Machine learning for multimodality genomic signal processing

This paper discusses how machine learning can be applied to genomic signal processing, particularly via fusion of multiple biological or algorithmic modalities, to improve prediction performance.     

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     

A signal processing application in genomic research: protein secondary structure prediction

The digital nature of genomic information makes it suitable for the application of signal processing techniques to better analyze and understand the characteristics of DNA, proteins, and their interaction. Prediction of genes, protein structure, and protein function greatly utilize pattern recognition techniques, in which hidden Markov models, neural networks, and support vector machines play a central role. Signal processing offers a variety of methods from pattern recognition and network analysis for the diagnosis and therapy of genetic diseases. In this paper, we focus on protein secondary structure prediction and discuss the problems in single sequence setting.     

数字信号处理研究

简单介绍了数字信号处理的应用领域,发展历史,并给出了数字信号处理中几种常用的基本运算的过程和算法以及数字滤波器和离散时间系统的相关知识.     

云计算平台下的语音信号处理

针对云计算平台下的语音信号处理模型进行研究。传统SVM语音信号处理识别模型是在单台计算机中完成所有数据的处理和运算。云计算环境的Hadoop平台下使用SVM对语音信号处理,能够发挥Map Reduce并行计算优势,通过Map和Reduce操作将所需要的数据处理和运算任务分配到多个计算机中同时进行。使用中科院自动化研究所建立的CASIA汉语情感数据库中的语音信号数据作为实验数据。实验结果表明,使用云计算平台下的语音识别模型针对研究的几种情感的识别率基本在70%以上,识别率可以满足要求。使用云计算平台处理这种数据比较庞大的计算任务时,相比传统单台计算机平台,效率较高,优势比较明显。     

Companding in signal processing

The authors consider the use in signal processing of noise reduction techniques such as syllabic companding (compressing and expanding), developed for transmission and storage. Applications discussed include increasing selectivity in single sinusoid detection. Whether or not companding will be useful in a given filtering application depends on the type and response of the filter and the type of signals present at the input.<>     

Signal processing techniques in genomic engineering

Now that the human genome has been sequenced, the measurement, processing, and analysis of specific genomic information in real time are gaining considerable interest because of their importance to better the understanding of the inherent genomic function, the early diagnosis of disease, and the discovery of new drugs. Traditional methods to process and analyze deoxyribonucleic acid (DNA) or ribonucleic acid data, based on the statistical or Fourier theories, are not robust enough and are time-consuming, and thus not well suited for future routine and rapid medical applications, particularly for emergency cases. In this paper, we present an overview of some recent applications of signal processing techniques for DNA structure prediction, detection, feature extraction, and classification of differentially expressed genes. Our emphasis is placed on the application of wavelet transform in DNA sequence analysis and on cellular neural networks in microarray image analysis, which can have a potentially large effect on the real-time realization of DNA analysis. Finally, some interesting areas for possible future research are summarized, which include a biomodel-based signal processing technique for genomic feature extraction and hybrid multidimensional approaches to process the dynamic genomic information in real time.     

Aperture-domain signal processing

An elementary reconsideration of first principles in aperture-domain processing of wave phenomena for reception (and, by reciprocity, for transmission) can yield revealing, and in some cases novel, insights into what can or cannot be achieved. The subjects covered here include direction-selective reception, superdirectivity, direction finding, focused near-field reception, circular arrays and circular modes in such arrays, and the new concepts of arrays composed of `random symmetrical pairs', and of real gain in omnidirectional receiving antennas. The ideas are basic to all wave directional analysis and imagining applications, be they electromagnetic or acoustic, in radar or sonar, communications, navigation, surveillance or medical imaging     

Baseband signal processing

The baseband signal processing of the ALTAIR wireless in-building network (WIN) is described. The discussion covers the 19-GHz oscillator, burst processing, packet detection, symbol clock synchronization and gain and offset correction. The algorithms described are carried out in a single ASIC composed of 60000 active gates. The implemented procedures allow for parallel processing, which significantly reduces the computation time and therefore leads to preserving high bandwidth efficiency. The learning processes that acquire information about packet parameters and the adjustment operations in the receivers are executed in 3 μs     

Quantum signal processing

In this article we present a signal processing framework that we refer to as quantum signal processing (QSP) (Eldar 2001) that is aimed at developing new or modifying existing signal processing algorithms by borrowing from the principles of quantum mechanics and some of its interesting axioms and constraints. However, in contrast to such fields as quantum computing and quantum information theory, it does not inherently depend on the physics associated with quantum mechanics. Consequently, in developing the QSP framework we are free to impose quantum mechanical constraints that we find useful and to avoid those that are not. This framework provides a unifying conceptual structure for a variety of traditional processing techniques and a precise mathematical setting for developing generalizations and extensions of algorithms, leading to a potentially useful paradigm for signal processing with applications in areas including frame theory, quantization and sampling methods, detection, parameter estimation, covariance shaping, and multiuser wireless communication systems. We present a general overview of the key elements in quantum physics that provide the basis for the QSP framework and an indication of the key results that have so far been developed within this framework. In the remainder of the article, we elaborate on the various elements in this figure.     

Electrical signal processing

This text presents a new approach to the processing of complicated signals. The method proposed provides the possibility of decomposing a complicated fluctuating signal into a deterministic part and a stochastic part. A procedure for finding a differential equation related to the deterministic part is presented     

Seismic signal processing

Seismic prospecting for oil and gas has undergone a digital revolution during the past decade. Most stages of the exploration process have been affected: the acquisition of data, the reduction of this data in preparation for signal processing, the design of digital filters to detect primary echoes (reflections) from buried interfaces, and the development of technology to extract from these detected signals information on the geometry and physical properties of the subsurface. The seismic reflection is genenlly weak, and it must be strengthened by the use of signal summing (stacking) procedures. The determination of depths to a target horizon requires knowledge of the propagational velocities of seismic stress waves, and a wealth of technology has evolved for this purpose. More recently, it has been possible to relate signal amplitude to the physical properties of the medium traversed and, in particular, to make inferences about the oil and gas content of the buried rocks. Much of the exploration effort occurs in offshore areas, where reverberations in the water layer mask reflections from below. The method of predictive deconvolution has been most effective in its ability to attenuate these reverberations, making it possible to detect reflections from structures at depth. Seismic signal processing is neither pure science nor pure art, and offers a continuing challenge to the practitioners of both cultures.     

Digital signal processing

Markets have always influenced the central thrust of the semiconductor industry. Beginning in the early eighties, the personal computer (PC) market has been the dominant market influencing the semiconductor industry. Single-chip microprocessors (MPUs) enabled what became the huge PC market, which ultimately overshadowed the earlier minicomputer and mainframe computer markets. The popularity of PCs led to investments in increasingly more powerful MPUs and memory chips of ever-growing capacity. MPUs and DRAMs became the semiconductor industry technology drivers for the data processing needs of the PC. But now, DSP, as opposed to conventional data processing, has become the major technology driver for the semiconductor industry as evidenced by its market growth and the fervour of chip vendors to provide new products based on DSP technology. The increasing need to digitally process analog information signals, like audio and video, is causing a major shift in the semiconductor business. Since DSP is the mathematical manipulation of those digitized information signals, specialized math circuitry is required for efficient signal processing-circuitry that was previously confined to classical DSP chips     

GPS软件接收机基带信号处理研究

GPS软件接收机相对传统硬件接收机,设计灵活,能够迅速分析、仿真、实现各类算法.本文主要针对GPS L1频率的C/A码信号,设计并实现了GPS软件接收机的基带处理部分,阐述了射频前端原理、并行码相位搜索捕获策略以及鉴频辅助跟踪环路,并推导了适应动态环境的三阶数字环路滤波器.仿真数据和实际中频数据测试表明:算法性能优越,能迅速捕获并锁定信号,获取导航数据.     

关于盲信号处理技术发展研究

本文主要介绍盲信号处理的意义和定义,阐述当前盲信号处理的研究现状.分析盲信号处理领域中仍需解决的问题,并为后续的研究提出建议与展望.     

Similarity methods in signal processing

A signal may contain information that is preserved by certain transformations of the signal. For example, the information phase-modulated signal is not altered by amplitude scaling of the signal. Many processing techniques have been developed to exploit such similarities. In the past, these algorithms have been developed in isolation without regard to common principles of invariance that tie them together. Similarity methods are presented as a unified method of designing processing algorithms invariant to specified transformations. These methods are based upon groups of continuous transformations known as local Lie groups and lead to a quasilinear partial differential equation. Solution of this partial differential equation specifies the form the signal processing operations must take. This form can then be applied using engineering judgment for algorithmic implementation. The paper presents an extended tutorial on Lie groups and similarity methods and quasilinear differential equations drawn from the mathematical literature. This is followed by several examples of signal processing interest that demonstrate that the similarity techniques may be applicable in certain kinds of signal processing problems     

Reproducible research in signal processing

What should we do to raise the quality of signal processing publications to an even higher level? We believe it to be crucial to maintain the precision in describing our work in publications, ensured through a high-quality reviewing process. We also believe that if the experiments are performed on a large data set, the algorithm is compared to the state-of-the-art methods, the code and/or data are well documented and available online, we will all benefit and make it easier to build upon each other's work. It is a clear win-win situation for our community: we will have access to more and more algorithms and can spend time inventing new things rather than recreating existing ones.