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人工智能多模态算法模型-概述说明以及解释1.引言1.1 概述概述部分旨在介绍本文所要讨论的主题——人工智能多模态算法模型。
随着科技进步的不断推动，人工智能技术正迅速发展，并在各个领域展现出巨大潜力。
多模态算法模型作为人工智能领域的重要研究方向之一，通过整合多个数据源的信息，实现了多种感知模态（如视觉、语音、文本等）之间的有机融合和相互协同，从而更全面地获取、理解和分析数据，在某些任务上取得了非常好的成果。
人工智能多模态算法模型的核心思想是通过利用多种感知模态之间的互补信息，提高任务处理的效果和准确度。
例如，当我们需要对一幅图像进行分类时，单一的视觉信息可能无法完全捕捉到图像中的细节，但是加入语音或者文本等其他感知模态的信息之后，就能够更加全面地理解图像的内容。
多模态算法模型的应用范围非常广泛，涉及到图像分类、音频处理、自然语言处理等诸多领域。
在图像领域，多模态算法模型可以应用于图像识别、目标检测和图像生成等任务；在音频领域，可以用于语音识别、情感分析和音乐生成等任务；在自然语言处理领域，可以用于文本分类、机器翻译和情感分析等任务。
通过将多模态的信息进行融合和分析，多模态算法模型能够更好地解决现实生活中复杂多变的问题。
本文旨在深入探讨多模态算法模型的概念、应用领域、优势以及发展前景。
通过对多模态算法模型的研究和实践，将有助于推动人工智能技术在多个领域的应用，为实现智能化社会做出更大贡献。
文章结构部分的内容如下：1.2 文章结构本文主要探讨人工智能中的多模态算法模型。
文章分为引言、正文和结论三个部分。
在引言部分，我们将对多模态算法模型进行一个概述，介绍其基本概念和重要性。
同时，我们还会对本文的结构进行简要的说明，以便读者对全文有一个整体的了解。
最后，我们会明确本文的目的，即为读者提供关于多模态算法模型的全面理解。
在正文部分，我们将进一步探讨多模态算法模型的概念，并介绍其在各个领域的应用。
我们将重点介绍多模态算法模型在语音识别、图像处理和自然语言处理等领域的应用，并阐述其在这些领域中的优势和挑战。
DATASHEET Overview The Synopsys OptSim tool is an award-winning photonic integrated circuit and fiber-optic system simulator. With state-of-the-art time- and frequency-domain split-step algorithms, OptSim provides engineers around the globe with a native photonic-domain environment to design and optimize photonic circuits and systems. OptSim can be used as a standalone solution with its own graphical user interface (Windows and Linux), or integrated into the OptoCompiler Photonic IC design platform (Linux). When used as an OptoCompiler-integrated simulator, OptSim:•Supports electro-optic (E-O) co-simulation with Synopsys PrimeSim HSPICE and PrimeSim SPICE electrical circuit simulators •Integrates seamlessly with the PrimeWave Design Environment for advanced simulation, analyses, and visualization including parametric scans, Monte Carlo and corner analyses •Provides single- and multimode fiber-optic system modeling capabilities.When used as a standalone simulator, OptSim’s GUI provides functionalities of schematic entry, simulation setup, and visualization.Introduction Photonic integration is an answer to the ever-increasing demands for more bandwidth, better energy efficiency, smaller footprint, and improved reliability. The adaptation of photonic ICs (PICs) is rapidly growing acrossindustry segments such as telecom, data centers, optical interconnects,automotive, sensing, aerospace & defense, artificial intelligence (AI), andphotonic computing. PICs are becoming complex and the component count isincreasing at a rapid pace. Co-packaged optics (CPO) and xPU I/O are drivingmore complex trade-offs between electronics and photonics. Gone are the dayswhen it was sufficient to model photonics on the back of an envelope, withsome homegrown code, or as electronics in electrical circuit simulators. WithOptSim, you use the most comprehensive optical simulator with the industry’sbest electrical circuit simulators on the respective portions of the design withinthe OptoCompiler platform.Features at a Glance•E lectronic-photonic co-design via Synopsys PrimeSim HSPICE and PrimeSim SPICE•Simulation of single and multimode fiber optic systems and photonic integrated circuits•Seamless integration with OptoCompiler and PrimeWave Design Environment•Extensive libraries of photonic andelectronic components and analysistools•Support for numerous foundryprocess design kits (PDKs)•Support for custom photonics (PDKsand devices) via Photonic DeviceCompiler•Support for hierarchical design and bidirectional signal flow•Design for manufacturing via MonteCarlo and corner analyses OptSim Electro-Optic Co-Simulation of Photonic Integrated Circuits and Fiber-Optic SystemsDesigning single- and multimode fiber-optic systems requires capabilities to support advanced intensity- and phase-modulation for both single- and multi-channel transmission with direct and coherent detection. The interplay of polarization-dependent transmission impairments with noise, crosstalk, and multi-path interference (MPI) can create challenges to the channel capacity. In addition to PIC modeling capabilities, OptSim provides rich libraries of components and powerful analyses options to facilitate the design of a diverse range of system applications such as coherent telecom systems, RF-over-fiber, high-speed Ethernet, passive-optical-networks, and free-space optics.Figure 1: Photonic and electronic circuit and system simulation from the OptoCompiler cockpitFeatures•Works with foundry model libraries and provides a complete library of generic model templates of integrated photonics devices, enabling engineers to tailor models to measured behavior. In addition to supporting PIC design models and features, OptSim provides a rich library of single- and multimode fiber-optic system design models to support testing a PIC at the system levelFigure 2: The OptSim library includes electrical and photonic models to simulate circuits and systems•Models bidirectional signal flow for both optical (single- and multi-wavelength) and electrical signals•Models multipath interference (MPI), reflections, and resonances from network and PIC devices•Supports Monte Carlo and corner analyses•Supports simulation of design hierarchies•Supports measurement- and datafile-driven modeling of active and passive photonic components, electroniccomponents, and circuits•Supports custom design, combining foundry models and custom devices•Co-simulation with PrimeSim HSPICE and PrimeSim SPICE enables simulation of electronics in the PIC using industry-leading electrical circuit simulators together with the simulation of photonic circuits in OptSimFigure 3: Co-simulating electronic and photonic circuits in OptSim•OptSim is integrated with the Synopsys PrimeWave Design Environment, for both electrical and photonic netlists allowing setup of test benches, specifying simulation engine and parameters, performing scans and analyses for both electrical, photonic, and combined schematicsFigure 4: Setting up a testbench and simulation in PrimeWave Design Environment•OptSim results and waveforms (logical, electrical, and optical) can be viewed in both the PrimeWave Design Environment WaveView and OptSim Viewer©2021 Synopsys, Inc. All rights reserved. Synopsys is a trademark of Synopsys, Inc. in the United States and other countries. A list of Synopsys trademarks isavailable at /copyright .html . All other names mentioned herein are trademarks or registered trademarks of their respective owners.Figure 5: OptSim: Viewing simulation waveforms in PrimeWave Design Environment WaveView•Standalone OptSim (Windows, Linux) has its own graphical user interface and provides an intuitive simulation experienceFigure 6: OptSim GUI: Simulation of a PAM4 fiber-optic systemApplications:•Single- and multi-stage PICs for photonic computing, optical neural networks, life sciences, photonic sensor PICs •Segmented-electrode (SE) and traveling-wave Mach-Zehnder modulators (TW-MZM), optical filters, ring resonators,ring modulators•Transceivers for coherent and non-coherent fiber optic communication systems (such as NRZ, RZ, m-PAM, BPSK, QPSK,m-QAM, and OFDM)•Single- and multimode fiber-optic systems and circuits•Free-space optics, RF-over-fiber: Intermodulation distortion (IMD), dynamic range, sensitivity•Datacenter and automotive interconnects•Photonic systems with multipath interference (MPI), reflections, and resonancesPlatform Support•Linux: Red Hat Enterprise (64-bit), CentOS (64-bit)•Windows (64-bit): Standalone OptSim。
常⽤集成电路名词缩写汇总（第⼆版）重要说明整个集成电路的设计和⽣产链路很长,相关专有名称很多;本⽂对常见的集成电路相关的名词缩写进⾏了汇总,特别聚焦与集成电路设计领域,意在整理常⽤的数字电路/DC/PT/ICC/DFV/DFT/RTL/ATE相关⽅⾯的知识点,⽅便⼤家快速学习和掌握相关知识,⽅便⼤家查询;同时希望对学⽣将来的培训/⾯试等活动给予最⼤的帮助;⽂章按照字母排序的⽅式进⾏编排,⽅便⼤家查询;本次⽂章内容为第⼆次发布,我们将定期更新,逐步完善;欢迎⼤家提供相关信息⾄xgcl_wei微信号,帮助我们逐步完善内容,⽅便更多的⼈查询和使⽤,感谢您的参与,谢谢!英⽂全称中⽂说明ABV Assertion based verification基于断⾔的验证AES Advanced Encryption Standard⾼级加密标准,是美国政府采⽤的⼀种区块加密标准ADC Analog-to-Digital Converter指模/数转换器或者模数转换器AHB Advanced High Performance Bus⾼级⾼性能总线ALF Advanced Library Format先进（时序）库格式ALU Arithmetic and logic unit算数逻辑单元AMBA Advanced Microcontroller Bus Architecture⾼级微控制器总线体系ANT antenna天线效应AOP Aspect Oriented Programming⾯向⽅⾯编程APB Advanced Peripheral Bus⾼级外部设备总线API Application Programming Interface应⽤程序编程接⼝APR Auto place and route⾃动布局布线ARM Advanced RISC Machines 英国Acorn公司(ARM公司的前⾝)设计的低功耗成本的第⼀款RISC微处理器。
embedding model 指标-概述说明以及解释1.引言1.1 概述概述:概述部分将介绍embedding model以及本文的主要研究内容。
在当今大数据时代，信息爆炸给数据处理和信息检索带来了极大的挑战。
为了更好地处理和利用这些海量数据，embedding model应运而生。
embedding model是一种将高维度数据映射到低维度连续向量空间的方法。
它可以将大规模的离散数据进行编码并进行有效的表示。
通过将每个离散数据映射到低维连续向量空间中的一个向量，embedding model可以保留原始数据之间的关系，并能够更好地捕捉到数据的语义信息。
本文将着重探讨embedding model在实际应用中的指标问题。
指标是衡量embedding model性能的重要标准，它可以用来评估embedding model对于特定任务的效果和表现。
在不同的应用领域中，常用的指标包括准确率、召回率、均方误差等。
本文将结合具体案例和实验结果，分析不同指标的优缺点，帮助读者更好地理解和评估embedding model的性能。
在接下来的章节中，我们将首先介绍embedding model的定义，包括其基本原理和核心概念。
然后，我们将探讨embedding model在各个领域的应用场景，包括自然语言处理、推荐系统、图像处理等。
通过分析不同领域的案例，我们将深入理解embedding model在解决实际问题中的作用和效果。
最后，在结论部分，我们将总结embedding model的优势和发展前景，并展望未来的研究方向。
通过本文的详细探讨，希望能够为读者提供一种全面的了解和评估embedding model的方法，推动其在各个领域的应用进一步发展。
1.2 文章结构文章结构部分的内容可以包括以下内容：文章结构部分旨在介绍整篇文章的组织结构，并说明各个部分的主要内容和目的。
本文分为引言、正文和结论三个部分。
引言部分以概述、文章结构和目的为核心内容。
Face tracking with automatic model constructionJesus Nuevo,Luis M.Bergasa ⁎,David F.Llorca,Manuel OcañaDepartment of Electronics,Universidad de Alcala.Esc.Politecnica,Crta Madrid-Barcelona,Km 33,600.28871Alcala de Henares,Madrid,Spaina b s t r a c ta r t i c l e i n f o Article history:Received 12November 2009Received in revised form 26August 2010Accepted 15November 2010Available online xxxx Keywords:Face trackingAppearance modeling Incremental clustering Robust fittingDriver monitoringThis paper describes an active model with a robust texture model built on-line.The model uses one camera and it is able to operate without active illumination.The texture model is de fined by a series of clusters,which are built in a video sequence using previously encountered samples.This model is used to search for the corresponding element in the following frames.An on-line clustering method,named leaderP is described and evaluated on an application of face tracking.A 20-point shape model is used.This model is built of fline,and a robust fitting function is used to restrict the position of the points.Our proposal is to serve as one of the stages in a driver monitoring system.To test it,a new set of sequences of drivers recorded outdoors and in a realistic simulator has been compiled.Experimental results for typical outdoor driving scenarios,with frequent head movement,turns and occlusions are presented.Our approach is tested and compared with the Simultaneous Modeling and Tracking (SMAT)[1],and the recently presented Stacked Trimmed Active Shape Model (STASM)[2],and shows better results than SMAT and similar fitting error levels to STASM,with much faster execution times and improved robustness.©2010Elsevier B.V.All rights reserved.Contents 1.Introduction ...............................................................02.Background ...............................................................03.Robust simultaneous modeling and tracking ................................................03.1.Appearance modeling .......................................................03.2.Shape model ...........................................................04.Tests and results .............................................................04.1.Test set ..............................................................04.2.Performance evaluation ......................................................04.3.Results ..............................................................04.3.1.R-SMAT with automatic initialization...........................................04.4.Timings..............................................................05.Conclusions and future work .......................................................0Acknowledgments...............................................................0References ..................................................................1.IntroductionDriver inattention is a major cause of traf fic accidents,and it has been found to be involved in some form in 80%of the crashes and 65%of the near crashes within 3s of the event [3].Monitoring a driver to detect inattention is a complex problem that involves physiological and behavioural elements.Different works have been presented in recentyears,focused mainly in drowsiness,with a broad range of techniques.Physiological measurements such as electro-encephalography (EEG)[4]or electro-oculography (EOG),provide the best data for detection [4].The problem with these techniques is that they are intrusive to the subject.Moreover,medical equipment is always expensive.Lateral position of the vehicle inside the lane,steering wheel movements and time-to-line crossing are commonly used,and some commercial systems have been developed [5,6].These techniques are not invasive,and to date they obtain the most reliable results.However,the measurements they use may not re flect behaviours such as the so-called micro-sleeps [7].They also require a trainingImage and Vision Computing 29(2011)209–218⁎Corresponding author.Tel.:+34918856569.E-mail addresses:jnuevo@depeca.uah.es (J.Nuevo),bergasa@depeca.uah.es(L.M.Bergasa),llorca@depeca.uah.es (D.F.Llorca),mocana@depeca.uah.es (M.Ocaña).0262-8856/$–see front matter ©2010Elsevier B.V.All rights reserved.doi:10.1016/j.imavis.2010.11.004Contents lists available at ScienceDirectImage and Vision Computingj o u r na l ho m e p a g e :w w w.e l s ev i e r.c o m /l o c a t e /i m av i speriod for each person,and thus are not applicable to the occasional driver.Drivers in fatigue exhibit changes in the way their eyes perform some actions,like moving or blinking.These actions are known as visual behaviours,and are readily observable in drowsy and distracted drivers.Face pose[8]and gaze direction also contain information and have been used as another element of inattention detection systems [9].Computer vision has been the tool of choice for many researchers to be used to monitor visual behaviours,as it is non-intrusive.Most systems use one or two cameras to track the head and eyes of the subject[10–14].A few companies commercialize systems[15,16]as accessories for installation in vehicles.These systems require user-specific calibration,and some of them use near-IR lighting,which is known to produce eye fatigue.Reliability of these systems is still not high enough for car companies to take on the responsibility of its production and possible liability in case of malfunctioning.Face location and tracking are thefirst processing stages of most computer vision systems for driver monitoring.Some of the most successful systems to date use near-IR active illumination[17–19],to simplify the detection of the eyes thanks to the bright pupil effect. Near-IR illumination is not as useful during the day because sunlight also has a near-IR component.As mentioned above,near-IR can produce eye fatigue and thus limits the amount of time these systems can be used on a person.Given the complexity of the problem,it has been divided in parts and in this work only the problem of face tracking is addressed.This paper presents a new active model with the texture model built incrementally.We use it to characterize and track the face in video sequences.The tracker can operate without active illumination. The texture model of the face is created online,and thus specific for each person without requiring a training phase.A new online clustering algorithm is described,and its performance compared with the method proposed in[1].Two shape models,trained online and off-line,are compared.This paper also presents a new video sequence database,recorded in a car moving outdoors and in a simulator.The database is used to assess the performance of the proposed face tracking method in the challenging environment a driver monitoring application would meet.No evaluations of face pose estimation and driver inattention detection are performed.The rest of the paper is structured as follows.Section2presents a few remarkable works in face tracking in the literature that are related to our proposal.Section3describes our approach.Section4describes the video dataset used for performance evaluation,and experimental results.This paper closes with conclusions and future work.2.BackgroundHuman face tracking is a broadfield in computing research[20], and a myriad of techniques have been developed in the last decades.It is of the greatest interest,as vast amounts of information are contained in face features,movements and gestures,which are constantly used for human communication.Systems that work on such data often use face tracking[21,22].Non-rigid object tracking has been a major focus of research in later years,and general purpose template-based trackers have been used to track faces in the literature with success.Several efficient approaches have been presented[23–26].Statistical models have been used for face modeling and tracking. Active Shape Models[27](ASM)are similar to the active contours (snakes),but include constraints from a Point Distribution Model (PDM)[28]computed in advance from a training set.Advances in late years have increased their robustness and precision to remarkable levels(STASM,[2]).Extensions of ASM that include modeling of texture have been presented,of which Active Appearance Models (AAMs)[29]are arguably the best known.Active Appearance Models are global models in the sense that the minimization is performed over all pixels that fall inside the mesh defined by the mean of the PDM.All these models have an offline training phase,which require comprehensive training sets so they can generalize properly to unseen instances of the object.This is time consuming process,and there is still the risk that perfectly valid instances of the object would not be modeled correctly.Several methods that work without a priori models have been presented in the literature.Most of them focus on patch tracking on a video sequence.The classic approach is to use the image patch extracted on thefirst frame of the sequence to search for similar patches on the following frames.Lukas–Kanade method[30]was one of thefirst proposed solutions and it is still widely used.Jepson et al.[31]presented a system with appearance model based on three components:a stable component that is learned over a long period based on wavelets,a2-frame tracker and an outlier rejection process.Yin and Collins[32]build an adaptive view-dependent appearance model on-line.The model is made of patches selected around Harris corners.Model and target patches are matched using correlation,and the change in position, rotation and scale is obtained with the Procrustes algorithm.Another successful line of work in object tracking without a priori training is based on classification instead of modeling.Collins and Liu [33]presented a system based on background/foreground discrimi-nation.Avidan[34]presents one of the many systems that use machine learning to classify patches[35,36].Avidan uses weak classifiers trained every frame and AdaBoost to combine them.Pilet et al.[37]train keypoint classifiers using Random Trees that are able to recognize hundreds of keypoints in real-time.Simultaneous Modeling and Tracking(SMAT)[1]is in line with methods like Lucas–Kanade,relaying on matching to track patches. Lukas–Kanade extracts a template at the beginning of the sequence and uses it for tracking,and will fail if the appearance of the patch changes considerably.Matthews et al.[38]proposed an strategic update of the template,which keeps the template from thefirst frame to correct errors that appear in the localization.When the error is too high,the update is blocked.In[39],a solution is proposed withfixed template that adaptively detected and selected the window around the features.SMAT builds a more complex model based on incremental clustering.In this paper we combine concepts from active models with the incremental clustering proposed in SMAT.The texture model is created online,making the model adaptative,while the shape model is learnt offline.The clustering used by SMAT has some limitations,and we propose some modifications to obtain a more robust model and better tracking.We name the approach Robust SMAT for this reason.Evaluation of face tracking methods is performed in most works with images captured indoors.Some authors use freely available image sets,but most of them test on internal datasets created by them,which limits the validity of a comparison with other systems.Only a few authors[40,41]have used images recorded in a vehicle,but the number of samples is limited.To the best of our knowledge,there is no publicly available video dataset of people driving,either in a simulator or in a real road.We propose a new dataset that covers such scenarios.3.Robust simultaneous modeling and trackingThis section describes the Simultaneous Modeling and Tracking (SMAT)of Dowson and Bowden[1],and some modifications we propose to improve its performance.SMAT tries to build a model of appearance of features and how their positions are related(the structure model,or shape),from samples of texture and shape obtained in previous frames.The models of appearance and shape are independent.Fitting is performed in the same fashion of ASM:the features arefirst found separately using correlation,and then theirfinal positions are constrained by the shape model.If thefinal positions are found to be reliable and not caused byfitting errors,the appearance model is updated,otherwise it is left unchanged.Fig.1shows aflow chart of the algorithm.210J.Nuevo et al./Image and Vision Computing29(2011)209–2183.1.Appearance modelingEach one of the possible appearances of an object,or a feature of it,can be considered as a point in a feature space.Similar appearances will be close in this space,away from other points representing dissimilar appearances of the object.These groups of points,or clusters,form a mixture model that can be used to de fine the appearance of the object.SMAT builds a library of exemplars obtained from previous frames,image patches in this case.Dowson and Bowden de fined a series of clusters by their median patch,also known as representative ,and their variance.A new incoming patch is made part of the cluster if the distance between it and the median of the cluster is below a threshold that is a function of the variance.The median and variance of a cluster are recalculated every time a patch is added to it.Up to M exemplars per cluster are kept.If the size limit is reached,the most distant element from the representative is removed.Every time a cluster is updated,the weight of the clusters is recalculated as in Eq.(1):w t +1ðÞk=w t ðÞk +α11+αifk =k u w t ðÞk11+αotherwise8>><>>:ð1Þwhere α∈[0,1)is the learning rate,and k u is the index of the updatedcluster.The number of clusters is also limited to K .If K is reached,the cluster with the lowest weight is discarded.In a later work,Dowson et al.[42],introduced a different condition for membership,that compares the probability of the exemplar belonging to foreground (a cluster)or to the background p fg j d x ;μn ðÞ;σfg n p bg j d x ;μn ðÞ;σbg nð2Þwhere σfg n is obtained from the distances between the representative and the other exemplars in the cluster,and σbg n is obtained from the distances between the representative and the exemplars in the cluster offset by 1pixel.We have found that this clustering method can be improved in several ways.The adapting nature of the clusters could theoretically lead two or more clusters to overlap.However,in our tests we have observed that the opposite is much more frequent:the representative of thecluster rarely changes after the cluster has reached a certain number of elements.Outliers can be introduced in the model in the event of an occlusion of the face by a hand or other elements like a scarf.In most cases,these exemplars would be far away from the representative in the cluster.To remove them and reduce memory footprint,SMAT keeps up to M exemplars per cluster.If the size limit is reached,the most distant element from the representative is removed.When very similar patches are constantly introduced,one of them will be finally chosen as the median,and the variance will decrease,over fitting the cluster and discarding valuable exemplars.At a frame rate of 30fps,with M set to 50,the cluster will over fit in less than 2s.This would happen even if the exemplar to be removed is chosen randomly.This procedure will discard valuable information and future,subtle changes to the feature will lead to the creation of another cluster.We propose an alternative clustering method,named leaderP ,to partially solve these and other problems.The method is a modi fication of the leader algorithm [43,44],arguably the simplest and most frequently used incremental clustering method.In leader ,each cluster C i is de fined by only one exemplar,and a fixed membership threshold T .It starts by making the first exemplar the representative of a cluster.If an incoming exemplar ful fills being within the threshold T it is marked as member of that cluster,otherwise it becomes a cluster on its own.The pseudocode is shown in Algorithm 1.Algorithm 1.Leader clustering.1:Let C ={C 1,…,C n }be a set of n clusters,withweights {w 1t ,…,w n t}2:procedure leader (E ,C )cluster patch E 3:for all C i ∈C do 4:if d (C k ,E )b T thenCheck if patch E ∈C k 5:UpdateWeights (w 1t ,…,w n t)As in Eq.(1)6:return 7:End If 8:End For 9:Create new cluster C n +1,with E asrepresentative.10:Set w n +1t +1←0Weight of new cluster C n +111:C ←C ∪C n +1Add new cluster to the model12:if n +1N K thenRemove the cluster with lowest weight13:Find C k |w k ≤w i i =1,…,n 14:C ←C ∖C k 15:end if16:end procedureFig.1.SMAT block diagram.211J.Nuevo et al./Image and Vision Computing 29(2011)209–218On the other hand,leaderP keeps the first few exemplars added to the cluster are kept,up to P .The median of the cluster is chosen as the representative,as in the original clustering of Dowson and Bowden.When the number of exemplars in the cluster reaches P ,all exemplars but the representative are discarded,and it starts to work under the leader algorithm.P is chosen as a small number (we use P =10).The membership threshold is however flexible:the distances between the representative and each of the exemplars that are found to be members of the cluster is saved,and the variance of those distances is used to calculate the threshold.Because the representative is fixed and distance is a scalar,many values can be kept in memory without having a impact on the overall performance.Keeping more values reduces the risk of over fitting.The original proposal of SMAT used Mutual Information (MI)as a distance measure to compare the image patches,and found it to perform better that Sum of Squared Differences (SSD),and slightly better than correlation in some tests.Any de finition of distance could be used.We have also tested Zero-mean Normalized Cross-Correlation (ZNCC).Several types of warping were tested in [42]:translation,Euclidean,similarity and af fine.The results showed an increasing failure rate as the degrees of freedom of the warps increased.Based on this,we have chosen to use the simplest,and the patches are only translated depending on the point distribution model.3.2.Shape modelIn the original SMAT of Dowson and Bowden,the shape was also learned on-line.The same clustering algorithm was used,but themembership of a new shape to a cluster was calculated using Mahalanobis distance.Our method relies on the pre-learned shape model.The restric-tions on using a pre-learned model for shape are less than those for an appearance model,as it is of lower dimensionality and the deforma-tions are easier to model.It has been shown [45]that location and tracking errors are mainly due to appearance,and that a generic shape model for faces is easier to construct.We use the method of classic ASM [27],which applies PCA to a set of samples created by hand and extracts the mean s 0and an orthogonal vector basis (s 1,…,s N ).The shapes are first normalized and aligned using Generalized Procrustes Analysis [46].Let s =(x 0,y 0,…,x n −1,y n −1)be a shape.A shape can be generated from this base as s =s 0+∑mi =1p i ·s i :ð3ÞUsing L 2norm,the coef ficients p =p 1;…;p N ðÞcan be obtained for a given shape s as a projection of s on the vector basis p =S Ts −s 0ðÞ;p i =s −s 0ðÞ·s i ð4Þwhere S is a matrix with the eigenvectors s i as rows.The estimation of p with Eq.(4)is very sensitive to the presence of outlier points:a high error value from one point will severely in fluence the values of p .We use M-estimators [47]to solve this problem.This technique hasbeenFig.2.Trayectory of the vehicle (map from ).Fig.3.Samples of outdoor videos.212J.Nuevo et al./Image and Vision Computing 29(2011)209–218applied to ASM and AAM in previous works [48,49],so it is only brie fly presented here.Let s be a shape,obtained by fitting each feature independently.The function to minimize is arg min p∑2ni =1ρr i;θð5Þwhere ρ:R ×R þ→R þis an M-estimator,and θis obtained from the standard deviation of the residues [50].r i is the residue for coordinate i of the shaper i=x i−s i o+∑mj =1p j s i j!!ð6Þwhere x i are the points of the shape s ,and s i j is the i th element of the vector s j .Minimizing function 5is a case of re-weighted least squared.The weight decreases more rapidly than the square of the residue,and thus a point with error tending to in finite will have zero weight in the estimation.Several robust estimators have been tested:Huber ,Cauchy ,Gaussian and Tukey functions [50].A study was made in [19]that resulted in similar performance for all of them in a similar scenario to that of this paper,and Huber function was chosen.Huber function performs correctly up to a number of outliers of 50%of the points.We use the 20-point distribution of the BioID database [51].Data from this database was used to train the model.This distribution places the points in some of the most salient locations of the face,and has been used in several other works [40].4.Tests and resultsThis section presents the video sequences used to test different tracking algorithms in a driving scenario.The dataset contains most actions that appear in everyday driving situations.A comparison between our approach and SMAT is presented.Additionally,we compare R-SMAT results with the recently introduced Stacked Trimmed ASM (STASM).4.1.Test setDriving scenarios present a series of challenges for a face tracking algorithm.Drivers move constantly,rotate their head (self-occluding part of the face)or occlude their face with their hands (or other elements such as glasses).If other people are in the car,talking and gesturing are common.There are also constant background changes and,more importantly,frequent illumination changes,produced by shadows of trees or buildings,streets lights,other vehicles,etc.A considerable amount of test data is needed to properly evaluate the performance of a system under all these situations.A new video dataset has been created,with sequences of subjects driving outdoor,and in a simulator.The RobeSafe Driver Monitoring Video (RS-DMV)dataset contains 10sequences,7recorded outdoors (Type A )and 3in a simulator (TypeB ).Outdoor sequences were recorded on RobeSafe's vehicle moving at the campus of the University of Alcala.Drivers were fully awake,talked frequently with other passengers in the vehicle and were asked to look regularly to the rear-view mirrors and operate the car sound system.The cameras are placed over the dashboard,to avoid occlusions caused by the wheel.All subjects drove the same streets,shown in Fig.2.The length of the track is around 1.1km.The weather conditions during the recordings were mostly sunny,which made noticeable shadows appear on the face.Fig.3shows a few samples from these video sequences.Type B sequences were recorded in a realistic truck simulator.Drivers were fully awake,and were presented with a demanding driving environment were many other vehicles were presentandFig.4.Samples of sequences in simulator.m e 17m e 17C u m u l a t i v e E r rD i s t r i b u t i o n00.10.20.30.40.50.60.70.80.91C u m u l a t i v e E r rD i s t r i b u t i o n(a)(b)Fig.5.Performance of different shape models,with leaderP clustering.213J.Nuevo et al./Image and Vision Computing 29(2011)209–218potentially dangerous situations took place.These situations increase the probability of small periods of distraction leading to crashes or near-crashes.The sequences try to capture both distracted behaviour and the reaction to dangerous driving situations.A few images from TypeB sequences can be seen in Fig.4.The recording took place in a low-light scenario that approached nighttime conditions.This forced the camera to increase exposure time to a maximum,which lead to motion blur being present during head movements.Low power near-IR illumination was used in some of the sequences to increase the available light.The outdoor sequences are around 2min long,and sequences in the simulator are close to 10min in length.The algorithms in this paper were tested on images of approximately 320×240pixels,but high resolution images were acquired so they can be used in other research projects.The images are 960×480pixels for the outdoor sequences and 1392×480for the simulator sequences,and are stored without compression.Frame rate is 30frames per second in both cases.The camera has a 2/3″sensor,and used 9mm standard lenses.Images are grayscale.The recording software controlled camera gain using values of the pixels that fell directly on the face of the driver.The RS-DMV is publicly available,free of charge,for research purposes.Samples and information on how to obtain the database are available at the authors'webpage.14.2.Performance evaluationPerformance of the algorithms is evaluated as the error between the estimated position of the features and their actual position,asgiven by a human operator.Hand-marking is a time consuming task,and thus not all frames in all videos have been marked.Approximately 1in 30frames (1per second)has been marked in the sequences in RS-DMV.We call this frames keyframes .We used the metric m e ,introduced by Cristinacce and Cootes [40].Let x i be the points of the ground-truth shape s ,and let ^xi be the points of the estimated shape ^s .Then,m e =1ns ∑n i =1d i ;d i=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix i −^x i T x i −^x ir ð7Þwhere n is the number of points and s is the inter-ocular distance.Wealso discard the point on the chin and the exterior of the eyes,because their location changes much from person to person.Moreover,the variance of their position when marked by human operators is greater than for the other points.Because only 17points are used,we note the metric as m e 17.In the event of a tracking loss,of if the face cannot be found,the value of m e 17for that frame is set to ∞.During head turns,the inter-eye distance reduces with the cosine of the angle.In these frames,s is not valid and is calculated from its value on previous frames.Handmarked points and software used to ease the marking process are distributed with the RS-DMV dataset.4.3.ResultsWe tested the performance of R-SMAT approach on the RS-DMV dataset,as well as that of SMAT.We compared these results with those obtained by STASM,using the implementation in [2].One of the most remarkable problems of (R-)SMAT is that it needs to be properly initialized,and the first frames of the sequence are key1/personal/jnuevo.Fig.7.Samples of type A sequence #1.Outlier points are drawn in red.m e 17FrameFig.6.m e 17error for a sequence.214J.Nuevo et al./Image and Vision Computing 29(2011)209–218to building a good model.We propose STASM to initialize (R-)SMAT in the first frame.STASM has been shown to be very accurate when the face is frontal.Nonetheless,a slightly incorrect initialization will make (R-)SMAT track the (slightly)erroneous points.To decouple this error from the evaluation of accuracy of (R-)SMAT in the tests,the shape was initialized in the first frame with positions from the ground-truth data.At the end of this section,the performance of R-SMAT with automatic initialization is evaluated.First,a comparison of the shape models is presented.With the best shape model,the original clustering algorithm and the proposed alternative are evaluated.Results are presented for outdoor and simulator sequences separately,as each has speci fic characteristics on their own.The incremental shape model of SMAT was found to produce much higher error than the pre-learned model.Fig.5shows the cumulative distribution error of the incremental shape model (on-line )with the robust pre-learned model (using Huber function)(robust ).Forcomparison purposes,the figure also shows the performance for the pre-learned shape model fitted using a L 2norm (non-robust ).All models use leaderP clustering,and patches of 15×15pixels.Clear improvements in performance are made by the change to a pre-learned model with robust fitting.The robust,pre-learned shape model is very important in the first frames,because it allows the model to have bigger certainties that the patches that are being included correspond to correct positions.Robust shape model is used in the rest of the experiments in this paper.Fig.6shows the plot of the m e 17distance of both models in a sequence.A clear example of the bene fits of the robust model is depicted in Fig.7.The online model diverges as soon as a few points are occluded by the hand,while the robust model keeps track of the face.The method is also able to keep track of the face during head rotations,although with increased fitting error.This is quite remarkable for a model that has only been trained with fully frontal faces.Fig.8shows the performance of the original SMAT clustering compared with the proposed leaderP clustering algorithm,as implemented in R-SMAT.R-SMAT presents much better performance than the original SMAT clustering.This is specially clear in Fig.8(b).We stated in Section 3.1that the original clustering method could lead to over fitting,and type B sequences are specially prone to this:patches are usually dark and do not change much from frame to frame,and the subject does not move frequently.When a movement takes place,it leads to high error values,because the model has problems finding the features.m e 17m e 17C u m u l a t i v e E r rD i s t r i b u t i o nC u m u l a t i v e E r rD i s t r i b u t i o n(a)(b)parison of the performance of clustering algorithms.Table 1Track losses for different clustering methods.MeanMaximum Minimum R-SMATType A 0.99% 2.08%(seq.#7)0%(seq.#1,#2,#6)Type B 0.71% 1.96%(seq.#9)0%(seq.#10)SMATType A 1.77% 5.03%(seq.#4)0%(seq.#1,#2,#5)TypeB1.03%2.45%(seq.#9)0%(seq.#10)m e 17m e 17C u m u l a t i v e E r rD i s t r i b u t i o n00.10.20.30.40.50.60.70.80.91C u m u l a t i v e E r r D i s t r i b u t i o n(a)(b)parison of the performance of STASM and SMAT.215J.Nuevo et al./Image and Vision Computing 29(2011)209–218。
Geometric ModelingGeometric modeling is a branch of mathematics that deals with the representation of objects in space. It is a fundamental tool in computer graphics, computer-aided design (CAD), and other applications that require the creation of 3D models. Geometric modeling involves the use of mathematical equations and algorithms to create and manipulate objects in space. In this essay, we will explore the different aspects of geometric modeling, including its history, applications, and challenges.The history of geometric modeling can be traced back to the early 19th century when mathematicians began to study the properties of curves and surfaces. In the early 20th century, the development of calculus and differential geometry led to the creation of new methods for representing complex objects in space. The introduction of computers in the mid-20th century revolutionized the field of geometric modeling, making it possible to create and manipulate 3D models with greater precision and ease.Today, geometric modeling is used in a wide range of applications, including computer graphics, animation, video games, virtual reality, and CAD. In computer graphics and animation, geometric modeling is used to create realistic 3D models of objects, characters, and environments. In video games, geometric modeling is used to create the game world and characters. In virtual reality, geometric modeling is used to create immersive environments that simulate real-world experiences. In CAD, geometric modeling is used to create precise 3D models of mechanical parts and assemblies.One of the biggest challenges in geometric modeling is the representation of complex shapes and surfaces. Many real-world objects, such as cars, airplanes, and human bodies, have complex shapes that are difficult to represent using simple geometric primitives such as spheres, cylinders, and cones. To overcome this challenge, researchers have developed advanced techniques such as NURBS (non-uniform rational B-splines), which allow for the creation of complex curves and surfaces by combining simple geometric primitives.Another challenge in geometric modeling is the optimization of models for efficient rendering and simulation. As the complexity of models increases, so doesthe computational cost of rendering and simulating them. To address this challenge, researchers have developed techniques such as level-of-detail (LOD) modeling,which involves creating multiple versions of a model at different levels of detail to optimize rendering and simulation performance.In conclusion, geometric modeling is a fundamental tool in computer graphics, animation, video games, virtual reality, and CAD. Its history can be traced backto the early 19th century, and its development has been closely tied to the advancement of mathematics and computer technology. Despite its many applications and successes, geometric modeling still faces challenges in the representation of complex shapes and surfaces, as well as the optimization of models for efficient rendering and simulation. As technology continues to advance, it is likely that new techniques and approaches will emerge to overcome these challenges and pushthe field of geometric modeling forward.。
Demo Abstract:MSPsim–an Extensible Simulator for MSP430-equipped Sensor Boards Joakim Eriksson,Adam Dunkels,Niclas Finne,Fredrik¨Osterlind,Thiemo V oigt,Nicolas TsiftesSwedish Institute of Computer Science{joakime,adam,nfi,fros,thiemo,nvt}@sics.seAbstract—Software development for wireless sensor networks is a challenging and time consuming task.The resource limited hardware with limited I/O and debugging abilities combined with the often cumbersome hardware debugging tools makes debugging on the target hardware difficult.We present MSPsim, an extensible sensor board platform and MSP430instruction level simulator.MSPsim is intended to be used for reducing development and debugging time by allowing low-level andfine grained instrumentation of various aspects of software execution. The use of a simulator also enables development and testing without access to the target hardware.I.I NTRODUCTIONDue to the distributed nature of sensor networks and resource-constraints of sensor nodes,code development for wireless sensor network is a challenging and time consuming task.Furthermore,the application development and debugging tools are still cumbersome.One of the most commonly used methods for debugging sensor nodes is using on-chip emulation via JTAG that makes it possible to single-step and debug a running application on the target hardware.This is useful for understanding execution patterns,stack usage,etc,but less useful for debugging com-munication,sensor drivers and other timing sensitive parts of the application.For the development of wireless sensor network applica-tions,system simulators exist that simplify the development of algorithms and enable researcher to study the algorithms’behaviour and interaction in a controlled environment[1]. Cross-level simulation enables simultaneous simulation at different levels of the sensor network and hence supports simultaneous low-level debugging and application develop-ment[2].For cross-level simulation of our MSP430-based sensor node platforms we required an extensible instruction level simulation.Towards,this end,we designed and imple-ment MSPsim.As Avrora[3]MSPsim is a sensor network simulator simulating nodes at the instruction-level,but for the MSP430.Unlike ATEMU that emulates the operations of indi-vidual nodes and simulates communication between them[4], MSPsim is designed for instruction-level simulation and for integration with COOJA’s cross-level simulation environment. The contribution of this paper is MSPsim,an extensible instruction level simulator for the MSP430microcontroller that can be used as a component in a larger sensor network simulation system supporting cross-level simulation[2].For this reason MSPsim is designed to run multiple instances of the simulator in a single process unlike otherMSP430Fig.1.MSPsim simulating Contiki’s Blinker application on a Sky mote. simulators such as the GDB MSP430simulator[5].MSPsim also contains a sensor board simulator that simulates hardware peripherals such as sensors,communication ports,LEDs,and sound devices such as a beeper.The design of MSPsim, together with its implementation in Java,makes it easy to adapt the simulator to new sensor boards.II.T HE MSP SIM S IMULATORThe MSPsim is a Java-based instruction level simulator for the MSP430microcontroller that simulates unmodified target platformfirmware.MSPsim is an instruction-level simulator which made it easy to achieve accurate timing simulation. Further,MSPsim can load and run unmodified target platform firmwarefiles in IHEX and ELF format.The simulator is easily extensible with peripheral devices making it possible to simulate various types of MSP430based sensor nodes.It is also easy to add instrumentation for monitoring the execution of the application.In addition to simulate the MSP430and sensor board hardware,MSPsim can show a graphical representation of the sensor board in an on-screen window.LEDs on the sensor board are displayed using the correct colors.Figure1shows the graphical output from MSPsim simulating a Sky mote. The graphical output and input(buttons)of the sensor board hardware combined with UART/Serial output allows a system designer to visually verify that an application is executing correctly by inspection of the LEDs and output over the serial interfaces.MSPsim have built-in support for setting break-points, read/write monitoring and C-level profiling.A.Sensor Board SimulationAt SICS we are working with the ESB[6]and the Telos Sky[7]platforms,which both use the MSP430microcon-troller.Therefore,one of the design objectives of the MSPsim simulator is to simplify the adaptation to different types of sensor node platforms.To add support for a new sensor node platform only implementations of peripherals such as sensors,actuators such as beepers or LEDs,and radio and communication peripherals are needed.The implementation of those peripherals are typically relatively easy to make as many of them do not need to conform to strict timing requirements. Figure2shows the complete MSPsim simulation system with an MSP430microcontroller and connected peripherals.Fig. 2.An MSPsim simulation with an MSP430microcontroller and connected peripherals.To illustrate how a peripheral is implemented in MSPsim, Figure3contains a complete MSPsim serial peripheral class. The class constructor attaches itself as a listener to the USART object.When thefirmware running on the simulated MSP430 writes data to the USART,the dataReceived method of the listener is invoked.In this example,the dataReceived method simply prints out the produced character on screen.public class SerialMonitor{public SerialMon(USART usart){usart.setUSARTListener(this);}public void dataReceived(USART source,int data){ System.out.print((char)data);}}Fig.3.Implementation of a serial output device class attached to a USART USART usart=(USART)cpu.getIOUnit("USART1");serial=new SerialMon(usart);Fig.4.Creating a serial data monitor and attaching it to serial port1(USART 1)in MSPsimFigure4shows how a sensor board simulation platform connects the MSP430USART1serial port with a the serial monitor from Figure3.III.E VALUATIONTo evaluate the extensibility of MSPsim we measure the number of interfaces that must be implemented when adding support for a new sensor board in MSPsim.The measurements in the Table I show the amount of interfaces that the ESB sensor board platform implements.Certain peripherals that are present on the ESB are not yet included in the simulator: EEPROM,real-time clock,and active IR.The table shows that the amount of interfaces that need to be implemented for a sensor board is small;only one or two methods need to be implemented for each peripheral.Most peripherals only need a single method for either reading or writing to the peripheral. The interface for the radio is slightly more complex as it requires two write interfaces,one for configuration and one for the data to be transmitted.TABLE IN UMBER OF INTERFACES IMPLEMENTED BY THE ESB SIMULATOR.Read WritePeripheral value valueLED01Beeper01Digital sensor(PIR,Vibration)10Analog sensor(Mic,RSSI)10Radio12Serial(RS232)01IV.C ONCLUSIONSIn this paper we have presented MSPsim,a simulator for MSP430based sensor nodes.MSPsim is extensible in that adapting the simulator to new sensor boards requires not more than the implementation of a few Java classes.If the sensors and other chips on the new board are already implemented even less work is involved.The source code of MSPsim is available from sourceforge at:/projects/mspsim/A CKNOWLEDGMENTSThis work was partlyfinanced by VINNOV A,the Swedish Agency for Innovation Systems.R EFERENCES[1]P.Levis,N.Lee,M.Welsh,and D.Culler,“Tossim:accurate and scalablesimulation of entire tinyos applications,”in Proceedings of thefirst international conference on Embedded networked sensor systems,2003, pp.126–137.[2] F.¨Osterlind,A.Dunkels,J.Eriksson,N.Finne,and T.V oigt,“Cross-levelsensor network simulation with cooja,”in Proceedings of the First IEEE International Workshop on Practical Issues in Building Sensor Network Applications(SenseApp2006),Tampa,Florida,USA,Nov.2006. [3] B.Titzer,D.Lee,and J.Palsberg,“Avrora:scalable sensor networksimulation with precise timing,”in IPSN’05:Proceedings of the4th international symposium on Information processing in sensor networks, 2005.[4]J.Polley,D.Blazakis,J.Mcgee,D.Rusk,and J.S.Baras,“Atemu:afine-grained sensor network simulator,”2004,pp.145–152.[5] D.Diky and C.Liechti,“The GCC toolchain for the Texas InstrumentsMSP430MCUs,”/Visited2006-11-11. [6]J.Schiller,H.Ritter,A.Liers,and T.V oigt,“Scatterweb-low powernodes and energy aware routing,”in Proceedings of Hawaii International Conference on System Sciences,Hawaii,USA,2005.[7]J.Polastre,R.Szewczyk,and D.Culler,“Telos:Enabling ultra-low powerwireless research,”in Proc.IPSN/SPOTS’05,Los Angeles,CA,USA, Apr.2005.。
