Successive state transitions with I O interface by molecules

I NTRODUCTIONWith the maturing of fourth generation (4G)standardization and the ongoing worldwide deployment of 4G cellular networks, research activities on 5G communication technologies have emerged in both the academic and industri-al communities. Various organizations from dif-ferent countries and regions have taken initiatives and launched programs aimed at potential key technologies of 5G: 5GNOW and METI S launched under the European Telecommunica-tions Standards Institute’s (ETSI’s) Framework 7study new waveforms and the fundamentals of 5G to meet the requirements in 2020; the 5G Research Center was established in the UnitedKingdom to develop a world-class testbed of 5G technologies; the Third Generation Partnership Project (3GPP) has drawn up its draft evolution roadmap to 2020; and China has kicked off its IMT-2020 Forum to start the study of user demands, spectrum characteristics, and technolo-gy trends [1]. There is a broad consensus that 5G requirements include higher spectral efficiency (SE) and energy efficiency (EE), lower end-to-end latency, and more connection nodes. From the perspective of China Mobile, 5G should reflect two major themes: green and soft.As global carbon emissions increase and sea levels rise, global weather and air pollution inmany large cities across the world is becoming more severe [2]. Consequently, energy saving has been recognized as an urgent issue rmation and communications technologies (ICT) take up a considerable proportion of total energy consumption. In 2012, the annual average power consumption by ICT industries was over 200 GW, of which telecoms infrastructure and devices accounted for 25 percent [3]. In the 5G era, it is expected that millions more base sta-tions (BSs) with higher functionality and billions more smart phones and devices with much high-er data rates will be connected. The largest mobile network in the world consumed over 14billion kWh of energy in 2012 in its network of 1.1 million BSs. If green communications tech-nologies are universally deployed across this net-work, significant energy savings can be realized,enabling larger infrastructure deployments for 4G and 5G capacity upgrades without requiring significant increase in average revenue per user (ARPU). Dramatic improvements in EE will be needed; consequently, new tools for jointly opti-mizing network SE and EE will be essential.Several research groups and consortia have been investigating EE of cellular networks,including Mobile VCE, EARTH, and Green-Touch. Mobile VCE has focused on the BS hard-ware, architecture, and operation, realizing energy saving gains of 75–92 percent in simula-tions [4]. EARTH has devised an array of new technologies including low-loss antennas, micro direct transmission (DTX), antenna muting, and adaptive sectorization according to traffic fluctu-ations, resulting in energy savings of 60–70 per-cent with less than 5 percent throughput degradation [5]. GreenTouch has set up a much more ambitious goal of improving EE 1000 times by 2020 [6]. Several operators have been actively developing and deploying green technologies,including green BSs powered solely by renew-able energies, and green access infrastructure such as cloud/collaborative/clean radio access network (C-RAN) [7].Carrier grade networks are complex and com-posed of special-purpose nodes and hardware.New standards and features often require a vari-ety of equipment to be developed and integrat-ed, thus leading to very long launch cycles. In order to accommodate the explosive mobileA BSTRACTAs the deployment and commercial operation of 4G systems are speeding up, technologists worldwide have begun searching for next genera-tion wireless solutions to meet the anticipated demands in the 2020 era given the explosive growth of mobile Internet. This article presents our perspective of the 5G technologies with two major themes: green and soft. By rethinking the Shannon theorem and traditional cell-centric design, network capacity can be significantly increased while network power consumption is decreased. The feasibility of the combination of green and soft is investigated through five inter-connected areas of research: energy efficiency and spectral efficiency co-design, no more cells,rethinking signaling/control, invisible base sta-tions, and full duplex radio.Chih-Lin I, Corbett Rowell, Shuangfeng Han, Zhikun Xu, Gang Li, and Zhengang Pan, China Mobile Research InstituteT oward Green and Soft: A 5G PerspectiveInternet traffic growth and a large number of new applications/services demanding much short-er times to market, much faster turnaround of new network capabilities is required. Dynamic RAN reconfiguration can handle both temporal and spatial domain variation of mobile traffic without overprovisioning homogeneously. Soft technologies are the key to resolve these issues.By separating software and hardware, control plane and data plane, building software over general-purpose processors (GPPs) via program-ming interfaces and virtualization technology, it is possible to achieve lower cost and higher effi-ciency using software defined networks (SDNs) and network functions virtualization (NFV) [8]. The OpenRoad project at Stanford University introduced Open-flow, FlowVisor, and SNMPVi-sor to wireless networks to enhance the control plane. Base station virtualization from NEC con-centrated on slicing radio resources at the medi-um access control (MAC) layer. CloudEPC from Ericsson modified Long Term Evolution (LTE) control plane to control open-flow switches. CellSDN from Alcatel-Lucent considered a logi-cally centralized control plane, and scalable dis-tributed enforcement of quality of service (QoS) and firewall policies in the data plane. C-RAN implements a soft and virtualized BS with multi-ple baseband units (BBUs) integrated as virtual machines on the same server, supporting multi-ple radio access technologies (RATs). A soft end-to-end solution from the core network to the RAN can enable the 5G goals of spectraland energy efficiency.In the following sections, this article will elab-orate on a green and soft 5G vision. In additionto the traditional emphasis on maximizing SE,EE must be positioned side by side for jointoptimization. We present an EE and SE co-design framework. The concept of no more cellsis highlighted later with user-centric design andC-RAN as key elements of a soft cell infra-structure. The rationale for a fundamentalrethinking of signaling and control design in 5Gis provided. This article further discusses theidea of invisible BSs incorporating line-sideanswer supervision (LSAS) technology. Finally,the fundamental interference management issuesin networks based on full duplex technologiesand potential solutions are identified; we thensummarize this article.R ETHINK S HANNON:EE AND SE C O-D ESIGNGiven limited spectrum and ever increasingcapacity demand, SE has been pursued fordecades as the top design priority of all majorwireless standards, ranging from cellular net-works to local and personal area networks. Thecellular data rate has been improved from kilo-bits per second in 2G to gigabits per second in4G. SE-oriented designs, however, have over-looked the issues of infrastructure power con-sumption. Currently, RANs consume 70 percentof the total power. In contrast to the exponentialgrowth of traffic volume on mobile Internet,both the associated revenue growth and the net-work EE improvement lag by orders of magni-tude. A sustainable future wireless network musttherefore be not only spectral efficient but alsoenergy efficient. Therefore EE and SE jointoptimization is a critical part of 5G research.Looking at traditional cellular systems, thereare many opportunities to become greener, fromequipment level such as more efficient poweramplifiers using envelop tracking, to networklevel such as dynamic operation in line with traf-fic variations both in time and space. For funda-mental principles of EE and SE co-design, onemust first revisit the classic Shannon theory andreformulate it in terms of EE and SE.In classic Shannon theory, the channel capac-ity is a function of the log of the transmit power(P t), noise power spectral density (N0), and sys-tem bandwidth (W). The total system power con-sumption is a sum of P t and the circuit power P c,that is,where r is power amplifier (PA) efficiencydefined as the ratio of the input of the PA to theoutput of the PA. From the definition of EE [9],EE is equal to the channel capacity normalizedby the system power consumption. SE is thechannel capacity normalized by system band-width. The relationship of EE and SE can beshown as a function of PA efficiency and P c inFig. 1a. From Fig. 1a, it can be observed thatwhen P c is zero, there is a monotonic trade-offbetween h EE and h SE as predicted by the classicShannon theory. For nonzero P c, however, h EEincreases in the low SE region and decreases inthe high SE region with h SE(for a given h EE,there are two values of h SE). As P c increases, theEE-SE curve appears flatter. Furthermore, whentaking the derivative of h EE over h SE, the maxi-mum EE (h*EE) and its corresponding SE (h*SE)then satisfy the following: log2h*EE= log2r/(N0ln2) –h*SE. This means there is a linear relation-ship between log2h*EE and h*SE, and the EE-SErelationship at the EE optimal points is indepen-dent of P c. This observation implies that as P cdecreases, an exponential EE gain may beobtained at the cost of linear SE loss.Figure 1b compares the EE-SE performanceof current Global System for Mobile Communi-cations (GSM) and LTE BSs. LTE performs bet-ter than GSM in terms of both SE and EE; both,however, are working in a low SE region, indi-cating room for improvement.Antenna muting is proposed in EARTH toimprove EE, while LSAS stipulates EE improve-ment by increasing the number of antennas. Theseemingly contradicting conclusions are actuallyconsistent with the analysis presented abovewhere the difference is that the former operatesin a low SE region, whereas the latter operatesin a high SE region.While some progress has been made in EEand SE co-design investigation, there is still along way to go to develop a unified frameworkand comprehensive understanding in this area.Ideally, the EE-SE curve in future systemsshould achieve the following criteria:•The EE value should be improved for eachSE operation point.ρ=+PPP,tottcAngle φ (degrees)Angle θ (degrees)80604020040806020806040200020406080arrival/angle of departure (AoA/AoD) and large-scale fading with regard to different antennas are assumed to be the same due to the regular spacing in the traditional 2D array. With irregu-lar antenna arrays, however, the spacing and rel-ative position of each antenna may invalidate the above assumption where AoA /AoD and large-scale fading may be different for each ray with regard to different LSAS antennas; therefore, modification to the current channel modeling is needed.F ULL D UPLEX R ADIOCurrent cellular systems are either frequency-division duplex (FDD) or TDD. To double SE as well as improve EE, full duplex operation should be considered for 5G. A full duplex BS transmits to and receives from different termi-nals simultaneously using the same frequency resource. Self-interference cancellation is the key to the success of a full duplex system since high DL interference will make the receivers unable to detect the UL signal. Significant research progress has been made recently in self-interference cancellation technologies, including antenna placement, orthogonal polarizations, analog cancellation, and digital cancellation [13]. Most of the research, however, has been on either point-to-point relay or a single-cell BS scenario. There is also inter-user UL-to-DL interference in the single-cell full duplex system.To mitigate such interference, the inter-userinterference channel must be measured andreported. The full duplex BS can then scheduleproper UL and DL user pairs, possibly with jointpower control.In the case of a multi-cell full duplex network,interference management becomes significantlymore complex. For current TDD or FDD sys-tems, the DL-to-DL interference received at UEand UL-to-UL interference received at BSs havebeen studied extensively in literature and stan-dardization bodies (e.g., CoMP in 3GPP LTE-Advanced and IEEE 802.16m). In a full duplexsystem, however, there are new interference situ-ations. For example, if there are two BSs, therewill be additional interference in the UL and DLbetween multiple UE mobile devices with thesame frequency and time resources. In additionto intracell interference, there are inter-BS DL-to UL-interference and intercell inter-user UL-to-DL interference. These additional types ofinterference will adversely impact full duplex sys-tem performance. Traditional transmit or receivebeamforming schemes can be applied to mitigateinter-BS DL-to-UL interference. The intracellinterference mitigation can be extended to han-dle intercell inter-user UL-to-DL interference.C ONCLUSIONSThis article has presented five promising areasof research targeting a green and soft 5G system.The fundamental differences between classicShannon theory and practical systems are firstidentified and then harmonized into a frame-work for EE-SE co-design. The characteristics ofno more cells are described from the perspectiveof infrastructure and architecture variations withparticular emphasis on C-RAN as a typical real-ization in order to enable various soft technolo-gies. Rethinking signaling/control based ondiverse traffic profiles and network loading isthen explored, and initial redesign mechanismsand results are discussed. Virtually invisible basestations with irregular LSAS array are envi-sioned to provide much larger capacity at lowerpower in high-density areas when integrated intobuilding signage. Optimal configuration oftransceivers and active antennas is investigatedin terms of EE-SE performance. Finally, newinterference scenarios are identified in fullduplex networks, and several candidate solutionsare discussed. These five areas provide potentialfor fundamental breakthroughs, and togetherwith achievements in other research areas, theywill lead to a revolutionary new generation ofstandards suitable for 2020 5G deployment.A CKNOWLEDGMENTThe authors would like to express gratitude toYami Chen, Jiqing Ni, Chengkang Pan, HualeiWang, and Sen Wang in the Green ResearchCommunication Center of the China MobileResearch Institute.R EFERENCES[1] .[2] .[3] T. C. Group, “Smart 2020: Enabling the Low CarbonEconomy in the Information Age,” 2008.[4] .[5] P. Skillermark and P. Frenger, “Enhancing Energy Effi-ciency in LTE with Antenna Muting,” IEEE VTC Spring’12, 2012, May 2012, pp. 1–9.[6] .[7] C. M. R. Institute, “C-RAN: The Road Towards GreenRAN,” Oct. 2011, available: /cran.[8] M. Chiosi, D. Clarke, and P. Willis, “Network FunctionsVirtualization,” Oct. 2012.[9] G. Y. Li et al., “Energy-Efficient Wireless Communica-tions: Tutorial, Survey, and Open Issues,” IEEE WirelessCommun., vol. 18, no. 6, Dec. 2011, pp. 28–35.[10] M. Gupta et al., “Energy Impact of Emerging MobileInternet Applications on LTE Networks: Issues and Solu-tions,” IEEE Commun. Mag., vol. 51, no. 2, Feb. 2013,pp. 90–97.[11] F. Rusek et al., “Scaling Up MIMO: Opportunities andChallenges with Very Large Arrays,” IEEE Signal Proc.Mag., vol. 30, no. 1, Jan. 2013, pp. 40–60.[12] T. Marzetta, “Noncooperative Cellular Wireless withUnlimited Numbers of Base Station Antennas,” IEEETrans. Wireless Commun., vol. 9, no. 11, Nov. 2010,pp. 3590–3600.[13] E. Aryafar et al., “Midu: Enabling MIMO Full Duplex,”Proc. ACM Mobicom ’12, 2012.B IOGRAPHIESC HIH L IN I(icl@) received her Ph.D. degreein electrical engineering from Stanford University and hasalmost 30 years experience in wireless communications.She has worked at various world-class companies andresearch institutes, including the Wireless CommunicationFundamental Research Department of AT&T Bell Labs; theheadquarters of AT&T, as director of Wireless Communica-tions Infrastructure and Access Technology; ITRI of Taiwan,as director of Wireless Communication Technology; HongKong ASTRI, as vice president and founding group directorof the Communications Technology Domain. She receivedthe IEEE Transactions on Communications Stephen RiceBest Paper Award and is a winner of the CCCP National1000 Talent program. Currently, she is China Mobile’s chiefscientist of wireless technologies in charge of advancedwireless communication R&D efforts of the China MobileResearch Institute. She established the Green Communica-tions Research Center of China Mobile, spearheading majorinitiatives including 5G key technologies R&D; high energyefficiency system architecture, technologies, and devices;green energy; and C-RAN and soft base stations. She was an elected Board Member of IEEE ComSoc, Chair of the ComSoc Meetings and Conferences Board, and Founding Chair of the IEEE WCNC Steering Committee. She is cur-rently an Executive Board Member of GreenTouch and a Network Operator Council Member of ETSI NFV. Her research interests are green communications, C-RAN, net-work convergence, bandwidth refarming, EE-SE co-design,massive MIMO, and active antenna arrays.C ORBETT R OWELL (corbettrowell@) received his B.A. degree (honors) in physics from the University of California Santa Cruz, his M.Phil. degree in electrical and electronic engineering from Hong Kong University of Sci-ence and Technology, and his Ph.D. degree in electrical and electronic engineering from Hong Kong University. He has worked extensively in industry with experience inside startups, research institutes, antenna manufacturers, and operators, designing a wide variety of products including cellular antennas, digital repeaters, radio units, MRI, NFC,MIMO, and base station RF systems. Currently, he is the research director of Antenna and RF Systems in the Green Communication Research Center at the China Mobile Research Institute in Beijing and is designing large-scale antenna systems for TD-LTE and future 5G systems. He has over 30 granted patents and 20 published papers with over 1300 citations. His research interests are digital RF,FPGA, miniature antennas, antenna arrays, active antennas,beamforming, isolation, and sensor arrays.SHUANGFENG H AN (hanshuangfeng@)received his M.S. and Ph.D. degrees in electrical engineer-ing from Tsinghua University in 2002 and 2006 respective-ly. He joined Samsung Electronics in Korea as a senior research engineer in 2006 working on MultiBS MIMO,MIMO codebook design, small cell/HetNet, millimeter wave communication, D2D, and distributed radio over fiber. Cur-rently he is a senior project manager in the Green Commu-nication Research Center at the China Mobile Research Institute. He has over 30 patent applications, and has pub-lished over 10 conference and journal papers. His research interests include green technologies R&D in 5G wireless communication systems, including large-scale antenna sys-tems, active antenna systems, a co-frequency co-time full duplex non-orthogonal multiple access scheme, and EE-SE co-design.Z HIKUN X U (xuzhikun@) received B.S.E. and Ph.D. degrees in electrical and computer engineering from Beihang University in 2007 and 2013, respectively. He was a visiting researcher in the School of Electrical and Com-puter Engineering, Georgia Institute of Technology, from 2009 to 2010. After graduation, he joined the Green Com-munication Research Center of the China Mobile Research Institute as a project manager. His current interests include green technologies, the fundamental relationships between energy efficiency and spectral efficiency, energy-efficient network deployment and operation, cross-layer resource allocation in cellular networks, and advanced signal pro-cessing and transmission techniques.G ANG L I (ligangyf@) received his B.A.degree in telecommunication engineering and M.E. degree in automation engineering from Sichuan University. After graduation, he worked for Lucent Technologies for four years as a team leader and software developer for the core network. He is now a senior researcher at the Green Com-munication Research Center of the China Mobile Research Institute and is working on the key technologies of next generation 5G wireless communication systems. His research interests include radio access network architecture optimization, service-aware signaling/control redesign, and radio and core network convergence.Z HENGANG P AN (panzhengang@) received his B.S.E. from Southeast University in Nanjing and his Ph.D. degree in electrical and electronic engineering from Hong Kong University. After graduation, he worked for NTT DoCoMo Beijing Communication Labs and ASTRI in Hong Kong working on wireless communication (WiFi, WiMax,LTE), mobile digital TV (T-DMB, DVB-T/H, CMMB), and wire-line broadband access (HomePlug, MoCA) for both sys-tem/algorithm design and terminal SoC chip implementation. He is currently a principal staff member of the Green Communication Research Center at the China Mobile Research Institute and is now leading a team work-ing on the key technologies for the next generation 5G wireless communication systems. He has published more than 40 papers in top journals and international confer-ences, and filed 45 patents (20 granted). His research inter-ests are time/frequency/sampling synchronization technology for OFDM/A-based systems, channel estimation,forward error correction coding, MIMO, space-time pro-cessing/coding, and cross-layer optimization.。

四年级下册语文第二单元英语作文短的好句全文共3篇示例，供读者参考篇1Good Sentences from English CompositionsEnglish class is one of my favorite subjects in school. I really enjoy learning a new language and getting to write compositions. In the latest unit we studied, we read some excellent English compositions from students around the world. While the whole compositions were very well-written, I noticed that many of them contained outstanding individual sentences that were descriptive, powerful, and impactful. Let me share some of my favorite sentences from these English compositions and explain why I think they are so well-crafted.The first great sentence that caught my eye is: "The sun smiled brilliantly, its rays dancing across the treacherous ocean waves like sparkling diamonds." This sentence is from a story about a sailing adventure. I love the personification of the sun smiling and the creative comparison of the sunlight reflections on the water to glistening diamonds. The adjectives "treacherous" and "sparkling" create such vivid imagery in mymind. With just one sentence, I can envision the bright sun, the churning ocean, and the glimmering light shimmering off the crashing waves. It's an excellent example of using descriptive language to paint a picture for the reader.Another fantastic sentence is: "Icy fear gripped my heart as the massive grizzly bear lumbered towards me, its claws cutting into the soil like knives through butter." This one is from a suspenseful story about an encounter with a wild bear. The words "icy fear" are so evocative - I can practically feel the terror and chills running through the narrator's body. Comparing the bear's claws to knives slicing butter is such a clear,easy-to-visualize metaphor too. The contrast of the "massive" bear against the vulnerability of the narrator made my heart start racing as I read this sentence. It uses just the right details to build tension and let readers share in the harrowing experience.Here's a sentence that makes me laugh every time: "When I tried Mom's famous cherry pie after months of begging, the sugary filling squirted across the kitchen, decorating the walls with a red fruity constellation." This one is from a humorous narrative about a cooking mishap. I love how it doesn't just state that the pie filling went everywhere, but instead uses entertaining descriptions like "squirted," "decorating," and"fruity constellation" to amplify the comedic effect. The comparison of the splattered filling to a constellation makes such an amusing visual in my head. This sentence is a perfect example of how to bring writing to life and get readers chuckling right along.One more sentence that left an impression: "Disappointment slumped my shoulders and crushed my spirit when I didn't make the travel basketball team after months of intensive training." This sentence comes from a personal narrative about perseverance. The words "slumped" and "crushed" clearly communicate the deep sadness and defeated feelings in that moment. Adding in the contrasting details of "months of intensive training" amplifies the emotional letdown even more. As I read it, I could empathize with the narrator's hopes being dashed despite so much dedicated hard work and preparation. With its expressive words and vivid scenario, this one sentence conveys profound disappointment extraordinarily well.The more examples of standout sentences I discover, the more I appreciate the incredible power of words. A single, carefully-crafted sentence can make readers laugh, cringe, imagine a whole world, or feel a character's emotions as their own. These are the kinds of sentences that linger in your mindand make written stories, experiences and thoughts come alive. As a student writer, I aspire to create sentences like these that leap off the page and into my reader's hearts and imaginations. Though an entire essay of complex ideas and storylines is impressive on its own, the true art of writing often lies in the perfect combination of individual words and phrases that crystallize the moment into something poignantly beautiful, hauntingly emotional, or delightfully whimsical. I will take the lessons from these standout English sentences and use them as inspiration to elevate my own writing and craft. Every sentence is an opportunity to engage readers through linguistic mastery, emotional intelligence and creative genius.篇2Good Sentences Make Great English CompositionsAs a fourth grader, one of the most important things we learn in language arts class is how to write a solid English composition. Our teacher always emphasizes that having good, well-constructed sentences is the foundation of any effective writing. In the second unit focusing on English compositions, we've studied all kinds of examples of excellent sentences that really make an impact. Let me share some of my favorites and why I think they work so well.One type of strong sentence I've noticed uses vivid descriptive details to paint a clear picture in the reader's mind. For example, this sentence describing a sunset is so vibrant: "The sky transformed into a brilliant canvas of fiery oranges, crimson reds, and dazzling pinks as the sun slowly dipped below the horizon." With phrasing like "brilliant canvas," "fiery oranges," and "dazzling pinks," I can almost see those colors streaking across the sky. The details make it jump off the page.Using the senses beyond just sight can also add wonderful texture to sentences. Check out this one depicting a bakery: "The warm, yeasty aroma of freshly baked bread embraced them as soon as they stepped inside the tiny shop." Words like "warm," "yeasty," and "aroma" bring the sense of smell into the experience. It makes my mouth water just reading it!Sometimes short, simple sentences can be immensely powerful too. This one about a outstanding student blows me away with its economy of words: "She devoured books." Those three words alone suggest so much about the character's hunger for knowledge and reading abilities. There's a reason that "less is more" is such a common saying – sentences doesn't need to be long to be incredible.Of course, the greatest sentences often combine multiple techniques to create a perfect storm of literary mastery. This one demonstrating anaphora (repetition of the same words) while personifying nature is a personal favorite: "The trees swayed to the earthly beat, the trees danced with the wind's rhythm, the trees performed for their sunny audience." The parallel construction of "the trees" highlights that wonderful personification. At the same time, phrases like "earthly beat," "wind's rhythm," and "sunny audience" immerse us in vivid natural imagery. It's an absolute gem of a sentence.Beyond just beautiful writing, clear sentences that guide readers logically from one point to the next are also crucial in English compositions. Take this transition sentence that seamlessly shifts from discussing reasons to suggest a solution: "Given all of these factors negatively impacting local businesses, a multifaceted economic revitalization plan prioritizing..." The words "Given all of these factors" let us know the reasons have been fully laid out, while "negatively impacting local businesses" succinctly summarizes what those reasons were. Then it effortlessly glides into proposing "a multifaceted economic revitalization plan" as the solution. Well-crafted transitions like this create amazing flow and cohesion.Another way to enhance cohesion is through the purposeful use of pronouns that connect ideas across sentences. As shown in this passage, effective pronoun referencing avoids confusion while sustaining the intended meaning: "People must learn to reduce their environmental footprint to help curb climate change. This crucial lifestyle shift involves making decisions that..." Here, both "their" and "This" refer back to the idea of reducing environmental impact introduced in the first sentence. Such clear replacements prevent having to restate that full concept repeatedly. Pronoun links thus keep writing nice and tight.Of course, mechanics like grammar, spelling, and punctuation are vital too. Flawless sentences exemplify pristine mechanics, like this one: "After scouring the beach for hours, the excited children finally discovered a kaleidoscope of vividly colored seashells glistening in the golden rays of the late afternoon sun." Every component - from subject/verb agreement to apostrophe usage to capitalization - is impeccable. Seeing mechanics handled so skillfully in action inspires me to be equally diligent in my own writing.As you can probably tell, I've picked up so many incredible sentence writing techniques already this school year! My Englishcompositions are dramatically stronger thanks to assembling masterful sentences using techniques like:•Vivid descriptors firing on all sensory cylinders•Concise yet powerful phrasing•Literary devices like personificatio n and repetition•Clear transitions avoiding abrupt jumps between ideas•Pronoun replacements enhancing cohesion•Airtight grammar, spelling, and punctuation mechanicsWhile individual sentences are the core building blocks, weaving them together into impactful multi-sentence passages is also crucial. For instance, this potent three-sentence passage builds thoughtfully off each preceding sentence:"The labyrinth of dark alleys appeared to be a sinister maze with no end. Paranoia crept up Jason's spine asie shadows danced ominously in his periphery. His pounding heart and shallow breaths were all he could hear over the deathly silence."With phrases like "sinister maze," "paranoia crept up," and "shadows danced ominously," each successive sentence ramps up the suspense and sense of rising action. Structuring longerpassages to steadily progress in this captivating manner is yet another advanced skill I'm determined to master.Of course, for any multi-sentence passage - or overall composition for that matter - to truly shine, the individual sentences themselves must be meticulously crafted master works. Awkward phrasing, improper mechanics, or vague descriptions will quickly undermine even the most promising setup. That's why studying how to construct flawless, eloquent sentences from the ground up is such a valuable investment. As the old saying goes, "perfection is the child of time" - but with enough diligent practice, I'm confident my sentence mastery will continue leveling up at a rapid pace.Examining this diverse array of excellent sentences from our textbook has been immensely inspiring and educational. Although I'm just in fourth grade, being intentionally exposed to these exceptional models is accelerating my skills at a pivotal stage. Whether it's packing sentences with luscious sensory details, thoughtfully guiding readers between ideas, or deploying literary techniques with surgical precision, I now know what sort of sentences I should constantly strive to produce. Writing at this high level won't be easy - but if I relentlessly studythe masters while tirelessly refining my craft, I know my own sentence skills will continue their exponential growth.English composition is truly an art form, with masterfully constructed sentences as the brushstrokes composing the greater picture. Just as regularly exercising any muscle group yields increases in strength over time, the more I flex my sentence-crafting abilities through deliberate writing practice, the more effortlessly I'll be able to seamlessly combine all these tools and techniques. With constants commitment, my sentences today will blossom into the seeds of my future artworks as an author capable of transporting readers into whole new worlds. For now, I'll keep practicing one gorgeous sentence at a time towards that grand dream. Every English composition, after all, is simply the sum of its stellar sentences - so that's where true writing mastery must start.篇3Title: Gems from the Second UnitEnglish class is always a highlight of my week. There's nothing quite like delving into the rich world of words and stories from different cultures. This term, our second unit was all about exploring well-crafted sentences from a diverse collectionof Grade 4 English essays. As we pored over these literary morsels, our teacher encouraged us to appreciate the artistry behind each carefully constructed phrase. Little did I know that this exercise would unveil a treasure trove of wisdom and beauty, forever shaping my perception of language.One of the first sentences that caught my eye was from an essay titled "A Day at the Beach." The author painted a vivid picture with these words: "The golden sun kissed the sparkling waves as they danced upon the sandy shore." I could almost feel the warmth of the sun and hear the gentle lapping of the water. This sentence was a true masterclass in using sensory details to transport the reader to a specific moment in time.Another standout was from an essay about a childhood memory: "Laughter echoed through the hallways, bouncing off the walls like a melodious symphony." This sentence was a brilliant example of using figurative language to convey emotion. The comparison of laughter to a symphony was not only creative but also evoked a sense of joy and harmony.As we progressed through the unit, I was struck by the sheer diversity of writing styles and perspectives represented in these essays. One sentence that particularly resonated with me was from an essay on the importance of perseverance: "Though thepath was paved with obstacles, I refused to surrender, for each challenge was an opportunity to grow stronger." This sentence embodied the indomitable spirit of determination, reminding me that true strength lies in facing adversity head-on.Another essay explored the wonders of nature, and one sentence left me in awe: "The ancient oak stood tall and proud, its branches reaching toward the heavens like arms embracing the sky." This poetic description personified the tree, imbuing it with a sense of majesty and grandeur. It was a testament to the power of language to breathe life into even the most mundane of subjects.As our exploration continued, I couldn't help but marvel at the sheer breadth of human experience captured within these essays. One sentence that deeply resonated with me came from an essay on the importance of family: "Though our roots may stretch across continents, our love knows no boundaries, binding us together like the threads of an intricate tapestry." This sentence beautifully encapsulated the universal longing for connection and belonging, reminding me of the unbreakable bonds that transcend physical distance.Nearing the end of the unit, I encountered a sentence that challenged my perception of what it means to truly live: "Life isnot a race to be won, but a journey to be savored, each step a potential masterpiece." This profound statement encouraged me to slow down and appreciate the beauty in the present moment, rather than constantly striving for some distant goal.As our exploration drew to a close, I couldn't help but feel a deep sense of gratitude for the opportunity to immerse myself in these literary gems. Each sentence was a carefully crafted work of art, a testament to the power of language to inspire, challenge, and transform. This unit not only enhanced my appreciation for the written word but also instilled in me a newfound respect for the art of storytelling.In the end, these good sentences from the second unit of Grade 4 English essays were more than just words on a page; they were windows into the human experience, inviting us to explore the depths of emotion, wisdom, and creativity that reside within us all. As I move forward in my literary journey, I carry with me the invaluable lessons learned from these carefully constructed phrases, forever enriching my relationship with language and the world around me.。

Project no.FP6-507752MUSCLENetwork of ExcellenceM ultimedia U nderstanding through S emantics,C omputation and LE arningProgress on Applications of Machine LearningTechniquesDue date of deliverable:31.09.2005Actual submission date:10.10.2005Start date of project:1March2004Duration:48MonthsWorkpackage:8Deliverable:D8.1c(5/5)Editors:Rozenn DahyotDepartment of StatisticsTrinity College DublinDublin2,Ireland Revision1.0Project co-funded by the European Commission within the Sixth Framework Programme(2002-2006)Dissemination LevelPU Public x PP Restricted to other programme participants(including the commission Services)RE Restricted to a group specified by the consortium(including the Commission Services) CO Confidential,only for members of the consortium(including the Commission Services)Keyword List:Contributing AuthorsArnaldo de Albuquerque Araújo ENSEA Patrick Bouthemy IRISA Guillermo Cámara Chávez ENSEA Matthieu Cord ENSEA Rozenn Dahyot TCD Alain Lehmann IRISA Attila Licsár SZTAKI Tamás Szirányi SZTAKI Jian-Feng Yao IRISAContentsWP7:State of the Art1 Contents6 1Introduction7 I Video Content Analysis9 2Introduction11 3Video model133.1Basic definitions (13)3.2Types of transitions (14)3.2.1Cuts (14)3.2.2Fades and dissolves (14)4Video Segmentation194.1Basic definitions (19)4.2Feature extraction (20)4.2.1Visual features (21)4.2.2Auditory modality (26)4.2.3Textual modality (27)4.3Pattern recognition for video segmentation (27)4.3.1Structural segmentation (28)4.3.2Content segmentation (28)5Shot boundary detection315.1Basic definitions (31)5.2Threshold-based approaches (32)5.2.1Sharp cut (32)5.2.2Gradual transition (36)5.2.3Performance comparison of shot boundary detection algorithms (38)5.2.4State of the art (39)5.3Learning-based approaches (41)5.4Final considerations (43)II Other Applications45 6Comparison of video dynamic contents without feature matching by using rank-tests476.1Introduction (47)6.2Classification Framework (48)6.2.1Dissimilarity measure between two video segments (48)6.2.2Wilcoxon Rank-Sum Test (49)6.2.3Feature combination by weighting (51)6.3Local Motion Features (52)6.3.1Interest Point Selection (52)6.3.2Trajectorial Information (53)6.3.3Motion Intensity Information (54)6.4Motion Event Classification (55)6.4.1Gesture Video Database (55)6.4.2Basketball Video Database (57)6.5Summary and Conclusions (57)7User-adaptive hand gesture recognition system with interactive training597.1Introduction (59)7.2Overview of gesture recognition in a camera-projector system (60)7.3Main steps of the camera-projector system (61)7.3.1Segmentation and background generation (62)7.3.2Hand segmentation (62)7.4Gesture recognition (63)7.5Overview of the interactive training method (64)7.5.1Unsupervised training of hand gestures (65)7.5.2Supervised training of hand gestures (65)7.6Experimental results (66)7.7Conclusions (68)Bibliography68Chapter1IntroductionThefirst part of this report proposes a review on shot change detection methods,starting with a set of definitions about the nature of possible changes and the presentation of visual and audio features used for the task.Occurrences of shot changes in the video-audio visual stream are made by using appropriate dissimilarity measures in between successive features in the stream.The choice of this measures relies also on the nature,abrupt or gradual,of the changes. The decision or classification of the frames into classes(frame intra-shot,frame in transition, etc.)can be performed by different techniques,from standard thresholding approaches to more sophisticated machine learning techniques.In the second part of this report,two methods are presented with applications to gesture recognition.In chapter6,a novel and efficient dissimilarity measure between video segments is presented.Local spatio-temporal descriptors are considered to be realizations of an unknown, but class-specific distribution.The similarity of two video segments is then calculated by eval-uating an appropriate statistic issued from a rank test.It does not require any matching of the local features between the two considered video segments,and can deal with a different number of computed local features in the two segments.Furthermore,the measure is self-normalized which allows for simple cue integration,and even on-line adapted class-dependent combination of the different descriptors.Satisfactory results have been obtained on real video sequences for two motion event recognition problems,gesture recognition and basket ball event classification.The last chapter7proposes a vision-based hand gesture recognition system with interactive training,aimed to achieve a user-independent application by on-line supervised ual recognition systems involve a preliminary off-line training phase,separated from the recogni-tion phase.If the system recognizes unknown(non-trainer)users the recognition rate of gesture classes could decrease.The recognition has to be suspended and all gestures need to be retrained with an improved training set,resulting in inconveniences.This new approach introduces an on-line training method embedded into the recognition process,being interactively controlled by the user and adapting to his/her gestures.The main goal is that any non-trainer users be able to use the system instantly and if the recognition accuracy decreases only the faulty de-tected gestures be retrained realizing fast adaptation.The proposed system is implemented as a camera-projector system in which users can directly interact with the projected image by hand gestures,realizing an augmented reality tool in a multi-user environment.The emphasis is on the novel approach of dynamic and quick follow-up training capabilities instead of handlinglarge pre-trained databases.Part IVideo Content AnalysisChapter2IntroductionThe development of shot boundary detection algorithms was initiated some decades ago by the intention to detect sharp cuts in video sequences.A vast majority of all works published in the area of content-based video analysis and retrieval are related in one way or another with the problem of shot boundary detection.So,it is not surprising that shot boundary detection provides a base for nearly all video abstraction and high-level video segmentation approaches. Therefore,resolving the problem of shot boundary detection is one of the principal prerequisites for revealing video content structure in a higher level.There are two kinds of shot boundaries in videos,sharp shot changes,called cuts,and gradual transition between two different shots.A common approach to detect shot boundaries is by computing the difference between two adjacent frames(color,motion,edge and/or texture features)and compare it to a preset threshold (threshold-based approach).A problem with this approach is that it is hard to select a threshold that will work with different shot transitions.To avoid this problem,we consider video shot segmentation from a different perspective,as a categorization task,using a machine learning approach.By turning the problem into a classification problem,the need for thresholding is eliminated and every frame in the video stream is classified as either a“common shot frame”, a“cut frame”,or a“gradual transition frame”.The classification framework allows us to use many different kinds of video features in an integrated structure.The objective of learning is to create systems that can improve their performance in a certain task whereas they acquire experience or data.In many natural learning tasks,this experience or data is gained interactively,by taking actions,doing queries,or making experiments.Most of machine learning research,nevertheless,treats the learner as a passive recipient of data to be processed.This“passive”approach does not pay attention to the fact that,in many situations, the learner’s most powerful tool is its ability to act,to collect data,and to influence the world it is trying to understand.Active learning is the study of how to use this ability effectively.The aim of active learning methods is to produce concise and highly informative training sets.This work focuses on the exploitation of features based on frame differences for pixel,block-based,histogram comparisons.Another visual features used are the phase correlation(motion-based)[70]and edge change ratio[78].After the feature extraction these features are classified.The text is organized as follows.In Chapter2,we present the video model,basic definitions that will hold within this text.In Chapter3,we show a description of video segmentation.In Chapter4,different approaches for shot boundary detection are presented,and also an overviewof the methods that are used for detecting cuts and gradual transitions.Chapter3Video modelDigital video now plays an important role in education,entertainment and other multimedia applications.It has become extremely important to developed mechanism for processing,filter-ing,indexing and organize the digital video information so that useful knowledge can be derived from the mass information that it is now available.The two most important aspects of video are its contents and its production style[24].The form is the information that is being transmitted and the latter is associated with the category of a video(commercial,drama,sciencefiction, etc.).In this chapter we will define some of the concepts used in literature;like shot,scene and keyframe.Also,we present the most popular types of transitions(sharp cuts and gradual transitions).All this basic definitions were abstracted from[28].3.1Basic definitionsLet A⊂N,A=0,...,H−1×0,...,W−1,where H and W are the height and width of a frame,respectively.Definition3.1(Frame)A frame f t is a function from A to N where for each spatial position (x,y)in A,f t(x,y)represents the value of the pixel(x,y)at time t.Definition3.2(Video)A video V is a sequence of frames f t with an accompanying audio track and can be described by V=(f t)t∈[0,T−1],where T is the number of frames contained in the video.The number of frames is directly associated with the frequency and the duration of visualization.A video is typically composed by a large amount of information that is hard to be physically or semantically related.The fundamental unit in a video is a shot(will be defined below)that captures an uninter-rupted recording of a camera,where camera motion and object motion is permitted.A scene is composed by a small number of shots.While a shot represents a physical video unit,a scene represents a semantic video unit.Definition3.3(Shot)A shot is the fundamental unit of a video,because it captures a continu-ous action from a single camera.A shot represents a spatio-temporally frame sequence.Definition3.4(Scene)A scene is composed by small number of shots that are interrelated and unified by similar features and by temporal proximity.Definition3.5(Key-frame)A key-frame is a frame that represents the content of a shot or scene.This content must be the most representative as possible.Different kinds of transitions bounder a shot from an other.There exists sharp and gradual transitions.3.2Types of transitionsThe process of video production involves shooting and edition operations.Thefirst is for pro-duction of shots and the second one if for compilation of the different shots into a structured visual presentation[24].When we refer to compilation we are talking about the transition between consecutive shots.Definition3.6(Transition)Shots are separated by editing effects,these effects are known as ually,a transition consists of the insertion of a series of artificial frames pro-duced by an editing tool.We will define some notations for understanding the transitions types.P denote the shot before a transition and Q the shot after a transition.P(t)represents the t-th frame in shot P.The transition between shots P and Q is denoted by C,the transition begins at time t0and ends at time t1.A frame C(t)represents a frame at time t in the transition C,where t∈]t0,t1[.Transitions are usually subdivided into sharp transitions(cuts)and gradual transitions(dis-solves,fades and wipes).3.2.1CutsThe simplest transition is the cut,and also is the easiest transition to identify.A cut is char-acterized by the abrupt change between consecutive shots.Figure3.1shows an example of a cut.Definition3.7(Cut)Also now as a sharp transition,a cut is a type of transition where two consecutive shots area concatenated,i.e.,the transition is abrupt.3.2.2Fades and dissolvesFades and dissolves are video editing operations that are done in a laboratory through optical processes.They make the boundary of two shots spread across a number o frames[14].So,the have an starting and ending frame that identifies the transition sequence.(a)(b)(c)(d)(d)(e)(f)(g)Figure3.1:An example of a cutDefinition3.8(Fade-out)The fade-out process is characterized by a progressive darkening of a shot P until the last frame becomes completely black.The frames of a fade-out can be obtained by(t)=α(t)×G+(1−α(t))×P(t)(3.1)T fwhereα(t)is a monotonic transformation function that it is usually linear,G represents the last frame,which is a monochromatic(e.g.white or black)and t∈]t0,t0+d[where d represents the duration of the fade.Definition3.9(Fade-in)The fade-in process is characterized by a progressive appearing of shot Q.Thefirst frame of the fade-in is a monochrome frame G.The frames of a fade-in can be obtained by(t)=(1−α(t))×G+α(t)×P(t)(3.2)T fiFigure3.2shows a fade-in and fade-out.Definition3.10(Dissolve)The dissolve is characterized by a progressive change of a shot P into a shot Q with non-null duration.Each transition frame can be defined byT d(t)=(1−α(t))×P(t)+α(t)×Q(t)(3.3) Figure3.3displays an example of dissolving.A video sequence is a rich multimodal[63],[37]information source,containing audio, speech,text(if closed caption is available),color patterns and shape of imaged object,and motion of these objects[33].Figure3.2:An example of fade-in(top)and fade-out(bottom).Figure 3.3:An example of dissolve.Chapter4Video SegmentationFor analysis purposes the authors of[63]treat video analysis and indexing as the inverse of an authoring process.Thefirst step in this inversion process is the segmentation of the video in its layouts and content elements.In many cases segmentation can be viewed as a classification problem.Therefore,patterns of interest must be distinguished that can be used for support decisions about layout and their content categories.First,we present some basic definitions that are used in this chapter.Then,we will discuss some approaches used for feature extraction. Finally,as we have already said,video segmentation could be viewed as classification problem so we develop a brief presentation of pattern recognition.4.1Basic definitionsIn this section we present definitions that will be used in the next sections.Definition4.1(Authoring process[63])Video made requires an author who conceives the idea for the video and produces thefinal result,consisting of specific content and a layout. An author uses visual,auditory and textual modalities to express his or her idea.Definition4.2(Multimodality[63])The capacity of an author of a video to express a pre-defined semantic idea,combining syntactic structure of each modality(visual,auditory and textual)with a specific content,using at least two information channels.Three information channels or modalities are considered within a video document:•Visual modality:contains everything,either naturally or artificially created,that can be seen in a video;•Auditory modality:contains speech,music,and environmental sounds that can be heard in a video;•Textual modality:contains textual resources that describe the content of a video.Definition4.3(Content[63])The content perspective relates segments to elements that an au-thor uses to create a video.The following elements can be distinguished[7]:•Setting:time and place where a history of a video occurs;•Objects:noticeable static or dynamic entities in the video;•People:human appears in the video.Definition4.4(Layout[63])Temporal sequence of fundamental units(syntactic structure)for each modality,which it self do not have a temporal dimension.The nature of these units is the main factor discriminating the different modalities.The fundamental units of visual modality are the single image frames,for auditory modality is a set of audio samples,and for textual modality are individual characters.A continuous sequence of fundamental units resulting from an uninterrupted sensor recording is known as sensor shot. For the visual and auditory modality this leads to:•Camera shots:result of an uninterrupted recording of a camera;and•Microphone shots:result of an uninterrupted recording of a microphone.For text,sensor recording do not exist.Definition4.5(Pattern[71])A pattern is a form,template,or model(or,more abstractly,a set of rules)which can be used to make or to generate things or parts of a thing,especially if the things that are generated have enough in common for the underlying pattern to be inferred or discerned,in which case the things are said to exhibit the pattern.The detection of underlying patterns is called pattern recognition.In our work the visual features are the patterns that will be classified.4.2Feature extractionMost of the activities in automatic detection of video is concentrated on the detection of shot boundaries.We have two kinds of shot boundaries:sharp shot transitions(cuts)and gradual shot transitions(fades,dissolves,wipes and mattes).Various algorithms,in visual modality,are proposed in video indexing literature for sharp cut detection[8],[20],[30],[51];all of these algorithms relies on comparison of successive frames with somefixed or dynamic threshold on either pixel,edge,block or frame level.Block level features can be derived from motion vectors.Transition edits are an important clue for shot boundaries,and because that transitions are gradual,comparison of successive frame is insufficient[14],[78].In the auditory modal-ity,detection of sharp cuts can be accomplished identifying silences and transition points,i.e., locations where the category of the underlying signal changes.4.2.1Visual featuresAutomatic detection is based on the information that it is extracted from the shots which can tell us when a scene cut occurs(brightness,color distribution change,motion,edges,etc.).It is easy to detect cuts between shots with little motion and constant illumination,this is done by looking for sharp brightness changes.In the presence of continuous object motion,or camera movements,or change of illumination in the shot is difficult to understand when the brightness changes are due to these conditions or to the transition from one shot to an other.So,it is necessary to use different visual features to avoid this kind of problems.In the next subsections we will review some visual features used for shot boundary detection.Color momentsThe basis of color moments lays in the assumption that the distribution of color in an image can be interpreted as a probability distribution.Probability distributions are characterized by a number of unique moments(e.g.Normal distributions are differentiated by their mean and vari-ance).It therefore follows that if the color in an image follows a certain probability distribution, the moments of that distribution can then be used as features to identify that image based on color.Color moments have been successfully used in many retrieval systems.Thefirst order (mean),the second(variance)and the third order(skewness)color moments have proven to be efficient and effective in representing color distributions of images[16].Thefirst three order moments are calculated as:µi=1NN∑j=1f i j(4.1)σi=(1NN∑j=1(f i j−µi)2)12(4.2)s i=(1NN∑j=1(f i j−µi)3)13(4.3)where f i j if the i th color channel at the j th image,and N is the number of pixels.Color coherence vectorIn[45]was proposed an other way to incorporate spatial information into the color histogram, color coherence vector(CCV).The authors define a color’s coherence as the degree to which pixels of that color are members of large similarity-colored regions,these are known as coherent regions.Each histogram bin is partitioned into two groups:1.coherent:if it belongs to a large uniformly-color region;2.incoherent:if it does not.The CCV of a image is defined as the vector<(α1,β1),(α1,β1),...,(αN,βN)>,whereαi denote the number of coherent pixels andβi denote the number of incoherent pixels.Figure4.1(a)shows the coherent and incoherent vector of Figure3.1(c),the threshold used was40,i.e.,a region is considerate coherent if it has more than40elements.In Figure4.1(b) we could that the sum of the coherent and incoherent vectors is equal to histogram vector.Color correlogramIn[26]the color correlogram was proposed to characterize the color distributions of pixels and the spatial correlation pairs of colors.Thefirst and the second dimension of the3D histogram are the colors of any pair of colors and the third dimension is their spatial distance.So,a color correlogram is a table where the k-th entry for(i,j)specifies the probability offinding a pixel of color i at a distance k from a pixel of color i.Let I represent the entire set of image pixels and I c(i)represent the set of pixels whose color are c(i).So,the correlogram is defined as:[p2∈I c(j)||p1−p2|=k](4.4)γ(k)i,j=Pr p1∈I c(i),p2∈Iwhere i,j∈1,2,...,N,k∈1,2,...,d,and|p1−p2|is the distance between pixels p1and p2.Compared to the color histograms and CCV,the correlogram provides the best retrieval,but is also the most computacional expensive.Figure4.2(a)shows the color correlogram of Figure3.1(c)just using the red band of the image and(b)shows the color correlogram using the three bands.Edge change ratioZabih et.al.proposed a video segmentation based on local features[78].This algorithm considers edge images and uses grey-level information.During scene breaks or fades new edges appears far from old edges and also old edges disappear far from new edges.For detecting cuts it is necessary to count the number of entering(ρin)and exiting(ρout)pixels in two consecutive frames.Figure4.3shows a sketch of this method.Thefirst step of the algorithm consists infinding the edges for each couple of frames f t and f t+1.For that,we smooth the frames using a gaussianfilter and then Canny edge detector [66].Then,they compute how many pixels in f t are close to their nearest neighbour in f t+1, and viceversa.Variablesρin andρout are used as a dissimilarity measure between a couple of consecutive frames.Frame dissimilarity is detected if:ρ=max(ρin,ρout)(4.5) exhibits a peak.For cut detection,they defined afixed threshold and afixed frame window.If ρexceeds the threshold,the frame is marked as a scene break.Phase correlation method(PCM)An other useful motion feature is the PCM between two frames[70].The phase-correlation method measures the motion directly from the phase correlation map(shift in the spatial domain(a)(b)Figure4.1:Coherence Color Vector,(a)coherent and incoherent vector of Figure3.1(c),and(b)histogram and sum of coherent and incoherent vector.(a)(b)Figure4.2:Color Correlogram,(a)Color Correlogram of red band of Figure3.1(c),and(b) using the three bands.is reflected as a phase change in the spectrum domain),it gives a more accurate and robust estimate of the motion vector,and a motionfield with much lower entropy.When one frame is the translation of the other,the PCM has a single peak at the location corresponding to the translation vector.When there are multiple objects moving,the PCM tends to have many peaks,see Figure4.4.The steps for calculating the PCM are:1.Given a block B1,B2from each image.pute2D FFT of each.CutEdge DetectionExiting edges Entering edges Figure4.3:Maps of entering and exiting pixels into consecutive frames.pute cross-power spectrum.Normalized value of:FB1FB2∗4.Take IFFT to get Phase Correlation Function.5.This is very similar to the correlation function between the two blocks(a)(b)Figure4.4:Phase correlation.4.2.2Auditory modalitySeveral researchers have started to investigate the potencial of analyzing the accompany audio signal for video classification[70],[33],also they realize that audio characteristics are equally, if not more,important when it comes to understanding the semantic contend of a video.For example,it is possible to determinate whether a TV program is a news report,a commercial,or sports game without watching the video.This possible,because the audio in a football game is very different from an audio of a news report.In[46]it is shown that average energy,E n,is sufficient measure for detecting silence seg-ments.E n is computed for a window of size n(audio frame-level),and if the average for all windows in a segment(audio clip-level)falls below a given threshold,a silence is marked.In [33]used the ratio of the number of nonsilence frames to the total number of frames in a clip where silence detection is based on both volume and zero crossing rate(ZCR).Saraceno et al.[58]classify audio according to silence,speech,music,or noise and use this information to ver-ify shot boundaries.Saunders proposed to use four statistics of the ZCR as feature[60].These are:1.standard deviation offirst order differences;2.third central moment about the mean;3.total number of zero crossing exceeding a threshold,and4.difference between the number of zero crossing above and below the mean values. Combined with the volume information can discriminate speech and music at high accurracy of 98Since the frame with a high energy has more influence on the perceived sound by human ear,[33]proposed using a weighted of corresponding audio frame-level features,where the weighting for an audio frame is proportional to the energy of the frame.This is very useful when there are many silent frames in an audio clip because the frequency features in silent audio frames are almost random.An other audio contend categorization was proposed by[72],which divides audio content into ten groups:animal,bells,crowds,laughter,male speech,female speech,telephone and water.An other interesting work related to general audio content classification was proposed in [82].They explorefive kinds of audio features:energy,ZCR,fundamental frequency,timber and rhythm.4.2.3Textual modalityTokenization is thefirst step in reconstruction the textual layout,in this phase the input text is divided into units called tokens and characters.Depending in the granularity used,the detection of text shots can be achieved in different ways.If we only are focused in singles words we can use the occurrence of white spaces as the main clue.However,this is not always possible, because of the occurrence of periods,single apostrophes and hyphenation[36].When more context is taken into account one can reconstruct sentences from textual layout.A basic heuristic for the reconstruction of sentences is the detection of periods,about90%of periods are sentence boundary indicators[36].Transitions are typically found by searching for predefined patterns.4.3Pattern recognition for video segmentationThefirst stage in the inverse of an authoring process is the segmentation of a video into struc-tural layout components(structural segmentation)and their associated content(content seg-mentation).First,patterns of interest(features extraction)must be distinguished so they can be。

Progressive Simplicial Complexes Jovan Popovi´c Hugues HoppeCarnegie Mellon University Microsoft ResearchABSTRACTIn this paper,we introduce the progressive simplicial complex(PSC) representation,a new format for storing and transmitting triangu-lated geometric models.Like the earlier progressive mesh(PM) representation,it captures a given model as a coarse base model together with a sequence of refinement transformations that pro-gressively recover detail.The PSC representation makes use of a more general refinement transformation,allowing the given model to be an arbitrary triangulation(e.g.any dimension,non-orientable, non-manifold,non-regular),and the base model to always consist of a single vertex.Indeed,the sequence of refinement transforma-tions encodes both the geometry and the topology of the model in a unified multiresolution framework.The PSC representation retains the advantages of PM’s.It defines a continuous sequence of approx-imating models for runtime level-of-detail control,allows smooth transitions between any pair of models in the sequence,supports progressive transmission,and offers a space-efficient representa-tion.Moreover,by allowing changes to topology,the PSC sequence of approximations achieves betterfidelity than the corresponding PM sequence.We develop an optimization algorithm for constructing PSC representations for graphics surface models,and demonstrate the framework on models that are both geometrically and topologically complex.CR Categories:I.3.5[Computer Graphics]:Computational Geometry and Object Modeling-surfaces and object representations.Additional Keywords:model simplification,level-of-detail representa-tions,multiresolution,progressive transmission,geometry compression.1INTRODUCTIONModeling and3D scanning systems commonly give rise to triangle meshes of high complexity.Such meshes are notoriously difficult to render,store,and transmit.One approach to speed up rendering is to replace a complex mesh by a set of level-of-detail(LOD) approximations;a detailed mesh is used when the object is close to the viewer,and coarser approximations are substituted as the object recedes[6,8].These LOD approximations can be precomputed Work performed while at Microsoft Research.Email:jovan@,hhoppe@Web:/jovan/Web:/hoppe/automatically using mesh simplification methods(e.g.[2,10,14,20,21,22,24,27]).For efficient storage and transmission,meshcompression schemes[7,26]have also been developed.The recently introduced progressive mesh(PM)representa-tion[13]provides a unified solution to these problems.In PM form,an arbitrary mesh M is stored as a coarse base mesh M0together witha sequence of n detail records that indicate how to incrementally re-fine M0into M n=M(see Figure7).Each detail record encodes theinformation associated with a vertex split,an elementary transfor-mation that adds one vertex to the mesh.In addition to defininga continuous sequence of approximations M0M n,the PM rep-resentation supports smooth visual transitions(geomorphs),allowsprogressive transmission,and makes an effective mesh compressionscheme.The PM representation has two restrictions,however.First,it canonly represent meshes:triangulations that correspond to orientable12-dimensional manifolds.Triangulated2models that cannot be rep-resented include1-d manifolds(open and closed curves),higherdimensional polyhedra(e.g.triangulated volumes),non-orientablesurfaces(e.g.M¨o bius strips),non-manifolds(e.g.two cubes joinedalong an edge),and non-regular models(i.e.models of mixed di-mensionality).Second,the expressiveness of the PM vertex splittransformations constrains all meshes M0M n to have the same topological type.Therefore,when M is topologically complex,the simplified base mesh M0may still have numerous triangles(Fig-ure7).In contrast,a number of existing simplification methods allowtopological changes as the model is simplified(Section6).Ourwork is inspired by vertex unification schemes[21,22],whichmerge vertices of the model based on geometric proximity,therebyallowing genus modification and component merging.In this paper,we introduce the progressive simplicial complex(PSC)representation,a generalization of the PM representation thatpermits topological changes.The key element of our approach isthe introduction of a more general refinement transformation,thegeneralized vertex split,that encodes changes to both the geometryand topology of the model.The PSC representation expresses anarbitrary triangulated model M(e.g.any dimension,non-orientable,non-manifold,non-regular)as the result of successive refinementsapplied to a base model M1that always consists of a single vertex (Figure8).Thus both geometric and topological complexity are recovered progressively.Moreover,the PSC representation retains the advantages of PM’s,including continuous LOD,geomorphs, progressive transmission,and model compression.In addition,we develop an optimization algorithm for construct-ing a PSC representation from a given model,as described in Sec-tion4.1The particular parametrization of vertex splits in[13]assumes that mesh triangles are consistently oriented.2Throughout this paper,we use the words“triangulated”and“triangula-tion”in the general dimension-independent sense.Figure 1:Illustration of a simplicial complex K and some of its subsets.2BACKGROUND2.1Concepts from algebraic topologyTo precisely define both triangulated models and their PSC repre-sentations,we find it useful to introduce some elegant abstractions from algebraic topology (e.g.[15,25]).The geometry of a triangulated model is denoted as a tuple (K V )where the abstract simplicial complex K is a combinatorial structure specifying the adjacency of vertices,edges,triangles,etc.,and V is a set of vertex positions specifying the shape of the model in 3.More precisely,an abstract simplicial complex K consists of a set of vertices 1m together with a set of non-empty subsets of the vertices,called the simplices of K ,such that any set consisting of exactly one vertex is a simplex in K ,and every non-empty subset of a simplex in K is also a simplex in K .A simplex containing exactly d +1vertices has dimension d and is called a d -simplex.As illustrated pictorially in Figure 1,the faces of a simplex s ,denoted s ,is the set of non-empty subsets of s .The star of s ,denoted star(s ),is the set of simplices of which s is a face.The children of a d -simplex s are the (d 1)-simplices of s ,and its parents are the (d +1)-simplices of star(s ).A simplex with exactly one parent is said to be a boundary simplex ,and one with no parents a principal simplex .The dimension of K is the maximum dimension of its simplices;K is said to be regular if all its principal simplices have the same dimension.To form a triangulation from K ,identify its vertices 1m with the standard basis vectors 1m ofm.For each simplex s ,let the open simplex smdenote the interior of the convex hull of its vertices:s =m:jmj =1j=1jjsThe topological realization K is defined as K =K =s K s .The geometric realization of K is the image V (K )where V :m 3is the linear map that sends the j -th standard basis vector jm to j 3.Only a restricted set of vertex positions V =1m lead to an embedding of V (K )3,that is,prevent self-intersections.The geometric realization V (K )is often called a simplicial complex or polyhedron ;it is formed by an arbitrary union of points,segments,triangles,tetrahedra,etc.Note that there generally exist many triangulations (K V )for a given polyhedron.(Some of the vertices V may lie in the polyhedron’s interior.)Two sets are said to be homeomorphic (denoted =)if there ex-ists a continuous one-to-one mapping between them.Equivalently,they are said to have the same topological type .The topological realization K is a d-dimensional manifold without boundary if for each vertex j ,star(j )=d .It is a d-dimensional manifold if each star(v )is homeomorphic to either d or d +,where d +=d:10.Two simplices s 1and s 2are d-adjacent if they have a common d -dimensional face.Two d -adjacent (d +1)-simplices s 1and s 2are manifold-adjacent if star(s 1s 2)=d +1.Figure 2:Illustration of the edge collapse transformation and its inverse,the vertex split.Transitive closure of 0-adjacency partitions K into connected com-ponents .Similarly,transitive closure of manifold-adjacency parti-tions K into manifold components .2.2Review of progressive meshesIn the PM representation [13],a mesh with appearance attributes is represented as a tuple M =(K V D S ),where the abstract simpli-cial complex K is restricted to define an orientable 2-dimensional manifold,the vertex positions V =1m determine its ge-ometric realization V (K )in3,D is the set of discrete material attributes d f associated with 2-simplices f K ,and S is the set of scalar attributes s (v f )(e.g.normals,texture coordinates)associated with corners (vertex-face tuples)of K .An initial mesh M =M n is simplified into a coarser base mesh M 0by applying a sequence of n successive edge collapse transforma-tions:(M =M n )ecol n 1ecol 1M 1ecol 0M 0As shown in Figure 2,each ecol unifies the two vertices of an edgea b ,thereby removing one or two triangles.The position of the resulting unified vertex can be arbitrary.Because the edge collapse transformation has an inverse,called the vertex split transformation (Figure 2),the process can be reversed,so that an arbitrary mesh M may be represented as a simple mesh M 0together with a sequence of n vsplit records:M 0vsplit 0M 1vsplit 1vsplit n 1(M n =M )The tuple (M 0vsplit 0vsplit n 1)forms a progressive mesh (PM)representation of M .The PM representation thus captures a continuous sequence of approximations M 0M n that can be quickly traversed for interac-tive level-of-detail control.Moreover,there exists a correspondence between the vertices of any two meshes M c and M f (0c f n )within this sequence,allowing for the construction of smooth vi-sual transitions (geomorphs)between them.A sequence of such geomorphs can be precomputed for smooth runtime LOD.In addi-tion,PM’s support progressive transmission,since the base mesh M 0can be quickly transmitted first,followed the vsplit sequence.Finally,the vsplit records can be encoded concisely,making the PM representation an effective scheme for mesh compression.Topological constraints Because the definitions of ecol and vsplit are such that they preserve the topological type of the mesh (i.e.all K i are homeomorphic),there is a constraint on the min-imum complexity that K 0may achieve.For instance,it is known that the minimal number of vertices for a closed genus g mesh (ori-entable 2-manifold)is (7+(48g +1)12)2if g =2(10if g =2)[16].Also,the presence of boundary components may further constrain the complexity of K 0.Most importantly,K may consist of a number of components,and each is required to appear in the base mesh.For example,the meshes in Figure 7each have 117components.As evident from the figure,the geometry of PM meshes may deteriorate severely as they approach topological lower bound.M 1;100;(1)M 10;511;(7)M 50;4656;(12)M 200;1552277;(28)M 500;3968690;(58)M 2000;14253219;(108)M 5000;029010;(176)M n =34794;0068776;(207)Figure 3:Example of a PSC representation.The image captions indicate the number of principal 012-simplices respectively and the number of connected components (in parenthesis).3PSC REPRESENTATION 3.1Triangulated modelsThe first step towards generalizing PM’s is to let the PSC repre-sentation encode more general triangulated models,instead of just meshes.We denote a triangulated model as a tuple M =(K V D A ).The abstract simplicial complex K is not restricted to 2-manifolds,but may in fact be arbitrary.To represent K in memory,we encode the incidence graph of the simplices using the following linked structures (in C++notation):struct Simplex int dim;//0=vertex,1=edge,2=triangle,...int id;Simplex*children[MAXDIM+1];//[0..dim]List<Simplex*>parents;;To render the model,we draw only the principal simplices ofK ,denoted (K )(i.e.vertices not adjacent to edges,edges not adjacent to triangles,etc.).The discrete attributes D associate amaterial identifier d s with each simplex s(K ).For the sake of simplicity,we avoid explicitly storing surface normals at “corners”(using a set S )as done in [13].Instead we let the material identifier d s contain a smoothing group field [28],and let a normal discontinuity (crease )form between any pair of adjacent triangles with different smoothing groups.Previous vertex unification schemes [21,22]render principal simplices of dimension 0and 1(denoted 01(K ))as points and lines respectively with fixed,device-dependent screen widths.To better approximate the model,we instead define a set A that associates an area a s A with each simplex s 01(K ).We think of a 0-simplex s 00(K )as approximating a sphere with area a s 0,and a 1-simplex s 1=j k 1(K )as approximating a cylinder (with axis (j k ))of area a s 1.To render a simplex s 01(K ),we determine the radius r model of the corresponding sphere or cylinder in modeling space,and project the length r model to obtain the radius r screen in screen pixels.Depending on r screen ,we render the simplex as a polygonal sphere or cylinder with radius r model ,a 2D point or line with thickness 2r screen ,or do not render it at all.This choice based on r screen can be adjusted to mitigate the overhead of introducing polygonal representations of spheres and cylinders.As an example,Figure 3shows an initial model M of 68,776triangles.One of its approximations M 500is a triangulated model with 3968690principal 012-simplices respectively.3.2Level-of-detail sequenceAs in progressive meshes,from a given triangulated model M =M n ,we define a sequence of approximations M i :M 1op 1M 2op 2M n1op n 1M nHere each model M i has exactly i vertices.The simplification op-erator M ivunify iM i +1is the vertex unification transformation,whichmerges two vertices (Section 3.3),and its inverse M igvspl iM i +1is the generalized vertex split transformation (Section 3.4).Thetuple (M 1gvspl 1gvspl n 1)forms a progressive simplicial complex (PSC)representation of M .To construct a PSC representation,we first determine a sequence of vunify transformations simplifying M down to a single vertex,as described in Section 4.After reversing these transformations,we renumber the simplices in the order that they are created,so thateach gvspl i (a i)splits the vertex a i K i into two vertices a i i +1K i +1.As vertices may have different positions in the different models,we denote the position of j in M i as i j .To better approximate a surface model M at lower complexity levels,we initially associate with each (principal)2-simplex s an area a s equal to its triangle area in M .Then,as the model is simplified,wekeep constant the sum of areas a s associated with principal simplices within each manifold component.When2-simplices are eventually reduced to principal1-simplices and0-simplices,their associated areas will provide good estimates of the original component areas.3.3Vertex unification transformationThe transformation vunify(a i b i midp i):M i M i+1takes an arbitrary pair of vertices a i b i K i+1(simplex a i b i need not be present in K i+1)and merges them into a single vertex a i K i. Model M i is created from M i+1by updating each member of the tuple(K V D A)as follows:K:References to b i in all simplices of K are replaced by refer-ences to a i.More precisely,each simplex s in star(b i)K i+1is replaced by simplex(s b i)a i,which we call the ancestor simplex of s.If this ancestor simplex already exists,s is deleted.V:Vertex b is deleted.For simplicity,the position of the re-maining(unified)vertex is set to either the midpoint or is left unchanged.That is,i a=(i+1a+i+1b)2if the boolean parameter midp i is true,or i a=i+1a otherwise.D:Materials are carried through as expected.So,if after the vertex unification an ancestor simplex(s b i)a i K i is a new principal simplex,it receives its material from s K i+1if s is a principal simplex,or else from the single parent s a i K i+1 of s.A:To maintain the initial areas of manifold components,the areasa s of deleted principal simplices are redistributed to manifold-adjacent neighbors.More concretely,the area of each princi-pal d-simplex s deleted during the K update is distributed toa manifold-adjacent d-simplex not in star(a ib i).If no suchneighbor exists and the ancestor of s is a principal simplex,the area a s is distributed to that ancestor simplex.Otherwise,the manifold component(star(a i b i))of s is being squashed be-tween two other manifold components,and a s is discarded. 3.4Generalized vertex split transformation Constructing the PSC representation involves recording the infor-mation necessary to perform the inverse of each vunify i.This inverse is the generalized vertex split gvspl i,which splits a0-simplex a i to introduce an additional0-simplex b i.(As mentioned previously, renumbering of simplices implies b i i+1,so index b i need not be stored explicitly.)Each gvspl i record has the formgvspl i(a i C K i midp i()i C D i C A i)and constructs model M i+1from M i by updating the tuple (K V D A)as follows:K:As illustrated in Figure4,any simplex adjacent to a i in K i can be the vunify result of one of four configurations in K i+1.To construct K i+1,we therefore replace each ancestor simplex s star(a i)in K i by either(1)s,(2)(s a i)i+1,(3)s and(s a i)i+1,or(4)s,(s a i)i+1and s i+1.The choice is determined by a split code associated with s.Thesesplit codes are stored as a code string C Ki ,in which the simplicesstar(a i)are sortedfirst in order of increasing dimension,and then in order of increasing simplex id,as shown in Figure5. V:The new vertex is assigned position i+1i+1=i ai+()i.Theother vertex is given position i+1ai =i ai()i if the boolean pa-rameter midp i is true;otherwise its position remains unchanged.D:The string C Di is used to assign materials d s for each newprincipal simplex.Simplices in C Di ,as well as in C Aibelow,are sorted by simplex dimension and simplex id as in C Ki. A:During reconstruction,we are only interested in the areas a s fors01(K).The string C Ai tracks changes in these areas.Figure4:Effects of split codes on simplices of various dimensions.code string:41422312{}Figure5:Example of split code encoding.3.5PropertiesLevels of detail A graphics application can efficiently transitionbetween models M1M n at runtime by performing a sequence ofvunify or gvspl transformations.Our current research prototype wasnot designed for efficiency;it attains simplification rates of about6000vunify/sec and refinement rates of about5000gvspl/sec.Weexpect that a careful redesign using more efficient data structureswould significantly improve these rates.Geomorphs As in the PM representation,there exists a corre-spondence between the vertices of the models M1M n.Given acoarser model M c and afiner model M f,1c f n,each vertexj K f corresponds to a unique ancestor vertex f c(j)K cfound by recursively traversing the ancestor simplex relations:f c(j)=j j cf c(a j1)j cThis correspondence allows the creation of a smooth visual transi-tion(geomorph)M G()such that M G(1)equals M f and M G(0)looksidentical to M c.The geomorph is defined as the modelM G()=(K f V G()D f A G())in which each vertex position is interpolated between its originalposition in V f and the position of its ancestor in V c:Gj()=()fj+(1)c f c(j)However,we must account for the special rendering of principalsimplices of dimension0and1(Section3.1).For each simplexs01(K f),we interpolate its area usinga G s()=()a f s+(1)a c swhere a c s=0if s01(K c).In addition,we render each simplexs01(K c)01(K f)using area a G s()=(1)a c s.The resultinggeomorph is visually smooth even as principal simplices are intro-duced,removed,or change dimension.The accompanying video demonstrates a sequence of such geomorphs.Progressive transmission As with PM’s,the PSC representa-tion can be progressively transmitted by first sending M 1,followed by the gvspl records.Unlike the base mesh of the PM,M 1always consists of a single vertex,and can therefore be sent in a fixed-size record.The rendering of lower-dimensional simplices as spheres and cylinders helps to quickly convey the overall shape of the model in the early stages of transmission.Model compression Although PSC gvspl are more general than PM vsplit transformations,they offer a surprisingly concise representation of M .Table 1lists the average number of bits re-quired to encode each field of the gvspl records.Using arithmetic coding [30],the vertex id field a i requires log 2i bits,and the boolean parameter midp i requires 0.6–0.9bits for our models.The ()i delta vector is quantized to 16bitsper coordinate (48bits per),and stored as a variable-length field [7,13],requiring about 31bits on average.At first glance,each split code in the code string C K i seems to have 4possible outcomes (except for the split code for 0-simplex a i which has only 2possible outcomes).However,there exist constraints between these split codes.For example,in Figure 5,the code 1for 1-simplex id 1implies that 2-simplex id 1also has code 1.This in turn implies that 1-simplex id 2cannot have code 2.Similarly,code 2for 1-simplex id 3implies a code 2for 2-simplex id 2,which in turn implies that 1-simplex id 4cannot have code 1.These constraints,illustrated in the “scoreboard”of Figure 6,can be summarized using the following two rules:(1)If a simplex has split code c12,all of its parents havesplit code c .(2)If a simplex has split code 3,none of its parents have splitcode 4.As we encode split codes in C K i left to right,we apply these two rules (and their contrapositives)transitively to constrain the possible outcomes for split codes yet to be ing arithmetic coding with uniform outcome probabilities,these constraints reduce the code string length in Figure 6from 15bits to 102bits.In our models,the constraints reduce the code string from 30bits to 14bits on average.The code string is further reduced using a non-uniform probability model.We create an array T [0dim ][015]of encoding tables,indexed by simplex dimension (0..dim)and by the set of possible (constrained)split codes (a 4-bit mask).For each simplex s ,we encode its split code c using the probability distribution found in T [s dim ][s codes mask ].For 2-dimensional models,only 10of the 48tables are non-trivial,and each table contains at most 4probabilities,so the total size of the probability model is small.These encoding tables reduce the code strings to approximately 8bits as shown in Table 1.By comparison,the PM representation requires approximately 5bits for the same information,but of course it disallows topological changes.To provide more intuition for the efficiency of the PSC repre-sentation,we note that capturing the connectivity of an average 2-manifold simplicial complex (n vertices,3n edges,and 2n trian-gles)requires ni =1(log 2i +8)n (log 2n +7)bits with PSC encoding,versus n (12log 2n +95)bits with a traditional one-way incidence graph representation.For improved compression,it would be best to use a hybrid PM +PSC representation,in which the more concise PM vertex split encoding is used when the local neighborhood is an orientableFigure 6:Constraints on the split codes for the simplices in the example of Figure 5.Table 1:Compression results and construction times.Object#verts Space required (bits/n )Trad.Con.n K V D Arepr.time a i C K i midp i (v )i C D i C Ai bits/n hrs.drumset 34,79412.28.20.928.1 4.10.453.9146.1 4.3destroyer 83,79913.38.30.723.1 2.10.347.8154.114.1chandelier 36,62712.47.60.828.6 3.40.853.6143.6 3.6schooner 119,73413.48.60.727.2 2.5 1.353.7148.722.2sandal 4,6289.28.00.733.4 1.50.052.8123.20.4castle 15,08211.0 1.20.630.70.0-43.5-0.5cessna 6,7959.67.60.632.2 2.50.152.6132.10.5harley 28,84711.97.90.930.5 1.40.453.0135.7 3.52-dimensional manifold (this occurs on average 93%of the time in our examples).To compress C D i ,we predict the material for each new principalsimplex sstar(a i )star(b i )K i +1by constructing an ordered set D s of materials found in star(a i )K i .To improve the coding model,the first materials in D s are those of principal simplices in star(s )K i where s is the ancestor of s ;the remainingmaterials in star(a i )K i are appended to D s .The entry in C D i associated with s is the index of its material in D s ,encoded arithmetically.If the material of s is not present in D s ,it is specified explicitly as a global index in D .We encode C A i by specifying the area a s for each new principalsimplex s 01(star(a i )star(b i ))K i +1.To account for this redistribution of area,we identify the principal simplex from which s receives its area by specifying its index in 01(star(a i ))K i .The column labeled in Table 1sums the bits of each field of the gvspl records.Multiplying by the number n of vertices in M gives the total number of bits for the PSC representation of the model (e.g.500KB for the destroyer).By way of compari-son,the next column shows the number of bits per vertex required in a traditional “IndexedFaceSet”representation,with quantization of 16bits per coordinate and arithmetic coding of face materials (3n 16+2n 3log 2n +materials).4PSC CONSTRUCTIONIn this section,we describe a scheme for iteratively choosing pairs of vertices to unify,in order to construct a PSC representation.Our algorithm,a generalization of [13],is time-intensive,seeking high quality approximations.It should be emphasized that many quality metrics are possible.For instance,the quadric error metric recently introduced by Garland and Heckbert [9]provides a different trade-off of execution speed and visual quality.As in [13,20],we first compute a cost E for each candidate vunify transformation,and enter the candidates into a priority queueordered by ascending cost.Then,in each iteration i =n 11,we perform the vunify at the front of the queue and update the costs of affected candidates.4.1Forming set of candidate vertex pairs In principle,we could enter all possible pairs of vertices from M into the priority queue,but this would be prohibitively expensive since simplification would then require at least O(n2log n)time.Instead, we would like to consider only a smaller set of candidate vertex pairs.Naturally,should include the1-simplices of K.Additional pairs should also be included in to allow distinct connected com-ponents of M to merge and to facilitate topological changes.We considered several schemes for forming these additional pairs,in-cluding binning,octrees,and k-closest neighbor graphs,but opted for the Delaunay triangulation because of its adaptability on models containing components at different scales.We compute the Delaunay triangulation of the vertices of M, represented as a3-dimensional simplicial complex K DT.We define the initial set to contain both the1-simplices of K and the subset of1-simplices of K DT that connect vertices in different connected components of K.During the simplification process,we apply each vertex unification performed on M to as well in order to keep consistent the set of candidate pairs.For models in3,star(a i)has constant size in the average case,and the overall simplification algorithm requires O(n log n) time.(In the worst case,it could require O(n2log n)time.)4.2Selecting vertex unifications fromFor each candidate vertex pair(a b),the associated vunify(a b):M i M i+1is assigned the costE=E dist+E disc+E area+E foldAs in[13],thefirst term is E dist=E dist(M i)E dist(M i+1),where E dist(M)measures the geometric accuracy of the approximate model M.Conceptually,E dist(M)approximates the continuous integralMd2(M)where d(M)is the Euclidean distance of the point to the closest point on M.We discretize this integral by defining E dist(M)as the sum of squared distances to M from a dense set of points X sampled from the original model M.We sample X from the set of principal simplices in K—a strategy that generalizes to arbitrary triangulated models.In[13],E disc(M)measures the geometric accuracy of disconti-nuity curves formed by a set of sharp edges in the mesh.For the PSC representation,we generalize the concept of sharp edges to that of sharp simplices in K—a simplex is sharp either if it is a boundary simplex or if two of its parents are principal simplices with different material identifiers.The energy E disc is defined as the sum of squared distances from a set X disc of points sampled from sharp simplices to the discontinuity components from which they were sampled.Minimization of E disc therefore preserves the geom-etry of material boundaries,normal discontinuities(creases),and triangulation boundaries(including boundary curves of a surface and endpoints of a curve).We have found it useful to introduce a term E area that penalizes surface stretching(a more sophisticated version of the regularizing E spring term of[13]).Let A i+1N be the sum of triangle areas in the neighborhood star(a i)star(b i)K i+1,and A i N the sum of triangle areas in star(a i)K i.The mean squared displacement over the neighborhood N due to the change in area can be approx-imated as disp2=12(A i+1NA iN)2.We let E area=X N disp2,where X N is the number of points X projecting in the neighborhood. To prevent model self-intersections,the last term E fold penalizes surface folding.We compute the rotation of each oriented triangle in the neighborhood due to the vertex unification(as in[10,20]).If any rotation exceeds a threshold angle value,we set E fold to a large constant.Unlike[13],we do not optimize over the vertex position i a, but simply evaluate E for i a i+1a i+1b(i+1a+i+1b)2and choose the best one.This speeds up the optimization,improves model compression,and allows us to introduce non-quadratic energy terms like E area.5RESULTSTable1gives quantitative results for the examples in thefigures and in the video.Simplification times for our prototype are measured on an SGI Indigo2Extreme(150MHz R4400).Although these times may appear prohibitive,PSC construction is an off-line task that only needs to be performed once per model.Figure9highlights some of the benefits of the PSC representa-tion.The pearls in the chandelier model are initially disconnected tetrahedra;these tetrahedra merge and collapse into1-d curves in lower-complexity approximations.Similarly,the numerous polyg-onal ropes in the schooner model are simplified into curves which can be rendered as line segments.The straps of the sandal model initially have some thickness;the top and bottom sides of these straps merge in the simplification.Also note the disappearance of the holes on the sandal straps.The castle example demonstrates that the original model need not be a mesh;here M is a1-dimensional non-manifold obtained by extracting edges from an image.6RELATED WORKThere are numerous schemes for representing and simplifying tri-angulations in computer graphics.A common special case is that of subdivided2-manifolds(meshes).Garland and Heckbert[12] provide a recent survey of mesh simplification techniques.Several methods simplify a given model through a sequence of edge col-lapse transformations[10,13,14,20].With the exception of[20], these methods constrain edge collapses to preserve the topological type of the model(e.g.disallow the collapse of a tetrahedron into a triangle).Our work is closely related to several schemes that generalize the notion of edge collapse to that of vertex unification,whereby separate connected components of the model are allowed to merge and triangles may be collapsed into lower dimensional simplices. Rossignac and Borrel[21]overlay a uniform cubical lattice on the object,and merge together vertices that lie in the same cubes. Schaufler and St¨u rzlinger[22]develop a similar scheme in which vertices are merged using a hierarchical clustering algorithm.Lue-bke[18]introduces a scheme for locally adapting the complexity of a scene at runtime using a clustering octree.In these schemes, the approximating models correspond to simplicial complexes that would result from a set of vunify transformations(Section3.3).Our approach differs in that we order the vunify in a carefully optimized sequence.More importantly,we define not only a simplification process,but also a new representation for the model using an en-coding of gvspl=vunify1transformations.Recent,independent work by Schmalstieg and Schaufler[23]de-velops a similar strategy of encoding a model using a sequence of vertex split transformations.Their scheme differs in that it tracks only triangles,and therefore requires regular,2-dimensional trian-gulations.Hence,it does not allow lower-dimensional simplices in the model approximations,and does not generalize to higher dimensions.Some simplification schemes make use of an intermediate vol-umetric representation to allow topological changes to the model. He et al.[11]convert a mesh into a binary inside/outside function discretized on a three-dimensional grid,low-passfilter this function,。

Vocal SetFEATURES• Engineered for professional live sound:Rugged all-in-one wireless system for singers andpresenters.• State-of-the-art live sound featuring Sennheiser‘s renowned e 835, e 845, e 865, e 935, e 945 capsules on a lightweight aluminium transmitter with integrated mute switch• True diversity half-rack receiver in a full-metal housing with intuitive LCD display for full control• Easy and flexible wireless synchronization between transmitter and receiver via infrared• Fast frequency allocation for up to 12 receivers via new linking functionality• Up to 20 compatible channels• Up to 42 MHz bandwidth with 1680 selectable frequen-cies, fully tunable in a stable UHF range• Transmission Range: up to 100 meters / 300 feet• High RF output power (up to 30 mW) depending on country regulations DELIVERY INCLUDES• EM 100 G4 true diversity receiver• SKM 100 G4-S handheld transmitter• MMD 935-1 microphone head (935-S variants only)• MMD 945-1 microphone head (945-S variants only)• GA 3 rackmount set• MZQ 1 microphone clamp• power supply• 2 AA batteries• 2 rod antennas• RJ 10 cable• quick guide• safety guide• manufacturer declaration sheet• frequency supplement sheetVersatile wireless systems for those who sing, speak or play instruments with up to 42 MHz tuning bandwidth in a stable UHF range and fast, simultaneous setup of up to 12 linked systems.State-of-the-art live sound featuring Sennheiser‘s renowned e 935 and e 945 capsules on a lightweight aluminum trans-mitter with integrated mute switch.Vocal Set SPECIFICATIONSEM 100 G4RF characteristicsModulation Wideband FM Frequency ranges A1: 470 - 516 MHzA: 516 - 558 MHzAS: 520 - 558 MHzG: 566 - 608 MHzGB: 606 - 648 MHzB: 626 - 668 MHzC: 734 - 776 MHzD: 780 - 822 MHzE: 823 - 865 MHzJB: 806 - 810 MHzK+: 925 - 937.5 MHz1G8: 1785 - 1800 MHz Receiving frequencies Max. 1680 receivingfrequencies, adjustable in25 k Hz steps20 frequency banks, eachwith up to 12 factory-presetchannels, no intermodula-tion1 frequency bank with up to12 programmable channels Switching bandwidth up to 42 MHzNominal/peak deviation±24 kHz / ±48 kHz Receiver principle True diversitySensitivity (with HDX, peak deviation)< 2.5 μV for 52 dBAeff S/NAdjacent channel selection Typically ≥ 65 dB Intermodulation attenua-tionTypically ≥ 65 dB Blocking≥ 70 dB Squelch low: 5 dBμVmiddle: 15 dBμVhigh: 25 dBμVPilot tone squelch Can be switched off Antenna inputs 2 BNC socketsAF characteristicsCompander system Sennheiser HDXEQ presets (switchable,act on line and monitoroutputs)Preset 1: FlatPreset 2:Low Cut (-3 dB at 180 Hz)Preset 3:Low Cut/High Boost(-3 dB at 180 Hz,+6 dB at 10 kHz)Preset 4:High Boost(+6 dB at 10 kHz)Signal-to-noise ratio (1 mV,peak deviation)≥ 110 dBATotal harmonic distortion(THD)≤ 0.9 %AF output voltage (at peakdeviation, 1 kHz AF)6.3 mm jack socket(unbalanced): +12 dBuXLR socket(balanced): +18 dBu Setting range “AF Out”48 dB (3 dB steps) Overall deviceTemperature range-10 °C to +55 °CPower supply12 V DCCurrent consumption300 mADimensions Approx. 190 x 212 x 43 mm Weight Approx. 980 gCONNECTIONSVocal Set SPECIFICATIONSSKM 100 G4-SRF characteristicsModulation Wideband FM Frequency ranges A1: 470 - 516 MHzA: 516 - 558 MHzA10: 516 - 558 MHzAS: 520 - 558 MHzG: 566 - 608 MHzGB: 606 - 648 MHzB: 626 - 668 MHzB10: 626 - 668 MHzC: 734 - 776 MHzD: 780 - 822 MHzJB: 806 - 810 MHzE: 823 - 865 MHzK+: 925 - 937,5 MHz1G8: 1785 - 1800 MHz Transmission frequencies Max. 1680 receivingfrequencies, adjustable in25 k Hz steps20 frequency banks, eachwith up to 12 factory-presetchannels, no intermodula-tion1 frequency bank with up to12 programmable channels Switching bandwidth up to 42 MHzNominal/peak deviation±24 kHz / ±48 kHz Frequency stability≤ ±15 ppmRF output power at 50 ΩMax. 30 mWPilot tone squelch Can be switched off AF characteristicsCompander system Sennheiser HDXAF frequency response80 – 18,000 HzSignal-to-noise ratio (1 mV,peak deviation)≥ 110 dBATotal harmonic distortion(THD)≤ 0.9 %Max. input voltage 3 VeffInput impedance40 kΩInput capacitance SwitchableSetting range for inputsensitivity48 dB,adjustable in 6 dB steps Overall deviceTemperature range-10 °C to +55 °CPower supply 2 AA batteries, 1.5 V orBA 2015 accupack Nominal voltage 3 V battery /2.4 V rechargeable battery Current consumption at nominal voltage:typ. 180 mAwith transmitter switchedoff: ≤ 25 μAOperating time Typically 8 h Dimensions Approx. Ø 50 x 265 mm Weight (incl. batteries)approx. 450 gSPECIFICATIONSMMD 935-1Transducer principle dynamic Sensitivity 2.5 mV/Pa Sound pressure level154 dB SPL Pick-up pattern cardioid MMD 945-1Transducer principle dynamic Sensitivity 1.8 mV/Pa Sound pressure level154 dB SPL Pick-up pattern supercardioidVocal Set PRODUCT VARIANTSew 100 G4-935-S-A1470 - 516 MHz Art. no. 509737 ew 100 G4-935-S-A516 - 558 MHz Art. no. 509728 ew 100 G4-935-S-AS520 - 558 MHz Art. no. 509805 ew 100 G4-935-S-G566 - 608 MHz Art. no. 509739 ew 100 G4-935-S-GB606 - 648 MHz Art. no. 509982 ew 100 G4-935-S-B626 - 668 MHz Art. no. 509740 ew 100 G4-935-S-C734 - 776 MHz Art. no. 509806 ew 100 G4-935-S-D780 - 822 MHz Art. no. 509807 ew 100 G4-935-S-JB806 - 810 MHz Art. no. 509862 ew 100 G4-935-S-E823 - 865 MHz Art. no. 509983 ew 100 G4-935-S-1G81785 - 1800 MHz Art. no. 509964 ew 100 G4-945-S-A1470 - 516 MHz Art. no. 509741 ew 100 G4-945-S-A516 - 558 MHz Art. no. 509742 ew 100 G4-945-S-AS520 - 558 MHz Art. no. 509808 ew 100 G4-945-S-G566 - 608 MHz Art. no. 509743 ew 100 G4-945-S-GB606 - 648 MHz Art. no. 509984 ew 100 G4-945-S-B626 - 668 MHz Art. no. 509744 ew 100 G4-945-S-C734 - 776 MHz Art. no. 509809 ew 100 G4-945-S-D780 - 822 MHz Art. no. 509810 ew 100 G4-935-S-JB806 - 810 MHz Art. no. 509863 ew 100 G4-945-S-E823 - 865 MHz Art. no. 509985 ew 100 G4-945-S-1G81785 - 1800 MHz Art. no. 509986Vocal Set DIMENSIONSEM 100 G4Vocal Set DIMENSIONSSKM 100 G4-SVocal SetSennheiser electronic GmbH & Co. KG · Am Labor 1 · 30900 Wedemark · Germany · ARCHITECT‘S SPECIFICATIONA wireless RF transmission system consisting of a stationary receiver and a handheld transmitter includings a micropho-ne head.The system shall operate within twelve UHF frequency ranges, with a switching bandwidth of up to 42 MHz: 470 –516 M Hz, 516 – 558 MHz, 520 – 558 MHz, 566 – 608 M Hz, 606 – 648 MHz, 626 – 668 MHz, 734 – 776 MHz, 780 – 822 M Hz, 823 – 865 MHz, 806 – 810 MHz, 925 – 937.5 M Hz, 1785 – 1800 MHz; receiving frequencies shall be 1,680 per range and shall be tunable in 25 kHz steps. The system shall feature 20 fixed frequency banks with up to 12 compatible frequency presets and 1 user bank with up to 12 user programmable frequencies.The receiver shall be menu-driven with a backlit LC display showing the current frequency, frequency bank and channel number, metering of RF level, metering of AF level, lock status, pilot tone evaluation, muting function, and battery status of the associated transmitter. An auto-lock feature shall be provided to prevent settings from being accidentally altered. The receiver shall feature an integrated guitar tuner and shall provide a sound check mode.Some receiver parameters such as receiving frequency, receiver name and pilot tone setting shall be synchronizable with the associated transmitter via an integrated infrared interface.The receiver shall feature a balanced XLR-3M audio output with a maximum output of +18 dBu along with an unbalanced ¼" (6.3 mm) audio output with a maximum output of +12 dBu. The receiver shall have two DATA ports (RJ 10) to set up a multichannel system. Two BNC-type input sockets shall be provided for connecting the antennas. Nominal/peak devia-tion shall be ±24 kHz/±48 kHz. Squelch threshold shall be adjustable to three levels: Low (5 dBμV), Middle (15 dBμV) and High (25 dBμV).The receiver shall incorporate the Sennheiser HDX compander system and a defeatable pilot tone squelch. Sensitivity shall be < 2 μV for 52 dBA eff S/N with HDX engaged at peak deviation. Adjacent channel rejection shall be ≥ 65 d B (ty-pical). Intermodulation attenuation shall be ≥ 65 d B (typical); blocking shall be ≥ 70 dB. Four selectable equalizer presets shall be provided: “Flat”, “Low Cut” (−3 d B at 180 Hz), “Low Cut/High Boost” (−3 d B at 180 H z/+6 dB at 10 kHz) and “High Boost” (+6 dB at 10 k Hz). Signal-to-noise ratio at 1 mV and peak deviation shall be ≥ 110 dBA. Total harmonic distortion (THD) shall be ≤ 0.9 %. The audio output level shall be adjustable within a 48 d B range in steps of 3 dB.The receiver shall operate on 12 V power supplied from the NT 2-3 CW mains unit (for 100 – 240 V AC, 50/60 Hz). Power consumption shall be 300 mA. The receiver shall have a rugged metal housing; dimensions shall be approximately 190 x 212 x 43 mm (7.48" x 8.35" x 1.69"). Weight shall be approximately 980 grams (2.16 lbs). Operating temperature shall range from −10 °C to +55 °C (+14 °F to +131 °F).The receiver shall be the Sennheiser EM 100 G4.The radio microphone shall be menu-driven with a backlit LC display showing the current frequency, frequency bank and channel number, metering of AF level, transmission status, lock status, pilot tone transmission, muting function, and bat-tery status. An auto-lock feature shall be provided to prevent settings from being accidentally altered.The radio microphone parameters shall either be configurable in the associated receiver’s menu and synchronized with the radio microphone via an integrated infrared interface or shall be programmable in the radio microphone menu. Recei-ver parameters such as receiving frequency, receiver name and pilot tone setting shall be synchronizable with the radio microphone via an integrated infrared interface.The handheld vocal radio microphone shall be equipped with a mute switch, which shall be switchable between “AF on/off”, “RF on/off” and “Disabled” via the user interface. Nominal/peak deviation shall be ±24 kHz/±48 kHz. Frequency stability shall be ≤ ±15 ppm. RF output power at 50 Ω shall be 30 mW (typical).The radio microphone shall incorporate the Sennheiser HDX compander system and a defeatable pilot tone squelch.Audio frequency response shall range from 80 – 18,000 Hz. Signal-to-noise ratio at 1 mV and peak deviation shall be ≥ 110 dBA. Total harmonic distortion (THD) shall be ≤ 0.9 %. Input sensitivity shall be adjustable within a 48 dB range in steps of 6 dB.Power shall be supplied to the radio microphone by two 1.5 V AA size batteries or by one Sennheiser BA 2015 recharge-able accupack. Nominal voltage shall be 2.4 V, current consumption shall be typical 180 mA at nominal voltage; ≤ 25 μA when radio microphone is switched off. Operating time shall be typical 8 hours. The radio microphone shall have a rugged metal housing; dimensions shall be approximately 50 mm (1.97") in diameter and 265 mm (10.43") in length. Weight inclu-ding the batteries shall be approximately 450 grams (0.99 lbs). Operating temperature shall range from −10 °C to +55 °C (+14 °F to +131 °F).A range of microphone heads shall be available for the radio microphone.The radio microphone shall be the Sennheiser SKM 100-S G4.。

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROLInt.J.Robust Nonlinear Control2001;11:515}539(DOI:10.1002/rnc.596)Hybrid control of force transients for multi-pointinjection enginesAndrea Balluchi ,Luca Benvenuti ,Maria Domenica Di Benedetto * R,Alberto L.Sangiovanni-VincentelliPARADES,<ia San Pantaleo,66,00186Roma,ItalyDip.di Informatica e Sistemistica,;niversita degli Studi di Roma**La Sapienza++,<ia Eudossiana18,00184Roma,ItalyDip.di Ingegneria Elettrica,;niversita degli Studi de¸+Aquila,Poggio di Roio,67040L+Aquila,Italy Department of Electrical Engineering and Computer Science,Uni v ersity of California at Berkeley,CA94720,U.S.A.SUMMARYWe address the problem of delivering as quickly as possible a requested torque produced by a spark ignition engine equipped with a multi-point port injection manifold and with electronic throttle.The optimal control problem,subject to the constraint that the air}fuel ratio stays within a pre-assigned range around the stoichiometric ratio,is solved for a detailed,cycle-accurate hybrid model with a hybrid control approach based on a two-step process.In the"rst step,a continuous approximation of the hybrid problem is solved exactly.Then,the control law so obtained is adjusted to satisfy the constraints imposed by the hybrid model. The quality of the control law has been in part analytically demonstrated and in part validated with simulations.Copyright 2001John Wiley&Sons,Ltd.KEY WORDS:hybrid systems;engine control;optimal control1.INTRODUCTIONIn this paper,we deal with the problem of delivering as quickly as possible a requested torque produced by a spark ignition engine equipped with a multi-point port injection manifold and electronics to control the throttle-valve position.The control variables are the amount of injected fuel and the voltage given to the electric motor controlling the position of the throttle valve.The optimization problem is subject to the constraint that the air}fuel(A/F)ratio stays within a pre-assigned range around the stoichiometric value of14.64(the ratio that guarantees minimum emission).Air and fuel dynamics depend on the pressure in the intake manifold}that is controlled by throttle valve}and on second order phenomena,the most important of which is the#uid"lm dynamics[1].The#uid"lm is created during the injection process:after fuel is injected in the intake runner,part of the vapourized fuel turns into#uid that deposits onto the*Correspondence to:M.D.Di Benedetto,Dip.di Ingegneria Elettrica,Universita degli Studi de L'Aquila,Poggio di Roio, 67040L'Aquila,Italy.-E-mail:dibenede@ing.univaq.itContract/grant sponsor:PARADES,P.F.MADESS II CNRCopyright 2001John Wiley&Sons,Ltd.516 A.BALLUCHI E¹A¸.walls of the intake runner and,hence,it is not immediately available for combustion inside the cylinders.The#uid"lm on the intake walls evaporates again,thus contributing to the combus-tion process but with a noticeable delay.This phenomenon(unfortunately)cannot be ignored, since it has a de"nite e!ect on the performance of the combustion process.The most used solutions to this problem consist of feed-forward compensation of fuel dynamics [2}4],based on mean value engine models[5}7].However,the mean values of the engine variables of interest may not be accurate enough to guarantee small transient deviations from the optimal A/F ratio.In this paper,we propose an approach that yields very small deviations with respect to the optimal A/F ratio,by using a hybrid model of the cyclic behaviour of the engine. The hybrid model describes accurately the detailed behaviour of the actuated throttle,of the injection systems and of the torque-generation mechanism,and,at the same time,allows to develop powerful closed-loop control laws.Our goal is to design a control law for the fuel injection durations and the voltage supplied to the throttle valve motor to drive the evolution of the system from an initial condition character-ized by the delivery of a torque u to a"nal condition characterized by the delivery of a requested torque u in minimum time subject to constraints on emissions.Note that the control problem described above is new not only because we use a detailed hybrid model for the injection process but also because we consider the entire control chain,from throttle motor to engine. Minimum time is not the only relevant criterion to follow when considering control schemes that might be implemented for engine control.Preventing signi"cant over/undershoot of the reference value,and being able to hold the desired torque value within some bounds are also desirable.However,minimum-time control problems have well-known solutions that allow us to obtain powerful control laws.These additional criteria can be cast into constraints in the same way as we have done for the A/F ratio.Then,the optimal control problem can still be solved analytically even though the number of details and the notational complexity would become overwhelming.For this reason,we have chosen to ignore these additional considerations since handling them could cloud our approach.Our approach to the optimal control problem is a two-step process:(1)we"rst introduce and solve an auxiliary optimal control problem in continuous time,and(2)we then map the optimal control law back in the hybrid domain trying to maintain as muchas possible the properties of the solution.The mapping process is critical for obtaining a satisfactory solution to the hybrid problem. Several mappings are possible that satisfy the restrictions imposed by the hybrid model but we want to choose a mapping that satis"es the original constraints and is as close as possible to the optimal solution.In Reference[8],we have solved with the same approach a related,but simpler, problem where throttle actuation was not taken into account,the torque requested was zero and the cost function was the amount of undesired oscillations of the power-train(the cut-o!problem). In this case,we were able to prove that the hybrid control law obtained by the mapping process ensured stability and constraints satisfaction.In this paper,given the additional details that have been considered,the control law obtained by the two-step process is more complex and depends on several engine and car parameters.For this reason,the theoretical results are weaker,but,under conservative assumptions on the relative speed of the crankshaft at the time of successive injection points and ignoring the dynamics of the throttle control loop,we can still prove that the original constraints on the problem are satis"ed with the hybrid control law.Simulation data are used to validate the control laws for the full chain on power-trains of existing cars.Copyright 2001John Wiley&Sons,Ltd.Int.J.Robust Nonlinear Control2001;11:515}539It can be argued that a dual approach,relying on a discretized,crank-angle-based abstraction of the cylinder 's FSM (see for example Reference [9]),would solve the problem tackled in this paper as well.Since we assume small excursions in engine speed,then the phase of mapping the auxiliary optimal solution into the hybrid domain is likely to be easier when discrete }time abstractions are used with respect to continuous-time ones.Indeed,the cut-o !problem has also been solved using this approach [10],i.e.a discrete-time abstraction of the hybrid model of the plant was used.However,the weakness of this approach lies on the lack of theoretical results asserting properties of the control law since only numerical solutions to the auxiliary optimal discrete-time control problem can be obtained.2.PLANT MODEL AND PROBLEM FORMULATIONIn this section,a hybrid model for vehicles with four-stroke four-cylinder gasoline engine equipped with a multi-point injection system and electronic throttle is illustrated.The model (an expansion of the model in Reference [8])consists of four parts (see Figure 1):two continuous-time systems,modelling the power-train dynamics and the air dynamics,respectively,and two parts each composed of four hybrid systems modelling the behaviour of each cylinder and of each injection system.Air Dynamics :The model of the quantity of air entering the cylinder during the intake run is obtained from the air #ow balance equation of the manifold.The air mass m ,loaded during an intake run,is subject to the manifold pressure (p )dynamics which is controlled by the throttle valve actuated by a DC motor.Since we consider short control horizon,then we can assume small variations of the crankshaft speed and neglect the dependence from it in the air dynamics.The model is then(t )"a ? (t )#b ?v (t )(1)p (t )"a N p (t )#b N(t )(2)m ?(t )"c Np (t )(3)where v 3[!<,#<]is the DC motor voltage and is the throttle angle that is subject to the constraint0) (t ))90(4)Powertrain model :Powertrain dynamics are modelled by the linear system(t )"A (t )#bu (t )where "[ C , , N ]2represents the axle torsion angle,the crankshaft revolution speed and the wheel revolution speed.The input signal u is the torque produced by the engine and acting on the crankshaft.Model parameters A and b ,depend on the transmission gear which is assumed not to change.A single-state hybrid system emits the event dead }point ,when pistons reach either the top or bottom dead centers,and produces the crank angle .¹orque -generation :The behaviour of each cylinder in the engine is abstractly represented by a "nite state machine (FSM)and a discrete event system (DES)modelling torque generation.The FSM state S G of the i th cylinder assumes values in the set +H G ,I G ,C G ,E G ,which correspond to the exhaust,intake,compression and expansion strokes,respectively,in the four-stroke engine 517TRANSIENTS CONTROL FOR INJECTION ENGINES Copyright 2001John Wiley &Sons,Ltd.Int .J .Robust Nonlinear Control 2001;11:515}539518 A.BALLUCHI E¹A¸.Figure1.Engine hybrid model E.The hybrid models of the cylinders and injection systems2,3and4arenot reported due to space limitation.Copyright 2001John Wiley&Sons,Ltd.Int.J.Robust Nonlinear Control2001;11:515}539cycle.An FSM transition occurs when the piston reaches a dead center,that is when the event dead }point is emitted.The DES describing the torque generation process of the i th cylinder increments its sequence index k by one at each transition of the FSM.Its inputs are the masses m ?G and m TG of air and fuel loaded during the intake phase;its output is the torque u G (k )generated by the cylinder.At the transition I G P C G ,that is at time t 'G ,the event int }end G is generated and the DES reads its inputs,storing in q ?G and q TG their values.The amount of torque achievable during the next expansion phase,obtained by the fuel-to-torque gain G ,is stored in z G .The DES output u G (k )is always zero except at the C G P E G transition when it is set to the value stored in z G .Input u G (t )to the powertrain dynamics is obtained from u G (k )by a zero }order holder latched on the event dead }point .Injection process :The i th injection system is abstractly represented by a hybrid system,whose discrete state F G assumes values in the set +J G ,B G ,=G ,D G,described below:(1)J G :the injector is open and delivers a constant #ow P of vapourized fuel.A fraction of it condenses in a fuel puddle and increases the mass m JG of liquid fuel,fraction 1! increases the mass m TG of vapourized fuel in the intake runner.The mass of liquid fuel evaporates o !with a time constant .The injector remains open for G seconds modelled by timer t G .(2)B G :the injector is closed and the evaporation process continues.When the next dead }point event is emitted,the intake valve opens,and the air }fuel mix begins to enter the cylinder.Atthe I G P C G transition,the intake valve closes and the int }end G event is generated.The mass m TG of vapour is reset to zero since all the vapour fuel has been loaded in the cylinder.(3)=G :the injector is closed and evaporation proceeds.(4)D G :the beginning of fuel injection is synchronized with respect to the beginning of the exhaust phase with a time delay of t B seconds measured by timer t G .This delay allows to locate the injection interval with respect to the engine cycle.The value of t B ,which in general depends on the crankshaft speed,is considered constant since small engine speed variations are assumed.In this state,fuel dynamics is as in state =G.Engine hybrid model :The overall model E of the engine is the combination of four hybrid systems representing the behaviour of each cylinder and related injection system,and the powertrain and intake manifold models which are shared among all cylinders.The pistons are connected to the crankshaft,so that dead-points are synchronous and the cycle of each one is delayed one step with respect to the cycle of the previous one.Then,the dead }point events and the sequence index k are shared among all the cylinders and only one signal u G (t )may be di !erent from zero at any time.Input signals are:the input voltage v (t )to the DC motor actuating the throttle valve,a scalar continuous time signal in the class of functions 1> P [!<,#<];the injection intervals (k ),a scalar discrete time signal in the class of functions Z > P [0, ],which is sequentially distributed over the four injectors synchronously with the corresponding exp }end G event.The state of the overall hybrid systems is a triple (q ,z ,x )where:(1)q "[S ,F ,S ,F ,S ,F ,S ,F ]is the state of the FSMs associated to each cylinder and each injection system;(2)z "[z ,q T ,q ? ,2,z ,q T ,q ? ]is the cylinder DES state;(3)x "[ , , ,p ,t ,t ,m T ,m J ,2,t ,t ,m T ,m J ]is the continuous state associated to the powertrain,air dynamics,and to each injection system.The output of the overall system is the generated torque u .519TRANSIENTS CONTROL FOR INJECTION ENGINES Copyright 2001John Wiley &Sons,Ltd.Int .J .Robust Nonlinear Control 2001;11:515}539520 A.BALLUCHI E¹A¸.2.1.Problem formulationIn order to reduce tailpipe emissions,the air}fuel ratio A/F of each cylinder has to be kept in a range[¸ ,¸ ]around the stoichiometric value¸ "14.64.This corresponds to requiring the following constraint:¸q TG!(k))q?G!(k))¸ q TG!(k)(5) where i!denotes the index of the cylinder which enters the state C.Given a value u of torque produced by the engine,de"ne in the hybrid state space the set T(u) of all the hybrid states(q(0),z(0),x(0))for which there exist v(t):1> P[!<,#<]and (k):Z> P[0, ]such that for all t*0and for all k*0,u(t)"u and constraints(4)and(5) are satis"ed.Note that the set T(u)consists of all the state trajectories of the hybrid model E such that along the entire trajectories a constant torque u is generated while constraints on inputs v, and states ,q?,q T are satis"ed.Problem1Consider the engine hybrid model E,shown in Figure1.Let u and u be the initial value and the desired value of the torque,respectively.Assume that,at the initial time t"0,the hybrid state (q ,z ,x )belongs to T(u ).Find v(t):1> P[!<,#<]and (k):Z> P[0, ]such that(1)the initial state(q ,z ,x )is steered to T(u )at some unspeci"ed time tଙ;(2)for all t*0and for all k*0constraints(4)and(5)are satis"ed;(3)the time tଙis minimized.3.AUXILIARY CONTINUOUS-TIME OPTIMAL CONTROL WITHOUTTHROTTLE DYNAMICSIn this section,the interactions between fuel and air dynamics,subject to constraint(5),are analysed by considering a continuous-time model approximating the behaviour of the engine hybrid model E.The hybrid nature of the intake process is abstracted away by using average continuous-time models for fuel and air whose outputs are the average fuel#ow,f T(t),and air #ow,f?(t),entering the cylinders.Moreover,we abstract away the throttle actuation dynamics and consider the throttle valve to be the air dynamics input.To solve the continuous optimal problem,we follow a two-step process:in the"rst step,we"nd the minimum-time control for the air dynamics alone;in the second step,we introduce the fuel dynamics and appropriately modify the optimal control law found in the"rst step to solve the continuous optimal problem at hand.The intake manifold dynamics is described by Equation(2)and constraint(4).The#ow of air f?(t)entering the cylinders is expressed as:f?(t)"c?p(t)where c?"( /30)c N.Furthermore,fuel dynamics is modelled by the average modelm J(t)"a J m J(t)#b Jf T(t)"c J m J(t)#d J (t)(6) Copyright 2001John Wiley&Sons,Ltd.Int.J.Robust Nonlinear Control2001;11:515}539where f T denotes the average fuel#ow entering the cylinders,m J denotes the average mass of liquid fuel,and a J"! \ ,b J" P( /30),c J" \ ,d J"(1! )P( /30).The A/F constraints are rewritten for the#ows f?and f T as follows:¸p(t)#¸K m J(t)) (t))¸ p(t)#¸K m J(t)(7) where¸ "c?/¸ d J'0,¸ "c?/¸ d J'0,and¸K"!c J/d J(0.For the continuous-time model considered here,the target set corresponding to the desired torque u isT(u )" (m J,p)"p"p "14.64c N G u (8) so that Problem1reduces to the following one:Problem2Consider the engine continuous-time model described by Equations(2)and(6).Let u and u be the initial value and the desired value of the torque,respectively.Assume that,at the initial time t"0,the state(m J,p )belongs to T(u ).Find (t):1> P[0,90]and (t):1> P[0, ] such that(1)the initial state(m J,p )is steered to T(u )at some unspeci"ed time tଙ;(2)for all t*0,constraints(7)are satis"ed;(3)the time tଙis minimized.Constraints(7)de"ne a set of feasible values for(m J,p),obtained for ranging in the interval [0, ].Since the liquid fuel mass is non-negative,the set of feasible states for the control problem at hand are de"ned by the following linear inequalities:¸p#¸K m J*0¸p#¸K m J)p,m J*0(9)Note that,when the manifold pressure p is zero,the unique feasible state value is(m J,p)"(0,0), which is obtained with injection "0(neither fuel nor air is loaded by the cylinders).As a matter of fact,the evaporation of any liquid fuel m J'0would produce a rich mixture with f?/f T(¸ . Hence,if a fuel puddle is present,the manifold pressure has to be greater than zero.However,by injecting a proper amount of fuel,some values(m J,p)on the line m J"0may be feasible even if there is no fuel puddle.These values lay on the segment with extremum points(0,0)and (0, /¸ ).Note that only the segment from(0,p )to(!(¸ /¸K)p ,p )of the target set T(u ) belongs to the feasible set(9).Constraints(7)couples between the manifold dynamics(2)and the fuel dynamics(6).If one considers the manifold dynamics(2)alone,the straightforward min-imum-time control to the target point p"p is" 0if p'p90if p(p!(aN/b N)p when p"p (10) 521TRANSIENTS CONTROL FOR INJECTION ENGINESCopyright 2001John Wiley&Sons,Ltd.Int.J.Robust Nonlinear Control2001;11:515}539The minimum-time t ଙneeded to steer an initial manifold pressure p (0)to p ist ଙ"1a N ln p p (0) if p (0)'p 1a N ln a N p #90b N a N p (0)#90b Nif p (0)(p (11)Given an initial fuel value m J (0),the manifold control (10)remains optimal when the fuel dynamics (6)are also considered and constraints (7)are introduced,if there exists a fuel injection signal (t ):[0,t ଙ]P [0, ]such that constraints (7)are satis "ed along the trajectory.In fact,if this is the case,the trajectory (m J (t ),p (t ))starting from (m J (0),p (0))reaches the target set T (u )at time t ଙwithout leaving the feasible set de "ned by (9).In the following we will:(1)"nd the feasible initial conditions for which the control law (10)remains optimal;(2)give the optimal control law for the remaining initial conditions.Consider "rst the initial conditions (m J (0),p (0))in the region delimited by (9)and p (p ,where the control "90is applied.For these conditions it can be shown that there are always values of satisfying (7)along the entire trajectory to the target set.Then,the control law "90and equal to any value satisfying (7)steer the state to the target set in minimum time satisfying the constraints.In this case,the time required to drive the initial condition to the target set is determined only by the pressure dynamics and is given by (11).On the other hand,when p (0)'p and the control "0is applied,it can be the case that no value of satisfying (7)exists for some point of the trajectory.Since p (t )is decreasing,this corresponds to violating the constraint f ?/f T '¸ .To avoid (if possible)this situation,the amount of fuel #ow entering the cylinders has to be minimized,by choosing the minimum admissible value for ,i.e. "max +0,¸ p #¸K m J ,.Then,for the initial conditions (m J (0),p (0))in the region delimited by (9)and p 'p ,where the control "0is applied,there are always values of satisfying (7)along the entire trajectory to the target set provided that (m J (0),p (0))is on the left of the trajectory obtained by backwards integration of dynamics (2)and (6)from the point (!(¸ /¸K )p ,p )with "0and "max +0,¸ p #¸K m J ,.See Figure 2.Hence,for these initial conditions,applying the control law "0and equal to any value satisfying (7),the target set is reached in minimum time and the constraints are satis "ed.Also in this case,the time needed to steer the initial condition to the target set is determined only by the pressure dynamics and is given by Equation (11).In summary,for all the initial conditions in the feasible set (9)and on the left of ,the optimal controls are" 0if p 'p90if p (p and "max +0,¸ p #¸K m J,Fuel dynamics is steered in such a way that it tracks the intake manifold dynamics to satisfy the air }fuel constraint (7).Since the amount of fuel puddle is small with respect to the values of the manifold pressure,then the air dynamics can be controlled in minimum time to the target 522 A.BALLUCHI E ¹A ¸.Copyright 2001John Wiley &Sons,Ltd.Int .J .Robust Nonlinear Control 2001;11:515}539Figure 2.Minimum-time trajectories without throttle dynamics.pressure p ,without any interference due to the air }fuel constraint,which is handled by the injection signal only.For conditions (m J (0),p (0))in the region delimited by (9)and lying to the right of the curve ,the air dynamics has to track the slower fuel evaporation dynamics to achieve air }fuel constraint (7)satisfation.Hence,for these initial conditions,the optimal feedback controls are as follows:" 0if ¸ p #¸K m J '0a J !a N b N p if ¸ p #¸K m J "0and "max +0,¸ p #¸K m J ,(12)According to (12),these initial conditions are "rst steered by the controls "0and "max +0,¸ p #¸K m J ,to the line ¸ p #¸K m J "0.Then,under the action of the control signals "[(a J !a N )/b N ]p and "0,they follow a sliding motion along this constraint until they reach the extremum (!(¸ /¸K )p ,p )of the target set.It is worth noting that,during the sliding motion,the closed-loop system ism J (t )"a J m J(t )p (t )"a N p #b N [(a C !a N )/b N ]p "a Jp Since "a J "("a N ",the pressure dynamics is slowed down to make it follow the fuel dynamics and to satisfy constraints (7).Thus,the control law for given by (12)is optimal since it minimizes the length of the arc of trajectory over the constraint.Summarizing:523TRANSIENTS CONTROL FOR INJECTION ENGINES Copyright 2001John Wiley &Sons,Ltd.Int .J .Robust Nonlinear Control 2001;11:515}539Theorem 1If the initial state (m J ,p )belongs to the feasible set described by inequalities (9),then the optimal control (t ):1> P [0,90]and (t ):1> P [0,]solving Problem 2is: (t )"0if ¸ p (t )#¸K m J (t )'0and p (t )'p ,90if p (t )(p ,!(a N /b N )p if p (t )"p ,[(a C !a N )/b N ]p (t )if ¸ p (t )#¸K m J(t )"0and p (t )'p (13) (t )"max +0,¸ p (t )#¸K m J(t ),.(14)This result can be proved by applying the Pontryagin Maximum Principle.Figure 2shows some minimum-time trajectories to the target set (8)for dynamics (2)and (6),and constraints (7).4.HYBRID CONTROL WITHOUT THROTTLE DYNAMICSThe continuous control law described in Section 3,must be approximated to yield a feasible control law for the hybrid model E introduced in Section 2.More precisely,in the continuous-time model adopted in Section 3,the air }fuel constraints (7)are expressed in terms of the continuous evolutions of the manifold pressure and liquid fuel.Moreover,the control signals and are assumed to be continuous-time signals.When dealing with the hybrid model E ,the air }fuel constraints (5)are expressed in terms of the event-based signals q ?and q T .In addition,the amount of air q ?loaded in the cylinder depends on the manifold pressure p at the dead center corresponding to the end of the intake.The amount of loaded fuel q T depends on the evolution of the hybrid model describing the fuel injection system,which models the delay between the time at which the injection control signal is set and the time at which the fuel is loaded.Then,the main issues to address when we move from the continuous case to the hybrid case are:(1)in model E there is a delay between the time at which the injection control G is set (at the end of the expansion phase)and the time at which the vapourized fuel q T is loaded (at the end of the intake phase);(2)feasible control actions on G are discrete-time signals synchronized with the crank angle.This issue is the main cause of di $culty for devising a hybrid control strategy;(3)in model E ,there exist four independent fuel dynamics,controlled by inputs ,2, ,whose evolutions are constrained with respect to the same air #ow evolution by A /Fbounds.The measurements available for closing the control loop are:the pressure p ,the angle and the crankshaft speed .Since in the solutions derived in Section 3, is chosen as the maximum between 0and ¸ p #¸K m J ,then fuel injection is regulated so to maintain in (5)q ?"¸ q T ,when the cylinder is in the compression stroke.The injection control G for the i th cylinder is set at the end of the expansion stroke (i.e.when the exp }end G event is generated).Consider "rst the design of the fuel injection control.The continuous optimal injection control law (14)has only two possible actions:either no fuel is injected,that is "0;or "¸ p #¸K m J ,which corresponds to producing a mixture with maximum feasible value of A /F ratio,is injected.524 A.BALLUCHI E ¹A ¸.Copyright 2001John Wiley &Sons,Ltd.Int .J .Robust Nonlinear Control 2001;11:515}539Hence,our strategy is mapped into the hybrid domain as follows.At time t I,corresponding to the end of the expansion stroke,the value of is set to one of the two possible values on the basis of the estimated values of q?(k#2)and q T(k#2)at time t I> corresponding to the end of the next intake stroke:( 1)either (k)"0,i.e.if q?(k#2)(¸ q T(k#2)no fuel is injected;( 2)or (k)such that q?(k#2)"¸ q T(k#2),i.e.a mixture with maximum feasible value of A/F ratio is produced(see(5)).The estimations of q?(k#2)and q T(k#2)are non-trivial since they depend not only on the values of the state components (t I),m T(k),m J(k)and p(t I),but also on the chosen control actions (k)and (t)over[t I,t I> ].Consider now the design of the throttle control.The continuous minimum-time control (13)assumes only four possible values: "0, "90, "[(a C!a N)/b N]p and"nally "!(a N/b N)p when the target set has been reached.This strategy is mapped into the hybrid domain as follows:( 1) "90,if p(p ;( 2) "0,if p'p and q?(k#2)'¸ q T(k#2);( 3) "[(a C!a N)/b N]p,if q?(k#2)"¸ q T(k#2),so that the manifold dynamics tracks the fuel dynamics to obtain a mixture with minimum feasible value of A/F ratio.( 4) "!(a N/b N)p when the target set has been reached.We will now show how to calculate the values of (k)and (t).Consider the cases p(t I)(p (Case1)and p(t I)'p (Case2)separately.Case1:p(t I)(p .According to( 1)suppose (t)"90for all t3[t I,t I> ].Then,the value p(t I> )of the manifold pressure at time t I> obtained by integration of the pressure dynamics, would bep(t I> )"p(t I)e?N R I> \R I !(1!e?N R I> \R I )90b Na N(15)Two cases are possible:Case1a:p(t I> ))p .In this case,we can indeed set (t)"90for all t3[t I,t I> ]and only the value of (k)needs to be computed.In order to compute the value of (k)the amount of fuel q T(k#2)loaded in the cylinder at time t I> needs to be evaluated.Integration of the fuel dynamics givesq T(k#2)"(1!e\ R I> \R I\ O)m J(t I\ )#e\ R I> \R I O e R B O(1!e O) P #P (16) Hence,according to( 1)and( 2),(k)" 0if q?(k#2)"c N p(t I> )(¸ q T(k#2)such that c N p(t > )"¸ q T(k#2),otherwiseCopyright 2001John Wiley&Sons,Ltd.Int.J.Robust Nonlinear Control2001;11:515}539。

3GPP TS 36.331 V13.2.0 (2016-06)Technical Specification3rd Generation Partnership Project;Technical Specification Group Radio Access Network;Evolved Universal Terrestrial Radio Access (E-UTRA);Radio Resource Control (RRC);Protocol specification(Release 13)The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP. The present document has not been subject to any approval process by the 3GPP Organizational Partners and shall not be implemented.This Specification is provided for future development work within 3GPP only. The Organizational Partners accept no liability for any use of this Specification. Specifications and reports for implementation of the 3GPP TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.KeywordsUMTS, radio3GPPPostal address3GPP support office address650 Route des Lucioles - Sophia AntipolisValbonne - FRANCETel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16InternetCopyright NotificationNo part may be reproduced except as authorized by written permission.The copyright and the foregoing restriction extend to reproduction in all media.© 2016, 3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TSDSI, TTA, TTC).All rights reserved.UMTS™ is a Trade Mark of ETSI registered for the benefit of its members3GPP™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational PartnersLTE™ is a Trade Mark of ETSI currently being registered for the benefit of its Members and of the 3GPP Organizational Partners GSM® and the GSM logo are registered and owned by the GSM AssociationBluetooth® is a Trade Mark of the Bluetooth SIG registered for the benefit of its membersContentsForeword (18)1Scope (19)2References (19)3Definitions, symbols and abbreviations (22)3.1Definitions (22)3.2Abbreviations (24)4General (27)4.1Introduction (27)4.2Architecture (28)4.2.1UE states and state transitions including inter RAT (28)4.2.2Signalling radio bearers (29)4.3Services (30)4.3.1Services provided to upper layers (30)4.3.2Services expected from lower layers (30)4.4Functions (30)5Procedures (32)5.1General (32)5.1.1Introduction (32)5.1.2General requirements (32)5.2System information (33)5.2.1Introduction (33)5.2.1.1General (33)5.2.1.2Scheduling (34)5.2.1.2a Scheduling for NB-IoT (34)5.2.1.3System information validity and notification of changes (35)5.2.1.4Indication of ETWS notification (36)5.2.1.5Indication of CMAS notification (37)5.2.1.6Notification of EAB parameters change (37)5.2.1.7Access Barring parameters change in NB-IoT (37)5.2.2System information acquisition (38)5.2.2.1General (38)5.2.2.2Initiation (38)5.2.2.3System information required by the UE (38)5.2.2.4System information acquisition by the UE (39)5.2.2.5Essential system information missing (42)5.2.2.6Actions upon reception of the MasterInformationBlock message (42)5.2.2.7Actions upon reception of the SystemInformationBlockType1 message (42)5.2.2.8Actions upon reception of SystemInformation messages (44)5.2.2.9Actions upon reception of SystemInformationBlockType2 (44)5.2.2.10Actions upon reception of SystemInformationBlockType3 (45)5.2.2.11Actions upon reception of SystemInformationBlockType4 (45)5.2.2.12Actions upon reception of SystemInformationBlockType5 (45)5.2.2.13Actions upon reception of SystemInformationBlockType6 (45)5.2.2.14Actions upon reception of SystemInformationBlockType7 (45)5.2.2.15Actions upon reception of SystemInformationBlockType8 (45)5.2.2.16Actions upon reception of SystemInformationBlockType9 (46)5.2.2.17Actions upon reception of SystemInformationBlockType10 (46)5.2.2.18Actions upon reception of SystemInformationBlockType11 (46)5.2.2.19Actions upon reception of SystemInformationBlockType12 (47)5.2.2.20Actions upon reception of SystemInformationBlockType13 (48)5.2.2.21Actions upon reception of SystemInformationBlockType14 (48)5.2.2.22Actions upon reception of SystemInformationBlockType15 (48)5.2.2.23Actions upon reception of SystemInformationBlockType16 (48)5.2.2.24Actions upon reception of SystemInformationBlockType17 (48)5.2.2.25Actions upon reception of SystemInformationBlockType18 (48)5.2.2.26Actions upon reception of SystemInformationBlockType19 (49)5.2.3Acquisition of an SI message (49)5.2.3a Acquisition of an SI message by BL UE or UE in CE or a NB-IoT UE (50)5.3Connection control (50)5.3.1Introduction (50)5.3.1.1RRC connection control (50)5.3.1.2Security (52)5.3.1.2a RN security (53)5.3.1.3Connected mode mobility (53)5.3.1.4Connection control in NB-IoT (54)5.3.2Paging (55)5.3.2.1General (55)5.3.2.2Initiation (55)5.3.2.3Reception of the Paging message by the UE (55)5.3.3RRC connection establishment (56)5.3.3.1General (56)5.3.3.1a Conditions for establishing RRC Connection for sidelink communication/ discovery (58)5.3.3.2Initiation (59)5.3.3.3Actions related to transmission of RRCConnectionRequest message (63)5.3.3.3a Actions related to transmission of RRCConnectionResumeRequest message (64)5.3.3.4Reception of the RRCConnectionSetup by the UE (64)5.3.3.4a Reception of the RRCConnectionResume by the UE (66)5.3.3.5Cell re-selection while T300, T302, T303, T305, T306, or T308 is running (68)5.3.3.6T300 expiry (68)5.3.3.7T302, T303, T305, T306, or T308 expiry or stop (69)5.3.3.8Reception of the RRCConnectionReject by the UE (70)5.3.3.9Abortion of RRC connection establishment (71)5.3.3.10Handling of SSAC related parameters (71)5.3.3.11Access barring check (72)5.3.3.12EAB check (73)5.3.3.13Access barring check for ACDC (73)5.3.3.14Access Barring check for NB-IoT (74)5.3.4Initial security activation (75)5.3.4.1General (75)5.3.4.2Initiation (76)5.3.4.3Reception of the SecurityModeCommand by the UE (76)5.3.5RRC connection reconfiguration (77)5.3.5.1General (77)5.3.5.2Initiation (77)5.3.5.3Reception of an RRCConnectionReconfiguration not including the mobilityControlInfo by theUE (77)5.3.5.4Reception of an RRCConnectionReconfiguration including the mobilityControlInfo by the UE(handover) (79)5.3.5.5Reconfiguration failure (83)5.3.5.6T304 expiry (handover failure) (83)5.3.5.7Void (84)5.3.5.7a T307 expiry (SCG change failure) (84)5.3.5.8Radio Configuration involving full configuration option (84)5.3.6Counter check (86)5.3.6.1General (86)5.3.6.2Initiation (86)5.3.6.3Reception of the CounterCheck message by the UE (86)5.3.7RRC connection re-establishment (87)5.3.7.1General (87)5.3.7.2Initiation (87)5.3.7.3Actions following cell selection while T311 is running (88)5.3.7.4Actions related to transmission of RRCConnectionReestablishmentRequest message (89)5.3.7.5Reception of the RRCConnectionReestablishment by the UE (89)5.3.7.6T311 expiry (91)5.3.7.7T301 expiry or selected cell no longer suitable (91)5.3.7.8Reception of RRCConnectionReestablishmentReject by the UE (91)5.3.8RRC connection release (92)5.3.8.1General (92)5.3.8.2Initiation (92)5.3.8.3Reception of the RRCConnectionRelease by the UE (92)5.3.8.4T320 expiry (93)5.3.9RRC connection release requested by upper layers (93)5.3.9.1General (93)5.3.9.2Initiation (93)5.3.10Radio resource configuration (93)5.3.10.0General (93)5.3.10.1SRB addition/ modification (94)5.3.10.2DRB release (95)5.3.10.3DRB addition/ modification (95)5.3.10.3a1DC specific DRB addition or reconfiguration (96)5.3.10.3a2LWA specific DRB addition or reconfiguration (98)5.3.10.3a3LWIP specific DRB addition or reconfiguration (98)5.3.10.3a SCell release (99)5.3.10.3b SCell addition/ modification (99)5.3.10.3c PSCell addition or modification (99)5.3.10.4MAC main reconfiguration (99)5.3.10.5Semi-persistent scheduling reconfiguration (100)5.3.10.6Physical channel reconfiguration (100)5.3.10.7Radio Link Failure Timers and Constants reconfiguration (101)5.3.10.8Time domain measurement resource restriction for serving cell (101)5.3.10.9Other configuration (102)5.3.10.10SCG reconfiguration (103)5.3.10.11SCG dedicated resource configuration (104)5.3.10.12Reconfiguration SCG or split DRB by drb-ToAddModList (105)5.3.10.13Neighbour cell information reconfiguration (105)5.3.10.14Void (105)5.3.10.15Sidelink dedicated configuration (105)5.3.10.16T370 expiry (106)5.3.11Radio link failure related actions (107)5.3.11.1Detection of physical layer problems in RRC_CONNECTED (107)5.3.11.2Recovery of physical layer problems (107)5.3.11.3Detection of radio link failure (107)5.3.12UE actions upon leaving RRC_CONNECTED (109)5.3.13UE actions upon PUCCH/ SRS release request (110)5.3.14Proximity indication (110)5.3.14.1General (110)5.3.14.2Initiation (111)5.3.14.3Actions related to transmission of ProximityIndication message (111)5.3.15Void (111)5.4Inter-RAT mobility (111)5.4.1Introduction (111)5.4.2Handover to E-UTRA (112)5.4.2.1General (112)5.4.2.2Initiation (112)5.4.2.3Reception of the RRCConnectionReconfiguration by the UE (112)5.4.2.4Reconfiguration failure (114)5.4.2.5T304 expiry (handover to E-UTRA failure) (114)5.4.3Mobility from E-UTRA (114)5.4.3.1General (114)5.4.3.2Initiation (115)5.4.3.3Reception of the MobilityFromEUTRACommand by the UE (115)5.4.3.4Successful completion of the mobility from E-UTRA (116)5.4.3.5Mobility from E-UTRA failure (117)5.4.4Handover from E-UTRA preparation request (CDMA2000) (117)5.4.4.1General (117)5.4.4.2Initiation (118)5.4.4.3Reception of the HandoverFromEUTRAPreparationRequest by the UE (118)5.4.5UL handover preparation transfer (CDMA2000) (118)5.4.5.1General (118)5.4.5.2Initiation (118)5.4.5.3Actions related to transmission of the ULHandoverPreparationTransfer message (119)5.4.5.4Failure to deliver the ULHandoverPreparationTransfer message (119)5.4.6Inter-RAT cell change order to E-UTRAN (119)5.4.6.1General (119)5.4.6.2Initiation (119)5.4.6.3UE fails to complete an inter-RAT cell change order (119)5.5Measurements (120)5.5.1Introduction (120)5.5.2Measurement configuration (121)5.5.2.1General (121)5.5.2.2Measurement identity removal (122)5.5.2.2a Measurement identity autonomous removal (122)5.5.2.3Measurement identity addition/ modification (123)5.5.2.4Measurement object removal (124)5.5.2.5Measurement object addition/ modification (124)5.5.2.6Reporting configuration removal (126)5.5.2.7Reporting configuration addition/ modification (127)5.5.2.8Quantity configuration (127)5.5.2.9Measurement gap configuration (127)5.5.2.10Discovery signals measurement timing configuration (128)5.5.2.11RSSI measurement timing configuration (128)5.5.3Performing measurements (128)5.5.3.1General (128)5.5.3.2Layer 3 filtering (131)5.5.4Measurement report triggering (131)5.5.4.1General (131)5.5.4.2Event A1 (Serving becomes better than threshold) (135)5.5.4.3Event A2 (Serving becomes worse than threshold) (136)5.5.4.4Event A3 (Neighbour becomes offset better than PCell/ PSCell) (136)5.5.4.5Event A4 (Neighbour becomes better than threshold) (137)5.5.4.6Event A5 (PCell/ PSCell becomes worse than threshold1 and neighbour becomes better thanthreshold2) (138)5.5.4.6a Event A6 (Neighbour becomes offset better than SCell) (139)5.5.4.7Event B1 (Inter RAT neighbour becomes better than threshold) (139)5.5.4.8Event B2 (PCell becomes worse than threshold1 and inter RAT neighbour becomes better thanthreshold2) (140)5.5.4.9Event C1 (CSI-RS resource becomes better than threshold) (141)5.5.4.10Event C2 (CSI-RS resource becomes offset better than reference CSI-RS resource) (141)5.5.4.11Event W1 (WLAN becomes better than a threshold) (142)5.5.4.12Event W2 (All WLAN inside WLAN mobility set becomes worse than threshold1 and a WLANoutside WLAN mobility set becomes better than threshold2) (142)5.5.4.13Event W3 (All WLAN inside WLAN mobility set becomes worse than a threshold) (143)5.5.5Measurement reporting (144)5.5.6Measurement related actions (148)5.5.6.1Actions upon handover and re-establishment (148)5.5.6.2Speed dependant scaling of measurement related parameters (149)5.5.7Inter-frequency RSTD measurement indication (149)5.5.7.1General (149)5.5.7.2Initiation (150)5.5.7.3Actions related to transmission of InterFreqRSTDMeasurementIndication message (150)5.6Other (150)5.6.0General (150)5.6.1DL information transfer (151)5.6.1.1General (151)5.6.1.2Initiation (151)5.6.1.3Reception of the DLInformationTransfer by the UE (151)5.6.2UL information transfer (151)5.6.2.1General (151)5.6.2.2Initiation (151)5.6.2.3Actions related to transmission of ULInformationTransfer message (152)5.6.2.4Failure to deliver ULInformationTransfer message (152)5.6.3UE capability transfer (152)5.6.3.1General (152)5.6.3.2Initiation (153)5.6.3.3Reception of the UECapabilityEnquiry by the UE (153)5.6.4CSFB to 1x Parameter transfer (157)5.6.4.1General (157)5.6.4.2Initiation (157)5.6.4.3Actions related to transmission of CSFBParametersRequestCDMA2000 message (157)5.6.4.4Reception of the CSFBParametersResponseCDMA2000 message (157)5.6.5UE Information (158)5.6.5.1General (158)5.6.5.2Initiation (158)5.6.5.3Reception of the UEInformationRequest message (158)5.6.6 Logged Measurement Configuration (159)5.6.6.1General (159)5.6.6.2Initiation (160)5.6.6.3Reception of the LoggedMeasurementConfiguration by the UE (160)5.6.6.4T330 expiry (160)5.6.7 Release of Logged Measurement Configuration (160)5.6.7.1General (160)5.6.7.2Initiation (160)5.6.8 Measurements logging (161)5.6.8.1General (161)5.6.8.2Initiation (161)5.6.9In-device coexistence indication (163)5.6.9.1General (163)5.6.9.2Initiation (164)5.6.9.3Actions related to transmission of InDeviceCoexIndication message (164)5.6.10UE Assistance Information (165)5.6.10.1General (165)5.6.10.2Initiation (166)5.6.10.3Actions related to transmission of UEAssistanceInformation message (166)5.6.11 Mobility history information (166)5.6.11.1General (166)5.6.11.2Initiation (166)5.6.12RAN-assisted WLAN interworking (167)5.6.12.1General (167)5.6.12.2Dedicated WLAN offload configuration (167)5.6.12.3WLAN offload RAN evaluation (167)5.6.12.4T350 expiry or stop (167)5.6.12.5Cell selection/ re-selection while T350 is running (168)5.6.13SCG failure information (168)5.6.13.1General (168)5.6.13.2Initiation (168)5.6.13.3Actions related to transmission of SCGFailureInformation message (168)5.6.14LTE-WLAN Aggregation (169)5.6.14.1Introduction (169)5.6.14.2Reception of LWA configuration (169)5.6.14.3Release of LWA configuration (170)5.6.15WLAN connection management (170)5.6.15.1Introduction (170)5.6.15.2WLAN connection status reporting (170)5.6.15.2.1General (170)5.6.15.2.2Initiation (171)5.6.15.2.3Actions related to transmission of WLANConnectionStatusReport message (171)5.6.15.3T351 Expiry (WLAN connection attempt timeout) (171)5.6.15.4WLAN status monitoring (171)5.6.16RAN controlled LTE-WLAN interworking (172)5.6.16.1General (172)5.6.16.2WLAN traffic steering command (172)5.6.17LTE-WLAN aggregation with IPsec tunnel (173)5.6.17.1General (173)5.7Generic error handling (174)5.7.1General (174)5.7.2ASN.1 violation or encoding error (174)5.7.3Field set to a not comprehended value (174)5.7.4Mandatory field missing (174)5.7.5Not comprehended field (176)5.8MBMS (176)5.8.1Introduction (176)5.8.1.1General (176)5.8.1.2Scheduling (176)5.8.1.3MCCH information validity and notification of changes (176)5.8.2MCCH information acquisition (178)5.8.2.1General (178)5.8.2.2Initiation (178)5.8.2.3MCCH information acquisition by the UE (178)5.8.2.4Actions upon reception of the MBSFNAreaConfiguration message (178)5.8.2.5Actions upon reception of the MBMSCountingRequest message (179)5.8.3MBMS PTM radio bearer configuration (179)5.8.3.1General (179)5.8.3.2Initiation (179)5.8.3.3MRB establishment (179)5.8.3.4MRB release (179)5.8.4MBMS Counting Procedure (179)5.8.4.1General (179)5.8.4.2Initiation (180)5.8.4.3Reception of the MBMSCountingRequest message by the UE (180)5.8.5MBMS interest indication (181)5.8.5.1General (181)5.8.5.2Initiation (181)5.8.5.3Determine MBMS frequencies of interest (182)5.8.5.4Actions related to transmission of MBMSInterestIndication message (183)5.8a SC-PTM (183)5.8a.1Introduction (183)5.8a.1.1General (183)5.8a.1.2SC-MCCH scheduling (183)5.8a.1.3SC-MCCH information validity and notification of changes (183)5.8a.1.4Procedures (184)5.8a.2SC-MCCH information acquisition (184)5.8a.2.1General (184)5.8a.2.2Initiation (184)5.8a.2.3SC-MCCH information acquisition by the UE (184)5.8a.2.4Actions upon reception of the SCPTMConfiguration message (185)5.8a.3SC-PTM radio bearer configuration (185)5.8a.3.1General (185)5.8a.3.2Initiation (185)5.8a.3.3SC-MRB establishment (185)5.8a.3.4SC-MRB release (185)5.9RN procedures (186)5.9.1RN reconfiguration (186)5.9.1.1General (186)5.9.1.2Initiation (186)5.9.1.3Reception of the RNReconfiguration by the RN (186)5.10Sidelink (186)5.10.1Introduction (186)5.10.1a Conditions for sidelink communication operation (187)5.10.2Sidelink UE information (188)5.10.2.1General (188)5.10.2.2Initiation (189)5.10.2.3Actions related to transmission of SidelinkUEInformation message (193)5.10.3Sidelink communication monitoring (195)5.10.6Sidelink discovery announcement (198)5.10.6a Sidelink discovery announcement pool selection (201)5.10.6b Sidelink discovery announcement reference carrier selection (201)5.10.7Sidelink synchronisation information transmission (202)5.10.7.1General (202)5.10.7.2Initiation (203)5.10.7.3Transmission of SLSS (204)5.10.7.4Transmission of MasterInformationBlock-SL message (205)5.10.7.5Void (206)5.10.8Sidelink synchronisation reference (206)5.10.8.1General (206)5.10.8.2Selection and reselection of synchronisation reference UE (SyncRef UE) (206)5.10.9Sidelink common control information (207)5.10.9.1General (207)5.10.9.2Actions related to reception of MasterInformationBlock-SL message (207)5.10.10Sidelink relay UE operation (207)5.10.10.1General (207)5.10.10.2AS-conditions for relay related sidelink communication transmission by sidelink relay UE (207)5.10.10.3AS-conditions for relay PS related sidelink discovery transmission by sidelink relay UE (208)5.10.10.4Sidelink relay UE threshold conditions (208)5.10.11Sidelink remote UE operation (208)5.10.11.1General (208)5.10.11.2AS-conditions for relay related sidelink communication transmission by sidelink remote UE (208)5.10.11.3AS-conditions for relay PS related sidelink discovery transmission by sidelink remote UE (209)5.10.11.4Selection and reselection of sidelink relay UE (209)5.10.11.5Sidelink remote UE threshold conditions (210)6Protocol data units, formats and parameters (tabular & ASN.1) (210)6.1General (210)6.2RRC messages (212)6.2.1General message structure (212)–EUTRA-RRC-Definitions (212)–BCCH-BCH-Message (212)–BCCH-DL-SCH-Message (212)–BCCH-DL-SCH-Message-BR (213)–MCCH-Message (213)–PCCH-Message (213)–DL-CCCH-Message (214)–DL-DCCH-Message (214)–UL-CCCH-Message (214)–UL-DCCH-Message (215)–SC-MCCH-Message (215)6.2.2Message definitions (216)–CounterCheck (216)–CounterCheckResponse (217)–CSFBParametersRequestCDMA2000 (217)–CSFBParametersResponseCDMA2000 (218)–DLInformationTransfer (218)–HandoverFromEUTRAPreparationRequest (CDMA2000) (219)–InDeviceCoexIndication (220)–InterFreqRSTDMeasurementIndication (222)–LoggedMeasurementConfiguration (223)–MasterInformationBlock (225)–MBMSCountingRequest (226)–MBMSCountingResponse (226)–MBMSInterestIndication (227)–MBSFNAreaConfiguration (228)–MeasurementReport (228)–MobilityFromEUTRACommand (229)–Paging (232)–ProximityIndication (233)–RNReconfiguration (234)–RNReconfigurationComplete (234)–RRCConnectionReconfiguration (235)–RRCConnectionReconfigurationComplete (240)–RRCConnectionReestablishment (241)–RRCConnectionReestablishmentComplete (241)–RRCConnectionReestablishmentReject (242)–RRCConnectionReestablishmentRequest (243)–RRCConnectionReject (243)–RRCConnectionRelease (244)–RRCConnectionResume (248)–RRCConnectionResumeComplete (249)–RRCConnectionResumeRequest (250)–RRCConnectionRequest (250)–RRCConnectionSetup (251)–RRCConnectionSetupComplete (252)–SCGFailureInformation (253)–SCPTMConfiguration (254)–SecurityModeCommand (255)–SecurityModeComplete (255)–SecurityModeFailure (256)–SidelinkUEInformation (256)–SystemInformation (258)–SystemInformationBlockType1 (259)–UEAssistanceInformation (264)–UECapabilityEnquiry (265)–UECapabilityInformation (266)–UEInformationRequest (267)–UEInformationResponse (267)–ULHandoverPreparationTransfer (CDMA2000) (273)–ULInformationTransfer (274)–WLANConnectionStatusReport (274)6.3RRC information elements (275)6.3.1System information blocks (275)–SystemInformationBlockType2 (275)–SystemInformationBlockType3 (279)–SystemInformationBlockType4 (282)–SystemInformationBlockType5 (283)–SystemInformationBlockType6 (287)–SystemInformationBlockType7 (289)–SystemInformationBlockType8 (290)–SystemInformationBlockType9 (295)–SystemInformationBlockType10 (295)–SystemInformationBlockType11 (296)–SystemInformationBlockType12 (297)–SystemInformationBlockType13 (297)–SystemInformationBlockType14 (298)–SystemInformationBlockType15 (298)–SystemInformationBlockType16 (299)–SystemInformationBlockType17 (300)–SystemInformationBlockType18 (301)–SystemInformationBlockType19 (301)–SystemInformationBlockType20 (304)6.3.2Radio resource control information elements (304)–AntennaInfo (304)–AntennaInfoUL (306)–CQI-ReportConfig (307)–CQI-ReportPeriodicProcExtId (314)–CrossCarrierSchedulingConfig (314)–CSI-IM-Config (315)–CSI-IM-ConfigId (315)–CSI-RS-Config (317)–CSI-RS-ConfigEMIMO (318)–CSI-RS-ConfigNZP (319)–CSI-RS-ConfigNZPId (320)–CSI-RS-ConfigZP (321)–CSI-RS-ConfigZPId (321)–DMRS-Config (321)–DRB-Identity (322)–EPDCCH-Config (322)–EIMTA-MainConfig (324)–LogicalChannelConfig (325)–LWA-Configuration (326)–LWIP-Configuration (326)–RCLWI-Configuration (327)–MAC-MainConfig (327)–P-C-AndCBSR (332)–PDCCH-ConfigSCell (333)–PDCP-Config (334)–PDSCH-Config (337)–PDSCH-RE-MappingQCL-ConfigId (339)–PHICH-Config (339)–PhysicalConfigDedicated (339)–P-Max (344)–PRACH-Config (344)–PresenceAntennaPort1 (346)–PUCCH-Config (347)–PUSCH-Config (351)–RACH-ConfigCommon (355)–RACH-ConfigDedicated (357)–RadioResourceConfigCommon (358)–RadioResourceConfigDedicated (362)–RLC-Config (367)–RLF-TimersAndConstants (369)–RN-SubframeConfig (370)–SchedulingRequestConfig (371)–SoundingRS-UL-Config (372)–SPS-Config (375)–TDD-Config (376)–TimeAlignmentTimer (377)–TPC-PDCCH-Config (377)–TunnelConfigLWIP (378)–UplinkPowerControl (379)–WLAN-Id-List (382)–WLAN-MobilityConfig (382)6.3.3Security control information elements (382)–NextHopChainingCount (382)–SecurityAlgorithmConfig (383)–ShortMAC-I (383)6.3.4Mobility control information elements (383)–AdditionalSpectrumEmission (383)–ARFCN-ValueCDMA2000 (383)–ARFCN-ValueEUTRA (384)–ARFCN-ValueGERAN (384)–ARFCN-ValueUTRA (384)–BandclassCDMA2000 (384)–BandIndicatorGERAN (385)–CarrierFreqCDMA2000 (385)–CarrierFreqGERAN (385)–CellIndexList (387)–CellReselectionPriority (387)–CellSelectionInfoCE (387)–CellReselectionSubPriority (388)–CSFB-RegistrationParam1XRTT (388)–CellGlobalIdEUTRA (389)–CellGlobalIdUTRA (389)–CellGlobalIdGERAN (390)–CellGlobalIdCDMA2000 (390)–CellSelectionInfoNFreq (391)–CSG-Identity (391)–FreqBandIndicator (391)–MobilityControlInfo (391)–MobilityParametersCDMA2000 (1xRTT) (393)–MobilityStateParameters (394)–MultiBandInfoList (394)–NS-PmaxList (394)–PhysCellId (395)–PhysCellIdRange (395)–PhysCellIdRangeUTRA-FDDList (395)–PhysCellIdCDMA2000 (396)–PhysCellIdGERAN (396)–PhysCellIdUTRA-FDD (396)–PhysCellIdUTRA-TDD (396)–PLMN-Identity (397)–PLMN-IdentityList3 (397)–PreRegistrationInfoHRPD (397)–Q-QualMin (398)–Q-RxLevMin (398)–Q-OffsetRange (398)–Q-OffsetRangeInterRAT (399)–ReselectionThreshold (399)–ReselectionThresholdQ (399)–SCellIndex (399)–ServCellIndex (400)–SpeedStateScaleFactors (400)–SystemInfoListGERAN (400)–SystemTimeInfoCDMA2000 (401)–TrackingAreaCode (401)–T-Reselection (402)–T-ReselectionEUTRA-CE (402)6.3.5Measurement information elements (402)–AllowedMeasBandwidth (402)–CSI-RSRP-Range (402)–Hysteresis (402)–LocationInfo (403)–MBSFN-RSRQ-Range (403)–MeasConfig (404)–MeasDS-Config (405)–MeasGapConfig (406)–MeasId (407)–MeasIdToAddModList (407)–MeasObjectCDMA2000 (408)–MeasObjectEUTRA (408)–MeasObjectGERAN (412)–MeasObjectId (412)–MeasObjectToAddModList (412)–MeasObjectUTRA (413)–ReportConfigEUTRA (422)–ReportConfigId (425)–ReportConfigInterRAT (425)–ReportConfigToAddModList (428)–ReportInterval (429)–RSRP-Range (429)–RSRQ-Range (430)–RSRQ-Type (430)–RS-SINR-Range (430)–RSSI-Range-r13 (431)–TimeToTrigger (431)–UL-DelayConfig (431)–WLAN-CarrierInfo (431)–WLAN-RSSI-Range (432)–WLAN-Status (432)6.3.6Other information elements (433)–AbsoluteTimeInfo (433)–AreaConfiguration (433)–C-RNTI (433)–DedicatedInfoCDMA2000 (434)–DedicatedInfoNAS (434)–FilterCoefficient (434)–LoggingDuration (434)–LoggingInterval (435)–MeasSubframePattern (435)–MMEC (435)–NeighCellConfig (435)–OtherConfig (436)–RAND-CDMA2000 (1xRTT) (437)–RAT-Type (437)–ResumeIdentity (437)–RRC-TransactionIdentifier (438)–S-TMSI (438)–TraceReference (438)–UE-CapabilityRAT-ContainerList (438)–UE-EUTRA-Capability (439)–UE-RadioPagingInfo (469)–UE-TimersAndConstants (469)–VisitedCellInfoList (470)–WLAN-OffloadConfig (470)6.3.7MBMS information elements (472)–MBMS-NotificationConfig (472)–MBMS-ServiceList (473)–MBSFN-AreaId (473)–MBSFN-AreaInfoList (473)–MBSFN-SubframeConfig (474)–PMCH-InfoList (475)6.3.7a SC-PTM information elements (476)–SC-MTCH-InfoList (476)–SCPTM-NeighbourCellList (478)6.3.8Sidelink information elements (478)–SL-CommConfig (478)–SL-CommResourcePool (479)–SL-CP-Len (480)–SL-DiscConfig (481)–SL-DiscResourcePool (483)–SL-DiscTxPowerInfo (485)–SL-GapConfig (485)。