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Congress on image and signal processing, CISP

作者： Ling Gao Shouxin Ren Department of Chemistry Inner Mongolia University for the Nationalities Huhhot China

A novel method named wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for simultaneous determination of Co (ii), Zn (ii) and Cu (ii) by combining wavelet packet denoising with Elman recurrent neural network. Wavelet packet representations of signals provided a local time–frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. Elman recurrent network was applied for non-linear multivariate calibration. In this case, by trials, wavelet function, decomposition level and numbers of hidden nodes for WPTERNN method were selected as Daubechies 2, 3 and 9 respectively. A program PWPTERNN was designed to perform simultaneous determination of Co (ii), Zn (ii) and Cu (ii). The relative standard errors of prediction (RSEP) for all components with WPTERNN, Elman recurrent neural network (ERNN) and partial least squares (PLS), principal component regression (PCR) and Fourier transform based PCR (FTPCR) were 6.7, 14.7, 9.2, 25.6 and 25.2 % respectively. Experimental results demonstrated that the WPTERRN method had the best performance among the five methods.

关键词： Predictive models Wavelet packets Recurrent neural networks Zinc Wavelet domain Wavelet transforms Noise reduction Calibration Least squares methods Fourier transforms

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Application of a Wavelet Packet Transform Based Radial Basis Function neural Network to Analyze Overlapping Spectra

Application of a Wavelet Packet Transform Based Radial Basis...

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Congress on image and signal processing, CISP

作者： Shouxin Ren Ling Gao Department of Chemistry Inner Mongolia University for the Nationalities Huhhot China

This paper suggested a novel method based on wavelet packet transform and radial basis function neural network (WPTRBFN) in simultaneous spectrophotometric determination of Mn (ii), Zn (ii), Co (ii) and Cd (ii) combining wavelet packet thresholding denoising with radial basis neural network. Wavelet packet representations of signals provided a local time–frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. Radial basis function network was applied for overcoming the convergence problem met in back propagation training and facilitating nonlinear calculation. In this case, by optimization, wavelet function, decomposition level, the numbers of hidden nodes and the width σ of RBFN for WPTRBFN method were selected as Symmlet 5, 1, 20 and 1.2 respectively. The relative standard errors of prediction (RSEP) for all components with WPTRBFN, RBFN and PLS were 7.4, 8.9 and 8.1 percent respectively. The proposed method has been successfully applied to analyze overlapping spectra and better than others.

关键词： Wavelet analysis Wavelet packets Wavelet transforms Radial basis function networks Wavelet domain Zinc Noise reduction neural networks Convergence Optimization methods

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A speech-and-speaker identification system: Feature extraction, description, and classification of speech-signal image

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2007年第2期54卷 887-897页

作者： Saeed, Khalid Nammous, Mohammad Kheir Bialystok Tech Univ PL-15351 Bialystok Poland

This paper discusses a speech-and-speaker (SAS) identification system based on spoken Arabic digit recognition. The speech signals of the Arabic digits from zero to ten are processed graphically (the signal is treated as an object image for further processing). The identifying and classifying methods are performed with Burg's estimation model and the algorithm of Toeplitz matrix minimal eigenvalues as the main tools for signal-image description and feature extraction. At the stage of classification, both conventional and neural-network-based methods are used. The success rate of the speaker-identifying system obtained in the presented experiments for individually uttered words is excellent and has reached about 98.8% in some cases. The miss rate of about 1.2% was almost only because of false acceptance (13 miss cases in 1100 tested voices). These results have promisingly led to the design of a security system-for SAS identification. The average overall,success rate was then 97.45% in recognizing one uttered word and identifying its speaker, and 92.5% in recognizing a three-digit password (three individual words), which is really a high success rate because, for compound cases, we should successfully test all the three uttered words consecutively in addition to and after identifying their speaker;hence, the probability of making an error is basically higher. The authors' major contribution to this task involves building a system to recognize both the uttered words and their speaker through an innovative graphical algorithm for feature extraction from the voice signal. This Toeplitz-based algorithm reduces the amount of computations from operations on an n x n matrix that contains n(2) different elements to a matrix (of Toeplitz form) that contains only n elements that are different from each other.

关键词： communication humatronics linear predictive coding processing and recognition speaker recognition speech analysis Toeplitz matrix (TM) eigenvalues understanding speech

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Gaussian sum approach with optimal experiment design for neural network

Gaussian sum approach with optimal experiment design for neu...

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9th IASTED International Conference on signal and image processing, SIP 2007

作者： Hering, Pavel Šimandl, Miroslav Department of Cybernetics Faculty of Applied Sciences University of West Bohemia Univerzitní 8 306 14 Pilsen Czech Republic

ISBN: (纸本)9780889866751

System identification is a discipline for construction of mathematical models of stochastic systems based on measured experimental data. Significant role in the system identification plays a selection of input signal which influences quality of obtained model. Design of optimal input signal for system modeled by multi-layer perceptron network is treated. Because the true system is unknown, the design can be constructed only from the actually obtained model. However, neural networks with the same structure differing only in parameters values are able to approximate various nonlinear mappings therefore it is crucial maximally to use available informations to select suitable input data. Hence a global estimation method allowing to determine conditional probability density functions of network parameters will be used. The Gaussian sumapproach based on approximation of arbitrary probability density function by a sum of normal distributions seems to be suitable to use. This approach is a less computationally demanding alternative to the sequential Monte Carlo methods and gives better results than the commonly used prediction error methods. The properties of the proposed experimental design are demonstrated in a numerical example.

关键词： Probability density function

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Identification of Dynamic Nonlinear Systems using Computational Intelligence Techniques

Identification of Dynamic Nonlinear Systems using Computatio...

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IEEE Symposium on Computational Intelligence in image and signal processing, CiiSP

作者： Claudio Turchetti Francesco Gianfelici Dipartimento di Elettronica Intelligenza Artificiale e Telecomunicazioni (DEIT) Università Politecnica delle Marche Ancona Italy

A novel approach based on computational intelligence techniques for the identification of nonlinear dynamic systems is presented in this paper. The technique encompasses both the properties of the Karhunen-Loeve transform in representing stochastic processes and the approximation capabilities of multi-layer neural networks. Experimental results on nonlinear systems governed by difference equations demonstrate the effectiveness of the proposed approach that is based on a real-time learning algorithm. Exhaustive experimentation on specific case studies was performed and some experimental results were compared with other existing techniques such as the Lee-Schetzen method, least mean square (LMS), recursive least square (RLS) and normalized least mean square (NLMS) algorithms. A better identification-accuracy was also achieved, and a reduction of some orders of magnitude in training-times compared with the well-known Lee-Schetzen method was obtained, thus making the proposed methodology one of the current best practices in this field

关键词： Nonlinear systems Computational intelligence Least squares approximation Karhunen-Loeve transforms stochastic processes Multi-layer neural network Difference equations Real time systems Least squares methods Resonance light scattering

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Prostate Cancer Biomarker Selection through a Novel Combination of Sequential Global Thresholding, Particle Swarm Optimization, and PNN Classification of MS-Spectra

Prostate Cancer Biomarker Selection through a Novel Combinat...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Panagiotis Bougioukos Dionisis Cavouras Antonis Daskalakis Spiros Kostopoulos George Nikiforidis Anastasios Bezerianos Department of Medical Physics School of Medicine University of Patras Rion Greece Medical Signal and Image Processing Laboratory Department of Medical Instrumentation Technology Technological Educational Institute of Athens Athens Greece

Proteomic analysis using mass spectrometry data is a powerful tool for biomarker discovery. However, proteomic data suffers from two crucial problems i/ are inherently very noisy and ii/ the number of features that finally characterize each spectrum is usually very large. In the present study, a well-established framework of data preprocessing steps was developed to deal with the problem of noise, incorporating smoothing, normalization, peak detection, and peak alignment algorithms. In addition, to alleviate the problem of feature dimensionality, a novel iterative peak selection method was developed for choosing peaks (features) from the pre- processed spectra, based on sequential global thresholding followed by particle swarm optimization. These features were fed into a probabilistic neural network algorithm, in order to discriminate healthy from prostate cancer cases and, thus, to determine, through the algorithm's optimal design, biomarkers related to prostate cancer.

关键词： Prostate cancer Biomarkers Particle swarm optimization Iterative algorithms Proteomics Mass spectroscopy Data preprocessing Smoothing methods Iterative methods neural networks

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File encryption software using fingerprint keys based on double random encoding

File encryption software using fingerprint keys based on dou...

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Frontiers in Optics, FiO 2005

作者： Suzuki, H. Tashima, H. Yamaguchi, M. Obi, T. Yachida, M. Ohyama, N. Tokyo Inst. of Technology Japan

ISBN: (纸本)1557527970

We propose an encryption method which uses the key created from fingerprint [1] by using double random phase encoding [2]. In conventional cipher methods, the encryption and decryption keys must match exactly. However, it is difficult to generate such an identical key from fingerprints even if they have almost the same patterns. The proposed method makes it possible to use fingerprints as keys because double random phase encoding is based on pattern matching technique. In this paper, we describe the development of a file encryption software for Windows using the above method.

关键词： Fourier transforms image sensors stochastic processes Fourier optics and signal processing (070.0070) Data processing by optical means (070.4560) image processing (100.0100) Analog optical image processing (100.1160)

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Multiple description coding models/multiple description sampling based multiple classifier systems and its application to automatic target recognition

Multiple description coding models/multiple description samp...

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Conference on Independent Component Analyses, Wavelets, Unsupervised Smart Sensors and neural Networks ii

作者： Asdornwised, W Jitapunkul, S Chulalongkorn Univ Dept Elect Engn Digital Signal Proc Res Lab Bangkok 10330 Thailand

ISBN: (纸本)0819453625

In this paper, we propose a new multiple classifier system (MCS) based on two concatenated stages of multiple description coding models (MDC) and multiple description sampling (MDS). This paper draws on concepts coming from a variety of disciplines that includes classical concatenated coding of error correcting codes, multiple description coding of wavelet based image compression, Adaboost and importance sampling of multiple classifier systems, and antithetic-common varaites of Monte Carlo methods. In our previous work, we proposed and extended several methods in MDC to MCS with inspirations from two frameworks. First, we found that one of our methods is equivalent to one of the variance reduction techniques, called antithetic-common variates, in the Monte Carlo methods (MCM). Having established that Adaboost can be interpreted as important sampling in MCM, and it can directly be interpreted as MDC,(1) we define the term "multiple description sampling (XDS)" for Adaboost. Second, another equivalent relation between one of our methods and transmitting data over heterogeneous network, especially wireless networks, are established. One of the benefits of our approach is that it allows us to formulate a generalized class of signal processing based weak classification algorithms. This will be very applicable for MDC-MDS in high dimensional classification problems, such as image/target recognition. Performance results for automatic target recognition are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our proposed method outperform state-of-the-art multiple classifier systems, such as Adaboost and SVM-ECOC etc.

关键词： Automatic target recognition Systems modeling Monte Carlo methods Synthetic aperture radar Detection and tracking algorithms image classification image compression signal processing Wavelets

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Supervised segmentation of remote-sensing multitemporal images based on the tree-structured Markov random field model

Supervised segmentation of remote-sensing multitemporal imag...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Cicala, L Poggi, G Scarpa, G Univ Naples Federico II DIET I-80125 Naples Italy

ISBN: (纸本)0780387422

We deal with the supervised segmentation of multi-temporal remote-sensing images following a statistical Bayesian approach. To take into account prior information on the class of images, like the correlation between neighboring pixels, as well as the available knowledge about the structure of the current image, we model the image as a tree-structured Markov random field. The data collected at two different dates are jointly processed as a single multi-component image, with the classes defined a priori based on ground truth information and grouped in changed and unchanged macro-classes. Experimental results in terms of classification accuracy prove the effectiveness of the proposed technique with respect to to non-contextual methods, as well as to a disjoint approach. In addition, the classification tree allows for a direct interpretation of the result.

关键词： geophysical signal processing terrain mapping image classification image segmentation Markov processes random processes Bayes methods statistical analysis trees (mathematics) supervised segmentation multitemporal remote sensing image statistical Bayesian process tree structured Markov random field model noncontextual methods image pixel image classification accuracy data collection ground truth information image segmentation Bayes methods geophysical signal processing terrain mapping image classification stochastic Processes Markov chain ground truth Statistical analysis Statistical methods Markov random fields

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Analysis of disturbed images

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BIOINFORMATICS 2004年第9期20卷 1381-1387页

作者： Kauer, G Blöcker, H GBF German Res Ctr Biotechnol Dept Genome Anal D-38124 Braunschweig Germany

Motivation: images in cellular and molecular biology (from microscopy, blots, biochips, etc.) are often disturbed, so that the detection and analysis of the respective relevant geometrical objects may be difficult or error-prone. The disturbances are either caused by the detector, usually a CCD camera, or by the experimental setup. Furthermore, microtechnology experiments often require simultaneous multiple-colour stainings. Therefore, the image analysis of such experiments should be colour-sensitive, and colour shadings should not only be detectable but also quantifiable. Results: Here, we describe a general solution as applied to the analysis of blots and DNA chips as well as to microscopy images of tissues. We decided to use (i) a stochastic filter as used by Wiener for image segmentation as the starting point for object detection, (ii) chaincodes as described by Freeman for object description, (iii) a novel 'rolling disc algorithm' to spot the objects to be analysed and (iv) an HSI instead of an RGB colour model for colour analysis. With this combination we succeeded in performing shape detection and colour-based analysis of disturbed images.

关键词： Algorithms Artifacts Color Colorimetry/methods image Enhancement/methods image Interpretation, Computer-Assisted/methods Models, Statistical signal processing, Computer-Assisted stochastic Processes

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