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IEEE International Conference on Communications (ICC)

作者： Lei Zhou Yu-Dong Yao H. Heffes Ruifeng Zhang Wireless Information Systems Engineering Laboratory Department of Electrical & Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Electrical & Computer Engineering Drexel University Philadelphia PA USA

The slotted ALOHA (S-ALOHA) scheme in Nakagami fading channel with the presence of in-cell and cochannel-cell interference is studied. The cases of asynchronous cochannel-cells are especially considered. The analysis is based on the signal capture model and gives closed-form expressions for the system throughput. Additional channel conditions and system parameters are examined in the study, including a minimal signal power requirement, lognormal shadowing and the cellular cluster size.

关键词： Fading Throughput Multiple access interference Radiofrequency interference Rician channels Rayleigh channels Traffic control Base stations Shadow mapping Diversity reception

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Two-Timescale Simultaneous Perturbation Stochastic Approximation using Deterministic Perturbation Sequences

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ACM Transactions on Modeling and computer Simulation 2003年第2期13卷 180-209页

作者： Bhatnagar, Shalabh Fu, Michael C. Marcus, Steven I. Wang, I.-Jeng Department of Computer Science Indian Institute of Science Bangalore 560 012 India Robert H. Smith School of Business Institut for Systems Research University of Maryland College Park MD 20742 Department of Electrical Engineering Institute for Systems Research University of Maryland College Park MD 20742 Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723

Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effective for high-dimensional simulation optimization problems. The main idea is to estimate the gradient using simulation output performance measures at only two settings of the N -dimensional parameter vector being optimized rather than at the N + 1 or 2N settings required by the usual one-sided or symmetric difference estimates, respectively. The two settings of the parameter vector are obtained by simultaneously changing the parameter vector in each component direction using random perturbations. In this article, in order to enhance the convergence of these algorithms, we consider deterministic sequences of perturbations for two-timescale SPSA algorithms. Two constructions for the perturbation sequences are considered: complete lexicographical cycles and much shorter sequences based on normalized Hadamard matrices. Recently, one-simulation versions of SPSA have been proposed, and we also investigate these algorithms using deterministic sequences. Rigorous convergence analyses for all proposed algorithms are presented in detail. Extensive numerical experiments on a network of M/G/1 queues with feedback indicate that the deterministic sequence SPSA algorithms perform significantly better than the corresponding randomized algorithms.

关键词： Stochastic programming

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A middleware substrate for integrating services on the grid

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2003年 2913卷 373-382页

作者： Bhat, Viraj Parashar, Manish Applied Software Systems Laboratory Department of Electrical and Computer Engineering 94 Brett Road Piscataway NJ 08854 United States

In this paper we present the design, implementation and evaluation of the Grid-enabled Discover middleware substrate. The middleware substrate enables Grid infrastructure services provided by the Globus Toolkit (security, information, resource management, storage) to interoperate with collaboratory services provided by Discover (collaborative application access, monitoring, and steering). Furthermore, it enables users to seamlessly access and integrate local and remote services to synthesize customized middleware configurations on demand. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Middleware

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Real time face detection from color video stream based on PCA method

Real time face detection from color video stream based on PC...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： R. Gottumukkal V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

We present a face detection system capable of detection of faces in real time from a streaming color video. Currently this system is able to detect faces as long as both the eyes are visible in the image plane. Extracting skin color regions from a color image is the first step in this system. Skin color detection is used to segment regions of the image that correspond to face regions based on pixel color. Under normal illumination conditions, skin color takes small regions of the color space. By using this information, we can classify each pixel of the image as skin region or non-skin region. By scanning the skin regions, regions that do not have shape of a face are removed. Principle Component Analysis (PCA) is used to classify if a particular skin region is a face or a non-face. The PCA algorithm is trained for frontal view faces only. The system is tested with images captured by a surveillance camera in real time.

关键词： Face detection Streaming media Principal component analysis Skin Real time systems Pixel Eyes Color Image segmentation Lighting

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Associative memory based on ratio learning for real time skin color detection

Associative memory based on ratio learning for real time ski...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Ming-Jung Seow V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A novel approach for skin color modeling using ratio rule learning algorithm is proposed in this paper. The learning algorithm is applied to a real time skin color detection application. The neural network learn, based on the degree of similarity between the relative magnitudes of the output of each neuron with respect to that of all other neurons. The activation/threshold function of the network is determined by the statistical characteristic of the input patterns. Theoretical analysis has shown that the network is able to learn and recall the trained patterns without much problem. It is shown mathematically that the network system is stable and converges in all circumstances for the trained patterns. The network utilizes the ratio-learning algorithm for modeling the characteristic of skin color in the RGB space as a linear attractor. The skin color will converge to a line of attraction. The new technique is applied to images captured by a surveillance camera and it is observed that the skin color model is capable of processing 420/spl times/315 resolution images of 24-bit color at 30 frames per second in a dual Xeon 2.2 GHz CPU workstation running Windows 2000.

关键词： Associative memory Skin Neurons Image converters Color Neural networks Pattern analysis Surveillance Cameras Image resolution

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Autonomic optimization of an oil reservoir using decentralized services

Autonomic optimization of an oil reservoir using decentraliz...

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International Workshop on Challenges of Large Applications in Distributed Environments

作者： V. Matossian M. Parashar The Applied Software Systems Laboratory Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA

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Neural network based skin color model for face detection

Neural network based skin color model for face detection

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Ming-Jung Seow D. Valaparla V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

This paper presents a novel neural network based technique for face detection that eliminates limitations pertaining to the skin color variations among people. We propose to model the skin color in the three dimensional RGB space which is a color cube consisting of all the possible color combinations. Skin samples in images with varying lighting conditions, from the Old Dominion University skin database, are used for obtaining a skin color distribution. The primary color components of each plane of the color cube are fed to a three-layered network, trained using the backpropagation algorithm with the skin samples, to extract the skin regions from the planes and interpolate them so as to provide an optimum decision boundary and hence the positive skin samples for the skin classifier. The use of the color cube eliminates the difficulties of finding the non-skin part of training samples since the interpolated data is consider skin and rest of the color cube is consider non-skin. Subsequent face detection is aided by the color, geometry and motion information analyses of each frame in a video sequence. The performance of the new face detection technique has been tested with real-time data of size 320/spl times/240 frames from video sequences captured by a surveillance camera. It is observed that the network can differentiate skin and non-skin effectively while minimizing false detections to a large extent when compared with the existing techniques. In addition, it is seen that the network is capable of performing face detection in complex lighting and background environments.

关键词： Neural networks Skin Face detection Video sequences Image databases Backpropagation algorithms Data mining Image color analysis Information geometry Motion analysis

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L2-norm approximation based learning in recurrent neural networks for expression invariant face recognition

L2-norm approximation based learning in recurrent neural net...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ming-Jung Seow D. Valaparla V.K. Asari VLSI Systems Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A new learning algorithm based on L2-norm approximation to define the relationship between two neurons in a recurrent neural network is proposed in this paper. The learning process utilizes the statistical relationship between each component of the input pattern with respect to every other component. The activation function of a neuron is a rectangular function whose position changes adaptively with respect to the input pattern and its left and right wings are decided by the mean of maximum variations of the training signals to that neuron. The new training algorithm is applied for recognition of faces images with varying expressions. 975 face images of 13 persons from the Carnegie Mellon University (CMU) face expression variant database are used for evaluating the performance of the network. The network has been trained with 5 images and tested with the remaining 70 images of each person. The recurrent neural network with the new learning algorithm recognized all the 13 persons in this database without error.

关键词： Intelligent networks Recurrent neural networks Face recognition Pattern recognition Neurons Biological neural networks Biology computing Artificial neural networks Biological system modeling Approximation algorithms

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AutoMate: enabling autonomic applications on the grid

AutoMate: enabling autonomic applications on the grid

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International Workshop on Active Middleware Services

作者： M. Agarwal V. Bhat H. Liu V. Matossian V. Putty C. Schmidt G. Zhang L. Zhen M. Parashar B. Khargharia S. Hariri The Applied Software Systems Laboratory Department of Electrical and Computer Engineering State University of New Jersey Rutgers USA High Performance Distributed Computing Laboratory Department of Electrical and Computer Engineering University of Arizona Tucson USA

The increasing complexity, heterogeneity and dynamism of networks, systems, services applications have made our computational/information infrastructure brittle, unmanageable and insecure. This has necessitated the investigation of a new paradigm for design, development and deployment based on strategies used by biological systems to deal with complexity, heterogeneity, and uncertainty, i.e. autonomic computing. This paper introduces the AutoMate project and describes its key components. The overall objective of AutoMate is to investigate key technologies to enable the development of autonomic grid applications that are context aware and are capable of self-configuring, self-composing, self-optimizing and self-adapting. Specifically, it will investigate the definition of autonomic components, the development of autonomic applications as dynamic composition of autonomic components, and the design of key enhancements to existing grid middleware and runtime services to support these applications.

关键词： Biology computing Application software Context awareness Middleware Grid computing Laboratories Distributed computing computer networks Biological systems Uncertainty

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Performance evaluation of CPCH with a geometrically-distributed message length in W-CDMA

Performance evaluation of CPCH with a geometrically-distribu...

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IEEE Conference on Vehicular Technology (VTC)

作者： Moon Young Choi Yu-Dong Yao H. Heffes Wireless Systems Engineering Laboratory (WISELAB) Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

Common packet channel (CPCH) access is an efficient approach to support packet data transmission in a wideband code division multiple access (W-CDMA) system. This paper presents a discrete-time analysis of the CPCH access scheme. It is assumed that a packet arrival process in Poisson distributed and the service time of each packet is geometrically distributed. The study focuses on examining the number of packet arrivals in each CPCH access slot. Performance is evaluated in terms of normalized throughput and blocking rate. It is observed that CPCH performs better when packet mean service time is larger. The performance results are also compared with previous studies using continuous-time analyses.

关键词： Multiaccess communication Wideband Monitoring Data engineering systems engineering and theory Laboratories Data communication Message service Electronic mail Access protocols

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