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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： A.V. Nefian M.H. Hayes School of Electrical and Computer Engineering Center for Signal and Image Processing Georgia Institute of Technology Atlanta GA USA

We describe an embedded hidden Markov model (HMM)-based approach for face detection and recognition that uses an efficient set of observation vectors obtained from the 2D-DCT coefficients. The embedded HMM can model the two dimensional data better than the one-dimensional HMM and is computationally less complex than the two-dimensional HMM. This model is appropriate for face images since it exploits an important facial characteristic: frontal faces preserve the same structure of "super states" from top to bottom, and also the same left-to-right structure of "states" inside each of these "super states".

关键词： Hidden Markov models Face detection Face recognition Embedded computing Density functional theory image recognition signal processing image processing Layout image databases

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Imaging and Detector Framework for Seismic Landmine Detection

Imaging and Detector Framework for Seismic Landmine Detectio...

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Sensor Array and Multichannel signal processing Workshop

作者： M. Alam J.H. McClellan Center for Signal and Image Processing Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA

In this paper, we consider the problem of detecting and locating buried landmines and subsurface objects by using seismic waves measured with sources and receivers on the surface. An imaging algorithm is proposed which uses the separated forward and reflected waves obtained via a Prony-based spectrum analysis technique. The imaging algorithm is followed by an energy detector. The resulting algorithms will are verified by using the experimental data containing mines and clutter

关键词： Landmine detection Detectors Sensor arrays Seismic waves Surface waves Data mining Resonance Laboratories Testing image reconstruction

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Omega-k quadtree UWB SAR focusing

Omega-k quadtree UWB SAR focusing

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IEEE National Conference on Radar

作者： M.C. Cobb J.H. McClellan Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)0780367073

A variation on the quadtree algorithm for ultra-wideband, wide-angle (UWB-WA) SAR imaging that uses the /spl omega/-k algorithm for final stage focusing is introduced. Several signal processing techniques appropriate to the requirements of UWB-WA SAR, and that prevent errors and artifacts in the /spl omega/-k focusing are reviewed. These techniques include spatial and temporal shifts for spotlight data, elimination of ghost targets, and adjustments needed to account for the virtual sampling of the quadtree algorithm. In order to justify these techniques, a simple Fourier-based model of the imaging problem appropriate to UWB-WA SAR is presented.

关键词： signal processing algorithms Focusing Computational efficiency Iterative algorithms Approximation algorithms Apertures Robustness Approximation error signal processing image processing

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An algorithm for compression of wideband diverse speech and audio signals

An algorithm for compression of wideband diverse speech and ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： T.R. Trinkaus M.A. Clements Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

A compression scheme for diverse speech and audio signals is proposed. In this scheme, signals are analyzed with a 2-band QMF filter bank followed by the application of a modulated lapped biorthogonal transform (MLBT) to each of the filter bank channels. Subsequent encoding of transform coefficients is performed using Laplacian optimized scalar and vector quantizers, whose rates are determined by an estimated noise threshold, i.e., masking threshold. Listening tests show that the coder achieves a quality at 32 kbits/s that is preferred over the ITU G.722 coder at 64 kbits/s, for speech, music, and more diverse signals consisting of speech in the presence of eventful background sounds. Both the delay of the coder, at 40 ms, and the level of complexity are moderate.

关键词： Wideband Speech Filter bank signal analysis Laplace equations Acoustic noise Masking threshold Acoustic testing Multiple signal classification Music

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A new directional filter bank for image analysis and classification

A new directional filter bank for image analysis and classif...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Sang-Il Park M.J.T. Smith R.M. Mersereau Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

A new directional filter bank for image analysis and classification is proposed. This paper introduces an improved structure in order to visualize subband outputs of the directional filter banks, while retaining the attractive properties of the original directional filter banks such as 1-D separable filtering, perfect reconstruction, and maximal decimation. Using this structure, any arbitrary 2/sup n/ band directional filter bank can be implemented by cascading simple directional filter bank blocks, unlike the original structure that needs a parallel structure for visualizing subband outputs. Also, in order to have nondistorted phase information in the subbands for visualization, both FIR and IIR filter prototypes that can be implemented efficiently are provided for linear phase filtering. This paper shows the approach proposed here can be applied to image analysis and classification.

关键词： Filter bank image analysis Visualization Finite impulse response filter IIR filters image reconstruction Prototypes Information filtering Information filters Nonlinear filters

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Stochastic Model for Simulation of Ground-Motion Sequences Using Kernel-Based Smoothed Wavelet Transform and Gaussian Mixture Distribution

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Journal of Earthquake engineering 2021年第11期25卷 2147-2177页

作者： Sharbati, R. Ramazi, H.R. Khoshnoudian, F. Amindavar, H.R. Rabbani, H. Department of Civil and Environmental Engineering Amirkabir University of Technology Tehran Iran Department of Mining & Metallurgical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Medical Image Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

In this paper, a stochastic-parametric model is developed for simulating the temporal and spectral nonstationary characteristics of ground motion sequences. In the proposed model, after extracting the wavelet coefficients of a ground motion sequence by using the complex discrete wavelet transform and smoothing them by the Normal kernel function, they are simulated by using the Gaussian mixture distribution. This model simulates multiple peaks in the time domain, several dominant frequency peaks at each time, the relaxation time between motions, and the steps of cumulative energy curve of ground motion sequences, while the previous models did not have these abilities. © 2019 Taylor & Francis Group, LLC.

关键词： Stochastic models

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Fast rate-constrained N-step search algorithm for motion estimation

Fast rate-constrained N-step search algorithm for motion est...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： M.Z. Coban R.M. Mersereau Center for Signal & Image Processing (CSIP) School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA USA

A fast N-step search algorithm for rate-constrained motion estimation is presented. The motion vectors are selected from a search window based on a rate-distortion criterion by successively eliminating the search positions at each step. The performance of the proposed algorithm is identical to the performance of the conventional rate-constrained N-step search algorithm, with considerable reduction in computation. Computational savings increase in parallel with the increase in the rate constraint and the number of steps.

关键词： Motion estimation Bit rate Video coding Computational efficiency Testing Cost function Lagrangian functions Video sequences signal processing image processing

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Computationally efficient exhaustive search algorithm for rate-constrained motion estimation

Computationally efficient exhaustive search algorithm for ra...

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IEEE International Conference on image processing

作者： M.Z. Coban R.M. Mersereau Center for Signal & Image Processing (CSIP) School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA USA

A computationally efficient exhaustive search algorithm for rate-constrained motion estimation is presented. The motion vectors are selected from a search window based on a rate-distortion criterion by successively eliminating the search positions depending on the rate constraint. The performance of the proposed algorithm is identical to the performance of the rate-constrained full search algorithm, with considerable reduction in computation. The number of matching calculations decreases as the constraint imposed on the rate increases.

关键词： Motion estimation Bit rate Video coding Computational complexity Constraint optimization Lagrangian functions signal processing image processing Electronic mail Rate-distortion

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Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

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Journal of engineering and Applied Science 2024年第1期71卷 12页

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Nounou, Hazem Bouzrara, Kais Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kairouan Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Monastir5019 Tunisia

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time. © 2023, The Author(s).

关键词： Failure analysis

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New signal subspace direction-of-arrival estimator for wideband sources

New signal subspace direction-of-arrival estimator for wideb...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yeo-Sun Yoon L.M. Kaplan J.H. McClellan Center for Signal and Image Processing Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA CTSPS Dept. of Engineering Clark Atlanta University Atlanta GA

signal subspace methods in DOA estimation for wideband sources require preprocessing to find initial values which are close enough to the true values or to convert sensor outputs into desired forms. The preprocessing procedure should be carefully done lest it introduce some distortion. Failure to find proper initial values may prevent convergence of the estimator or cause biases in the estimator. The proposed method detects uncorrelated wideband sources using the signal subspace and the noise subspace of decomposed wideband signals. It does not require any initial values. The only preprocessing is narrowband decomposition of the sensor output which is very common in other wideband methods. computer simulation showed that the proposed method has less bias than and comparable variance to CSSM with small focusing errors.

关键词： Wideband Direction of arrival estimation Narrowband Frequency estimation Array signal processing Multiple signal classification Collaboration Government signal processing image processing

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