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IEEE Swarm Intelligence Symposium, SIS

作者： Ahmed Y. Saber Ganesh K. Venayagamoorthy Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology MO USA

This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by cell-to-cell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and elimination-dispersal steps of BFT are very promising. On the other hand, particles move toward promising locations depending on best values from memory and knowledge in particle swarm optimization (PSO). Therefore, best cell (or particle) biased velocity (vector) is added to the random velocity of BFT to reduce randomness in movement (evolution) and to increase swarming in the proposed method to solve ELD. Finally, a data set from a benchmark system is used to show the effectiveness of the proposed method and the results are compared with other methods.

关键词： Microorganisms Particle swarm optimization Power generation economics Space technology USA Councils Power system economics Optimization methods Gradient methods Neural networks Hopfield neural networks

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Particle Swarm Optimization with Quantum Infusion for the design of digital filters

Particle Swarm Optimization with Quantum Infusion for the de...

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IEEE Swarm Intelligence Symposium, SIS

作者： Bipul Luitel Ganesh Kumar Venayagamoorthy The Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

In this paper, particle swarm optimization with quantum infusion (PSO-QI) has been applied for the design of digital filters. In PSO-QI, Global best (gbest) particle (in PSO star topology) obtained from particle swarm optimization is enhanced by doing a tournament with an offspring produced by quantum behaved PSO, and selecting the winner as the new gbest. Filters are designed based on the best approximation to the ideal response by minimizing the maximum ripples in passband and stopband of the filter response. PSO-QI, as is shown in the paper, converges to a better fitness. This new algorithm is implemented in the design of finite impulse response (FIR) and infinite impulse response (IIR) filter.

关键词： Particle swarm optimization Digital filters Finite impulse response filter IIR filters Algorithm design and analysis Chebyshev approximation Frequency Passband Signal processing algorithms Band pass filters

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real-time Collaborative Routing Algorithm for Wireless Sensor Network Longevity

Real-Time Collaborative Routing Algorithm for Wireless Senso...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： Shishir Bashyal Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department ofElectrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

ISBN: (纸本)9781424422241

Wireless sensor networks are being used in different scenarios including safety critical applications that require real-time communication framework such that the events detected by sensor nodes reach the base station within a limited timeframe. The ad-hoc nature of the sensor network makes it impossible to develop a static communication scheme that works well throughout the network lifetime. The network needs to be capable of adapting to the changes due to node failures and traffic congestions. This paper proposes an ant inspired dynamic routing algorithm in which individual sensor nodes collaborate with each other to discover a route that optimizes the network longevity while maintaining network delays to an acceptable level. Fuzzy logic is used to develop ant-like ability in sensor nodes to determine the suitability of the node to act as the cluster head. Using this algorithm, the nodes that are in the energy-rich region and have low end-to-end delay are more likely to be the cluster heads and thus the overall route offers lower communication delays and longer network life in terms of effective performance. As the algorithm relies on local collaboration for the route formation, the algorithm is highly scalable. Better end-to-end delay is achieved in simulation using this algorithm.

关键词： Availability Wireless sensor networks Delay Batteries Clustering algorithms Robot sensing systems Collaboration

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Network-centric localization in MANETs based on particle swarm optimization

Network-centric localization in MANETs based on particle swa...

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IEEE Swarm Intelligence Symposium, SIS

作者： Raghavendra V. Kulkarni Ganesh K. Venayagamoorthy Ann Miller Cihan H. Dagli Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Systems Engineering and Engineering Management Missouri University of Science and Technology Rolla MO USA

There exist several application scenarios of mobile ad hoc networks (MANET) in which the nodes need to locate a target or surround it. Severe resource constraints in MANETs call for energy efficient target localization and collaborative navigation. Centralized control of MANET nodes is not an attractive solution due to its high network utilization that can result in congestions and delays. In nature, many colonies of biological species (such as a flock of birds) can achieve effective collaborative navigation without any centralized control. Particle swarm optimization (PSO), a popular swarm intelligence approach that models social dynamics of a biological swarm is proposed in this paper for network-centric target localization in MANETs that are enhanced by mobile robots. Simulation study of two application scenarios is conducted. While one scenario focuses on quick target localization, the other aims at convergence of MANET nodes around the target. Reduction of swarm size during PSO search is proposed for accelerated convergence. The results of the study show that the proposed algorithm is effective in network-centric collaborative navigation. Emergence of converging behavior of MANET nodes is observed.

关键词： Particle swarm optimization Mobile ad hoc networks Collaboration Navigation Centralized control Biological system modeling Convergence Energy efficiency Birds Mobile robots

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MIMO beam-forming with neural network channel prediction trained by a novel PSO-EA-DEPSO algorithm

MIMO beam-forming with neural network channel prediction tra...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chris Potter Ganesh K. Venayagamoorthy Kurt Kosbar Electrical Engineering at the Missouri University of Science and Technology Rolla MO USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Electrical Engineering Missouri University of Science and Technology Rolla MO USA

A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received signal-to-noise ratio is derived for un-coded and beam-forming MIMO systems to see how the RNN error affects the performance. This error is shown to deteriorate the accuracy of the weak singular modes, making beam-forming more desirable. Bit error rate simulations are performed to validate these results.

关键词： MIMO Recurrent neural networks Prediction algorithms Artificial neural networks Signal to noise ratio Manganese Training

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Statistical learning approaches for discriminant features selection

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Journal of the Brazilian computer Society 2008年第2期14卷 7-22页

作者： Giraldi, Gilson A. Rodrigues, Paulo S. Kitani, Edson C. Sato, João R. Thomaz, Carlos E. Department of Computer Science National Laboratory for Scientific Computing LNCC Petrópolis Rio de Janeiro Brazil Department of Electrical Engineering University of São Paulo USP São Paulo São Paulo Brazil University of São Paulo USP São Paulo São Paulo Brazil Department of Computer Science Centro Universitário da FEI FEI São Bernardo do Campo São Paulo Brazil Department of Electrical Engineering Centro Universitário da FEI FEI São Bernardo do Campo São Paulo Brazil

Supervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sample groups. Our method, called here as Discriminant Feature Analysis (DFA), is not restricted to any particular probability density function and the number of meaningful discriminant features is not limited to the number of groups. To evaluate the discriminant features selected, two case studies have been investigated using face images and breast lesion data sets. In both case studies, our experimental results show that the DFA approach provides an intuitive interpretation of the differences between the groups, highlighting and reconstructing the most important statistical changes between the sample groups analyzed.

关键词： Support vector machines

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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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Frequency offset estimation for multi-static radar system

Frequency offset estimation for multi-static radar system

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Eunjung Yang Joohwan Chun Scientific Computing Laboratory Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Daejeon South Korea

Bi-static and multi-static radar requires synchronization between transmitter(s) and receiver(s) due to their separated locations. Especially, for the target Doppler estimation, the carrier frequency offset occurred by the discord between the transmit and the receive ends should be estimated before the processing. In this paper, the system model for multi-static radar is provided as a similar form of multi-input multi-output (MIMO) communication system. However, unlike the MIMO case the frequency offset for each receiver is not identical to each other which makes the modeling and problem complicated. Based on the system model, the joint maximum likelihood (ML) solution and the suboptimal estimation is presented for frequency offset compensation. In this approximated approach, each receiver frequency offset is decoupled to others and the computational complexity becomes extremely low while it provides reasonable error performance for small frequency offsets and high signal to noise ratio (SNR).

关键词： Frequency estimation Maximum likelihood estimation Frequency synchronization MIMO Transmitters Delay effects Doppler radar Signal to noise ratio Oscillators Global Positioning System

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Lattice Reduction Aided Precoding Combined with SDM for Clusters of Correlated Users

Lattice Reduction Aided Precoding Combined with SDM for Clus...

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IEEE Conference on Wireless Communications and Networking

作者： Jaehyun Park Joohwan Chun Scientific Computing Laboratory Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Daejeon South Korea

We present a lattice reduction (LR) aided preceding scheme combined with spatial division multiplexing (SDM) for clusters of correlated multiple users. Previously, the LR aided precoding schemes are deeply studied to transmit different symbols, simultaneously, to multiple users since it shows superior performance to linear precoding schemes like zero-forcing (ZF) algorithm. However, it has high complexity compared with linear precoding schemes. To resolve it, we exploit the spatial separability between clusters. That is, we apply the LR aided scheme for highly correlated users in a same cluster and to suppress the interferences from other clusters, we apply the ZF algorithm. Furthermore, we also propose a new SDM scheme to maximize the transmit signal to interference and noise ratio, which is formulated by generalized eigenvalue problem. We verify the performance of the proposed schemes by Monte-Carlo simulations and compare the computation complexities of them with the conventional LR aided precoding scheme.

关键词： Lattices MIMO Clustering algorithms Signal to noise ratio Transmitting antennas Linear discriminant analysis Interference Eigenvalues and eigenfunctions Receiving antennas Communications Society

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Flexible Electronics: Ultrathin Silicon Circuits With Strain‐Isolation Layers and Mesh Layouts for High‐Performance Electronics on Fabric, Vinyl, Leather, and Paper (Adv. Mater. 36/2009)

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Advanced Materials 2009年第36期21卷

作者： Dae‐Hyeong Kim Yun‐Soung Kim Jian Wu Zhuangjian Liu Jizhou Song Hoon‐Sik Kim Yonggang Y. Huang Keh‐Chih Hwang John A. Rogers Department of Materials Science and Engineering Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana Illinois 61801 (USA) Department of Civil and Environmental Engineering Northwestern University Evanston IL 60208 (USA) Department of Mechanical Engineering Northwestern University Evanston IL 60208 (USA) Institute of High Performance Computing 1 Fusionopolis Way #16‐16 Connexis 138632 Singapore (Singapore) Department of Mechanical and Aerospace Engineering University of Miami Coral Gables FL 33146 (USA) FML Departments of Engineering Mechanics Tsinghua University Beijing 100084 (P. R. China) Department of Chemistry Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana Illinois 61801 (USA) Department of Mechanical Science and Engineering Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana Illinois 61801 (USA) Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana Illinois 61801 (USA)

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