CR 2009 (version 1)

ZTE BLADE V7 LITE Quick Start Guide LEGAL INFORMATIONCopyright © 2016 ZTE CORPORATION.All rights reserved.No part of this publication may be quoted,reproduced, translated or used in any form or byany means, electronic or mechanical, includingphotocopying and microfilm, without the priorwritten permission of ZTE Corporation.NoticeZTE Corporation reserves the right to makemodifications on print errors or updatespecifications in this guide without prior notice.We offer self-service for our smart terminaldevice users. Please visit the ZTE official website(at ) for more informationon self-service and supported product models.Information on the website takes precedence.Visit to download theuser manual. 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March 2009High Quality DXT Compression using OpenCL for CUDAIgnacio Castaño*******************Document Change HistoryVersion Date Responsible Reason for Change0.1 02/01/2007 Ignacio Castaño First draft0.2 19/03/2009 Timo Stich OpenCL versionAbstractDXT is a fixed ratio compression format designed for real-time hardware decompression of textures. While it’s also possible to encode DXT textures in real-time, the quality of the resulting images is far from the optimal. In this white paper we will overview a more expensivecompression algorithm that produces high quality results and we will see how to implement it using CUDA to obtain much higher performance than the equivalent CPU implementation.MotivationWith the increasing number of assets and texture size in recent games, the time required to process those assets is growing dramatically. DXT texture compression takes a large portion of this time. High quality DXT compression algorithms are very expensive and while there are faster alternatives [1][9], the resulting quality of those simplified methods is not very high. The brute force nature of these compression algorithms makes them suitable to be parallelized and adapted to the GPU. Cheaper compression algorithms have already been implemented [2] on the GPU using traditional GPGPU approaches. However, with the traditional GPGPU programming model it’s not possible to implement more complex algorithms where threads need to share data and synchronize.How Does It Work?In this paper we will see how to use CUDA to implement a high quality DXT1 texturecompression algorithm in parallel. The algorithm that we have chosen is the cluster fit algorithm as described by Simon Brown [3]. We will first provide a brief overview of the algorithm and then we will describe how did we parallelize and implement it in CUDA.DXT1 FormatDXT1 is a fixed ratio compression scheme that partitions the image into 4x4 blocks. Each block is encoded with two 16 bit colors in RGB 5-6-5 format and a 4x4 bitmap with 2 bits per pixel. Figure 1 shows the layout of the block.Figure 1. DXT1 block layoutThe block colors are reconstructed by interpolating one or two additional colors between the given ones and indexing these and the original colors with the bitmap bits. The number of interpolated colors is chosen depending on whether the value of ‘Color 0’ is lower or greater than ‘Color 1’.BitmapColorsThe total size of the block is 64 bits. That means that this scheme achieves a 6:1 compression ratio. For more details on the DXT1 format see the specification of the OpenGL S3TCextension [4].Cluster FitIn general, finding the best two points that minimize the error of a DXT encoded block is a highly discontinuous optimization problem. However, if we assume that the indices of the block are known the problem becomes a linear optimization problem instead: minimize the distance from each color of the block to the corresponding color of the palette.Unfortunately, the indices are not known in advance. We would have to test them all to find the best solution. Simon Brown [3] suggested pruning the search space by considering only the indices that preserve the order of the points along the least squares line.Doing that allows us to reduce the number of indices for which we have to optimize theendpoints. Simon Brown provided a library [5] that implements this algorithm. We use thislibrary as a reference to compare the correctness and performance of our CUDAimplementation.The next section goes over the implementation details.OpenCL ImplementationPartitioning the ProblemWe have chosen to use a single work group to compress each 4x4 color block. Work items that process a single block need to cooperate with each other, but DXT blocks are independent and do not need synchronization or communication. For this reason the number of workgroups is equal to the number of blocks in the image.We also parameterize the problem so that we can change the number of work items per block to determine what configuration provides better performance. For now, we will just say that the number of work items is N and later we will discuss what the best configuration is.During the first part of the algorithm, only 16 work items out of N are active. These work items start reading the input colors and loading them to local memory.Finding the best fit lineTo find a line that best approximates a set of points is a well known regression problem. The colors of the block form a cloud of points in 3D space. This can be solved by computing the largest eigenvector of the covariance matrix. This vector gives us the direction of the line.Each element of the covariance matrix is just the sum of the products of different colorcomponents. We implement these sums using parallel reductions.Once we have the covariance matrix we just need to compute its first eigenvector. We haven’t found an efficient way of doing this step in parallel. Instead, we use a very cheap sequential method that doesn’t add much to the overall execution time of the group.Since we only need the dominant eigenvector, we can compute it directly using the Power Method [6]. This method is an iterative method that returns the largest eigenvector and only requires a single matrix vector product per iteration. Our tests indicate that in most cases 8iterations are more than enough to obtain an accurate result.Once we have the direction of the best fit line we project the colors onto it and sort them along the line using brute force parallel sort. This is achieved by comparing all the elements against each other as follows:cmp[tid] = (values[0] < values[tid]);cmp[tid] += (values[1] < values[tid]);cmp[tid] += (values[2] < values[tid]);cmp[tid] += (values[3] < values[tid]);cmp[tid] += (values[4] < values[tid]);cmp[tid] += (values[5] < values[tid]);cmp[tid] += (values[6] < values[tid]);cmp[tid] += (values[7] < values[tid]);cmp[tid] += (values[8] < values[tid]);cmp[tid] += (values[9] < values[tid]);cmp[tid] += (values[10] < values[tid]);cmp[tid] += (values[11] < values[tid]);cmp[tid] += (values[12] < values[tid]);cmp[tid] += (values[13] < values[tid]);cmp[tid] += (values[14] < values[tid]);cmp[tid] += (values[15] < values[tid]);The result of this search is an index array that references the sorted values. However, this algorithm has a flaw, if two colors are equal or are projected to the same location of the line, the indices of these two colors will end up with the same value. We solve this problem comparing all the indices against each other and incrementing one of them if they are equal:if (tid > 0 && cmp[tid] == cmp[0]) ++cmp[tid];if (tid > 1 && cmp[tid] == cmp[1]) ++cmp[tid];if (tid > 2 && cmp[tid] == cmp[2]) ++cmp[tid];if (tid > 3 && cmp[tid] == cmp[3]) ++cmp[tid];if (tid > 4 && cmp[tid] == cmp[4]) ++cmp[tid];if (tid > 5 && cmp[tid] == cmp[5]) ++cmp[tid];if (tid > 6 && cmp[tid] == cmp[6]) ++cmp[tid];if (tid > 7 && cmp[tid] == cmp[7]) ++cmp[tid];if (tid > 8 && cmp[tid] == cmp[8]) ++cmp[tid];if (tid > 9 && cmp[tid] == cmp[9]) ++cmp[tid];if (tid > 10 && cmp[tid] == cmp[10]) ++cmp[tid];if (tid > 11 && cmp[tid] == cmp[11]) ++cmp[tid];if (tid > 12 && cmp[tid] == cmp[12]) ++cmp[tid];if (tid > 13 && cmp[tid] == cmp[13]) ++cmp[tid];if (tid > 14 && cmp[tid] == cmp[14]) ++cmp[tid];During all these steps only 16 work items are being used. For this reason, it’s not necessary to synchronize them. All computations are done in parallel and at the same time step, because 16 is less than the warp size on NVIDIA GPUs.Index evaluationAll the possible ways in which colors can be clustered while preserving the order on the line are known in advance and for each clustering there’s a corresponding index. For 4 clusters there are 975 indices that need to be tested, while for 3 clusters there are only 151. We pre-compute these indices and store them in global memory.We have to test all these indices and determine which one produces the lowest error. In general there are indices than work items. So, we partition the total number of indices by the number of work items and each work item loops over the set of indices assigned to it. It’s tempting to store the indices in constant memory, but since indices are used only once for each work group, and since each work item accesses a different element, coalesced global memory loads perform better than constant loads.Solving the Least Squares ProblemFor each index we have to solve an optimization problem. We have to find the two end points that produce the lowest error. For each input color we know what index it’s assigned to it, so we have 16 equations like this:i i i x b a =+βαWhere {}i i βα, are {}0,1, {}32,31, {}21,21, {}31,32 or {}1,0 depending on the index and the interpolation mode. We look for the colors a and b that minimize the least square error of these equations. The solution of that least squares problem is the following:∑∑⋅∑∑∑∑= −i i i i i ii i i i x x b a βαββαβαα122 Note: The matrix inverse is constant for each index set, but it’s cheaper to compute it everytime on the kernel than to load it from global memory. That’s not the case of the CPU implementation.Computing the ErrorOnce we have a potential solution we have to compute its error. However, colors aren’t stored with full precision in the DXT block, so we have to quantize them to 5-6-5 to estimate the error accurately. In addition to that, we also have to take in mind that the hardware expands thequantized color components to 8 bits replicating the highest bits on the lower part of the byte as follows:R = (R << 3) | (R >> 2); G = (G << 2) | (G >> 4); B = (B << 3) | (B >> 2);Converting the floating point colors to integers, clamping, bit expanding and converting them back to float can be time consuming. Instead of that, we clamp the color components, round the floats to integers and approximate the bit expansion using a multiplication. We found the factors that produce the lowest error using an offline optimization that minimized the average error.r = round(clamp(r,0.0f,1.0f) * 31.0f); g = round(clamp(g,0.0f,1.0f) * 63.0f); b = round(clamp(b,0.0f,1.0f) * 31.0f); r *= 0.03227752766457f; g *= 0.01583151765563f; b *= 0.03227752766457f;Our experiment show that in most cases the approximation produces the same solution as the accurate solution.Selecting the Best SolutionFinally, each work item has evaluated the error of a few indices and has a candidate solution.To determine which work item has the solution that produces the lowest error, we store the errors in local memory and use a parallel reduction to find the minimum. The winning work item writes the endpoints and indices of the DXT block back to global memory.Implementation DetailsThe source code is divided into the following files:•DXTCompression.cl: This file contains OpenCL implementation of the algorithm described here.•permutations.h: This file contains the code used to precompute the indices.dds.h: This file contains the DDS file header definition. PerformanceWe have measured the performance of the algorithm on different GPUs and CPUscompressing the standard Lena. The design of the algorithm makes it insensitive to the actual content of the image. So, the performance depends only on the size of the image.Figure 2. Standard picture used for our tests.As shown in Table 1, the GPU compressor is at least 10x faster than our best CPUimplementation. The version of the compressor that runs on the CPU uses a SSE2 optimized implementation of the cluster fit algorithm. This implementation pre-computes the factors that are necessary to solve the least squares problem, while the GPU implementation computes them on the fly. Without this CPU optimization the difference between the CPU and GPU version is even larger.Table 1. Performance ResultsImage TeslaC1060 Geforce8800 GTXIntel Core 2X6800AMD Athlon64 DualCore 4400Lena512x51283.35 ms 208.69 ms 563.0 ms 1,251.0 msWe also experimented with different number of work-items, and as indicated in Table 2 we found out that it performed better with the minimum number.Table 2. Number of Work Items64 128 25654.66 ms 86.39 ms 96.13 msThe reason why the algorithm runs faster with a low number of work items is because during the first and last sections of the code only a small subset of work items is active.A future improvement would be to reorganize the code to eliminate or minimize these stagesof the algorithm. This could be achieved by loading multiple color blocks and processing them in parallel inside of the same work group.ConclusionWe have shown how it is possible to use OpenCL to implement an existing CPU algorithm in parallel to run on the GPU, and obtain an order of magnitude performance improvement. We hope this will encourage developers to attempt to accelerate other computationally-intensive offline processing using the GPU.High Quality DXT Compression using CUDAMarch 2009 9References[1] “Real-Time DXT Compression”, J.M.P. van Waveren. /cd/ids/developer/asmo-na/eng/324337.htm[2] “Compressing Dynamically Generated Textures on the GPU”, Oskar Alexandersson, Christoffer Gurell, Tomas Akenine-Möller. http://graphics.cs.lth.se/research/papers/gputc2006/[3] “DXT Compression Techniques”, Simon Brown. /?article=dxt[4] “OpenGL S3TC extension spec”, Pat Brown. /registry/specs/EXT/texture_compression_s3tc.txt[5] “Squish – DXT Compression Library”, Simon Brown. /?code=squish[6] “Eigenvalues and Eigenvectors”, Dr. E. Garcia. /information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html[7] “An Experimental Analysis of Parallel Sorting Algorithms”, Guy E. Blelloch, C. Greg Plaxton, Charles E. Leiserson, Stephen J. Smith /blelloch98experimental.html[8] “NVIDIA CUDA Compute Unified Device Architecture Programming Guide”.[9] NVIDIA OpenGL SDK 10 “Compress DXT” sample /SDK/10/opengl/samples.html#compress_DXTNVIDIA Corporation2701 San Tomas ExpresswaySanta Clara, CA 95050 NoticeALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, “MATERIALS”) ARE BEING PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT,MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or otherrights of third parties that may result from its use. No license is granted by implication or otherwise under any patent or patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all information previously supplied. NVIDIA Corporation products are not authorized for use as critical components in life support devices or systems without express written approval of NVIDIA Corporation.TrademarksNVIDIA, the NVIDIA logo, GeForce, and NVIDIA Quadro are trademarks or registeredtrademarks of NVIDIA Corporation in the United States and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Copyright© 2009 NVIDIA Corporation. All rights reserved.。

Cooperative Communications for Cognitive Radio NetworksDistributed network users can collaborate to avoid the degrading effectsof signal fading by automatically adjusting their coding structurewith changes in the wireless environment.By Khaled Ben Letaief,Fellow IEEE,and Wei Zhang,Member IEEEABSTRACT|Cognitive radio is an exciting emerging technol-ogy that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum.Such revolu-tionary and transforming technology represents a paradigm shift in the design of wireless systems,as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation,and dynamic spectrum sharing.Cooperative communications and networking is another new communica-tion technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels.In this paper,we consider the application of these technologies to spectrum sensing and spectrum sharing.One of the most important challenges for cognitive radio systems is to identify the presence of primary(licensed)users over a wide range of spectrum at a particular time and specific geographic location.We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users.We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cog-nitive radios.We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network.To exploit the maximum spectrum opportunities,we present a cognitive space–time–frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.KEYWORDS|Cognitive radio;cooperative communications; spectrum sensing;spectrum sharingI.INTRODUCTIONAs wireless technologies continue to grow,more and more spectrum resources will be needed.Within the current spectrum regulatory framework,however,all of the fre-quency bands are exclusively allocated to specific services, and no violation from unlicensed users is allowed.A recent survey of spectrum utilization made by the Federal Com-munications Commission(FCC)has indicated that the actual licensed spectrum is largely underutilized in vast temporal and geographic dimensions[1].For instance,a field spectrum measurement taken in New York City has shown that the maximum total spectrum occupancy is only 13.1%from30MHz to3GHz[2],[3].Similar results,ob-tained in the most crowded area of downtown Washington, D.C.,indicated an occupancy of less than35%of the radio spectrum below3GHz.Moreover,the spectrum usage varies significantly in various time,frequency,and geographic locations.Spectrum utilization can be improved significantly by allowing a secondary user to utilize a licensed band when the primary user(PU)is absent.Cognitive radio(CR),as an agile radio technology,has been proposed to promote the efficient use of the spectrum[4].By sensing and adapting to the environment,a CR is able to fill in spectrum holes and serve its users without causing harmful interference to the licensed user.To do so,the CR must continuously sense the spectrum it is using in order to detect the reappearance of the PU.Once the PU is detected,the CR should withdraw from the spectrum so as to minimize the interference it mayManuscript received October20,2008.First published April29,2009;current versionpublished May1,2009.This work was supported in part by the Hong KongResearch Grant Council under Grant N_HKUST622/06.K.B.Letaief is with the Department of Electronic and Computer Engineering,Hong Kong University of Science and Technology,Kowloon,Hong Kong(e-mail:eekhaled@t.hk).W.Zhang is with the School of Electrical Engineering and Telecommunications,The University of New South Wales,Sydney,Australia(e-mail:wzhang@.au).Digital Object Identifier:10.1109/JPROC.2009.2015716878Proceedings of the IEEE|Vol.97,No.5,May20090018-9219/$25.00Ó2009IEEEpossibly cause.This is a very difficult task,as the various PUs will be employing different modulation schemes,data rates, and transmission powers in the presence of variable propagation environments and interference generated by other secondary users.Another great challenge of imple-menting spectrum sensing is the hidden terminal problem, which occurs when the CR is shadowed,in severe multipath fading or inside buildings with a high penetration loss while a PU is operating in the vicinity.Cooperative communications is an emerging and pow-erful solution that can overcome the limitation of wireless systems[5],[6].The basic idea behind cooperative trans-mission rests on the observation that,in a wireless envi-ronment,the signal transmitted or broadcast by a source to a destination node,each employing a single antenna,is also received by other terminals,which are often referred to as relays or partners.The relays process and retransmit the signals they receive.The destination then combines the signals coming from the source and the partners,thereby creating spatial diversity by taking advantage of the multiple receptions of the same data at the various terminals and transmission paths.In addition,the interference among terminals can be dramatically suppressed by distributed spatial processing technology.By allowing multiple CRs to cooperate in spectrum sensing,the hidden terminal problem can be addressed[7].Indeed,cooperative spectrum sensing in CR networks has an analogy to a distributed decision in wireless sensor networks,where each sensor makes a local decision and those decision results are reported to a fusion center to give a final decision according to some fusion rule [8].The main difference between these two applications lies in the wireless pared to wireless sensor networks,CRs and the fusion center(or common receiver) are distributed over a larger geographic area.This difference brings out a much more challenging problem to cooperative spectrum sensing because sensing channels(from the PU to CRs)and reporting channels(from the CRs to the fusion center or common receiver)are normally subject to fading or heavy shadowing.In this paper,we propose several robust cooperative spectrum sensing techniques to address these challenging issues.With fast and agile sensing ability,CR can opportunis-tically fill in spectrum holes to improve the spectrum occu-pancy utilization.However,once the PU returns to access the licensed band,the CR should immediately stop operating in the PU licensed band.This fast switching off of the CR can guarantee minimum interference to the primary system. However,from the point of view of the cognitive system,the interruptive transmissions will lead to a discontinuous data service and intolerable delay.To cope with this problem,we propose a cognitive relay network in which distributed cognitive users collaborate with each other so that they can share their distinct spectrum bands.By utilizing a cognitive space–time–frequency(STF)coding in the cognitive relay network,seamless data transmission within the cognitive system can also be realized.The remainder of this paper is organized as follows.In Section II,the CR and cooperative communication technol-ogies will be briefly reviewed.In Section III,spectrum sensing techniques for CR are surveyed and compared.In Section IV,cooperative spectrum sensing is considered and performance analysis will be given.The limitation of coop-erative spectrum sensing in realistic cognitive wireless networks is then derived.In Section V,several robust coop-erative spectrum sensing techniques are proposed.In Section VI,cooperative spectrum sharing is investigated and a new cognitive wireless relay network proposed.In particular,a cognitive STF coding technique is proposed to realize high-data-rate seamless service for cognitive wireless networks.In Section VII,we draw our conclusions.II.PRELIMINARYA.Cognitive RadioAs the demand for additional bandwidth continues to increase,spectrum policy makers and communication technologists are seeking solutions for the apparent spectrum scarcity[9],[10].Meanwhile,measurement studies have shown that the licensed spectrum is relatively unused across many time and frequency slots[3].To solve the problem of spectrum scarcity and spectrum underutilization,the use of CR technology is being considered because of its ability to rapidly and autonomously adapt operating parameters to changing requirements and conditions.Recently,the FCC has issued a Notice of Proposed Rulemaking regarding CR[11] that requires rethinking of the wireless communication architectures so that emerging radios can share spectrum with PUs without causing harmful interference to them.In the pioneering work[4],Mitola and Maguire stated that B radio etiquette is the set of RF bands,air interfaces, protocols,and spatial and temporal patterns that moderate the use of radio spectrum.CR extends the software radio with radio-domain model-based reasoning about such etiquettes.[ In Haykin’s paper[12],it was stated that B cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment(i.e.,its outside world),and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming radio frequency(RF)stimuli by making corresponding changes in certain operating param-eters(e.g.,transmit power,carrier frequency,and modulation strategy)in real time,with two primary objectives in mind: 1)Highly reliable communications whenever and wherever needed;and2)Efficient utilization of the radio spectrum.[ Another CR description is found in Jondral’s paper[13], which states that B an SDR that additionally senses its environment,tracks changes,and reacts upon its findings.[ More specifically,the CR technology will enable the users to[14]:•determine which portions of the spectrum are available and detect the presence of licensed usersLetaief and Zhang:Cooperative Communications for Cognitive Radio NetworksVol.97,No.5,May2009|Proceedings of the IEEE879when a user operates in a licensed band(spectrumsensing);•select the best available channel(spectrum management);•coordinate access to this channel with other users (spectrum sharing);•vacate the channel when a licensed user is detected (spectrum mobility).IEEE has also endeavored to formulate a novel wireless air interface standard based on CR.The IEEE802.22 working group aims to develop wireless regional area net-work physical(PHY)and medium access control(MAC) layers for use by unlicensed devices in the spectrum allo-cated to TV bands[15],[16].For an overview of recent advances in CR,readers are referred to[17]–[21].B.Cooperative CommunicationsTraditional wireless networks have predominantly used direct point-to-point or point-to-multipoint(e.g.,cellular) topologies.In contrast to conventional point-to-point com-munications,cooperative communications and networking allows different users or nodes in a wireless network to share resources and to create collaboration through distributed transmission/processing,in which each user’s information is sent out not only by the user but also by the collaborating users[22].Cooperative communications and networking is a new communication paradigm that promises significant capacity and multiplexing gain increase in wireless networks [23],[24].It also realizes a new form of space diversity to combat the detrimental effects of severe fading[25].There are mainly three relaying protocols:amplify-and-forward(AF),decode-and-forward(DF),and compress-and-forward(CF).In AF,the received signal is amplified and retransmitted to the destination.The advantage of this protocol is its simplicity and low cost implementation.But the noise is also amplified at the relay.In DF,the relay attempts to decode the received signals.If successful,it reencodes the information and retransmits stly,CF attempts to generate an estimate of the received signal. This is then compressed,encoded,and transmitted in the hope that the estimated value may assist in decoding the original codeword at the destination.In[5]and[6],Sendonaris et al.introduced and examined the concept of user cooperation diversity.The implemented strategy uses a pair of transmitting,full-duplex users who cooperate in sending independent data from both users to a common destination.In essence,each user is acting as a relay for others while using the AF relaying strategy.The DF and CF strategies are thoroughly examined for wireless channels in[24].In addition to providing a thorough survey of relay networks,[24]showed that under certain condi-tions,the DF strategy is capable of achieving rates of up to the ergodic capacity of the channel.Cooperative techniques have already been considered for wireless and mobile broadband radio[26]and also have been under investigation in various IEEE802standards. The IEEE802.11standard is concerned with wireless local-area networks(WLANs)in unlicensed bands in indoor environments.A recent evolution of IEEE802.11using mesh networking,i.e.,802.11s is considering the update of 802.11MAC layer operation to self-configuration and multihop topologies[27].The mesh point that has the ability to function as the802.11access point collects the information about the neighboring mesh points,communi-cating with them and forwarding the traffic.The IEEE802.16 standard is an orthogonal frequency-division multiplexing (OFDM),orthogonal frequency-division multiple access (OFDMA),and single-carrier based fixed wireless metropol-itan-area network in licensed bands of10–66GHz.As an amendment of802.16networks,IEEE802.16j is concerned with multihop relay to enhance coverage,throughput,and system capacity[28].III.SPECTRUM SENSING TECHNIQUES One of the most important components of CR is the ability to measure,sense,learn,and be aware of the parameters related to the radio channel characteristics,availability of spectrum and power,interference and noise temperature,radio’s operating environment,user requirements,and applications [29].In CR,the PUs are referred to those users who have higher priority or legacy rights on the usage of a part of the spectrum.Spectrum sensing is a key element in CR com-munications,as it enables the CR to adapt to its environment by detecting spectrum holes.The most effective way to detect the availability of some portions of the spectrum is to detect the PUs that are receiving data within the range of a CR. However,it is difficult for the CR to have a direct mea-surement of a channel between a primary transmitter and receiver.Therefore,most existing spectrum sensing algo-rithms focus on the detection of the primary transmitted signal based on the local observations of the CR.In the following,we denote xðtÞthe received signal at the CR.To enhance the detection probability,many signal-detection techniques can be used in spectrum sensing.In this section,we give an overview of some well-known spectrum sensing techniques.1)Matched Filter Detection:When a secondary user has a prior knowledge of the PU signal,the optimal signal detection is a matched filter,as it maximizes the signal-to-noise ratio(SNR)of the received signal.A matched filter is obtained by correlating a known signal,or template,with an unknown signal to detect the presence of the template in the unknown signal.This is equivalent to convolving the unknown signal with a time-reversed version of the template.The main advantage of matched filter is that it needs less time to achieve high processing gain due to coherent detection[30].Another significant disadvantage of the matched filter is that it would require a dedicated sensing receiver for all primary user signal types.Letaief and Zhang:Cooperative Communications for Cognitive Radio Networks 880Proceedings of the IEEE|Vol.97,No.5,May2009In the CR scenario,however,the use of the matched filter can be severely limited since the information of the PU signal is hardly available at the CRs.The use of this approach is still possible if we have partial information of the PU signal such as pilot symbols or preambles,which can be used for coherent detection[7].For instance,to detect the presence of a digital television(DTV)signal,we may detect its pilot tone by passing the DTV signal through a delay-and-multiply circuit.If the squared magnitude of the output signal is larger than a threshold,the presence of the DTV signal can be detected.2)Energy Detection:If prior knowledge of the PU signal is unknown,the energy detection method is optimal for detecting any zero-mean constellation signals[30]. In the energy detection approach,the radio-frequency (RF)energy in the channel or the received signal strength indicator is measured to determine whether the channel is idle or not.First,the input signal is filtered with a band-pass filter to select the bandwidth of interest.The output signal is then squared and integrated over the observation stlly,the output of the integrator is compared to a predetermined threshold to infer the presence or not of the PU signal.When the spectral is analyzed in the digital domain,fast Fourier transform(FFT)based methods are used.Specifically,the received signal xðtÞ,sampled in a time window,is first passed through an FFT device to get the power spectrum j XðfÞj2.The peak of the power spec-trum is then located.After windowing the peak of the spectrum,we get j YðfÞj2.The signal energy is then col-lected in the frequency domain.Although the energy-detection approach can be imple-mented without any prior knowledge of the PU signal,it still has some drawbacks.The first problem is that it has poor performance under low SNR conditions.This is because the noise variance is not accurately known at the low SNR,and the noise uncertainty may render the energy detection useless[30].Another challenging issue is the inability to differentiate the interference from other secondary users sharing the same channel and the PU[31].Furthermore,the threshold used in energy selection depends on the noise variance,and small noise power estimation errors can result in significant performance loss.3)Cyclostationary Detection:Cyclostationary detection is more robust to noise uncertainty than an energy detection. If the signal of the PU exhibits strong cyclostationary properties,it can be detected at very low SNR values by exploiting the information(cyclostationary feature)em-bedded in the received signal.A signal is said to be cyclostationary(in the wide sense)if its autocorrelation is a periodic function of time t with some period[32].The cyclostationary detection can be performed as follows.•First,the cyclic autocorrelation function(CAF)of the observed signal xðtÞis calculated as E f xðtþ ÞxÃðtÀ ÞeÀi2 t g,where E fÁg denotes the statisti-cal expectation operation and is called the cyclicfrequency.•The spectral correlation function(SCF)Sðf; Þis then obtained from the discrete Fourier transfor-mation of the CAF.The SCF is also called cyclicspectrum,which is a two-dimension function interms of frequency f and cyclic frequency .•The detection is completed by searching for the unique cyclic frequency corresponding to the peak inthe SCF plane.This detection approach is robust to random noise and interference from other modulated signals because the noise has only a peak of SCF at the zero cyclic frequency and the different modulated signals have different unique cyclic frequencies.In[33],the cyclostationary detection method is employed for the detection of the Advanced Television Sys-tems Committee DTV signals in wireless region-area net-work systems.Experimental results show superior detection performance even in very low SNR region.In[34],dis-tributed detection is considered for scanning spectrum holes,where each CR employs a generalized likelihood ratio test for detecting primary transmissions with multiple cyclic frequencies.The above approach can detect the PU signal from other CR users signals over the same frequency band provided that the cyclic features of the PU and the CR signals differ from each other,which is usually the case,because different wireless systems usually employ different signal structures and parameters.By exploiting the distinct cyclostationary characteristics of the PU and the CR signals,a strategy of extracting channel-allocation information is proposed in spectrum pooling systems[35],where the PU is a GSM network and the CR is an OFDM-based WLAN system. However,cyclostationary detection is more complex to im-plement than the energy detection and requires a prior knowledge of PU signal such as modulation format.4)Wavelet Detection:Wavelet transform is a multi-resolution analysis mechanism where an input signal is decomposed into different frequency components,and then each component is studied with resolutions matched to its scales.Unlike the Fourier transform,using sines and cosines as basic functions,the wavelet transforms use irregularly shaped wavelets as basic functions and thus offer better tools to represent sharp changes and local features[36].For signal detection over wide-band channels,the wavelet approach offers advantages in terms of both implementation cost and flexibility in adapting to the dynamic spectrum,as opposed to the conventional use of multiple narrow-band bandpass filters[37].In order to identify the locations of vacant frequency bands,the entire wide-band is modeled as a train of consecutive frequency subbands where the power spectral characteristic is smooth within each subband but changes abruptly on the border of two neighboring subbands.By employing a wavelet transform of the power spectral density (PSD)of the observed signal xðtÞ,the singularities of theLetaief and Zhang:Cooperative Communications for Cognitive Radio NetworksVol.97,No.5,May2009|Proceedings of the IEEE881PSD S ðf Þcan be located and thus the vacant frequency bands can be found.One critical challenge of implementing the wavelet approach in practice is the high sampling rates for characterizing the large bandwidth.In [38],a dual-stage spectrum sensing technique is proposed for wide-band CR systems,in which a wavelet transform-based detection is employed as a coarse sensing stage and a temporal signature detection is used as a fine sensing stage.5)Covariance Detection:Given that the statistical covari-ance matrices or autocorrelations of the signal and noise are generally different,covariance-based signal detection meth-ods were proposed in [39].By observing the fact that off-diagonal elements of the covariance matrix of the received signal are zero when the primary user signal is not present and nonzero when it is present,the authors in [39]devel-oped two detection methods:covariance absolute value detection and covariance Frobenius norm detection.The methods can be used for various signal detection and appli-cations without knowledge of the signal,channel,and noise ter,and by applying eigendecomposition of the covariance matrix,the authors further developed other two detection methods,called max-min eigenvalue detection and max-eigenvalue detection in [40]and [41],respectively.The essence of the eigendetection methods lies in the significant difference of the eigenvalue of the received signal covariance matrix when the primary user signal is present or not.IV.COOPERATIVE SPECTRUM SENSINGA.General ConceptThe critical challenging issue in spectrum sensing is the hidden terminal problem,which occurs when the CR is shadowed or in severe multipath fading.Fig.1shows that CR 3is shadowed by a high building over the sensing channel.In this case,the CR cannot sense the presence of the primary user,and thus it is allowed to access thechannel while the PU is still in operation.To address this issue,multiple CRs can be designed to collaborate in spectrum sensing [7].Recent work has shown that coop-erative spectrum sensing can greatly increase the probability of detection in fading channels [42].For an overview of recent advances in cooperative spectrum sensing,readers are referred to [42]–[52].In general,cooperative spectrum sensing can be performed as described below.Cooperative Spectrum Sensing:1)Every CR performs its own local spectrum sensingmeasurements independently and then makes a bi-nary decision on whether the PU is present or not.2)All of the CRs forward their decisions to a com-mon receiver.3)The common receiver fuses the CR decisions andmakes a final decision to infer the absence or pre-sence of the PU.1)Decision Fusion Versus Data Fusion:The above coop-erative spectrum sensing approach can be seen as a DF protocol for cooperative networks,where each coopera-tive partner makes a binary decision based on the local observation and then forwards one bit of the decision to the common receiver.At the common receiver,all 1-bit decisions are fused together according to an OR logic.We shall refer to this approach as decision fusion .An alter-native form of cooperative spectrum sensing can be performed as follows.Instead of transmitting the 1-bit decision to the common receiver in step 2)of the above algorithm,each CR can just send its observation value directly to the common receiver [51].This alternative approach can then be seen as an AF protocol for coop-erative networks.We shall refer to this approach as data fusion .Obviously,the 1-bit decision needs a low-bandwidth control channel.2)Sensing Diversity Gain:It can be seen that cooperative spectrum sensing will go through two successive channels:1)sensing channel (from the PU to CRs)and 2)reporting channel (from the CRs to the common receiver).The merit of cooperative spectrum sensing primarily lies in the achievable space diversity brought by the sensing channels,namely,sensing diversity gain,provided by the multiple CRs.Even though one CR may fail to detect the signal of the PU,there are still many chances for other CRs to detect it.With the increase of the number of cooperative CRs,the probability of missed detection for all the users will be extremely small.Another merit of cooperative spectrum sensing is the mutual benefit brought forward by communicating with each other to improve the sensing performance [48].When one CR is far away from the primary user,the received signal may be too weak to be detected.However,by employing a CR that is located nearby the PU as a relay,the signal of the PU can be detected reliably by the faruser.Fig.1.Cooperative spectrum sensing in CR networks.CR 1isshadowed over the reporting channel and CR 3is shadowed over the sensing channel.Letaief and Zhang:Cooperative Communications for Cognitive Radio Networks882Proceedings of the IEEE |Vol.97,No.5,May 2009B.Performance Analysis1)Local Spectrum Sensing:The essence of spectrum sen-sing is a binary hypothesis-testing problemH 0:Primary user is absentH 1:Primary user is in operation :The key metrics of the spectrum sensing are the probabilities of correct detection given by Prob f Decision ¼H 1j H 1g and Prob f Decision ¼H 0j H 0g ,the false alarm probability given by Prob f Decision ¼H 1j H 0g ,and the missed detection probability given by Prob f Decision ¼H 0j H 1g .We consider a CR network composed of K CRs (sec-ondary users)and a common receiver,as shown in Fig.2.The common receiver manages the CR network and all associated K CRs.We assume that each CR performs local spectrum sensing independently.In order to see how the energy detector works,we only consider the i th CR in the following.The local spectrum sensing problem is to decide between the following two hypotheses:x i ðt Þ¼n i ðt Þ;H 0h i s ðt Þþn i ðt Þ;H 1&(1)where x i ðt Þis the observed signal at the i th CR,s ðt Þis the signal coming from the primary transmitter,n i ðt Þis the additive white Gaussian noise,and h i is the complex channel gain of the sensing channel between the PU and the i th CR.We assume that the sensing channel is time-invariant during the sensing process.The energy detection is performed by measuring the energy of the received signal x i ðt Þin a fixed bandwidth W over an observation time window T .The energy collected in the frequency domain is denoted by E i ,which serves as a decision statistic with the following distribution [53]–[55]:E i $ 22u ;H 0 22u ð2 i Þ;H 1&(2)where 22u denotes a central chi-square distribution with 2u degrees of freedom and 22u ð2 i Þdenotes a noncentral chi-square distribution with u degrees of freedom and a noncentrality parameter 2 i ,respectively.The instanta-neous SNR of the received signal at the i th CR is i ,and u ¼TW is the time–bandwidth product.By comparing the energy E i with a threshold i ,the detection of PU signal is made.Therefore,the probability of false alarm is given by P ði Þf ¼Prob f E i > i j H 0g and the probability of detection isgiven by P ði Þd ¼Prob f E i > i j H 1g .Over Rayleigh fading channels,the average probability of false alarm,the average probability of detection,and the average probability of missed detection are given by [55],respectivelyP ði Þf¼Àu ; i 2ÀÁÀðu Þ(3)P ði Þd¼e À i 2X u À2p ¼01p ! i 2 p þ1þ" i " i u À1Âe À i 2ð1þ"i ÞÀe À i 2X u À2p ¼01p ! i " i 2ð1þ i Þ p "#(4)andP ði Þm ¼1ÀP ði Þd(5)where "i denotes the average SNR at the i th CR,Àða ;x Þis the incomplete gamma function given by Àða ;x Þ¼R 1x ta À1e Àtdt ,and Àða Þis the gamma function.In Fig.3,the complementary receiver operating char-acteristic (ROC)curves (probability of misseddetectionFig.2.Spectrum sensing structure in a cognitive radionetwork.Fig.3.Spectrum sensing performance over Rayleigh fading channelswith SNR "¼0;10;20dB for one cognitive radio.Letaief and Zhang:Cooperative Communications for Cognitive Radio NetworksVol.97,No.5,May 2009|Proceedings of the IEEE 883。

实体肿瘤的疗效评价标准1.1 版( Response Evaluation Criteria in Solid Tumors RECIST Version 1.1)1肿瘤在基线水平的可测量性1.1 定义在基线水平上，肿瘤病灶/淋巴结将按以下定义分为可测量和不可测量两种：1.1.1 可测量病灶肿瘤病灶：至少有一条可以精确测量的径线（记录为最大径），其最小长度如下：●CT扫描10 mm（CT扫描层厚不大于5mm）●临床常规检查仪器10 mm（肿瘤病灶不能用测径仪器准确测量的应记录为不可测量）●胸部X-射线20 mm●恶性淋巴结：病理学增大且可测量，单个淋巴结CT扫描短径须≥15 mm（CT扫描层厚推荐不超过5 mm）。

基线和随访中，仅测量和随访短径。

1.1.2 不可测量病灶所有其他病灶，包括小病灶（最长径＜10 mm或者病理淋巴结短径≥10 mm至＜15 mm）和无法测量的病灶。

无法测量的病灶包括：脑膜疾病、腹水、胸膜或者心包积液、炎性乳腺癌、皮肤/肺的癌性淋巴管炎、影像学不能确诊和随诊的腹部包块，以及囊性病变。

1.1.3 关于病灶测量的特殊考虑骨病灶、囊性病灶和先前接受过局部治疗的病灶需要特别注明：骨病灶：●骨扫描，PET扫描或者平片不适合于测量骨病灶，但是可用于确认骨病灶的存在或者消失；●溶骨性病灶或者混合性溶骨/成骨病灶有确定的软组织成分，且软组织成分符合上述可测量性定义时，如果这些病灶可用断层影像技术如CT或者MRI进行评价，那么这些病灶可以作为可测量病灶；●成骨病灶属不可测量病灶。

囊性病灶：●符合放射影像学单纯囊肿定义标准的病灶，不应因其为定义上的单纯性囊肿，而认为是恶性病灶，既不属于可测量病灶，也不属于不可测量病灶；●若为囊性转移病灶，且符合上述可测量性定义的，可以作为是可测量病灶。

但如果在同一病人中存在非囊性病灶，应优先选择非囊性病灶作为靶病灶。

局部治疗过的病灶：●位于曾放疗过或经其他局部区域性治疗的部位的病灶，一般作为不可测量病灶，除非该病灶出现明确进展。

Microsoft Dynamics® AX 2009Team Foundation Server Version Control SetupWhite PaperThis document describes how to set up the Version Control System (VCS) for Microsoft Dynamics AX 2009 by using Microsoft® Visual Studio® Team Foundation Server as the version control system.Date: June 2008/dynamics/axTable of ContentsIntroduction (3)Version Control System Considerations (3)Setup overview (3)Install Microsoft Visual Studio Team Foundation Server (3)Install Microsoft Visual Studio Team Explorer (4)Configure Microsoft Visual Studio Team Foundation Server (4)Connect Team Explorer to the Team Foundation Server. (4)Create a project. (4)Add all of the developers to the project as members. (4)Restrict developer access to the system settings. (4)Install and Set Up the Team Server (ID Server) (5)Set Up the Developer and Administrator Computers (5)Install the Operating System (5)Install MS SQL Server or Oracle (5)Install Microsoft Dynamics AX 2009 (5)Install Microsoft Visual Studio Team Explorer (5)Connect Team Explorer to the Source Control Server Database (5)Set Up Local Version Control Parameters (Developer and Administrator) (5)Create a Local Repository Folder (5)Set Up the Version Control Parameters (6)Configure Global Version Control System Settings (Administrator) (7)Create Repository (7)Create a Local Repository Folder (8)Create Repository (8)Synchronize Objects (8)Optional Task (9)Add Ax32Serv.exe as an exception on the Win2K3 Database Server Firewall (9)2[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]3[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]IntroductionYou can use Microsoft Visual SourceSafe (VSS), Microsoft Visual Studio Team Foundation Server (TFS), or MorphX VCS for version control in Microsoft Dynamics AX 2009. This document describes how to set up the Version Control System (VCS) for Microsoft Dynamics AX 2009 by using TFS as your VCS. Thesteps involved in setting up version control by using TFS are:Set up Microsoft Visual Studio Team Foundation Server on the version control server.Set up Team Server (the ID server) on the Team Server computer.Set up Microsoft Dynamics AX 2009 and Microsoft Visual Studio Team Explorer on theadministrator computer. Configure version control settings in Microsoft Dynamics AX 2009.Version Control System ConsiderationsBefore you configure Microsoft Dynamics AX 2009 to use TFS for version control, review therequirements and features of each of the VCS options. For more information about VCS options, see the version control documentation in Microsoft Dynamics AX 2009.Setup Overview Visual Studio TeamFoundation Serverand prerequisites Supported Operating System (See Set up the Developer andAdministratorComputers later in thisdocument)Team Server databaseon Microsoft SQL Serveror Oracle Supported Operating System (See Set up the Developer and Administrator Computers later in this document) Microsoft SQL Server or Oracle Microsoft Dynamics AX2009 application,Application Object Server(AOS), and clientVisual Studio TeamExplorerInstall and set up (Admin) Install and set up(Admin)Install on each computer(Admin and developers) Install Microsoft Visual Studio Team Foundation ServerInstall Microsoft Visual Studio Team Foundation Server on the source control server by following the installation instructions in the Visual Studio Team Foundation Installation Guide. If the message,“Navigation to the webpage was canceled”, appears when you try to access the installation guide, thenthe .chm documentation file might be blocked by your computer. To unblock the documentation file, right-click the file, and then click Properties. On the General tab, click Unblock.The documentation for Visual Studio Team System 2008 is located at/fwlink/?LinkId=116810.The documentation for Visual Studio Team System 2005 is located at/fwlink/?LinkId=113137.Install Microsoft Visual Studio Team ExplorerThe client portion of TFS is called Team Explorer. Install Team Explorer on the source control server, on the administrator computer, and optionally on developer computers.The documentation for Visual Studio Team System 2008 is located at/fwlink/?LinkId=116810.The documentation for Visual Studio Team System 2005 is located at/fwlink/?LinkId=113137.Configure Microsoft Visual Studio Team Foundation ServerConnect Team Explorer to the Team Foundation Server.1.Open Team Explorer. Click Tools > Connect to Team Foundation Server.2.Type the name of the Team Foundation Server if it does not appear, and then click OK.Create a project.1.Click File > New Team Project.2.Follow the instructions in the New Team Project Wizard to create a project.Add all of the developers to the project as members.1.Click View > Team Explorer to display the Team Explorer.2.Right-click the project, point to Team Project Settings, and then click Group Membership.Restrict developer access to the system settings.We recommend that you restrict access to the VCSdef.xml file to prevent developers from modifying global version control system settings.1.Click View > Source Control Explorer.2.Expand the project, and then double-click Definition.3.Right-click VCSdef.xml, and then click Properties.4.Click the Security tab.5.Modify the permissions of the group that contains the developers so that developers cannotmodify the VCSdef.xml file.Team Foundation Administrators help is available at /fwlink/?LinkId=117754. Learn about widely used Team Foundation source control tasks at/fwlink/?LinkID=113138&clcid=0x409.4[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]Install and Set Up the Team Server (ID Server)Note:Team Server should not be confused with Team Foundation Server. Team Server is an ID server that handles the task of issuing unique IDs to objects and labels when they are created. See the Team Server (ID Server) Setup white paper to set up Team Server at/fwlink/?LinkId=120292.Set Up the Developer and Administrator ComputersInstall the Operating SystemSee /fwlink/?LinkId=113164&clcid=0x409 for system requirements.Install MS SQL Server or OracleSee /fwlink/?LinkId=113164&clcid=0x409 for system requirements.Install Microsoft Dynamics AX 2009See /fwlink/?LinkId=117123 for access to the Microsoft Dynamics AX 2009 Implementation Guide.Install Microsoft Visual Studio Team ExplorerThe client portion of TFS is called Team Explorer. Installing Team Explorer on developer computers is optional. To install Team Explorer on developer computers, follow the instructions in the installation documentation at /fwlink/?LinkId=113137.Connect Team Explorer to the Source Control Server Database1.Open Team Explorer. Click Tools > Connect to Team Foundation Server.2.Type the name of the Team Foundation Server if it does not appear, select the Team Project, andthen click OK.Note: Installing Team Explorer on developer computers is optional.Learn about widely used Team Foundation source control tasks at/fwlink/?LinkID=113138&clcid=0x409.Set Up Local Version Control Parameters (Developer and Administrator)Create a Local Repository Folder1.Open Windows Explorer.2.Create a new folder to use as the local repository folder. For example, C:\VCS_Repository.Note: Consider the folder you choose for the local repository folder. If a Team Explorer instance that runs against the source control server shares a local repository folder with a Microsoft Dynamics AX 2009 client and a change is checked in by using Team Explorer, the Microsoft Dynamics AX 2009 client will not reflect those changes when you synchronize the Microsoft Dynamics AX client unless you select Force when you synchronize. Installing Team Explorer on developer computers is optional. 5[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]6[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]Set Up the Version Control Parameters1. Click Microsoft Dynamics AX > Tools > Development tools > Version control > Setup >Parameters.2. In Source control status , select Enable .3. In Version control system , select Team Foundation Server .4. In Repository folder , type the path of the repository folder.5. Choose environment settings.6. Click the Team Foundation Servertab.7[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]7. In the Team Foundation Server URL field, type the URL of the server that has the TFS project.8. In the Team Foundation project name field, type the name of the TFS project.Configure Global Version Control System Settings (Administrator)1. Click Microsoft Dynamics AX > Tools > Development tools > Version control > Setup >System settings.2. In Microsoft SQL Server or Oracle , select the kind of database that the Team Server uses.3. If you selected Microsoft SQL Server in step 2, type the name of the Team Server in Team Servername . If you selected Oracle in step 2, type or select the TNS Service name in Team Server TNS Service Name .4. If you selected Microsoft SQL Server in step 2, type or select the name of the Team Serverdatabase, such as AXTS, in Team Server Database name . If you selected Oracle in step 2, type or select the Team Server schema, such as AXTS, in Team Server Schema .5. Specify best practice settings.Create RepositoryYou must add all objects from your master copy of Microsoft Dynamics AX 2009 to the VCS server. When you add the objects to the server that runs the VCS, they are automatically registered in the Team Server so that IDs are assigned to them. Assigning IDs to all existing objects ensures that the Team Server can issue unique IDs for new objects when they are created.8[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]Create a Local Repository FolderCreate a repository folder on your computer. When you add the Application Object Tree (AOT) objects to the server, a copy of each object is also added to your repository folder.Note: Consider the folder you choose for the local repository folder. If a VSS client that runsagainst the VSS server shares a local repository folder with a Microsoft Dynamics AX 2009 client and a change is checked in by using the VSS client, the Microsoft Dynamics AX 2009 client will not reflect those changes when you synchronize the Microsoft Dynamics AX 2009 client unless you select Force when you synchronize.1. Open Windows Explorer.2. Create a new folder to use as your local repository folder, such as C:\VCS_Repository.Create Repository1. Click Microsoft Dynamics AX > Tools > Development tools > Version control > Createrepository.2. In Check-in description , type a description of the check-in, such as “C reation of repository.”3. Click OK .Synchronize ObjectsAt first, the local version of Microsoft Dynamics AX 2009 may not be synchronized with the master version that is deployed by the version control administrator. To access the latest version of all AOT objects, synchronize the local version with the version that is on the server that is running the version control system.9[TEAM FOUNDATION SERVER VERSION CONTROL SETUP]1. Click Microsoft Dynamics AX > Tools > Development tools > Version control > Synchronize.2. Indicate whether you want to force the synchronization (optional).3. Click OK to start synchronization.Optional TaskAdd Ax32Serv.exe as an exception on the Win2K3 Database Server Firewall1. Click Start > Control panel > Windows Firewall .2. Select On to turn on the firewall.3. On the Exceptions tab, click the Add program button.4. In the Add a program dialog box, browse to locate the Ax32Serv.exe file on your computer, andthen click OK to add it to the list of exceptions.Microsoft Dynamics is a line of integrated, adaptable business management solutions that enables you and your people to make business decisions with greater confidence. Microsoft Dynamics works like and with familiar Microsoft software, automating and streamlining financial, customer relationship and supply chain processes in a way that helps you drive business success.U.S. and Canada Toll Free 1-888-477-7989Worldwide +1-701-281-6500/dynamicsThe information contained in this document represents the current view of Microsoft Corporation on the issues discussed as of the date of publication. Because Microsoft must respond to changing market conditions, this document should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information presented after the date of publication.This document is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED, OR STATUTORY, AS TO THE INFORMATION IN THIS DOCUMENT.Complying with all applicable copyright laws is the responsibility of the user. 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Recist标准（Response Evaluation Criteria in Solid Tumors）是用于检测恶性肿瘤治疗效果的标准，其发展历程如下：1.WHO标准：1979年，在世界卫生组织 （WHO）的倡议下，关于癌症治疗报告结果标准化的会议先后举行。

制定了有关记录与患者、肿瘤、实验及放射学数据相关的数据、急性/亚急性的毒性分级、癌症治疗结果报告以及治疗结果报告的标准化等的方法。

这些建议已得到若干组织的赞同并被提出供国际化使用，以使调查人员能够有效地将其结果与其他人的结果进行比较。

这代表着实体瘤双径测量疗效评价标准的确定。

该测量方式为肿瘤面积=a*b （其中a、b分别为最大径、最大垂直径），以其变化来代表肿瘤的体积变化，是二维测量法。

2.RECIST （Version 1.0）标准：1999年，RECIST在美国ASCO会议上首次出现于大众视野，并于同年发表在JNCI（Journal of the National Cancer Institute）杂志。

其对先前的WHO疗效评价标准进行了必要的修改和补充，采用了更为简易精确的单径测量代替传统的双径测量方法。

其中，单径指的是肿瘤最大径。

3.RECIST （Version 1.1）标准：2009年，RECIST修订版 （即RECIST-1.1版）首次公布。

与RECIST-1.0版一样，RECIST-1.1版也运用了基于肿瘤负荷的解剖成像技术进行疗效评估。

该版本与前两版不同的是具备充足的文献基础，收集和使用了来自欧洲癌症治疗研究组织 （EORTC）实体瘤临床试验数据库中的6500例患者、18000多处靶病灶的检验数据，因此数据来源广泛，所制定标准的可借鉴性和规范性更强。

Recist标准主要用于记录肿瘤的体积变化，并将之分成四个分类：完全缓解（CR）、部分缓解（PR）、疾病稳定（SD）和疾病进展（PD）。

其中，完全缓解指的是除结节性疾病外，所有目标病灶完全消失，所有目标结节须缩小至正常大小 （短轴＜10mm），所有目标病灶均须评价；部分缓解指的是如果经过治疗之后靶病灶的长径总和缩小超过30%，即为“疾病缓解”。

Table of Contents! WARNING - USER RESPONSIBILITYFAILURE OR IMPROPER SELECTION OR IMPROPER USE OF THE PRODUCTS DESCRIBED HEREIN OR RELATED ITEMS CAN CAUSE DEATH, PERSONAL INJURY AND PROPERTY DAMAGE.This document and other information from Parker Hannifin Corporation, its subsidiaries and authorized distributors provide product or system options for further investigation by users having technical expertise.The user, through its own analysis and testing, is solely responsible for making the final selection of the system and components and assuring that all performance, endurance, maintenance, safety and warning requirements of the application are met. The user must analyze all aspects of the application, follow applicable industry standards, and follow the information concerning the product in the current product catalog and in any other materials provided from Parker or its subsidiaries or authorized distributors.To the extent that Parker or its subsidiaries or authorized distributors provide component or system options based upon data or specifications provided by the user, the user is responsible for determining that such data and specifications are suitable and sufficient for all applications and reasonably foreseeable uses of the components or systems.OFFER OF SALEThe items described in this document are hereby offered for sale by Parker Hannifin Corporation, its subsidiaries or its authorized distributors. This offer and its acceptance by the provisions stated in the detailed ‘Offer of Sale’ which is available upon request.Global PartnershipsGlobal SupportParker is committed to helping makeour customers more productiveand more profitable through ourglobal offering of motion andcontrol products and systems. Inan increasingly competitive globaleconomy, we seek to developcustomer relationships as technologypartnerships. Working closely withour customers, we can ensure the bestselection of technologies to suit theneeds of our customers’ applications.Electromechanical Technologiesfor High Dynamic Performanceand Precision MotionParker electromechanicaltechnologies form an importantpart of Parker’s global motion andcontrol offering. Electromechanicalsystems combine high performancespeed and position control with theflexibility to adapt the systems tothe rapidly changing needs of theindustries we serve.Parker HannifinThe global leader in motion and control technologies and systemsWith annual sales exceeding$12 billion in fiscal year 2011,Parker Hannifin is the world’sleading diversified manufacturer ofmotion and control technologiesand systems, providing precision-engineered solutions for a widevariety of mobile, industrial andaerospace markets. The companyemploys approximately 58,000people in 47 countries aroundthe world. Parker has increasedits annual dividends paid toshareholders for 55 consecutiveyears, among the top five longest-running dividend-increase recordsin the S&P 500 index. For moreinformation, visit the company’s website at , or itsinvestor information site at http://About Parker Hannifin CorporationWith more than 30 years of worldwide application experience, Parker assists its customers in improving productivity and reducing energy consumption with a comprehensive, robust range of DC drives and drive systems. Parker DC drive products are sold, supported and serviced worldwide, with solutions from simple speed control to complex multi-motor coordinated process control. Parker DC drive products are easy to configure and commission, with simple but flexible function block-based configuration tools and connectivity with all major industrial fieldbus networks.Digital DC Drives Maximize Flexibility and Functionality Using the same 32-bit control architecture as our current range of AC drive products, Parker’s range of digital DC drives provides the same high level of functionality - and with it flexibility and performance - as comparable AC drive systems, while simultaneously allowing the user to integrate both AC and DC drive systems in a single machine with the same interface and software.Retrofit Existing Applications with the Latest TechnologyBy retrofitting existing DC motor applications with Parker digital DC drives, the user can avoid the cost of replacing an existing functioning, DC motor with a similar AC drive system, while still enjoying the benefits of a flexible control platform and high performance drive.DC590+ Integrator Series 2 Digital DC DriveThe DC590+ uses an advanced control platform to provide high levels of flexibility and performance for a wide range of applications. Designed for machine integrators, the DC590+ features function block programming, multiple communications and feedback options and support worldwide. Available as non-regenerative or full fourquadrant regenerative. Available from 1-2400A maximum. Fieldbus options include Profibus-DP, CANopen, Modbus RTU, Ethernet and DeviceNet.Typical applications include • Converting machinery • Hoists and cranes• Plastics processing machinery • Wire and cable manufacturing • Automotive test standsProcessSimple2400A35A15ADC Drives Product Range OverviewGlobal DC Drive Solutions to Maximize Flexibility and Increase performanceDRV Package - “Ready to Install” DC DrivesSave design time, panel space and the time and cost of component sourcing and installation with Parker’s unique DRV drive format. DRV drives include all peripheral power components typically required in a DC drive system, integrated in a self-contained package. This packagecontains the additional components within the footprint of the standard drive module and saves significant panel space while reducing complexity and improving the appearance.01010010002000HPSystemsSingle Phase Analog Non-Isolated Converter: 506/507/508Economical, compact torque and speed control of permanent magnet or shunt wound DC motors. Selectable between 110VAC or 230VAC single phase supply. Tachometer or armature voltage feedback, 3, 6, or 12A armature options.Typical applications include:• Conveyors, basic speed control • Packaging machineryDC590+ DRV - “Ready to Install” Series 2The DC590+ DRV Series version is a complete packaged drive solution, including AC line contactor, AC line fuses, DC fuse, control / field fuse and provisions for a motor blower starter. The DRV series reduces panel complexity while saving on panel space. Available to 1750 HP (2400A) maximum.Single Phase Two Quadrant Analog Isolated Converter: 512CThe 512C provides effective torque and speed control of permanent magnet or wound field DC motors. Extremely linear speed and current loops in an isolated package, ideal for single or multiple motor applications up to 32A (10 HP at highest input voltage).Typical applications include:• Centrifugal fans and pumps • Extruders and mixers• Small paper converting machinesSingle Phase Four Quadrant Analog Isolated Converter: 514CThe 514C offers full four quadrant regenerative control of permanent magnet or wound field DC motors. Ideal for applications requiring accurate or rapid deceleration of high inertia loads. Effective for single or multiple motor applications to 32A (10 HP at highest input voltage).Typical applications include:• Machine tool spindles • Wire drawing machines •Winders/Reelers01010010002000 HP1010010002000 HP010*******2000 HP010*******2000 HPAnalog DC Drives RangeDescriptionThe 506, 507 and 508 series drives break new ground in cost-effective DC motor control. Available in 3, 6 or 12A armature ratings, the feature packed minimum footprint design is ideal for speed or torque control of permanent magnet or shunt wound DC motors fed from single phase supplies.Typical applications include:• Fans and pumps • Conveyors• Packaging machineryLow cost high featured design IP20 protected coversCompact footprint and DIN rail mounting Selectable 110V or 230V supplySelectable tach or armature voltage feedbackStandardsMarkedEN61800-3 (EMC) with external filterEN50178 (safety, low voltage directive)andTechnical SpecificationAnalog DC Drives506/507/508 SeriesUp to 2 HP/12ADimensions (in/mm)Note: Color of enclosure may vary from illustrationDescriptionIsolated control circuitry, a host of features, and extremely linear control loop make the 512C ideal for single motor or multi-drive applications. The 512C is suitable for controlling permanent magnet or field wound DC motors in speed or torque control, and can be used “open loop” with armature voltage feedback, or with DC tach feedback for enhanced regulation and speed range. Chassis mount, IP00 rating. Typical applications include:• Centrifugal fans and pumps • Extruders and mixers • ConveyorsTechnical SpecificationsAnalog DC Drives512C SeriesUp to 32AStandards:Marked EN61800-3 (EMC) with external filterEN50178 (safety, low voltage directive)Common Specifications: 512C and 514C Voltage Ratings:Dimensions (in/mm):DescriptionThe regenerative 514C DC drive offers full four quadrant control of DC motors from single phase supplies. As such it is ideal for applications involving overhauling loads or where rapid and accurate deceleration is required. 514C can be used “open loop” with armature voltage feedback, or with DC tach feedback for enhanced regulation and speed range. Chassis mount, IP00 rating. Typical applications include:• Machine tool spindles • Wire drawing machines • Winders/ReelersTechnical SpecificationAnalog DC Drives514C SeriesUp to 32ANote: Color of front panel may vary from illustrationDrive mounted on a “footprint” filterEMC Filtersfor AC and DC DrivesDescriptionA range of pre-selected EMC (Electromagnetic Compatibil-ity)/RFI (Radio Frequency Interference) Filters are avail-able, suitable for all drives. These filters are a cost effective and easily implemented solution for the abatement of EMC in order to meet certain directives. Installation of the drive must be in accordance with the installation guidelines in the product manual.Filters described as “footprint” type are designed to save panel space by mounting behind the drive.Ordering© 2012 Parker Hannifin Corporation. All rights reserved.Parker Hannifin CorporationSSD Drives Division 9225 Forsyth Park Dr.Charlotte, NC 28273 USATel: (704) 588-3246 Fax: (704) 588-3249AE – UAE, Dubai Tel: +971 4 8127100 ********************AR – Argentina, Buenos Aires Tel: +54 3327 44 4129AT – Austria, Wiener Neustadt Tel: +43 (0)2622 23501-0 *************************AT – Eastern Europe, Wiener NeustadtTel: +43 (0)2622 23501 900 ****************************AU – Australia, Castle Hill Tel: +61 (0)2-9634 7777AZ – Azerbaijan, Baku Tel: +994 50 2233 458****************************BE/LU – Belgium, Nivelles Tel: +32 (0)67 280 900*************************BR – Brazil, Cachoeirinha RS Tel: +55 51 3470 9144BY – Belarus, Minsk Tel: +375 17 209 9399*************************CA – Canada, Milton, Ontario Tel: +1 905 693 3000CH – Switzerland, Etoy Tel: +41 (0)21 821 87 00*****************************CL – Chile, Santiago Tel: +56 2 623 1216CN – China, Shanghai Tel: +86 21 2899 5000CZ – Czech Republic, Klecany Tel: +420 284 083 111*******************************DE – Germany, Kaarst Tel: +49 (0)2131 4016 0*************************DK – Denmark, Ballerup Tel: +45 43 56 04 00*************************ES – Spain, Madrid Tel: +34 902 330 001 ***********************FI – Finland, Vantaa Tel: +358 (0)20 753 2500 *************************FR – France, Contamine s/Arve Tel: +33 (0)4 50 25 80 25 ************************GR – Greece, Athens Tel: +30 210 933 6450 ************************HK – Hong Kong Tel: +852 2428 8008HU – Hungary, Budapest Tel: +36 1 220 4155*************************IE – Ireland, Dublin Tel: +353 (0)1 466 6370 *************************IN – India, MumbaiTel: +91 22 6513 7081-85IT – Italy, Corsico (MI) Tel: +39 02 45 19 21 ***********************JP – Japan, Tokyo Tel: +81 (0)3 6408 3901KR – South Korea, Seoul Tel: +82 2 559 0400KZ – Kazakhstan, Almaty Tel: +7 7272 505 800****************************LV – Latvia, Riga Tel: +371 6 745 2601 ************************MX – Mexico, Apodaca Tel: +52 81 8156 6000MY – Malaysia, Shah Alam Tel: +60 3 7849 0800NL – The Netherlands, OldenzaalTel: +31 (0)541 585 000 ********************NO – Norway, Ski Tel: +47 64 91 10 00************************NZ – New Zealand, Mt Wellington Tel: +64 9 574 1744PL – Poland, Warsaw Tel: +48 (0)22 573 24 00 ************************PT – Portugal, Leca da Palmeira Tel: +351 22 999 7360**************************Parker WorldwideRO – Romania, Bucharest Tel: +40 21 252 1382*************************RU – Russia, Moscow Tel: +7 495 645-2156************************SE – Sweden, Spånga Tel: +46 (0)8 59 79 50 00 ************************SG – Singapore Tel: +65 6887 6300SK – Slovakia, Banská Bystrica Tel: +421 484 162 252**************************SL – Slovenia, Novo Mesto Tel: +386 7 337 6650**************************TH – Thailand, Bangkok Tel: +662 717 8140TR – Turkey, Istanbul Tel: +90 216 4997081 ************************TW – Taiwan, Taipei Tel: +886 2 2298 8987UA – Ukraine, Kiev Tel +380 44 494 2731*************************UK – United Kingdom, WarwickTel: +44 (0)1926 317 878 ********************US – USA, Cleveland Tel: +1 216 896 3000VE – Venezuela, Caracas Tel: +58 212 238 5422ZA – South Africa, Kempton ParkTel: +27 (0)11 961 0700*****************************。

感受时间的重量记Acer 2009全球记者发布会
佚名
【期刊名称】《电脑迷》
【年(卷),期】2009(000)010
【摘要】2009年第二季度初，Acer亚太地区重要合作伙伴以及数百家媒体齐聚北京，在名为“Sign of The Time”的全球记者会上共同见证Acer发展历程上的重要时刻……
【总页数】1页(P34)
【正文语种】中文
【中图分类】TP368.3
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