基于嵌入式多核SoPC平台的说话人识别系统应用研究

针对当前基于DSP、ARM等硬核处理器设计的嵌入式说话人识别系统训练和辨认时间长等缺陷,根据MFCC提取过程的特点与遗传聚类算法中适应度计算的原理,提出一种基于SoPC平台与矢量量化原理的说话人识别系统实现方案。经测试,该实现方案在保证识别率前提下,可有效提高训练与识别速度。     

Fast pattern recognition using a neurocomputer

This paper presents a new pattern recognition system based on moment invariants using a neurocomputer. The new pattern recognition system consists of a CCD video camera, an image processing system named FDM, a monitor, two stand lights, an NEC PC-9801 microcomputer and a RICOH RN-2000 neurocomputer; these two different types of computers can be considered to constitute an artificial brain. Experimental studies to recognize five dynamic patterns of Japanese chestnuts were performed. From the studies, a high speed of both learning and recognition has been achieved compared with the former pattern recognition system based on the software of artificial neural networks developed by us. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

The artificial intelligence approach to pattern recognition—a perspective and an overview

The present paper reviews the techniques for automated extraction of information from signals. The techniques may be classified broadly into two categories—the conventional pattern recognition approach and the artificial intelligence (AI) based approach. The conventional approach comprises two methodologies—statistical and structural. The paper reviews salient issues in the application of conventional techniques for extraction of information. The systems that use the artificial intelligence approach are characterized with respect to three key properties. The basic differences between the approaches and the computational aspects are reviewed. Current trends in the use of the AI approach are indicated. Some key ideas in current literature are reviewed.     

Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways

In this paper, a new artificial neural network model is proposed for visual object recognition, in which the bottom-up, sensory-driven pathway and top-down, expectation-driven pathway are fused in information processing and their corresponding weights are learned based on the fused neuron activities. During the supervised learning process, the target labels are applied to update the bottom-up synaptic weights of the neural network. Meanwhile, the hypotheses generated by the bottom-up pathway produce expectations on sensory inputs through the top-down pathway. The expectations are constrained by the real data from the sensory inputs, which can be used to update the top-down synaptic weights accordingly. To further improve the visual object recognition performance, the multi-scale histograms of oriented gradients (MS-HOG) method is proposed to extract local features of visual objects from images. Extensive experiments on different image datasets demonstrate the efficiency and robustness of the proposed neural network model with features extracted using the MS-HOG method on visual object recognition compared with other state-of-the-art methods.     

“模式识别”课程虚拟教学平台的研究与实现

针对目前研究生"模式识别"课程教学中所呈现的问题,并结合课程自身的特点,分析了构建"模式识别"虚拟教学平台应解决的主要问题,借助计算机技术和网络技术进行平台的构建。此平台主要由课程介绍、成员管理、在线学习、项目开发、在线测试和在线交流等功能模块构成。该虚拟教学平台极大地扩展了教学空间,丰富了教学方法,提高了教学效率,为师生提供了一个便捷、有效的网上教学环境。     

基于多核计算平台和高速缓存感知的Haar小波变换算法

针对目前多核计算平台的普及性以及多核平台的强大计算能力,通过充分利用高速缓存的工作原理以及多线程程序设计的优势,提出了一种在多核平台上高速缓存优化的并行Haar小波计算算法.通过测定算法运行过程中高速缓存缺失率以及算法在不同多核计算平台上的运算性能,反映了该算法极大地降低了缺失率和减少了计算时间.如在数据规模8192条件下缺失率从95%降低到8.37%,计算时间从4.35s减至0.89s.由此证明了该计算方法具有高速且可移植的特性.     

Invariant pattern recognition using contourlets and AdaBoost

In this paper, we propose new methods for palmprint classification and handwritten numeral recognition by using the contourlet features. The contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images and handwritten numeral images. AdaBoost is used as a classifier in the experiments. Experimental results show that the contourlet features are very stable features for invariant palmprint classification and handwritten numeral recognition, and better classification rates are reported when compared with other existing classification methods.     

Task complexity analysis and QoS management for mapping dynamic video-processing tasks on a multi-core platform

This paper addresses efficient mapping and reconfiguration of advanced video applications onto a general purpose multi-core platform. By accurately modeling the resource usage for an application, allocation of processing resources on the platform can be based on the actually needed resources instead of a worst-case approach, thereby improving Quality-of-Service (QoS). Here, we exploit a new and strongly upcoming class of dynamic video applications based on image and content analysis for resource management and control. Such applications are characterized by irregular computing behavior and memory usage. It is shown that with linear models and statistical techniques based on the Markov modeling, a rather good accuracy (94?C97%) for predicting the resource usage can be obtained. This prediction accuracy is so good that it allows resource prediction at runtime, thereby leading to an actively controlled system management.     

A comparison study of pattern recognition algorithms implemented on a microcontroller for use in an electronic tongue for monitoring drinking waters

A portable electronic tongue has been developed using an array of eighteen thick-film electrodes of different materials forming a multi-electrode array. A microcontroller is used to implement the pattern recognition. The classification of drinking waters is carried out by a Microchip PIC18F4550 micro-controller and is based on neural networks algorithms. These algorithm are initially trained with the multi-electrode array on a Personal Computer (PC) using several samples of waters (still, sparkling and tap) to obtain the optimum architecture of the networks. Once it is trained, the computed data are programmed into the microcontroller, which then gives the water classification directly for new unknown water samples. A comparative study between a Fuzzy ARTMAP, a Multi-Layer Feed-Forward network (MLFF) and a Linear Discriminant Analysis (LDA) has been done in order to obtain the best implementation on a microcontroller.     

Unnatural pattern recognition on control charts using correlation analysis techniques

This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts. The system is based on correlation analysis, where a set of optimal matched filters are generated. To illustrate the design methodology and operation of the system, a set of commonly encountered patterns is utilized, such as the trend, the systematic, and the cyclic patterns. A training algorithm that minimizes the probabilities of Type I and Type II errors is presented. To evaluate the system performance, a testing algorithm as well as a set of newly-defined performance measures are introduced. The obtained results have shown the effectiveness of correlation analysis for control chart pattern recognition.     

基于Blackfin-uClinux嵌入式平台的设计与实现

提出了一种基于Blackfin-uClinux的嵌入式平台设计方案,选用ADI公司生产的16/32位高性能ADSP-BF533芯片为该平台的核心控制器.ADSP-BF533是针对嵌入式微控制而设计的数字信号处理芯片,详细介绍了该嵌入式平台部分的硬件电路设计,并且将嵌入式操作系统uClinux的移植到该平台上.该平台既可用于嵌入式系统的开发,也可以实现各种软件算法,有利于应用此类芯片的人员快速理解,为今后开发基于该平台的应用系统提供可靠参考.     

基于ARM和Linux的嵌入式平台的构建

首先介绍了嵌入式系统的概念,及相关硬件平台和软件版本。然后,主要介绍了嵌入式Linux的引导程序U-Boot的移植,以及开源、免费操作系统Linux2.6.32.2的移植。最后,构建了基于Nand Flash存储器的Yaffs2文件系统,利用BusyBox创建根文件系统。基于ARM和嵌入式Linux的嵌入式系统平台搭建基本完成,可以在此平台上添加更多驱动,以便更好地开发应用程序。     

Automatic recognition of vigilance state by using a wavelet-based artificial neural network

In this study, 5-s long sequences of full-spectrum electroencephalogram (EEG) recordings were used for classifying alert versus drowsy states in an arbitrary subject. EEG signals were obtained from 30 healthy subjects and the results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron (MLP), was used for the classification of EEG signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg–Marquardt algorithm was used to discriminate the alertness level of the subject. In order to determine the MLPNN inputs, spectral analysis of EEG signals was performed using the discrete wavelet transform (DWT) technique. The MLPNN was trained, cross-validated, and tested with training, cross-validation, and testing sets, respectively. The correct classification rate was 93.3% alert, 96.6% drowsy, and 90% sleep. The classification results showed that the MLPNN trained with the Levenberg–Marquardt algorithm was effective for discriminating the vigilance state of the subject.     

Induction motor fault detection and diagnosis using a current state space pattern recognition

In the last few decades the continuous monitoring of complex dynamic systems has become an increasingly important issue across diverse engineering areas. This paper presents a pattern recognition based system that uses visual-based efficient invariants features for continuous monitoring of induction motors. The procedures presented here are based on the image identification of the 3-D current state space patterns that allow the identification of distinct fault types and, furthermore, their corresponding severity. This automatic fault detection system deals with time-variant electric currents and is based on the identification of three-phase stator currents specified patterns. Several simulation and experimental results are also presented in order to verify the effectiveness of the proposed methodology.     

Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation

The ability to quickly locate one or more instances of a model in a grey scale image is of importance to industry. The recognition/localization must be fast and accurate. In this paper we present an algorithm which incorporates normalized correlation into a pyramid image representation structure to perform fast recognition and localization. The algorithm employs an estimate of the gradient of the correlation surface to perform a steepest descent search. Test results are given detailing search time by target size, effect of rotation and scale changes on performance, and accuracy of the subpixel localization algorithm used in the algorithm. Finally, results are given for searches on real images with perspective distortion and the addition of Gaussian noise.     

Human home daily living activities recognition based on a LabVIEW implemented hidden Markov model

The recognition of human daily living activities within a house represents an efficient tool to model its power consumption and is also a good indicator for monitoring the health status of the inhabitants. The problematic of activities recognition in smart homes has been extensively addressed in several studies. In this paper, we present an original interactive tool developed under LabVIEW environment with a graphical user interface allowing the modeling of the daily living activities, based on a machine learning Hidden Markov Model. After an overview of the advantage for the consideration of this model in current human activities, we examine how the associated scientific problematic can find an interest and a solution by the integration of machine learning tools. Thus, the application based on a Hidden Markov model approach, is presented and evaluated using two sets of experimental data from literature. Comparing with results obtained by other daily living activities recognition methods, we point out the very satisfactory recognition performance of the Hidden Markov Model and the likelihood of our development associated to a user-friendly graphical interface. This work opens the way to applications dedicated to the supervision of human daily living activities and / or to the management of the electrical consumption within a smart home equipped with non-intrusive sensors.