Genomic signal processing: diagnosis and therapy

Genomics entails the study of large sets of genes with the goal of understanding collective gene function, rather than just that of individual genes. Genomic signal processing (GSP) is the engineering discipline that studies the processing of genomic signals. Since regulatory decisions within the cell utilize numerous inputs, analytical tools are necessary to model the multivariate influences on decision-making produced by complex genetic networks. Genomic signals must be processed to characterize their regulatory effects and their relationship to changes at both the genotypic and phenotypic levels. The aim of GSP is to integrate the theory and methods of signal processing with the global understanding of genomics, placing special emphasis on genomic regulation. GSP encompasses various methodologies related to signal profiles: detection, prediction, classification, control, and statistical and dynamical modeling of gene networks. In this article, we give an overview of GSP and describe how pattern recognition and network analysis are central to diagnosis and therapy for genetic diseases.     

Nanotechnology for genomic signal processing in cancer research - A focus on the genomic signal processing hardware design of the nanotools for cancer ressearch

Nanotechnology has recently been applied to study dynamic cellular processes, such as cell cycles and cell migration, providing rich spatial and temporal phenotype information. Tremendous opportunities and challenges exist in combining nanotechnology with signal processing techniques to develop faster, smaller, yet more accurate and sensitive biomedical devices for cancer genomics and proteomics to obtain a better understanding of the cellular and molecular mechanisms of different kinds of cancers. In this article, we present the applications of new nanoscale solutions that include nanoparticles and sensing devices for genomic signal processing (GSP) in cancer research     

Fads and fallacies in the name of small-sample microarray classification - A highlight of misunderstanding and erroneous usage in the applications of genomic signal processing

The purpose of this article is to highlight those topics that are believed to have displayed a record of misunderstanding and erroneous usage in the applications of genomic signal processing (GSP). We focus on the related subjects of feature selection, classifier design, and error estimation, which together form a microarray classification pipeline, discussing some of the most common "fads" and "fallacies" regarding classification methods that are routinely applied in the analysis of small-sample microarray data     

Signal Processing in Genomics [From The Guest Editors]

Genomic signal processing has been defined as the analysis, processing, and use of genomic signals for gaining biological knowledge and the translation of that knowledge into systems-based applications.     

基于Wigner分布的脑电信号处理

临床实践表明,脑电信号中包含有大量的生理与疾病信息。对脑电信号进行行之有效的处理,不仅可以为医生提供临床诊断信息,而且可以为某些脑疾病的治疗提供有效的治疗手段。目前,随着信号处理技术的发展,在脑电信号处理中已应用了多种信号分析方法来提取脑电信号中所包含的信息,但大多数还是停留在理论研究阶段。本文在研制虚拟式脑电图仪的过程中,考虑到Wigner分布在各种时频分布中具有最简单的形式和良好的性质,从临床应用及医学研究相结合的角度出发,应用Wigner分布对脑电信号进行时频分析以提取脑电信号中的特征信息。对实测脑电数据的分析表明,应用此方法可获得较好的分析效果。     

扩展信息最大化盲源分离算法的研究

盲源分离技术是信号处理和神经网络领域近年来的一个热点研究课题,由于其能够从观测的混合信号中恢复出源信号,而对源信号和混合系统的先验知识要求很少,因此在语音信号处理、无线信号处理、生物医学信号处理、地震信号处理,以及图像增强等方面都具有非常重要的理论意义和实用价值。信息最大化盲源分离算法能够有效地分离语音信号的瞬时混合,但是不能分离超高斯信号(如语音信号)和亚高斯信号(如正弦信号)的混合。基于此,本文讨论了扩展信启、最大化盲源分离算法,通过仿真表明,该算法可以有效的对各种源信号的线性即时混合进行分离,实验证明了该算法的有效性。     

DNA sequence compression - Based on the normalized maximum likelihood model

Genomic data provide challenging problems that have been studied in a number of fields such as statistics, signal processing, information theory, and computer science. This article shows that the methodologies and tools that have been recently developed in these fields for modeling signals and processes appear to be most promising for genomic research     

图信号处理在高光谱图像处理领域的典型应用

高光谱图像(HSI)具有纳米级的光谱分辨能力且同时对地物目标的光谱维和空间维进行联合成像的优势,能够精细化感知场景目标的本征判别属性,在遥感探测、医疗诊断和国防安全等具有重要应用价值,是高精度遥感探测的科技制高点之一。不同于传统1维时间信号、2维图像信号,高光谱图像具有多阶、高维的信号属性。为解决传统信号处理方法在高光谱图像处理领域中的不足,图信号处理(GSP)理论与方法被逐渐引入高光谱图像处理与解译等任务中。该文以短综述的形式,介绍了图信号处理在高光谱图像处理领域的理论发展并列举了在高光谱特征提取、图像重构和解译分类3个主要方面的典型应用。最后,进一步探讨了该方向未来发展所面临的挑战和相应解决办法。     

Parameter estimation in speech: A lesson in unorthodoxy

The accurate estimation of both discrete and continuous parameters of speech signals has played a central role in speech processing and research. Interestingly, the most successful estimation procedures have often relied on intuition based on knowledge of speech signals and their production in the human vocal apparatus rather than routine applications of well-established theoretical methods. This experience in speech processing should serve as a reminder that a thorough understanding of the signal is paramount for successful analyses of real-world processes.     

Design of Voltage Overscaled Low-Power Trellis Decoders in Presence of Process Variations

In hardware implementations of many signal processing functions, timing errors on different circuit signals may have largely different importance with respect to the overall signal processing performance. This motivates us to apply the concept of unequal error tolerance to enable the use of voltage overscaling at minimal signal processing performance degradation. Realization of unequal error tolerance involves two main issues, including how to quantify the importance of each circuit signal and how to incorporate the importance quantification into signal processing circuit design. We developed techniques to tackle these two issues and applied them to two types of trellis decoders including Viterbi decoder for convolutional code decoding and Max-Log-Maximum A Posteriori (MAP) decoder for Turbo code decoding. Simulation results demonstrated promising energy saving potentials of the proposed design solution on both trellis decoding computation and memory storage at small decoding performance degradation.      

混沌相位调制雷达信号的模糊函数

本文研究了一类混沌编码信号,利用混沌信号进行雷达信号的相位编码,并且计算了这类编码信号的模糊函数。理论和仿真结果表明,混沌编码信号的模糊函数十分逼近理想的图钉型,信号具有很高的时延和多普勒分辨力,揭示了混沌用于雷达信号处理的可能性和优越性。     

Analysis of the Electromagnetic Signals of the Human Brain: Milestones, Obstacles, and Goals

Measuring the functioning of the human brain is one of the most formidable scientific/engineering endeavors ever undertaken. It is difficult to extract information about any particular processing function from brain electromagnetic signals (BEMS) since, at any instant, only a small fraction of the brain's hundreds of simultaneously active major systems might be performing processing related to the function being studied. With recent developments, a new era of research is dawning based on an interdisciplinary approach in which advanced signal processing methods are focused on increasingly more specific neuroanatomical, neurophysiological, and neuropsychological research questions and clinical applications. This brief review highlights the major accomplishments of the last several decades in human BEMS analysis and discusses obstacles to progress. Five main topics are addressed: 1) the historical problem of developing a computerized expert clinical electroencephalogram (EEG) system; 2) advances in signal processing methods, including primary analysis, feature extraction, and statistical hypothesis testing and pattern classification; 3) integrated computing systems for BEMS analysis; 4) biophysical, basic science, practical and conceptual obstacles to progress; and 5) the long-term goal of developing a device for measuring the functional integrity of major neural systems, and the related topic of neurocybernetics. Cutting-edge issues discussed include measurement and modeling of nonstationary event-related signals, characterization of spatial processes, single-trial signal detection, location of the sources of scalp-recorded field distributions, and studies of the functional significance of BEMS.     

Signal processing issues in diffraction and holographic 3DTV

Image capture and image display will most likely be decoupled in future 3DTV systems. Due to the need to convert abstract representations of 3D images to display driver signals, and to explicitly consider optical diffraction and propagation effects, it is expected that signal processing issues will be of fundamental importance in 3DTV systems. Since diffraction between two parallel planes can be represented as a 2D linear shift-invariant system, various signal processing techniques naturally play an important role. Diffraction between tilted planes can also be modeled as a relatively simple system, leading to efficient discrete computations. Two fundamental problems are digital computation of the optical field arising from a 3D object, and finding the driver signals for a given optical display device which will then generate a desired optical field in space. The discretization of optical signals leads to several interesting issues; for example, it is possible to violate the Nyquist rate while sampling, but still achieve full reconstruction. The fractional Fourier transform is another signal processing tool which finds applications in optical wave propagation.     

Noncoherent ultra-wideband systems

The need for low-complexity devices with low-power consumption motivates the application of suboptimal noncoherent ultra-wideband (UWB) receivers. This article provides an overview of the state of the art of recent research activities in this field. It introduces energy detection and autocorrelation receiver front ends with a focus on architectures that perform the initial signal processing tasks in the analog domain, such that the receiver does not need to sample the UWB received signals at Nyquist rate. Common signaling and multiple access schemes are reviewed for both front ends. An elaborate section illustrates various performance tradeoffs to highlight preferred system choices. Practical issues are discussed, including, for low-data-rate schemes, the allowed power allocation per pulse according to the regulator?s ruling and the estimated power consumption of a receiver chip. A large part is devoted to signal processing steps needed in a digital receiver. It starts with synchronization and time-of-arrival estimation schemes, introduces studies about the narrowband interference problem, and describes solutions for high-data-rate and multiple access communications. Drastic advantages concerning complexity and robustness justify the application of noncoherent UWB systems, particularly for low-data-rate systems.     

A general framework for frequency domain multi-channel signalprocessing

The authors provide a general framework for performing processing of stationary multichannel (MC) signals that is linear shift-invariant within channel and shift varying across channels. Emphasis is given to the restoration of degraded signals. It is shown that, by utilizing the special structure of semiblock circulant and block diagonal matrices, MC signal processing can be easily carried out in the frequency domain. The generalization of many frequency-domain single-channel (SC) signal processing techniques to the MC case is presented. It is shown that in MC signal processing each frequency component of a signal and system is presented, respectively, by a small vector and a matrix (of size equal to the number of channels), while in SC signal processing each frequency component in both cases is a scalar.     

相关熵与循环相关熵信号处理研究进展

在无线电监测和目标定位等应用中,接收信号经常会受到脉冲噪声和同频带干扰等复杂电磁环境的影响,传统的基于2阶统计量的信号处理方法往往不能正常工作,基于分数低阶统计量的信号处理方法也由于对信号噪声统计先验知识的依赖性而遇到困难。近年来提出并受到信号处理领域普遍关注的相关熵和循环相关熵信号处理理论与方法,是解决复杂电磁环境下信号分析处理、参数估计、目标定位和其他应用问题的有效技术手段,有力促进了非高斯、非平稳信号处理理论方法和应用的发展。该文系统性地综述了相关熵和循环相关熵信号处理的基本理论和基本方法,包括相关熵与循环相关熵的起源背景、定义概念、性质特点,以及所包含的数学物理意义。该文还介绍了相关熵与循环相关熵信号处理在多个领域的应用问题,希望对非高斯、非平稳统计信号处理的研究和应用有所裨益。     

从稀疏到结构化稀疏:贝叶斯方法

稀疏分解算法是稀疏表达理论和压缩感知理论中的核心问题,也是当前信号处理领域的一个热门话题。近年来,研究人员发现除了稀疏以外,如果引入稀疏系数之间的相关性先验信息,可以大大提高稀疏分解算法的精度,这种方法称为“结构化稀疏分解算法”。本文归纳和总结了从稀疏到结构化稀疏的信号模型,并且介绍了两种不同的贝叶斯稀疏（或者结构化稀疏）算法,以及从稀疏到结构化稀疏贝叶斯稀疏分解算法的扩展。同时,本文还介绍了结构化稀疏分解算法在医学信号处理和语音信号处理中的应用。      

着眼非单频破解通信信息类理论课教学难题

基于通信信息类专业特点,有针对性地对理论课教学难点的讲解问题进行阐述研究。从信号的分解及其可能的物理意义出发,紧贴专业实际,深入阐述说明了信号的非单频特征,继而在此基础上对多普勒扩展、傅里叶变换分析、邻信道干扰等几个具有典型代表意义的知识点的讲解问题进行了分析,对于有效地解决信号与系统、信号处理、通信原理、信号检测、电子测量、电磁理论、移动通信、泛函分析等课程和密码学、光学等学科中的许多难点的讲解问题有一定的参考意义。     

生物医学工程专业"信号处理原理"课程教学分析

“信号处理原理”是由“信号与系统”和“数字信号处理”整合而成的。根据生物医学工程专业的特点,课程整合的内容突出信号分析与应用。在课程教学过程中,注重知识联系,采用多种教学方式以及强化课程习题训练。“信号处理原理”课程的突出问题是内容跨度大。建议保留和加强课程内容的层次性和逻辑顺序关系,注重有重点、分阶段、分层次的教学过程,并要求合理使用现代教学手段。     

基于波束域预处理的阵列盲信号分离方法

相控阵雷达采用多个阵元进行信号的接收处理,其多通道信号处理可以采用盲信号处理的方法进行目标源信号分离.由于阵元间距导致信号在阵元间产生相位延迟,在进行盲分离的时候一般只能采用卷积混合模型,盲分离过程是较为复杂的多通道反卷积问题.文中对阵列接收信号进行波束域预处理,通过确定的空间波束相位补偿,将阵元域的多时延混合信号变换为瞬时混合信号,从而采用简单的实数分离算法即可完成信号分离,分离信号可用于后置处理.所提方法简单有效,相比常规阵列信号处理方法,可直接适用于宽带信号.仿真实验验证了其有效性.