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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Ming Z. Zhang Vijayan K. Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

Design of a fully pipelined multiplierless digital architecture for computing 2D convolution utilizing the quadrant symmetry of the kernels is proposed in this paper. Pixels in the four quadrants of the kernel region with respect to an image pixel are considered simultaneously for computing the partial results of the convolution sum. The new architecture performs computations in log-domain by utilizing low complexity log2 and inverse-log2 approximation modules. An effective data handling strategy is developed in conjunction with the logarithmic modules to eliminate the necessity of the multipliers in the architecture. The proposed architecture is capable of performing convolution operations for 181.3 1024 times 1024 frames or 190.1 million outputs per second with 22 times 22 kernels in a Xilinx's Virtex XC2V2000-4ff896 FPGA at maximum clock frequency of 190.1 MHz. The throughput of the new design is 3.17 times higher when compared with that of the previous implementations (Zhang et al., 2005). Evaluation in Xilinx's core generator showed that the proposed design results in 60% reduction in hardware resource when compared to the design using pipelined multipliers

关键词： Convolution Kernel computer architecture Pixel Data handling Field programmable gate arrays Clocks Frequency Throughput Hardware

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Gabor Wavelet Based Modular PCA Approach for Expression and Illumination Invariant Face Recognition

Gabor Wavelet Based Modular PCA Approach for Expression and ...

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applied Imagery Pattern Recognition Workshop (AIPR)

作者： Neeharika Gudur Vijayan Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

A Gabor wavelet based modular PCA approach for face recognition is proposed in this paper. The proposed technique improves the efficiency of face recognition, under varying illumination and expression conditions for face images when compared to traditional PCA techniques. In this algorithm the face images are divided into smaller sub-images called modules and a series of Gabor wavelets at different scales and orientations are applied on these localized modules for feature extraction. A modified PCA approach is then applied for dimensionality reduction. Due to the extraction of localized features using Gabor wavelets, the proposed algorithm is expected to give improved recognition rate when compared to other traditional techniques. The performance of the proposed technique is evaluated under conditions of varying illumination, expression and variation in pose up to a certain range using standard face databases.

关键词： Principal component analysis Lighting Face recognition Image recognition Feature extraction Vectors Statistical analysis Image databases Linear discriminant analysis Application software

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An Adaptive and Non Linear Technique for Enhancement of Extremely High Contrast Images

An Adaptive and Non Linear Technique for Enhancement of Extr...

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applied Imagery Pattern Recognition Workshop (AIPR)

作者： Saibabu Arigela K. Vijayan Asari Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. A novel non linear image enhancement algorithm for digital images captured under such extremely non-uniform lighting conditions is proposed in this paper. The new technique constitutes three processes viz. adaptive intensity enhancement, contrast enhancement and color restoration. Adaptive intensity enhancement uses a specifically designed nonlinear transfer function which is capable of reducing the intensity of bright regions and at the same time enhancing the intensity of dark regions. Contrast enhancement tunes the intensity of each pixels magnitude based on its surrounding pixels. Finally, a linear color restoration process based on the chromatic information of the input image frame is applied to convert the enhanced intensity image back to a color image.

关键词： Layout Dynamic range Image coding Color Image restoration Image enhancement Displays Histograms computational intelligence Machine vision

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A hardware-efficient high performance digital architecture for run-time computation of normalized cross correlation

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WSEAS Transactions on Circuits and Systems 2006年第9期5卷 1416-1423页

作者： Zhang, Ming Z. Asari, Vijayan K. Computational Intelligence and Machine Vision Laboratory Department of Electrical and Computer Engineering Old Dominion University 231 Kaufman Hall Norfolk VA 23529 United States

A novel architecture for performing high speed computation of normalized cross correlation (NCC) is proposed in this paper. The approach promotes the concept of digital filter design to simplify window related operations and log-domain computations to eliminate all multiplications, divisions, squares, and square-roots utilizing the approximation techniques for efficient estimation of log2 and inverse-log2. The new design is capable of performing run-time calculation of NCC and the energy normalization on the fly, eliminating the frame buffering and frame sized integral tables. A new concept of uniform filter design is also presented to reduce the number of processing element arrays (PEAs) from V PEAs to 2 PEAs for a U × V window. This concept is applied to simplify the calculation of average value and energy in NCC equation. It is observed that the performance of the system with parallel pipelined architecture is able to achieve 178.5 million outputs per second (MOPS), or equivalently 142.8 billion multiplications per second, on Xilinx's Virtex II XC2V6000-4ff1152 FPGA with maximum clock frequency of 178.5MHz with 20 × 20 feature template of 8-bit resolution. Given a 1024 × 1024 data frame, the system can process over 170 frames per second (fps). Similarly, the improved full precision logarithmic module is able to sustain 212.2 MOPS at 212.2MHz maximum clock frequency with 32-bit resolution.

关键词： Digital filters

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Image Recognition Systems Based on Random Local Descriptors

Image Recognition Systems Based on Random Local Descriptors

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International Joint Conference on Neural Networks (IJCNN)

作者： E. Kussul T. Baidyk D. Wunsch O. Makeyev A. Martin Laboratory of Micromechanics and Mechatronics CCADET UNAM Mexico Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Taras Shevchenko National University of Kiev Kiev Ukraine

Two image recognition systems based on random local descriptors are described. Random local descriptors play the role of features that have to be extracted from the image. The advantage of this type of features is a possibility to create sufficiently general description of the image. This approach was tested in different image recognition tasks: handwritten digit recognition, face recognition, metal surface texture recognition and micro work piece shape recognition. The best result for handwritten digit recognition on the MNIST database is the error rate of 0.37% and for face recognition on the ORL database is the error rate of 0.1%. The results for texture and micro work piece shape recognition are also promising.

关键词： Image recognition Neurons Face recognition Handwriting recognition Animals Shape Image databases Error analysis System testing Surface texture

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A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks

A General Recurrent Neural Network Approach to Model Genetic...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Xiao Hu A. Maglia D.C. Wunsch Applied Computational Intelligence Lab Department of Electrical & Computer Engineering University of Missouri Rolla Rolla MO USA Department of Biology and Sciences University of Missouri Rolla Rolla MO USA

There is an urgent need for tools to unravel the complex interactions and functionalities of genes. As such, there has been much interest in reverse-engineering genetic regulatory networks from time series gene expression data. We use an artificial neural network to model the dynamics of complicated gene networks and to learn their parameters. The positive and negative regulations of genes are defined by a weight matrix, and different genes are allowed to have different decaying time constants. We demonstrate the effectiveness of the method by recreating the SOS DNA repair network of Escherichia coli bacterium, previously discovered through experimental data

关键词： Recurrent neural networks Genetics Gene expression Biological system modeling DNA Bioinformatics Neural networks computational intelligence computational biology Biology computing

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Probabilistic Framework for Reliability Analysis of Information-Theoretic CAD Systems in Mammography

Probabilistic Framework for Reliability Analysis of Informat...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Piotr A. Habas Jacek M. Zurada Adel S. Elmaghraby Georgia D. Tourassi Computational Intelligence Laboratory University of Louisville Louisville KY USA Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Computer Engineering and Computer Science University of Louisville Louisville KY USA Duke Advanced Imaging Laboratories Department of Radiology Duke University Medical Center Durham NC USA

The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic framework to facilitate application of the reliability analysis technique for knowledge-based CAD systems that are not feature-based. The study was based on an information-theoretic CAD system developed for detection of masses in screening mammograms from the Digital Database for Screening Mammography (DDSM). The experimental results reveal that the query-specific reliability estimate provided by the proposed probabilistic framework is an accurate predictor of CAD performance for the query case. It can also be successfully applied as a base for stratification of CAD predictions into clinically meaningful reliability groups (i.e., HIGH, MEDIUM, and LOW). Based on a leave-one-out sampling scheme and ROC analysis, the study demonstrated that the diagnostic performance of the IT-CAD is significantly higher for cases with HIGH reliability (Az=0.92plusmn0.03) than for those stratified as MEDIUM (Az=0.84plusmn0.02) or LOW reliability predictions (Az=0.78plusmn0.02)

关键词： Information analysis Mammography Design automation Laboratories Cities and towns USA Councils Supervised learning Application software Spatial databases Delta-sigma modulation

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Using a qualitative sketch to control a team of robots

Using a qualitative sketch to control a team of robots

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IEEE International Conference on Robotics and Automation (ICRA)

作者： M. Skubic D. Anderson S. Blisard D. Perzanowski A. Schultz Electrical and Computer Engineering Department University of Missouri Columbia Columbia MO USA Navy Center for Applied Research in Artificial Intelligence Naval Research Laboratory Inc. Washington D.C. DC USA

In this paper, we describe a prototype interface that facilitates the control of a mobile robot team by a single operator, using a sketch interface on a tablet PC. The user sketches a qualitative map of the scene and includes the robots in approximate starting positions. Both path and target position commands are supported as well as editing capabilities. Sensor feedback from the robots is included in the display such that the sketch interface acts as a two-way communication device between the user and the robots. The paper also includes results of a usability study, in which users were asked to perform a series of tasks

关键词： Robot kinematics Robot sensing systems Mobile robots Layout Prototypes Displays Navigation computer interfaces Military computing Design engineering

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Effects of Acute Hippocampal Stimulation on EEG Dynamics

Effects of Acute Hippocampal Stimulation on EEG Dynamics

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sandeep P. Nair J. Chris Sackellares Deng-Shan Shiau Wendy M. Norman Linda K. Dance Panos M. Pardalos Jose C. Principe Paul R. Carney Department of Biomedical Engineering University of Florida Gainesville FL USA Department of Neurology University of Florida Gainesville FL USA Department of Industrial and Systems Engineering Center for Applied Optimization University of Florida Gainesville FL USA Department of Electrical and Computer Engineering Computational NeuroEngineering Laboratory University of Florida Gainesville FL USA Department of Pediatrics Division of Neurology University of Florida Gainesville FL USA

Progressive preictal dynamical convergence and postictal divergence of dynamical EEG descriptors among brain regions has been reported in human temporal lobe epilepsy (TLE) and in a rodent model of TLE. There are also reports of anticonvulsant effects of high frequency stimulation of the hippocampus in humans. We postulate that this anticonvulsant effect is due to dynamical resetting by the electrical stimulation. The following study investigated the effects of acute hippocampal electrical stimulation on dynamical transitions in the brain of a spontaneously seizing animal model of TLE to test the hypothesis of divergence in dynamical values by electrical stimulation of the hippocampus

关键词： Electroencephalography electrical stimulation Humans Brain modeling Hippocampus Convergence Temporal lobe Epilepsy Rodents Frequency

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Engine data classification with simultaneous recurrent network using a hybrid PSO-EA algorithm

Engine data classification with simultaneous recurrent netwo...

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International Joint Conference on Neural Networks, IJCNN 2005

作者： Cai, Xindi Wunsch II, Donald C. Applied Computational Intelligence Laboratory Dept. of Electrical and Computer Engineering University of Missouri - Rolla Rolla MO 65409 United States

ISBN: (纸本)0780390482

We applied an architecture which automates the design of simultaneous recurrent network (SRN) using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the simultaneous recurrent network for the engine data classification. The experimental results show that our approach gives solid performance in categorizing the nonlinear car engine data. © 2005 IEEE.

关键词： Pattern recognition

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