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.     

Active and cooperative learning in signal processing courses

This work describes positive effects of using active and cooperative learning (ACL) methods to improve signal processing instruction. It provides examples, references, and assessment data that encourage other instructors to consider this approach. Conclusions are based on impressions gathered through conversations with students during office hours as well as on responses from anonymous student opinion surveys. In addition to these subjective assessments, preliminary quantitative data measured with the signals and systems concept inventory (SSCI) support the benefits of ACL techniques in signal processing courses.     

一种基于支持向量机的心电信号处理算法

新一代心电图(Electrocardiography,ECG)系统中,可以使用可穿戴设备来监测人体生理信号.心电图信号是一种生物医学信号,基本上与人体心脏的电活动相对应,根据其波形可以初步判断人体是否存在疾病.本文首先对ECG信号进行了预处理,然后使用自适应阈值对QRS波进行定位,最后使用支持向量机对心电信号进行分类....     

Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal

Multidimensional Systems and Signal Processing - In this research paper we present designing and evaluating the electrocardiography (ECG) and Myoelectric signal (EMG) pattern recognition methods...     

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.<>     

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     

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.     

An approach to iterative learning control for spatio-temporal dynamics using nD discrete linear systems models

Iterative Learning Control (ILC) is now well established in terms of both the underlying theory and experimental application. This approach is specifically targeted at cases where the same operation is repeated over a finite duration with resetting between successive executions. Each execution is known as a trial and the key idea is to use information from previous trials to update the control input used on the current one with the aim of improving performance from trial-to-trial. In this paper, the subject area is the application of ILC to spatio-temporal systems described by a linear partial differential equation (PDE) using a discrete approximation of the dynamics, where there are a number of construction methods that could be applied. Here explicit discretization is used, resulting in a multidimensional, or nD, discrete linear system on which to base control law design, where n denotes the number of directions of information propagation and is equal to the total number of indeterminates in the PDE. The resulting control laws can be computed using Linear Matrix Inequalities (LMIs) and a numerical example is given. Finally, a natural extension to robust control is noted and areas for further research briefly discussed.     

ASSP-based radar signal processing

This paper proposes the Application Specific Signal Processor(ASSP)-based implementation of the real-time signal processing system in both spatial domain and time domain for a phased-array radar. This paper also proposes the system-on-silicon hardware design of some ASSPs including the adaptive beamformer, FFT appliation specific integrated circuit, clutter map former and update, moving target extractor and video integrator. The advantages of the processing system are compact, efficient, and robust.     

Wavelets and signal processing

A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes. The discussion includes nonstationary signal analysis, scale versus frequency, wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing. The main definitions and properties of wavelet transforms are covered, and connections among the various fields where results have been developed are shown     

Dense target signal processing

The problem of determining the density of targets at every range and velocity is addressed. These targets consist of a dense group of reflecting objects moving with different velocities and at different ranges. The problem of how to choose the outgoing signals and process the echoes of those signals from the targets to determine the density function is discussed. The problem is a classical inverse problem. The objective is to reconstruct a function of two variables (range and velocity) from limited information. Two schemes are given. The first of these methods modifies the method of J.R. Klauder et al. (1960) to the case of signals with a large range of frequency components (wideband signals). The second is an improvement on the first that uses a formula of I. Khalil (1974) from affine group theory. Numerical solutions support the conclusions. The method has applications in echocardiography, radar, sonar and fluid flow measurement