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conference on advanced signal processing algorithms, Architectures, and Implementations IX

作者： Lo Presti, L Olmo, G Galleani, L Politecn Torino Dipartimento Elettron I-10129 Turin Italy

ISBN: (纸本)0819432938

In this paper, a method for signal component separation, operating in the Time-Frequency (TF) plane and employing a Turbo Estimation Algorithm (TEA), is described. A novel 2D distribution is proposed, named Two Window Spectrogram (TWS), which is free from crossterms and able to yield good time anti frequency resolution. Then, a set of parameters is defined in the time-frequency plane, which are able to carry the relevant information on the signal components. An algorithm of estimation of these parameters is proposed, making use of a TEA scheme to yield improved performance. The algorithm has been tested by simulation, yielding very encouraging performance.

关键词： Thermal weapon sites Time-frequency analysis Detection theory Interference (communication) Prototyping signal processing signal to noise ratio Tantalum Yield improvement Fourier transforms

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Neural model-based motion estimation

Neural model-based motion estimation

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conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Szostakowski, J Skoneczny, S Iwanowski, M Warsaw Univ. of Technology (Poland)

ISBN: (纸本)0819425842

In this paper a novel method of estimating displacement of moving objects from one frame to the next in the image sequence is presented. This method is based on using the artificial neural networks for different models of motion. The two model is examined: affine flow and planar surface motion. Various circuit architectures of simple neuron-like processors are considered for estimation of motion parameters. The efficiency of the proposed networks are investigated by computer simulation for using in video processing.

关键词： image processing neural networks video processing motion estimation

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Real time correlation processing of television communication channel video data

Real time correlation processing of television communication...

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conference on advanced signal processing algorithms, Architectures, and Implementations XI

作者： Larkin, A Volkov, L Moscow Engn Phys Inst Coherent Photon Ctr Moscow 115409 Russia

ISBN: (纸本)0819441880

Electron-optical image converters (EOIC) have been known to be useful in recording and investigating highspeed processes, nuclear physics experiments, automatic environmental control, medicine etc. In this paper the cathode ray tubes with the cathodoluminescent screen having a sufficiently high level of the radiation temporal coherence (particularly on the basis of rare-earth phosphors) are proposed to be utilised as devices for the dynamic data input into the holographic correlator for realisation of TV signal recognition in real time. This approach allows combining both the radiation source and the spatial light modulator functions in one compact device.

关键词： Image processing Holography CRTs Optical correlators Data communications Temporal coherence Televisions Data processing Video Video processing

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Improvement in Available Methods for Simultaneous and Proportional Control using the Kernel Technique for Unsupervised Myoelectric Intention Estimation of Individual Fingers

Improvement in Available Methods for Simultaneous and Propor...

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IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Jamal, Muhammad Zahak Lee, Dong-Hyun Hyun, Dong Jin Hyundai Motor Co Articulated Robot Res Grp Hyundai Robot Lab Uiwang Res Ctr Uiwang Si 16082 Gyeonggi Do South Korea

ISBN: (纸本)9781728167947

Recent advances in the field of biomedical engineering has prompted modern research to focus on challenges of human machine interface. This paper provides an improvement in unsupervised learning methods already available for estimating myoelectric intention of individual fingers using the kernel technique. The unsupervised methods which have been improved upon for simultaneous and proportional intention estimation are NMF and NMF-HP. These methods are called semi unsupervised algorithms as models are evaluated blindly using only the target finger. The algorithms implemented with kernels are named as kNMF and kNMF-HP. The kernel technique increases the feature matrix for the NMF and NMF-HP models and improves the performance of these algorithms. The algorithms were analyzed in terms of signal to noise ratio using the strength of the signal of the activated finger and the levels of other fingers not activated. Significant improvements were seen through the implementation of the kernel matrix on the parameters analyzed. An in-house eight channel signal instrumentation scheme was used to acquire the EMG signals using dry electrodes. In addition, a comprehensive signal filtering scheme was designed in order to remove the acquired EMG signal of noise. Finally, we used the algorithms to successfully drive a robotic hand.

关键词： Electromyography NMF NMF-HP semi unsupervised kNMF kNMF-HP dry surface EMG EMG signal processing

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Time-frequency representations of astronomical signals

Time-frequency representations of astronomical signals

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conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Rao, AW Cohen, L Scargle, JD CUNY/Hunter College (United States) NASA Ames Research Ctr. (United States)

ISBN: (纸本)0819425842

We discuss applications of time-frequency analysis to the investigation of astronomical type signals. In particular, we apply time-frequency techniques to a data set consisting of the kinetic energy in the three body problem We explain how the methods of time-frequency analysis shed light on these signals and also how the concept of multicomponent signals is applied to their decomposition. We also discuss methods to do simple filtering and estimation of the signal parameters.

关键词： Time-frequency analysis Astronomy signal analyzers Electronic filtering Optical filters Bandpass filters Fourier transforms Iterated function systems signal processing Strontium

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Generalized reaction-diffusion equation for image processing

Generalized reaction-diffusion equation for image processing

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conference on advanced signal processing algorithms, Architectures, and Implementations IX

作者： Wei, GW Natl Univ Singapore Dept Computat Sci Singapore Singapore

ISBN: (纸本)0819432938

This paper presents a robust algorithm for image processing using generalized reaction-diffusion equations. An edge enhancing functional is proposed for image enhancement. A number of super diffusion operators is introduced for fast and effective smoothing. Statistical information is utilized for robust edge-stopping and diffusion rate estimation. A unification of computational methods is discussed. The unified computational method is employed for the numerical integration of the generalized reaction-diffusion equations. Computer experiments indicate that the present algorithm is very efficient for edge-detecting and noise-removing.

关键词： image processing generalized reaction-diffusion equations unified computational method

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Information theoretic signal detection on the time-frequency plane

Information theoretic signal detection on the time-frequency...

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conference on advanced signal processing algorithms Architectures and Implementations XIII

作者： Aviyente, S Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)0819450782

A comprehensive theory for time-frequency based signal detection has been developed during the past decade. The time-frequency detectors proposed in literature are linear structures operating on the time-frequency representation of the signals and are equivalent to quadratic receivers that are defined in the,time domain. In this paper, an information theoretic approach for signal detection on the time-frequency plane is introduced. In recent years, Renyi entropy has been proposed as an effective measure for quantifying signal complexity on the time-frequency plane and some important properties of this measure have been proven. In this paper, a new approach that uses the entropy functional as the test statistic for signal detection is developed. The minimum error detection algorithm is derived and the performance of this new signal detection method is demonstrated through examples.

关键词： signal detection Time-frequency analysis Detection and tracking algorithms Interference (communication) Sensors Radar Algorithm development Backscatter signal to noise ratio Receivers

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A method to identify and restore a class of aliased signals

A method to identify and restore a class of aliased signals

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conference on advanced signal processing algorithms, Architectures, and Implementations XIV

作者： Loughlin, P Walia, A Univ Pittsburgh Pittsburgh PA 15261 USA

ISBN: (纸本)0819454974

When a continuous-time signal is sampled at a rate less than the Nyquist criterion, the signal is aliased. This distortion is usually irrecoverable. However, we show that for certain AM-FM signals, the distortion due to aliasing can be mitigated and an unaliased version of the signal can be recovered from its aliased samples. We present a method for determining whether or not a signal has potentially been distorted by aliasing and an algorithm for recovering an unaliased version of the signal. The method is based on the manifestation of aliasing in the time-frequency plane, and estimating the instantaneous phase/frequency of the aliased signal.

关键词： Time-frequency analysis Distortion signal analyzers Amplitude modulation Fourier transforms signal generators Spectrographs Statistical analysis Fermium Frequency modulation

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Seismic signal processing - A new millennium perspective

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GEOPHYSICS 2001年第1期66卷 18-20页

作者： Cary, P Sensor Geophys Ltd Calgary AB T2P 3T7 Canada

In the introduction to his comprehensive SEG textbook, Seismic Data processing, Oz Yilmaz selects deconvolution, common‐midpoint stacking and migration as being the three principal processes that are applied during routine seismic processing. Since Yilmaz’s tome was first published in 1987, a vast number of papers have been published and conference presentations have been given on virtually every aspect of seismic processing. However, I think it is still accurate to say that the same three processes dominate the processing flow of the vast majority of seismic data that is processed now, at the beginning of the twenty‐first century. This is not to say that important progress has not been made in many aspects of seismic processing and that much more sophisticated processing flows are now applied to some datasets. But it is a great tribute to the real pioneers of our profession—the people who advanced our ideas of seismic processing from examining raw analog records in the field to creating crisp computer‐generated images of the subsurface with processes such as deconvolution, stack and migration—that the very same, or similar, algorithms that they invented still form the backbone of everyday processing that is done around the world today. In fact, there are times when it seems that the last great geophysicist was Carl Friedrich Gauss, because the method that he published back in 1823 of minimizing the sum of the squared errors seems to be used almost everywhere one looks in seismic processing, from deconvolution to migration.

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Detection and Classification of Neurodegenerative Diseases by Automatic Speech Analysis 7

Detection and Classification of Neurodegenerative Diseases b...

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IEEE 7th International conference on advanced Technologies, signal and Image processing (ATSIP)

作者： Kehili, Ahlem Bouafif, Lamia Cherif, Adnen Univ Tunis Manar ATSSEE Lab FST Tunis 2092 Tunisia

ISBN: (纸本)9798350351491;9798350351484

This study presents a novel non-invasive method for detecting and classifying neuro-degenerative diseases such as Parkinson's (PD) and Alzheimer's (AD) through automatic speech analysis and artificial intelligence. The analysis of the voice recordings was carried out using different parametric extraction methods based on the MFCC and prosodic coefficients (VOT, Jitter, Shimmer, HNR, ... ) followed by a classification step based on CNN and FC-DNN neural network. These methods made it possible to extract relevant speech parameters and use them for training and classification. The results obtained showed vocal disturbances in mild and preclinical stages of PD and AD such as articulation, prosody and rhythmic abilities. Developed machine learning algorithms were able to detect subjects with PD with 98% accuracy from rapid syllable repetitions and 96% accuracy for subjects with AD from voice parameters.

关键词： Disease detection Parkinson & Alzheimer speech processing Artificial intelligence classification

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