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Conference on advanced signal processing - algorithms, architectures, and implementations vII

作者： Rabinkin, Dv Renomeron, RJ French, JC Flanagan, JL Rutgers Univ. (United States)

ISBN: (纸本)0819425842

Microphone arrays can be used for high-quality sound pick up in reverberant and noisy environments. The beamforming capabilities of microphone array systems allow highly directional sound capture, providing superior signal-to-noise ratio (SNR) when compared to single microphone performance. There are two aspects in microphone array system performance: The ability of the system to locate and track sound sources, and its ability to selectively capture sound from those sources. Both aspects of system performance are strongly affected by the spatial placement of microphone sensors. A method is needed to optimize sensor placement based on geometry of the environment and assumed sound source behavior. The objective of the optimization is to obtain the greatest average system SNR using a specified number of sensors. A method is derived to evaluate array performance for a given array configuration defined by the above mentioned metrics. An overall performance function is described based on these metrics. A framework for optimum placement of sensors under the practical considerations of possible sensor placement and potential location of sound sources is also characterized.

关键词： signal to noise ratio Sensors Error analysis Interference (communication) Acoustics Monte Carlo methods signal attenuation Statistical analysis Environmental sensing signal processing

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Local regularization and Bayesian hypermodels

Local regularization and Bayesian hypermodels

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advanced signal processing algorithms, architectures, and implementations Xv

作者： Calvetti, Daniela Somersalo, Erkki Department of Mathematics Center for Modeling Integrated Metabolic Systems Case Western Reserve University Cleveland OH United States Department of Mathematics Helsinki University of Technology Helsinki Finland

In this paper, we restore a one-dimensional signal that a priori is known to be a smooth function with a few jump discontinuities from a blurred, noisy specimen signal using a local regularization scheme derived in a Bayesian statistical inversion framework. The proposed method is computationally effective and reproduces well the jump discontinuities, thus is an alternative to using a total variation (Tv) penalty as a regularizing functional. Our approach avoids the non-differentiability problems encountered in Tv methods and is completely data driven in the sense that the parameter selection is done automatically and requires no user intervention. A computed example illustrating the performance of the method when applied to the solution of a deconvolution problem is also presented.

关键词： signal processing

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RECURSIvE LEAST-SQUARES LEARNING algorithms FOR NEURAL NETWORKS

RECURSIVE LEAST-SQUARES LEARNING ALGORITHMS FOR NEURAL NETWO...

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CONF ON advanced signal-processing algorithms, architectures, AND implementations

作者： LEWIS, PS HWANG, JN Los Alamos National Lab. (United States) Univ. of Washington (United States)

ISBN: (纸本)0819404098

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the

关键词： Algorithm development Detection and tracking algorithms Evolutionary algorithms Neural networks Network architectures Artificial neural networks Pattern recognition Chemical elements Machine learning signal processing

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An efficient array processor for accurate downdating of ULv decomposition

An efficient array processor for accurate downdating of ULV ...

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Conference on advanced signal processing algorithms, architectures, and implementations XI

作者： Yoon, PA Sibul, LH Trinity Coll Dept Comp Sci Hartford CT 06106 USA

ISBN: (纸本)0819441880

We present an efficient array processor for downdating the ULvD. The array processor exploits the block structure of the decomposition to determine the effective rank at every step of modification and maintains the rank-revealing nature of the decomposition throughout the steps by tracking exact quantities of Frobenius norms of all three blocks of the lower triangular factor in the decomposition. Thus. a deflation step that is necessary to compute its numerical rank can be often avoided. This feature of the algorithm is particularly attractive to its array processor implementation because the deflation steps usually require some condition estimation. and most of good condition estimators are not well pipelined.

关键词： signal processing null space rank detection array processors

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Convergence results on adaptive approximate filtering 6

Convergence results on adaptive approximate filtering

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Conference on advanced signal processing algorithms, architectures, and implementations vI

作者： Ludwig, JT Nawab, SH Chandrakasan, AP MIT ELECT RES LABCAMBRIDGEMA 02139

ISBN: (纸本)0819422347

We present an IIR filtering technique based upon a recently proposed approach for reducing power consumption in implementation of frequency-selective filters. The basic idea in such techniques is to utilize the most recent input and output signal samples to estimate the current SNR (defined as the ratio of the in-band signal power to the out-of-band signal power) at the filter's input. This estimated input SNR is then used to update the filter order to the minimum value which would guarantee a minimum tolerable SNR at the filter's output. A key issue addressed in this paper is how well the estimated filter order converges to the theoretical minimum for situations satisfying the assumptions behind the derivation of the technique. Experimental results are used to verify that convergence to the correct filter order depends (1) upon the number of input and output samples used for estimating the input SNR, (2) upon the filter order applied in generating the output samples that are used in estimating the input SNR and (3) upon the proximity of the actual input SNR to boundaries in the input-SNR space corresponding to changes in the optimal choice for filter order.

关键词： low power approximate processing incremental refinement structure frequency-selective filtering Butterworth filters adaptive filtering

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Redundant arithmetic, algorithms and implementations

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INTEGRATION-THE vLSI JOURNAL 2000年第1期30卷 13-53页

作者： González, AF Mazumder, P Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Performance in many very-large-scale-integrated (vLSI) systems such as digital signal processing (DSP) chips, is predominantly determined by the speed of arithmetic modules like adders and multipliers. Even though redundant arithmetic algorithms produce significant improvements in performance through the elimination of carry propagation, efficient circuit implementations of these algorithms have been traditionally difficult to obtain. This work presents a survey of circuit implementations of redundant arithmetic algorithms. The described implementations are divided into three main groups: (1) conventional binary logic circuits, which encode the multivalued digits of redundant arithmetic into two or more binary digital signals;(2) current-mode multiple-valued logic circuits, which directly represent multivalued redundant digits using non-binary digital current signals;and (3) heterostructure and quantum electronic circuits. intended for very compact designs capable of operating at extremely high speeds. For each of the circuits, the operating principle is described and the main advantages and disadvantages of the approach are discussed and compared. (C) 2000 Published by Elsevier Science B.v.

关键词： redundant number systems redundant binary logic current-mode logic multiple-valued logic quantum electronic circuits

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Novel volterra predictor based on state-space equilibrium of nonlinear single- or multi-fractal signals

Novel volterra predictor based on state-space equilibrium of...

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Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Sabry-Rizk, M Zgallai, W City Univ London Dept Elect Elect & Informat Engn London EC1V 0HB England

ISBN: (纸本)0819437611

It has been quite common in the analysis of single- or multi-fractal signals originating from complex nonlinear systems to make a time-delayed construction of the state space attractor in which the dynamics can be qualitatively viewed. This involves the calculations of the embedding dimension and an appropriate time delay based on the signal nonlinear correlation behavioral pattern. This is usually followed by a sub-optimal short-term linear prediction in the signal time/subspace domain instead of the optimal nonlinear prediction in the time/frequency domain. In this paper, we propose to alleviate the sub-optimality problem and exploit the nonlinear signal dynamics embedded in the attractor and integrate them in the design of a new family of temporal multiple-step volterra predictors. Essentially, this is done by including relevant past information preserved in the signal up to a time t(d) = the embedded dimension X embedded time delay, and sampled at instances coincident with the embedded time delays, to predict one-step ahead adaptively, using the LMS criteria. The results obtained using several nonlinear (chaotic or non-chaotic) synthetic and measured biomedical signals and performing the novel quadratic- and cubic-volterra predictions show superior MSE performance, of as much as 30 dB, over those obtained using an optimized conventional one-step volterra predictor of the same order, particularly in the case of electrocardiogram signals. This is not achieved at the expense of any increase in the CPU time as the algorithm is designed to cater for new parallel volterra structures, with progressive delayed inputs.

关键词： state-space attractor nonlinear correlation multiple-step nonlinear quadratic- and cubic-volterra predictions

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FPGA-based artificial neural network using CORDIC modules

FPGA-based artificial neural network using CORDIC modules

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Conference on advanced signal processing algorithms, architectures, and implementations XvI

作者： Liddicoat, Albert A. Slivovsky, Lynne A. McLenegan, Tim Heyer, Don Calif Polytech State Univ San Luis Obispo Dept Elect Engn San Luis Obispo CA 93407 USA

ISBN: (纸本)0819463922

Artificial neural networks have been used in applications that require complex procedural algorithms and in systems which lack an analytical mathematic model. By designing a large network of computing nodes based on the artificial neuron model, new solutions can be developed for computational problems in fields such as image processing and speech recognition. Neural networks are inherently parallel since each neuron, or node, acts as an autonomous computational element. Artificial neural networks use a mathematical model for each node that processes information from other nodes in the same region. The information processing entails computing a weighted average computation followed by a nonlinear mathematical transformation. Some typical artificial neural network applications use the exponential function or trigonometric functions for the nonlinear transformation. various simple artificial neural networks have been implemented using a processor to compute the output for each node sequentially. This approach uses sequential processing and does not take advantage of the parallelism of a complex artificial neural network. In this work a hardware-based approach is investigated for artificial neural network applications. A Field Programmable Gate Arrays (FPGAs) is used to implement an artificial neuron using hardware multipliers, adders and CORDIC functional units. In order to create a large scale artificial neural network, area efficient hardware units such as CORDIC units are needed. High performance and low cost bit serial CORDIC implementations are presented. Finally, the FPGA resources and the performance of a hardware-based artificial neuron are presented.

关键词： computer arithmetic field programmable gate arrays (FPGAs) higher-level functions reconfigurable computing digital signal processing and artificial neural networks

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Discrete time processing of linear scale invariant signals and systems

Discrete time processing of linear scale invariant signals a...

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Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Izzetoglu, M Onaral, B Chitrapu, P Bilgutay, N Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)0819437611

In this paper, we formulate a framework for discrete-time processing of Linear Scale Invariant (LSI) systems which are invariant to scale changes in time. Continuous-time LSI systems can be processed by Mellin and Modified Scale Transforms analogous to the use of the Laplace and Fourier Transforms in the continuous-time processing of LTI systems. In this work, we present the geometric sampling theorem to prevent aliasing in the scale domain. We also derive the perfect reconstruction filter in time domain, the discrete-time convolution sum, the Discrete Time Modified Scale Transform (DTMST) and the Discrete Modified Scale Transform (DMST) for geometrically sampled signals.

关键词： Transform theory Fourier transforms Convolution Electronic filtering signal processing Statistical analysis Toxic industrial chemicals Biomedical optics Computing systems Telecommunications

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Toward blind removal of unwanted sound from orchestrated music

Toward blind removal of unwanted sound from orchestrated mus...

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Conference on advanced signal processing algorithms, architectures, and implementations X

作者： Chang, SY Chun, J Univ Suwon Dept Elect Engn Kyungki do South Korea

ISBN: (纸本)0819437611

The problem addressed in this paper is to removing unwanted sounds from music sound. The sound to be removed could be disturbance such as cough. We shall present some preliminary results on this problem using statistical properties of signals. Our approach consists of three steps. We first estimate the fundamental frequencies and partials given noise-corrupted music sound. This gives us the autoregressive (AR) model of the music sound. Then se filter the noise-corrupted sound using the AR parameters. The filtered signal is then subtracted from the original noise-corrupted signal to get the disturbance. Finally the obtained disturbance is used as reference signal to eliminate the disturbance from the noise-corrupted music signal. Above three steps are carried out in a recursive manner using a sliding window or an infinitely growing window with an appropriate forgetting factor.

关键词： Wiener filter music signal processing interference cancellation

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