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pattern recognition system Using Evolvable Hardware

systems and Computers in Japan 2000年第4期31卷 1-11页

作者： Iwata, Masaya Kajitani, Isamu Murakawa, Masahiro Hirao, Yuji Iba, Hitoshi Higuchi, Tetsuya Electrotechnical Laboratory Ibaraki 305-8568 Japan Doctoral Program in Engineering University of Tsukuba Ibaraki 305-8573 Japan Graduate School of Engineering University of Tokyo Tokyo 113-8656 Japan Graduate School of Engineering University of Tokushima Tokushima 770-8506 Japan

We have developed a high-speed pattern recognition system using evolvable hardware (EHW). EHW is hardware that can change its own structure by genetic learning for maximum adaptation to the environment. The purpose of the system is to show that recognition devices based on EHW are possible and that they have the same robustness to noise as devices based on an artificial neural network (ANN). The advantage of EHW compared with ANN is the high processing speed and the readability of the learned result. In this paper, we describe the learning algorithm, the architecture, and the experiment involving a pattern recognition system that uses EHW. We also compare the processing speed of the pattern recognition system with two types of ANN dedicated hardware and discuss the performance of the system. © 2000 Scripta Technica.

关键词： Genetic algorithms evolvable hardware adaptive hardware pattern recognition system intelligibility evolvable hardware Genetic Algorithm (GA)

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A pattern recognition system for JPEG steganography detection

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OPTICS COMMUNICATIONS 2012年第21-22期285卷 4252-4261页

作者： Chen, C. L. Philip Chen, Mei-Ching Agaian, Sos Zhou, Yicong Roy, Anuradha Rodriguez, Benjamin M. Univ Macau Dept Comp & Informat Sci Macau Peoples R China Montgomery Coll Rockville MD 20850 USA Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA Univ Texas San Antonio Dept Management Sci & Stat San Antonio TX 78249 USA Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

This paper builds up a pattern recognition system to detect anomalies in JPEG images, especially steganographic content. The system consists of feature generation, feature ranking and selection, feature extraction, and pattern classification. These processes tend to capture image characteristics, reduce the problem dimensionality, eliminate the noise inferences between features, and further improve classification accuracies on clean and steganography JPEG images. Based on the discussion and analysis of six popular JPEG steganography methods, the entire recognition system results in higher classification accuracies between clean and steganography classes compared to merely using individual feature subset for JPEG steganography detection. The strength of feature combination and preprocessing has been integrated even when a small amount of information is embedded. The work demonstrated in this paper is extensible and can be improved by integrating various new and current techniques. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Feature generation Feature ranking and selection pattern classification pattern recognition system Steganography detection

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Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a pattern recognition system

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2016年第2期11卷 416-424页

作者： Cira, Ferhat Arkan, Muslum Gumus, Bilal Dicle Univ Dept Elect & Elect Engn Diyarbakir Turkey Inonu Univ Dept Elect & Elect Engn Malatya Turkey

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

关键词： Condition monitoring Digital signal processing Fault detection Fault severity classification pattern recognition system Permanent magnet synchronous motors

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Grey wolf optimization (GWO) with the convolution neural network (CNN)-based pattern recognition system

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IMAGING SCIENCE JOURNAL 2022年第4期70卷 238-252页

作者： Jamshed, Aatif Mallick, Bhawna Bharti, Rajendra Kumar Veer Madho Singh Bhandari Uttarakhand Tech Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Meerut Inst Engn & Technol Meerut Uttar Pradesh India Bipin Tripathi Kumaon Inst Technol Dwarahat Uttarakhand India 1241-A Tower 1BLandcraft Golflinks Ghaziabad 201002 Uttar Pradesh India

The dynamic video frame dataset's automated feature analysis addresses the complexity of intensity mapping with normal and abnormal classes. Iterative modelling is needed to learn the component of a video frame in several patterns for various video frame data types for threshold-based data clustering and feature analysis. GWO optimises the Convoluted pattern of Wavelet Transform (CPWT) feature vectors employed in this paper's CNN feature analysis technique. A median filter reduces noise and smooths the video frame before normalising it. Edge information represents the video frame's bright spot boundary. Neural network based video frame classification clusters pixels using feature recurrent learning with minimal dataset training. The filtered video frame's features were evaluated using complex wavelet transformation feature extraction algorithms. These features demonstrate video frame spatial and textural classifications. CNN classifiers help analyse video frame instances and classify action labels. Categorization improves with the fewest training datasets. This strategy may be beneficial if compared to optimal practises.

关键词： Action recognition pattern recognition system convoluted pattern of wavelet transform (CPWT) grey wolf optimization (GWO) convolution neural network (CNN) Deep bidirectional long short-term memory (DBiLSTM) Deep Convolution Symmetric Neural Network with PCANET Weaklysupervisedaction localization (WSAL) UCF sports dataset

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Neural Network Based Iris pattern recognition system Using Discrete Walsh Hadamard Transform Features

Neural Network Based Iris Pattern Recognition System Using D...

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2nd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Khedkar, Mrunal M. Ladhake, S. A. Sipna Coll Engg & Tech Amravati 444605 Maharashtra India

ISBN: (纸本)9781479924325;9781479926596

An iris pattern recognition system is developed for CASIA database using discrete 2D Walsh Hadamard (WH) transform as a tool for feature extraction. Experimental prototype pattern recognition (PR) system is designed in which iris images of ten different persons are given as input to the system. After localizing region of interest (ROI), features are extracted with respect to image statistics, texture and 2D WH transform domain. Based upon these features, an optimal feature vector comprising of only 23 features is selected and it is given as input to neural network based pattern recognition (PR) system. Two different neural network configurations including Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and a different learning machine, known as Support Vector Machine (SVM) are investigated for their suitability as a PR system. It is observed that MLP neural network based PR system comprising of only one hidden layer containing 20 processing elements (PEs) outperforms others in respect of performance measures on cross-validation (CV) dataset.

关键词： Multi layer Perceptron pattern recognition system Radial Basis Function Support vector machine Walsh Hadamard Transform

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Robust human Iris pattern recognition system Using Neural Network Approach

Robust human Iris Pattern Recognition System Using Neural Ne...

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International Conference on Information Communication and Embedded systems (ICICES)

作者： Khedkar, Mrunal M. Ladhake, S. A. Sipna Coll Engg & Tech Amravati 444605 Maharashtra India

ISBN: (纸本)9781467357869;9781467357876

This paper proposes a prototype model of robust iris pattern recognition (PR) system for classification of ten different persons using neural network. Feature extraction algorithms are developed and an optimal feature vector comprising of features in relation to image statistics, texture and 2-D transform domain is formed. It is observed that 2D Walsh Hadamard Transform (WHT) entails the best performance as compared to other image transforms. Different neural network configurations, such as, Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) are implemented after systematically varying the concerned parameters of the respective networks and MLP with single hidden layer is seen to outperform all others with respect to performance on cross validation dataset derived from CASIA iris image database. Further, the sensitivity analysis is carried out over this network in order to reduce time and space complexity of the network. To make this network more robust;controlled Gaussian and Uniform noise are injected in all input features and it is noticed that for both types of noise, the proposed PR system can sustain the variance up to 0.1.

关键词： pattern recognition system Multi layer Perceptron Radial Basis Function Support vector machine Walsh Hadamard Transform sensitivity analysis Gaussian Noise Uniform Noise

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Special issue on real-time image and video processing for pattern recognition systems and applications

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2016年第2期11卷 247-249页

作者： Wachs, Juan Mejail, Marta Fishbain, Barak Alvarez, Luis Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Purdue Univ W Lafayette IN 47907 USA

The overarching goal of the pattern recognition community consists of presenting hypotheses to describe classes of objects using mathematical models, processing the information to eliminate the presence of noise, and selecting the model that best explains the given observations; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. Image segmentation is implemented on a low-power embedded GPU, which delivers the necessary computation power for on-board execution in mobile robots. The main feature of the method discussed in the paper is high quality compressed images by using multi-scale/multi-orientation approach.

关键词： real image pattern recognition system Videotapes

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Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications

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JOURNAL OF BIOPHOTONICS 2013年第4期6卷 371-381页

作者： Dingari, Narahara Chari Barman, Ishan Saha, Anushree McGee, Sasha Galindo, Luis H. Liu, Wendy Plecha, Donna Klein, Nina Dasari, Ramachandra Rao Fitzmaurice, Maryann MIT Cambridge MA 02139 USA Case Western Reserve Univ Cleveland OH 44106 USA Univ Hosp Case Med Ctr Cleveland OH 44106 USA

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. Here, we develop and compare different approaches for developing Raman classification algorithms to diagnose invasive and in situ breast cancer, fibrocystic change and fibroadenoma that can be associated with microcalcifications. In this study, Raman spectra were acquired from tissue cores obtained from fresh breast biopsies and analyzed using a constituent-based breast model. Diagnostic algorithms based on the breast model fit coefficients were devised using logistic regression, C4.5 decision tree classification, k-nearest neighbor (k -NN) and support vector machine (SVM) analysis, and subjected to leave-one-out cross validation. The best performing algorithm was based on SVM analysis (with radial basis function), which yielded a positive predictive value of 100% and negative predictive value of 96% for cancer diagnosis. Importantly, these results demonstrate that Raman spectroscopy provides adequate diagnostic information for lesion discrimination even in the presence of microcalcifications, which to the best of our knowledge has not been previously reported. ((c) 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

关键词： Raman spectroscopy breast cancer stereotactic biopsy pattern recognition system decision trees support vector machines

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Linear Subspace Learning-Based Dimensionality Reduction

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IEEE SIGNAL PROCESSING MAGAZINE 2011年第2期28卷 16-26页

作者： Jiang, Xudong ASTAR Inst Infocomm Res Biometr Lab Singapore Singapore

The ultimate goal of pattern recognition is to discriminate the class membership of the observed novel objects with the minimum misclassification rate. An observed object is often represented by a high-dimensional real-valued vector after some preprocessing while its class membership can be represented by a much lower dimensional binary vector. Thus, in the discriminating process, a pattern recognition system intrinsically reduces the dimensionality of the input data into the number of classes.

关键词： Subspace pattern recognition system Binary vectors incoming data dimensionality reduction pattern recognition Misclassification

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Effects of Interelectrode Distance on the Robustness of Myoelectric pattern recognition systems

Effects of Interelectrode Distance on the Robustness of Myoe...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Aaron J. Young Levi J. Hargrove Department of Biomedical Engineering at Northwestern University Department of Physical Medicine and Rehabilitation at Northwestern University

ISBN: (纸本)9781424441211

Myoelectric pattern recognition control can potentially provide upper limb amputees with intuitive control of multiple prosthetic functions. However, the lack of robustness of myoelectric pattern recognition algorithms is a barrier for clinical implementation. One issue that can contribute to poor system performance is electrode shift, which is a change in the location of the electrodes with respect to the underlying muscles that occurs during donning and doffing and daily use. We investigated the effects of interelectrode distance and feature choice on system performance in the presence of electrode shift. Increasing the interelectrode distance from 2 cm to 4 cm significantly (p<0.01) improved classification accuracy in the presence of electrode shifts of up to 2 cm. In a controllability test, increasing the interelectrode distance from 2 cm to 4 cm improved the user's ability to control a virtual prosthesis in the presence of electrode shift. Use of an autoregressive feature set significantly (p<0.01) reduced sensitivity to electrode shift when compared to use of a traditional time-domain feature set.

关键词： system performance interelectrode Electrodes pattern recognition system DOFFING Curium Centimetre Shift Robustness

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