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Full-Text Articles in Engineering
Pso Tuned Flatness Based Control Of A Magnetic Levitation System, Ganesh K. Venayagamoorthy, E. C. Anene
Pso Tuned Flatness Based Control Of A Magnetic Levitation System, Ganesh K. Venayagamoorthy, E. C. Anene
Electrical and Computer Engineering Faculty Research & Creative Works
Investigation on the application of flatness-based feedback linearization to the magnetic levitation model of INTECOTm Maglev system is presented in this paper. The MAGLEV system dynamics studied consists of a set of third order nonlinear differential equations. Using computational techniques proposed by Levine, it is verified that the ball position is the flat output. The derived flat output is applied in the construction of a nonlinear control law used to control the levitation to a set point as well as tracking a sine function trajectory. The controller gains are obtained and optimized using particle swarm optimization. The simulation results compared …
Particle Swarm Optimization Tuned Flatness-Based Generator Excitation Controller, Ganesh K. Venayagamoorthy, E. C. Anene, U. O. Aliyu
Particle Swarm Optimization Tuned Flatness-Based Generator Excitation Controller, Ganesh K. Venayagamoorthy, E. C. Anene, U. O. Aliyu
Electrical and Computer Engineering Faculty Research & Creative Works
An optimal transient controller for a synchronous generator in a multi-machine power system is designed using the concept of flatness-based feedback linearization in this paper. The computation of the flat output and corresponding controller for reduced order model of the synchronous generator is presented. The required feedback gains used to close the linearization loop is optimized using particle swarm optimization for maximum damping. Typical results obtained for transient disturbances on a two-area, four-generator power system equipped with the proposed controller on one generator and conventional power system stabilizers on the remaining generators are presented. The effectiveness of the flatness-based controller …
Learning Functions Generated By Randomly Initialized Mlps And Srns, R. Cleaver, Ganesh K. Venayagamoorthy
Learning Functions Generated By Randomly Initialized Mlps And Srns, R. Cleaver, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) and two benchmark functions are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - PSO-QI is introduced. PSO-QI is a standard particle swarm optimization (PSO) algorithm with the addition of a quantum step utilizing the probability density property of a quantum particle. The results from PSO-QI are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs …
Real-Time Implementation Of Intelligent Modeling And Control Techniques On A Plc Platform, Curtis Alan Parrott, Ganesh K. Venayagamoorthy
Real-Time Implementation Of Intelligent Modeling And Control Techniques On A Plc Platform, Curtis Alan Parrott, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Programmable logic controllers (PLCs) have been used for many decades for standard control in industrial and factory environments. Over the years, PLCs have become computational efficient and powerful, and a robust platform with applications beyond the standard control and factory automation. Due to the new advanced PLC's features and computational power, they are ideal platforms for exploring advanced modeling and control methods, including computational intelligence based techniques such as neural networks, particle swarm optimization (PSO) and many others. Some of these techniques require fast floating-point calculations that are now possible in real-time on the PLC. This paper focuses on the …
Real Time Implementation Of An Artificial Immune System Based Controller For A Dstatcom In An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy
Real Time Implementation Of An Artificial Immune System Based Controller For A Dstatcom In An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
A new adaptive control strategy based on artificial immune system (AIS) for a DSTATCOM in an electric ship power system is presented in this paper. DSTATCOM is a shunt compensation device, which can be used to improve the power quality during the pulse power requirements in a naval shipboard system. The role of DSTATCOM controller is very important to meet this objective. In this paper, the DSTATCOM controller parameters are first tuned by particle swarm optimization (PSO) technique, so that it can provide innate immunity to common system disturbances. Then, these optimum parameters are modified online by an artificial immune …
Enhanced Particle Swarm Optimizer For Power System Applications, Yamille Del Valle, M. Digman, A. Gray, J. Perkel, Ganesh K. Venayagamoorthy, Ronald G. Harley
Enhanced Particle Swarm Optimizer For Power System Applications, Yamille Del Valle, M. Digman, A. Gray, J. Perkel, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Power system networks are complex systems that are highly nonlinear and non-stationary, and therefore, their performance is difficult to optimize using traditional optimization techniques. This paper presents an enhanced particle swarm optimizer for solving constrained optimization problems for power system applications, in particular, the optimal allocation of multiple STATCOM units. The study focuses on the capability of the algorithm to find feasible solutions in a highly restricted hyperspace. The performance of the enhanced particle swarm optimizer is compared with the classical particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and bacterial foraging algorithm (BFA). Results show that the enhanced PSO …
Economic Load Dispatch Using Bacterial Foraging Technique With Particle Swarm Optimization Biased Evolution, Ahmed Yousuf Saber, Ganesh K. Venayagamoorthy
Economic Load Dispatch Using Bacterial Foraging Technique With Particle Swarm Optimization Biased Evolution, Ahmed Yousuf Saber, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
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 …
A Dstatcom Controller Tuned By Particle Swarm Optimization For An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy
A Dstatcom Controller Tuned By Particle Swarm Optimization For An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
In an all-electric ship power system, the power quality problems mainly arise due to the pulsed loads, which cause the degradation of the overall system performance. The paper proposes the application of DSTATCOM to improve these power quality problems of an electric ship. DSTATCOM is a shunt compensation device, which regulates the bus voltage by injecting reactive power during the pulsed load operations. The control strategy of DSTATCOM plays an important role to meet the objectives. The paper proposes a controller design strategy which is based on particle swarm optimization (PSO). PSO, an algorithm that falls into swarm intelligence family, …
Mimo Beam-Forming With Neural Network Channel Prediction Trained By A Novel Pso-Ea-Depso Algorithm, Chris G. Potter, Ganesh K. Venayagamoorthy, Kurt Louis Kosbar
Mimo Beam-Forming With Neural Network Channel Prediction Trained By A Novel Pso-Ea-Depso Algorithm, Chris G. Potter, Ganesh K. Venayagamoorthy, Kurt Louis Kosbar
Electrical and Computer Engineering Faculty Research & Creative Works
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 …
Comparative Application Of Differential Evolution And Particle Swarm Techniques To Reactive Power And Voltage Control, G. A. Bakare, G. Krost, Ganesh K. Venayagamoorthy, U. O. Aliyu
Comparative Application Of Differential Evolution And Particle Swarm Techniques To Reactive Power And Voltage Control, G. A. Bakare, G. Krost, Ganesh K. Venayagamoorthy, U. O. Aliyu
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the comparative application of two metaheuristic approaches: Differential Evolution (DE) and Particle Swarm Optimization (PSO) to the solution of the reactive power and voltage control problem. Efficient distribution of reactive power in an electric network leads to minimization of the system losses and improvement of the system voltage profile. It can be achieved by varying the excitation of generators or the on-load tap changer positions of transformers as well as by switching of discrete portions of inductors or capacitors etc. This constitutes a typical mixed integer non-linear optimization problem for the solution of which metaheuristic techniques have …
Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank
Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank
Electrical and Computer Engineering Faculty Research & Creative Works
Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene …
Miso Damping Controller Design For A Tcsc Using Particle Swarm, Swakshar Ray, Ganesh K. Venayagamoorthy, Balarko Chaudhuri, Rajat Majumder
Miso Damping Controller Design For A Tcsc Using Particle Swarm, Swakshar Ray, Ganesh K. Venayagamoorthy, Balarko Chaudhuri, Rajat Majumder
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a new approach for designing multi-input-single-output (MISO) damping controller for a TCSC in a multi-machine power system. The damping controller design uses particle swarm optimization (PSO) to determine the coefficients of single or multi-stage lead-lag compensators. The classical technique works well in the design of lead-lag compensators for SISO controllers. But, there is no proper step-by-step procedure to achieve the desired performance characteristics for a MISO controller. Hence, in this paper, a computational optimization tool has been used to determine the optimal gains and time constants of a linear MISO damping controller. The damping controller is implemented …
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding nonuniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs and particle swarm optimization, aiming to maximize the signal-to-noise ratio. The comparison of these optimal quantizer designs over a bit-rate range of 3-6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality standard
Multiclass Cancer Classification Using Semisupervised Ellipsoid Artmap And Particle Swarm Optimization With Gene Expression Data, Georgios C. Anagnostopoulos, Donald C. Wunsch, Rui Xu
Multiclass Cancer Classification Using Semisupervised Ellipsoid Artmap And Particle Swarm Optimization With Gene Expression Data, Georgios C. Anagnostopoulos, Donald C. Wunsch, Rui Xu
Electrical and Computer Engineering Faculty Research & Creative Works
It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. with the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to research on binary classification such as normal versus tumor samples, which attracts numerous efforts from a variety of disciplines, the discrimination of multiple tumor types is also important. Meanwhile, the selection of genes which are relevant to a certain cancer type not only improves the performance of the classifiers, but also provides …
Bio-Inspired Algorithms For The Design Of Multiple Optimal Power System Stabilizers: Sppso And Bfa, Tridib Kumar Das, Ganesh K. Venayagamoorthy
Bio-Inspired Algorithms For The Design Of Multiple Optimal Power System Stabilizers: Sppso And Bfa, Tridib Kumar Das, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Power System Stabilizers (PSSs) provide stabilizing control signals to excitation systems to damp out inter-area and intra-area oscillations. The PSS must be optimally tuned to accommodate the variations in the system dynamics. Designing multiple optimal PSSs is a challenging task for researchers. This paper presents the comparison between two bio-inspired algorithms: a Small Population based Particle Swarm Optimization (SPPSO) and the Bacterial Foraging Algorithm (BFA) for the simultaneous tuning of a number of PSSs in a multi-machine power system. The cost function to be optimized by both algorithms takes into consideration the time domain transient responses. The effectiveness of the …
Optimal Allocation Of A Statcom In A 45 Bus Section Of The Brazilian Power System Using Particle Swarm Optimization, J. C. Hernandez, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Optimal Allocation Of A Statcom In A 45 Bus Section Of The Brazilian Power System Using Particle Swarm Optimization, J. C. Hernandez, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper introduces the application of Particle Swarm Optimization (PSO) to solve the optimal allocation of a STATCOM in a 45 bus system which is part of the Brazilian power network. The criterion used in finding the optimal location is based on the voltage profile of the system, i.e. the voltage deviation at each bus, with respect to its optimum value, is minimized. In order to test the performance of the PSO algorithm in this particular application, different approaches for inertia weight are investigated; also different values of acceleration constants, number of iterations and maximum velocity are considered. A sensitivity …
Comparison Of Pso And Ga For K-Node Set Reliability Optimization Of A Distributed System, G. A. Bakare, I. N. Chiroma, Ganesh K. Venayagamoorthy
Comparison Of Pso And Ga For K-Node Set Reliability Optimization Of A Distributed System, G. A. Bakare, I. N. Chiroma, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Particle Swarm Optimization (PSO), as a novel evolutionary computing technique, has succeeded in many continuous problems, but quite a little research on discrete problem especially combinatorial optimization problem has been reported. In this paper, a discrete PSO algorithm is proposed to solve a typical combinatorial optimization problem: K-Node Set Reliability (KNR) optimization of a distributed computing system (DCS) which is a well-known NP-hard problem is presented. It computes the reliability of a subset of network nodes of a DCS such that the reliability is maximized and specified capacity constraint is satisfied. The feasibility of the proposed algorithm is demonstrated on …
Density Estimation Using A Generalized Neuron, R. Kiran, Ganesh K. Venayagamoorthy, M. Palaniswami
Density Estimation Using A Generalized Neuron, R. Kiran, Ganesh K. Venayagamoorthy, M. Palaniswami
Electrical and Computer Engineering Faculty Research & Creative Works
Neural networks have been shown to be useful tools for density estimation. However, the training of neural network structures is time consuming and requires fast processors for practical applications. A new method with a generalized neuron (GN) for density estimation is presented in this paper. The GN is trained with the particle swarm optimization algorithm which is known to have fast convergence than the standard backpropagation algorithm. Results are presented to show that the GN can estimate the density functions for distribution functions with different means and variances. This density estimation method can also be applied to the multi-sensor data …
Particle Swarm Optimization Based Defensive Islanding Of Large Scale Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, David A. Cartes
Particle Swarm Optimization Based Defensive Islanding Of Large Scale Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, David A. Cartes
Electrical and Computer Engineering Faculty Research & Creative Works
Defensive islanding is an efficient way to avoid catastrophic failures and wide area blackouts. Power system splitting especially for large scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution (if one exists) for large scale power system in real time. This paper proposes to utilize the computational efficiency property of Binary Particle Swarm Optimization (BPSO) to find some efficient splitting solutions in limited timeframe. The solutions are optimized based on a cost function considering the balance between real power generation and consumption, the relative importance of customers, the capacities of distribution …
Comparison Of Two Optimal Control Strategies For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
Comparison Of Two Optimal Control Strategies For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents two optimal control strategies for a grid independent photovoltaic system consisting of a PV collector array, a storage battery, and loads (critical and non-critical loads). The first strategy is based on Action Dependent Heuristic Dynamic Programming (ADHDP), a model-free adaptive critic design (ACD) technique which optimizes the control performance based on a utility function. ADHDP critic network is used in a PV system simulation study to train an action neural network (optimal neurocontroller) to provide optimal control for varying PV system output energy and loadings. The second optimal control strategy is based on a fuzzy logic controller …
Gene Regulatory Networks Inference With Recurrent Neural Network Models, Rui Xu, Donald C. Wunsch
Gene Regulatory Networks Inference With Recurrent Neural Network Models, Rui Xu, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Large-scale time series gene expression data generated from DNA microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand their relations and interactions. To infer gene regulatory networks from these data with effective computational tools has attracted intensive efforts from artificial intelligence and machine learning. Here, we use a recurrent neural network (RNN), trained with particle swarm optimization (PSO), to investigate the behaviors of regulatory networks. The experimental results, on a synthetic data set and a real data set, show that the proposed model and algorithm can effectively capture the dynamics of …
A Comparison Of Pso And Backpropagation For Training Rbf Neural Networks For Identification Of A Power System With Statcom, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
A Comparison Of Pso And Backpropagation For Training Rbf Neural Networks For Identification Of A Power System With Statcom, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.
Fuzzy Pso: A Generalization Of Particle Swarm Optimization, S. Abdelshahid, Donald C. Wunsch, Ashraf M. Abdelbar
Fuzzy Pso: A Generalization Of Particle Swarm Optimization, S. Abdelshahid, Donald C. Wunsch, Ashraf M. Abdelbar
Electrical and Computer Engineering Faculty Research & Creative Works
In standard particle swarm optimization (PSO), the best particle in each neighborhood exerts its influence over other particles in the neighborhood. In this paper, we propose fuzzy PSO, a generalization which differs from standard PSO in the following respect: charisma is defined to be a fuzzy variable, and more than one particle in each neighborhood can have a non-zero degree of charisma, and, consequently, is allowed to influence others to a degree that depends on its charisma. We evaluate our model on the weighted maximum satisfiability (maxsat) problem, comparing performance to standard PSO and to Walk-Sat.
Optimal Control Parameters For A Upfc In A Multimachine Using Pso, Ganesh K. Venayagamoorthy
Optimal Control Parameters For A Upfc In A Multimachine Using Pso, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) is an effective means for controlling the power flow and can provide damping capability during transient conditions. The UPFC is controlled conventionally using PI controllers. The optimal design of the PI controllers for a UPFC is a challenging task and time consuming using the conventional techniques. This paper presents an approach using particle swarm optimization (PSO) for the design of optimal conventional controllers for a UPFC in a multimachine power system. Simulation results are presented to show the effectiveness of the …
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
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 …
Evolving Combinational Logic Circuits Using A Hybrid Quantum Evolution And Particle Swarm Inspired Algorithm, Phillip W. Moore, Ganesh K. Venayagamoorthy
Evolving Combinational Logic Circuits Using A Hybrid Quantum Evolution And Particle Swarm Inspired Algorithm, Phillip W. Moore, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, an algorithm inspired from quantum evolution and particle swarm to evolve combinational logic circuits is presented. This algorithm uses the framework of the local version of particle swarm optimization with quantum evolutionary algorithms, and integer encoding. A multi-objective fitness function is used to evolve the combinational logic circuits in order obtain feasible circuits with minimal number of gates in the design. A comparative study indicates the superior performance of the hybrid quantum evolution-particle swarm inspired algorithm over the particle swarm and other evolutionary algorithms (such as genetic algorithms) independently.
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Quantization is a crucial link in the process of digital speech communication. Non-uniform quantizer such as the logarithm quantizers are commonly used in practice. In this paper, a companding non-uniform quantizer is designed using two neural networks to perform the nonlinear transformation. Particle swarm optimization is applied to find the weights of neural networks such that the signal to noise ratio (SNR) is maximized. Simulation results on different speech samples are presented and the proposed quantizer design is compared with the logarithm quantizer for bit rates ranging from 3 to 8.
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding non-uniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs (ACD) and particle swarm optimization (PSO), aiming to maximize the signal to noise ratio (SNR). The comparison of these optimal quantizer designs over bit rate range of 3 to 6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union''s Perceptual …
Unmanned Vehicle Navigation Using Swarm Intelligence, Sheetal Doctor, Ganesh K. Venayagamoorthy
Unmanned Vehicle Navigation Using Swarm Intelligence, Sheetal Doctor, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned vehicles are used to explore physical areas where humans are unable to go due to different constraints. There have been various algorithms that have been used to perform this task. This paper explores swarm intelligence for searching a given problem space for a particular target(s). The work in this paper has two parts. In the first part, a set of randomized unmanned vehicles are deployed to locate a single target. In the second part, the randomized unmanned vehicles are deployed to locate various targets and are then converged at one of targets of a particular interest. Each of the …
Swarm Intelligence For Digital Circuits Implementation On Field Programmable Gate Arrays Platforms, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Swarm Intelligence For Digital Circuits Implementation On Field Programmable Gate Arrays Platforms, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Electrical and Computer Engineering Faculty Research & Creative Works
Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA's resources is an efficient placement and routing mechanism. This paper presents an optimization technique based on swarm intelligence for FPGA placement and routing. Mentor graphics technology mapping netlist file is used to generate initial FPGA placements and routings which are then optimized by particle swarm optimization (PSO). Results for the implementation of a binary coded decimal bidirectional counter and an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving …