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。     

基于仿生模式识别的构造型神经网络分类方法*

从BP神经元模型和RBF神经元模型几何意义出发,将仿生模式识别理论引入到神经网络分类中,提出了一种基于仿生模式识别的构造型神经网络分类方法,通过构造不同结构神经元结合的神经网络,实现了对不同类样本在高维空间中形成的不同形状几何体的覆盖。实验证明该算法是非常有效的。     

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     

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.     

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.     

基于以太网和PCIe的多核DSP开发平台

多核数字信号处理器（DSP）在航空、航天等领域的信号处理系统中应用较广泛,在实际工程应用时,由于JTAG接口的性能限制,存在速度慢、不稳定、难操作等问题,导致开发效率低下并严重影响项目进度。建立了一种以千兆以太网和PCIe作为加载和调试接口的多核DSP软硬件开发平台,包括标准化的硬件模块和软件开发环境,具有易重构、易扩展、不再依赖JTAG接口、兼容多个软件操作系统、资源占用低等优点。以多核DSP TMS320C 6678为例,描述了多片DSP组成的信号处理系统开发中的关键技术,包括商用标准（COTS）模块、系统架构、硬件诊断、软件加载、软件调试等。用该平台可以显著降低使用门槛,大幅度提高开发效率。     

“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。     

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.     

An efficient static gesture recognizer embedded system based on ELM pattern recognition algorithm

Millions of people throughout the world describe themselves as being deaf. Some of them suffer from severe hearing loss and consequently use an alternative manner with which to communicate with society by means of either written or visual language. There are several sign languages capable of dealing with such a need. Nonetheless, a communication gap still exists even when using such languages, since only a small fraction of the population is able to use them. Over the last few years, due to the increasing need for universal accessibility when using computational resources, gesture recognition has been widely researched. Thus, in an attempt to reduce this communication gap, our approach proposes a computational solution in order to translate static gesture symbols into text symbols, through computer vision, without the use of hand sensors or gloves. In order to guarantee the highest quality, with emphasis on the reliability of the system and real-time translation, we have developed an approach based on the Extreme Learning Machine (ELM) pattern recognition algorithms fully implemented in hardware, and have assessed it to measure these two metrics. Hardware components were designed in order to perform the best image processing and pattern recognition tasks used within the project. As a case study, and so as to validate the technique, a recognition system for the Brazilian Sign Language (LIBRAS) was implemented. Besides ensuring that this approach could be used for any static hand gesture symbol recognition, our main goal was to guarantee fast, reliable gesture recognition for communication between humans. Experimental results have demonstrated that the system is able to recognize LIBRAS symbols with an accuracy of 97%, a response time of 6.5ms per letter recognition, and using only 43% (about 64,851 logic elements) of the FPGA area.     

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.     

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.     

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.     

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。