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12th Americas Conference on Information Systems, AMCIS 2006

作者： Liebau, Nicolas C. Heckmann, Oliver Kovacevic, Aleksandra Tafreschi, Omid Fidler, Markus Mauthe, Andreas Steinmetz, Ralf Multimedia Communications Lab Technische Universität Darmstadt Germany IT-Security Group Technische Universität Darmstadt Germany Department of Electrical and Computer Engineering University of Toronto Canada Computing Department Lancaster University United Kingdom Technische Universität Darmstadt Germany

ISBN: (纸本)9781604236262

Closed user group based on a Peer-to-Peer system require a membership mechanism that is fully decentralized. Several suggestions have been made in the literature. However, the proposed mechanisms have some significant drawbacks since any peer can easily bypass the proposed membership mechanism. In this paper we present a new fully decentralized and secure membership mechanism for Peer-to-Peer systems. A quorum of peers decides about membership requests from new peers. In order to prevent the requesting peer from manipulating the membership decision two mechanisms are applied: (1) The quorum peers must make there membership decision unanimously. This is sufficient to guarantee a correct membership decision, if a fraction of the peers in the P2P system is honest and if the quorum peers are selected in a random manner. (2) In order to ensure a random selection of the quorum peer a random value is created in a distributed way that is used to select the quorum. It can be verified, that the quorum was correctly selected. The presented mechanism is evaluated in terms of the offered security and the created traffic overhead.

关键词： Access control

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A hierarchical approach for solving large-scale traveling salesman problem

A hierarchical approach for solving large-scale traveling sa...

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International Joint Conference on Neural Networks (IJCNN)

作者： D.C. Park A.L. Figueras C. Chen Intelligent Computing Research Lab Department of Electrical and Computer Engineering Florida International University Miami FL USA

A hierarchical approach which combines an unsupervised learning algorithm and a recursive Hopfield neural network is proposed for solving the large-scale traveling salesman problem (TSP). For a TSP with given number of cities, an unsupervised learning algorithm was first used to find the clusters for decomposing the given problem and then a recursive version of the Hopfield network was applied to the centroids of the clusters. The proposed recursive Hopfield network was also applied to cities in each cluster in order to find an optimal path. A final tour was obtained by connecting together the resultant paths of each cluster.< >

关键词： Large-scale systems Traveling salesman problems Cities and towns Neurons Clustering algorithms Temperature Annealing Unsupervised learning Intelligent networks Electronic mail

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Gradient based fuzzy c-means (GBFCM) algorithm

Gradient based fuzzy c-means (GBFCM) algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： D.C. Park I. Dagher Intelligent Computing Research Lab Department of Electrical and Computer Engineering Florida International University Miami FL USA

In this paper, a clustering algorithm based on the fuzzy c-means algorithm (FCM) and the gradient descent method is presented. In the FCM, the minimization process of the objective function is proceeded by solving two equations alternatively in an iterative fashion. Each iteration requires the use of all the data at once. In our proposed approach one datum at a time is presented to the network, and the minimization is proceeded using the gradient descent method. Compared to FCM, the experimental results show that our algorithm is very competitive in terms of speed and stability of convergence for large number of data.< >

关键词： Clustering algorithms Iterative algorithms Minimization methods Equations Convergence Neurons Stability

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A comparative study of the suitability of a custom computing machine and a VLIW DSP for use in 3G applications

A comparative study of the suitability of a custom computing...

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IEEE Workshop on Signal Processing Systems (SIPS)

作者： J. Neel S. Srikanteswara J.H. Reed P.M. Athanas Mobile and Portable Radio Research Group Bradley Department of Electrical and Computer Engineering Virginia Technology USA Configurable Computing Lab Bradley Department of Electrical and Computer Engineering Virginia Technology USA

As wireless communications advances, the complexity of waveform processing places an ever increasing demand on baseband processing resources. As this is a difficult problem for traditional processing solutions to address, many have looked to reconfigurable computing as a solution. For 3G systems, whose intense processing requirements exacerbate these problems, we have previously proposed in (S. Srikanteswara et al, Soft Radio Impl. for 3G and Future High Data Rate Syst., Globecom 2001) the use of custom computing machines (CCMs), also known as adaptive computing machines (ACMs). This paper introduces a method for measuring processor performance which is independent of chip architecture. This method is then used to perform a comparative analysis on the suitability of Virginia Tech's Stallion CCM and Texas, Instrument's TMS320C6201 processor for use in 3G applications.

关键词： VLIW Digital signal processing computer architecture Application software Semiconductor device measurement Field programmable gate arrays Hardware Software radio Baseband Analog integrated circuits

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A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving:Deep Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 169-178页

作者： Mohammad Al-Sharman David Murdoch Dongpu Cao Chen Lv Yahya Zweiri Derek Rayside William Melek the Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterloo N2L 3G1Canada the Cognitive Autonomous Driving Lab University of WaterlooWaterloo N2L 3G1Canada the School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 999002Singapore the Faculty of Science Engineering and ComputingKingston University LondonLondon SW153DWUK the Khalifa University Center for Autonomous Robotic Systems Department of Aerospace EngineeringKhalifa UniversityAbu Dhabi 127788UAE

In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the *** a typical CPS,the braking system is crucial for the vehicle design and safe ***,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of *** this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified *** techniques are utilized to obtain more accurate model for brake pressure state estimation *** proposed model is trained using real experimental training data which were collected via conducting real vehicle *** vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving *** on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated *** results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.

关键词： Brake pressure state estimation cyber-physical system(CPS) deep learning dropout regularization approach

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

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Bilinear recurrent neural network

Bilinear recurrent neural network

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International Joint Conference on Neural Networks (IJCNN)

作者： D.C. Park Yan Zhu Intelligent Computing Research Lab Department of Electrical and Computer Engineering Florida International University Miami FL USA

A recurrent neural network and its training algorithm are proposed in this paper. Since the proposed algorithm is based on the bilinear polynomial, it can model many nonlinear systems with much more parsimony than the higher order neural networks based on Volterra series. The proposed bilinear recurrent neural network (BLRNN) is compared with multilayer perceptron neural networks (MLPNN) for time series prediction problems. The results show that the BLRNN is robust and outperforms the MLPNN in terms of prediction accuracy.< >

关键词： Recurrent neural networks Neurons Polynomials Neural networks Nonlinear systems Artificial neural networks Feedforward neural networks Intelligent networks computer networks Electronic mail

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Myoelectric signal recognition using fuzzy clustering and artificial neural networks in real time

Myoelectric signal recognition using fuzzy clustering and ar...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Del Boca D.C. Park Intelligent Computing Research Lab Department of Electrical and Computer Engineering Florida International University Miami FL USA

Application of EMG-controlled functional neuromuscular stimulation to a denervated muscle depends largely on the successful discrimination of the myoelectric signal (MES) by which the subject desires to execute control over the impeded movement. This can be achieved by an adaptive and flexible interface that is robust regardless of electrode location, strength of remaining muscle activity or even personal conditions. A real-time application of an artificial neural network that can accurate recognize the MES signature is proposed in this paper. MES features are first extracted through Fourier analysis and clustered using the fuzzy c-means algorithm. Data obtained by this unsupervised learning technique are then automatically targeted and presented to a multilayer perceptron type neural network. For real-time operation, a digital signal processor operates over the resulting set of weights and maps the incoming signal to the stimulus control domain. Results show a highly accurate discrimination of the control signal over interference patterns.< >

关键词： Muscles Artificial neural networks Automatic control Neuromuscular stimulation Impedance Robustness Electrodes Feature extraction Data mining Algorithm design and analysis

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Statistical Methods in Video Processing 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Dorin Comaniciu Rudolf Mester Kenichi Kanatani David Suter

ISBN: (数字)9783540302124

ISBN: (纸本)9783540239895

The 2nd International Workshop on Statistical Methods in Video Processing, SMVP 2004, was held in Prague, Czech Republic, as an associated workshop of ECCV 2004, the 8th European Conference on computer Vision. A total of 30 papers were submitted to the workshop. Of these, 17 papers were accepted for presentation and included in these proceedings, following a double-blind review process. The workshop had 42 registered participants. The focus of the meeting was on recent progress in the application of - vanced statistical methods to solve computer vision tasks. The one-day scienti?c program covered areas of high interest in vision research, such as dense rec- struction of 3D scenes, multibody motion segmentation, 3D shape inference, errors-in-variables estimation, probabilistic tracking, information fusion, optical ?owcomputation,learningfornonstationaryvideodata,noveltydetectionin- namic backgrounds, background modeling, grouping using feature uncertainty, and crowd segmentation from video. We wish to thank the authors of all submitted papers for their interest in the *** external reviewers for their commitment of time and e?ort in providing valuable recommendations for each submission. We are thankful to Vaclav Hlavac, the General Chair of ECCV 2004, and to Radim Sara, for the local organization of the workshop and registration management. We hope you will ?nd these proceedings both inspiring and of high scienti?c quality.

关键词： Image Processing and computer Vision computer Graphics Pattern Recognition Probability and Statistics in computer Science Artificial Intelligence Algorithm Analysis and Problem Complexity

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CRNN based jersey-bib number/text recognition in sports and marathon images 15

CRNN based jersey-bib number/text recognition in sports and ...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Nag, Sauradip Ramachandra, Raghavendra Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Kankanhalli, Mohan Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781728128610

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets. © 2019 IEEE.

关键词： Sports

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