基于太赫兹光谱成像技术的智能处理方法分析

太赫兹图谱的智能处理技术可以将样本的特性提取出来,并有目的地加以分类和识别,能够提高太赫兹成像的准确性,是太赫兹成像技术发展的重要标志和方向。介绍了几种智能处理太赫兹成像技术的方法的基本概念和原理,分析了它们的优缺点,并从实际应用场景出发对空间成分识别、化学模式识别、人工神经网络做了具体的分析及分类。     

具有领域独立性的通用模式识别方法的研究

统计方法是当前广泛采用的模式识别方法，但是这种方法的专业性较强，缺乏通用性。针对这种现状文章提出了基于结构模式识别理论的一种通用模式识别方法，该方法使用一系列结构检测子来提取信号特征．具有领域独立性。设计了对比实验，通过同传统的统计模式识别方法的比较验证了通用模式识别方法的有效性．通过两个不同领域的模式识别问题验证了该方法的领域独立性和有效性。     

基于数字图像处理的矿石鉴定方法研究

随着数字图像处理技术的高速发展,计算机图像处理技术在科学研究应用越来越广泛。矿产、资源、环境,等领域,图像处理技术也得到了广泛的应用。文章正是应用数字图像处理技术实现对矿石特征的学习和矿石的自动判别,探索将模式识别方法应用到矿物识别与检测领域,从而实现对目标矿物的鉴定。     

Weaning from mechanical ventilation: a retrospective analysis leading to a multimodal perspective

Practitioners' decision for mechanical aid discontinuation is a challenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Recently, an increasing interest on respiratory pattern variability as an extubation readiness indicator has appeared. Reliable assessment of this variability involves a set of signal processing and pattern recognition techniques. This paper presents a suitability analysis of different methods used for breathing pattern complexity assessment. The contribution of this analysis is threefold: 1) to serve as a review of the state of the art on the so-called weaning problem from a signal processing point of view; 2) to provide insight into the applied processing techniques and how they fit into the problem; 3) to propose additional methods and further processing in order to improve breathing pattern regularity assessment and weaning readiness decision. Results on experimental data show that sample entropy outperforms other complexity assessment methods and that multidimensional classification does improve weaning prediction. However, the obtained performance may be objectionable for real clinical practice, a fact that paves the way for a multimodal signal processing framework, including additional high-quality signals and more reliable statistical methods.     

Signal processing in acoustic imaging

A review of signal processing methods which can be used to improve the effectiveness of systems designed for acoustic imaging and bearing estimation is presented. Topics covered include a) signal processing for increased resolution, b) the processing of stochastic acoustic signals, c) image processing, enhancement, and pattern recognition. The discussion of resolution processing includes lateral resolution improvement by both superresolution techniques and aperture synthesis, and improvement of both range and Doppler resolution. The stochastic signal-processing section addresses adaptive processing, as well as methods of imaging in the case of incoherent, noisy signals.     

光敏蛋白菌紫质分子及其在光信息处理中的应用

有机高分子和生物分子材料近年来在分子、光子器件的研究已引起了广泛重视。文章介绍其中一种新型光敏蛋白分子－紫膜菌紫质的结构和光致色变特性，重点阐述了其在全息干涉测量和实时模式识别等光子器件和系统中的应用。     

MATLAB平台下图像处理实验教学软件的实现

着重介绍了基于MATLAB下图像处理实验教学软件的开发,该软件的基本内容主要包括图像的变换、增强、压缩、分割、恢复等知识,并为学生提供一个良好的能调整各算法参数的交互式GUI学习平台,使学生在短暂的时间时熟悉并消化图像处理基本算法。     

Structural Bayesian Linear Regression for Hidden Markov Models

Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. The regression parameters are usually shared among sets of Gaussians in HMMs where the Gaussian clusters are represented by a tree. This paper realizes a fully Bayesian treatment of linear regression for HMMs considering this regression tree structure by using variational techniques. This paper analytically derives the variational lower bound of the marginalized log-likelihood of the linear regression. By using the variational lower bound as an objective function, we can algorithmically optimize the tree structure and hyper-parameters of the linear regression rather than heuristically tweaking them as tuning parameters. Experiments on large vocabulary continuous speech recognition confirm the generalizability of the proposed approach, especially when the amount of adaptation data is limited.     

脸部序列图像的模型基编码

本文以头部线框模型和图像处理、模式识别、计算机图形学技术为基础，提出了一套完整的会议电视头肩序列图像的分析—综合模型。在低于６４ｋｂｉｔ／ｓ的码率下，得到主观质量很高的图像。     

Feature extraction: A survey

A survey of computer algorithms and philosophies applied to problems of feature extraction and pattern recognition in conjunction with image analysis is presented. The main emphasis is on usable techniques applicable to practical image processing systems. The various methods are discussed under the broad headings of microanalysis and macroanalysis.     

Three-Dimensional Imaging and Processing Using Computational Holographic Imaging

Digital holography is a technique that permits digital capture of holograms and subsequent processing on a digital computer. This paper reviews various applications of this technique. The presented applications cover three-dimensional (3-D) imaging as well as several associated problems. For the case of 3-D imaging, optical and digital methods to reconstruct and visualize the recorded objects are described. In addition, techniques to compress and encrypt 3-D information in the form of digital holograms are presented. Lastly, 3-D pattern recognition applications of digital holography are discussed. The described techniques constitute a comprehensive approach to 3-D imaging and processing.     

汉语小词汇量表语音识别算法的研究和应用

论文研究了汉语小词汇表语音识别算法的基本原理,提出了具有鲁棒性的两级端点检测语音识别技术,在语音信号采集时,根据过零率、短时能量对数据进行提取并压缩,采用了多模板匹配算法识别。硬件采用51内核单片机,用较少的存储空间和计算空间实现语音数据处理,不需要额外的器件。实验用20个字的汉语小词汇量系统进行了测试,识别成功率大于90%,显示该算法比通常采用的算法性能更好。     

Automatic contour definition on left ventriculograms by image evidence and a multiple template-based model

An algorithm which utilizes digital image processing and pattern recognition methods for automated definition of left ventricular (LV) contours is presented. Digital image processing and pattern recognition techniques are applied to digitally acquired radiographic images of the heart to extract the LV contours required for quantitative analysis of cardiac function. Knowledge of the image domain is invoked at each step of the algorithm to orient the data search and thereby the complexity of the solution. A knowledge-based image transformation, directional gradient search, expectations of object versus background location, least-cost path searches by dynamic programming, and a digital representation of possible versus impossible ventricular shape are exploited. The digital representation, composed of a set of characteristic templates, was created using contours obtained by manual tracing. The algorithm was tested by application of three sets of 25 images each. Test set one and two were used as training sets for creation of the model for contour correction. Model-based correction proved to be an effective technique, producing significant reduction of error in the final contours.     

Some Applications of Pattern Recognition to Oil And Gas Exploration

The application of pattern recognition to oil and gas prospection is very recent, and results are still sparse. This paper presents an introduction to pattern recognition, a summary of previous applications in seismic processing, and several new pattern recognition approaches. Topics discussed include impluse recognition, horizon correlation, determination of the boundaries and nature of a reservoir, and two-dimensional Fourier (f-k) filtering to attenuate ground roll. To conclude, we examine the outlook for pattern recognition and propose increased use of the theory of fuzzy sets for pattern recognition applications in oil and gas exploration.     

Spoken language understanding

This article is intended to serve as an introduction to the field of statistical SLU, based on the mainstream statistical modeling approach that shares a similar mathematical framework with many other statistical pattern recognition applications such as speech recognition. In particular, we formulated a number of statistical models for SLU in the literature as extensions to HMMs as segment models, where a multiple-word block (segment) with word dependency is generated from each underlying Markov state corresponding to each individual semantic slot defined from the application domain. In the past, due partly to its nature of symbolic rather than numeric processing, the important field of SLU in human language technology has not been widely exposed to the signal processing research community. However, many key techniques in SLU originated from statistical signal processing. And because SLU is becoming increasingly important, as one major target application area of ASR that has been dear to many signal processing researchers, we contribute this article to provide a natural bridge between ASR and SLU in methodological and mathematical foundation. It is our hope that when the mathematical basis of SLU becomes well known through this introductory article, more powerful techniques established by signal processing researchers may further advance SLU to form a solid application area, making speech technology a successful component for intelligent human-machine communication.     

Memory Access Optimized VLSI for 5000-Word Continuous Speech Recognition

We have developed a memory access reduced VLSI chip for 5,000 word speaker-independent continuous speech recognition. This chip employs a context-dependent HMM (hidden Markov model) based speech recognition algorithm, and contains parallel and pipelined hardware units for emission probability computation and Viterbi beam search. To maximize the performance, we adopted several memory access reduction techniques such as sub-vector clustering and multi-block processing for the emission probability computation. We also employed a custom DRAM controller for efficient access of consecutive data. Moreover, we analyzed the access pattern of data to minimize the internal SRAM size while maintaining high performance. The experimental results show that the implemented system performs speech recognition 2.4 and 1.8 times faster than real-time utilizing 32-bit DDR SDRAM and SDR SDRAM, respectively.     

Intelligent techniques for electronic component and systemalignment

The application of intelligent systems is investigated for the automatic tuning of microwave waveguide filters and the alignment of quadrature-amplitude-modulated (QAM) digital microwave radio equipment. The intelligent techniques include conventional rule-based expert systems as well as a machine-learning system which combines techniques from pattern recognition and adaptive signal processing. These approaches are shown to significantly reduce the time and skill required for manual filter alignment and, in addition, they can readily diagnose both type and magnitude of selected faults in the radio communication equipment. This is accomplished via a set of feature data whose parameters are monitored under fault or misalignment conditions, and the deviations from a good pattern are used to adjust the device under test. The selection of appropriate features and alignment of six-cavity 11 GHz waveguide filters and 16-state QAM digital microwave radio-relay-system equipments are discussed     

Knowledge-Based Aerial Image Understanding Systems and Expert Systems for Image Processing

This paper discusses roles of artificial intelligence in the automatic interpretation of remotely sensed imagery. We first discuss several image understanding systems for analyzing complex aerial photographs. The discussion is mainly concerned with knowledge representation and control structure in the aerial image understanding systems: a blackboard model for integrating diverse object detection modules, a symbolic model representation for three-dimensional object recognition, and integration of bottom-up and top-down analyses. Then, a model of expert systems for image processing is introduced that discusses which and what combinations of image processing operators are effective to analyze an image. Various information about image processing techniques is used to find efficient and reliable image analysis processes. In general, two kinds of knowledge, that is, knowledge about objects and about analysis tools (i. e., image processing techniques) are required to realize versatile photointerpretation systems.     

A microprocessor-based data acquisition and processing system for studying the kinematics of labor

The design, development and preliminary evaluation of a microprocessor-based data acquisition and processing system which continuously and simultaneously acquires signals of fetal heart rate, intrauterine pressure, uterine cervical dilatation and variables for calculation of spatial position or pathway of fetal descent are presented. The project consists of four phases: 1) Design and fabrication of the data acquisition system; 2) Development of signal processing and pattern recognition techniques using a PDP-11/45 based signal processing system; 3) Transfer of algorithms produced in phase 2) to the microprocessor-based data acquisition system; and 4) Long term physiological study of acquired data directed toward obtaining more conclusive diagnostic or clinical results. This paper reports the development of the first two phases.