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Full-Text Articles in Engineering
A Gray-Code Type Bit Assignment Algorithm For Unitary Space-Time Constellations, Adam Panagos, Kurt Louis Kosbar
A Gray-Code Type Bit Assignment Algorithm For Unitary Space-Time Constellations, Adam Panagos, Kurt Louis Kosbar
Electrical and Computer Engineering Faculty Research & Creative Works
Many techniques for constructing unitary space- time constellations have been proposed. To minimize bit-error rate (BER) in a wireless communication system, constellations constructed using these techniques should be given a Gray- code type bit assignment, where symbols which are close in signal space have bit assignments which have small Hamming distance. To the authors' knowledge, no efficient general strategy for making this bit assignment has been suggested. This work proposes a prioritized distance (PD) algorithm for making this assignment in an optimal manner by minimizing the probability of bit error union bound. The algorithm can be used on constellations constructed …
A General Purpose Framework For Wireless Sensor Network Applications, Ayman Z. Faza, Sahra Sedigh
A General Purpose Framework For Wireless Sensor Network Applications, Ayman Z. Faza, Sahra Sedigh
Electrical and Computer Engineering Faculty Research & Creative Works
Wireless sensor networks are becoming a basis for a rapidly increasing range of applications. Habitat, flood, and wildfire monitoring are interesting examples of such applications. Each application has different requirements in terms of node functionalities, network size, complexity and cost; therefore, it is worthwhile time investment to design and implement a general purpose framework for wireless sensor networks that would be adaptable to any monitoring application of interest with a minimum amount of effort. In this manuscript, we propose a basic structure for such a framework and highlight a number of challenges anticipated during the course of this doctoral research.
Efficiently Managing Security Concerns In Component Based System Design, Ammar Masood, Sahra Sedigh, Arif Ghafoor
Efficiently Managing Security Concerns In Component Based System Design, Ammar Masood, Sahra Sedigh, Arif Ghafoor
Electrical and Computer Engineering Faculty Research & Creative Works
Component-based software development (CBSD) offers many advantages like reduced product time to market, reduced complexity and cost etc. Despite these advantages its wide scale utilization in developing security critical systems is currently hampered because of lack, of suitable design techniques to efficiently manage the complete system security concerns in the development process. The use of commercial of the shelf (COTS) components can introduce various security and reliability risks in the system. In this paper we propose a methodology for efficient management of all the system security concerns involved in the design of component based systems. Our methodology is based on …
Static And Quasi-Dynamic Load Balancing In Parallel Fdtd Codes For Signal Integrity, Power Integrity, And Packaging Applications, Sarah A. Seguin, Michael A. Cracraft, James L. Drewniak
Static And Quasi-Dynamic Load Balancing In Parallel Fdtd Codes For Signal Integrity, Power Integrity, And Packaging Applications, Sarah A. Seguin, Michael A. Cracraft, James L. Drewniak
Electrical and Computer Engineering Faculty Research & Creative Works
The Finite-Difference Time-Domain (FDTD) method is a robust technique for calculating electromagnetic fields, but practical problems, involving complex or large geometries, can require a long time to calculate on any one single-processor computer. One computer with many processors or many single-processor computers can reduce the computation time. However, some FDTD cell types, e.g., PML cells, require more computation time than others. Thus, the size and shape of the individual process allocations can significantly influence the computation time. This paper addresses these load balancing issues with static and quasi-dynamic approaches. The Message-Passing Interface (MPI) library is applied to a three-dimensional (3D) …
Representation Of Permittivity For Multiphase Dielectric Mixtures In Fdtd Modeling, Marina Koledintseva, J. Wu, H. Zhang, James L. Drewniak, Konstantin Rozanov
Representation Of Permittivity For Multiphase Dielectric Mixtures In Fdtd Modeling, Marina Koledintseva, J. Wu, H. Zhang, James L. Drewniak, Konstantin Rozanov
Electrical and Computer Engineering Faculty Research & Creative Works
A simple method of approximating frequency characteristics of composites in a form convenient for time-domain numerical modeling is proposed. The frequency characteristics can be obtained from experiment or calculations based on the Maxwell Garnett mixing formalism. The resultant frequency characteristic might be of a complex shape corresponding to a combination of a number of absorption peaks. The approximation is made by a series of Debye-like terms using a genetic algorithm (GA). This leads to the necessity of taking a number of terms in the approximating series. Every term corresponds to its pole, i.e., the frequency where the maximum loss occurs. …
Testing Layered Interconnection Networks, Bin Liu, Fabrizio Lombardi, Nohpill Park, Minsu Choi
Testing Layered Interconnection Networks, Bin Liu, Fabrizio Lombardi, Nohpill Park, Minsu Choi
Electrical and Computer Engineering Faculty Research & Creative Works
We present an approach for fault detection in layered interconnection networks (LINs). An LIN is a generalized multistage interconnection network commonly used in reconfigurable systems; the nets (links) are arranged in sets (referred to as layers) of different size. Switching elements (made of simple switches such as transmission-gate-like devices) are arranged in a cascade to connect pairs of layers. The switching elements of an LIN have the same number of switches, but the switching patterns may not be uniform. A comprehensive fault model for the nets and switches is assumed at physical and behavioral levels. Testing requires configuring the LIN …
Optimal Control Synthesis Of A Class Of Nonlinear Systems Using Single Network Adaptive Critics, Radhakant Padhi, Nishant Unnikrishnan, S. N. Balakrishnan
Optimal Control Synthesis Of A Class Of Nonlinear Systems Using Single Network Adaptive Critics, Radhakant Padhi, Nishant Unnikrishnan, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Adaptive critic (AC) neural network solutions to optimal control designs using dynamic programming has reduced the need of complex computations and storage requirements that typical dynamic programming requires. In this paper, a "single network adaptive critic" (SNAC) is presented. This approach is applicable to a class of nonlinear systems where the optimal control (stationary) equation is explicitly solvable for control in terms of state and costate variables. The SNAC architecture offers three potential advantages; a simpler architecture, significant savings of computational load and reduction in approximation errors. In order to demonstrate these benefits, a real-life micro-electro-mechanical-system (MEMS) problem has been …
A Nested Sensor Array Focusing On Near Field Targets, Y. Rosa Zheng, M. El-Tanany, R. A. Goubran
A Nested Sensor Array Focusing On Near Field Targets, Y. Rosa Zheng, M. El-Tanany, R. A. Goubran
Electrical and Computer Engineering Faculty Research & Creative Works
A nested virtual array subband beamforming system is proposed for applications where broadband signal targets are located within the near field of the array. Subband multirate processing and near field beamforming techniques are used jointly for the nested array to improve the performances and reduce the computational complexity. A new noise model, namely the broadband near field spherically isotropic noise model, is also proposed for the optimization design of near field beamformers. It is shown that near field beamforming is essential for better distance discrimination of near field targets, reduced beampattern variations for broadband signals, and stronger reverberation suppression.
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN) for the online identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization and online global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster and global minimum convergence is achieved even when operating conditions change.
Incorporating Two-Port Networks With S-Parameters Into Fdtd, Xiaoning Ye, James L. Drewniak
Incorporating Two-Port Networks With S-Parameters Into Fdtd, Xiaoning Ye, James L. Drewniak
Electrical and Computer Engineering Faculty Research & Creative Works
A modeling approach for incorporating a two-port network with S-parameters in the finite-difference time-domain (FDTD) method is reported in this paper. The proposed method utilizes the time-domain Y-parameters to describe the network characteristics, and incorporates the Y-parameters into the FDTD algorithm. The generalized pencil-of-function (GPOF) technique is applied to improve the memory efficiency of this algorithm by generating a complex exponential series for the Y-parameters and using recursive convolution in the FDTD updating equations. A modeling example is given, which shows that this approach is effective and accurate. This modeling technique can be extended for incorporating any number of N-port …
Infinite Time Optimal Neuro Control For Distributed Parameter Systems, S. N. Balakrishnan, Radhakant Padhi
Infinite Time Optimal Neuro Control For Distributed Parameter Systems, S. N. Balakrishnan, Radhakant Padhi
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The conventional dynamic programming methodology for the solution of optimal control, despite having many desirable features, is severely restricted by its computational requirements. However, in recent times, an alternate formulation, known as the adaptive-critic synthesis, has given it a new perspective. In this paper, we have attempted to use the philosophy of adaptive-critic design to the optimal control of distributed parameter systems. An important contribution of this study is the derivation of the necessary conditions of optimality for distributed parameter systems, described in discrete domain, following the principle of approximate dynamic programming. Then the derived necessary conditions of optimality are …
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian
Electrical and Computer Engineering Faculty Research & Creative Works
Strategies to efficiently train a neural network for an aerospace problem with a large multidimensional input space are developed and demonstrated. The neural network provides classification for over 100,000,000 data points. A query-based strategy is used that initiates training using a small input set, and then augments the set in multiple stages to include important data around the network decision boundary. Neural network inversion and oracle query are used to generate the additional data, jitter is added to the query data to improve the results, and an extended Kalman filter algorithm is used for training. A causality index is discussed …
Double-Talk Robust Fast Converging Algorithms For Network Echo Cancellation, T. Gansler, Steven L. Grant, J. Benesty, M. M. Sondhi
Double-Talk Robust Fast Converging Algorithms For Network Echo Cancellation, T. Gansler, Steven L. Grant, J. Benesty, M. M. Sondhi
Electrical and Computer Engineering Faculty Research & Creative Works
Echo cancelers which cover longer impulse responses (greater than or equal to 64 ms) are desirable. Long responses create a need for more rapidly converging algorithms in order to meet the specifications for network echo cancelers devised by the ITU (International Telecommunication Union). In general, faster convergence implies a higher sensitivity to near-end disturbances, especially "double-talk". Recently, a fast converging algorithm called proportionate NLMS (normalized least mean squares) algorithm (PNLMS) has been proposed. This algorithm exploits the sparseness of the echo path. In this paper we propose a method for making the PNLMS algorithm more robust against double-talk. The slower …
Dynamically Regularized Fast Rls With Application To Echo Cancellation, Steven L. Grant
Dynamically Regularized Fast Rls With Application To Echo Cancellation, Steven L. Grant
Electrical and Computer Engineering Faculty Research & Creative Works
This paper introduces a dynamically regularized fast recursive least squares (DR-FRLS) adaptive filtering algorithm. Numerically stabilized FRLS algorithms exhibit reliable and fast convergence with low complexity even when the excitation signal is highly self-correlated. FRLS still suffers from instability, however, when the condition number of the implicit excitation sample covariance matrix is very high. DR-FRLS, overcomes this problem with a regularization process which only increases the computational complexity by 50%. The benefits of regularization include: (1) the ability to use small forgetting factors resulting in improved tracking ability and (2) better convergence over the standard regularization technique of noise injection. …
The Fast Affine Projection Algorithm, Steven L. Grant, S. Tavathia
The Fast Affine Projection Algorithm, Steven L. Grant, S. Tavathia
Electrical and Computer Engineering Faculty Research & Creative Works
This paper discusses a new adaptive filtering algorithm called fast affine projections (FAP). FAP''s key features include LMS like complexity and memory requirements (low), and RLS like convergence (fast) for the important case where the excitation signal is speech. Another of FAP''s important features is that it causes no delay in the input or output signals. In addition, the algorithm is easily regularized resulting in robust performance even for highly colored excitation signals. The combination of these features make FAP an excellent candidate for the adaptive filter in the acoustic echo cancellation problem. A simple, low complexity numerical stabilization method …
A Study Of Numerically Efficient Algorithms For Power System Dynamic Analysis, J. G. Chen, Mariesa Crow
A Study Of Numerically Efficient Algorithms For Power System Dynamic Analysis, J. G. Chen, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, the multirate method is introduced to analyze power system behavior including linear and nonlinear systems with widely varying time constants. The development and study of time domain simulation techniques and error detection are discussed. The results, both in terms of accuracy and computation time, are compared to traditional simulation methods in a small nonlinear power system example