Discrete signal representation by the method of kernel expansion

In digital processing of continuous-time signals it is necessary to represent the signal by a sequence. In this letter a number of transforms are presented, generalizing the Poisson transform, which in addition to generating the sequences they also map continuous time-invariant convolution to discrete shift-invariant convolution.     

OFDM with reduced peak-to-average power ratio by multiple signal representation

In this paper two highly effective, flexible and distortionless peak power reduction schemes for orthogonal frequency division multiplexing (ofdm) with low amount of additional complexity and almost vanishing redundancy are presented. The schemes work with arbitrary numbers of subcarriers and signal sets. The first approach generates a set of several alternative multicarrier signals and selects that transmit signal with the lowest peak power value. The second method optimally combines partial transmit sequences to minimize the peak-to-average power ratio (PAR-coefficient). The schemes are analyzed theoretically and their performance is covered by simulations.     

Optimized signal expansions for sparse representation

Traditional signal decompositions such as transforms, filterbanks, and wavelets generate signal expansions using the analysis-synthesis setting: the expansion coefficients are found by taking the inner product of the signal with the corresponding analysis vector. In this paper, we try to free ourselves from the analysis-synthesis paradigm by concentrating on the synthesis or reconstruction part of the signal expansion. Ignoring the analysis issue completely, we construct sets of synthesis vectors, which are denoted waveform dictionaries, for efficient signal representation. Within this framework, we present an algorithm for designing waveform dictionaries that allow sparse representations: the objective is to approximate a training signal using a small number of dictionary vectors. Our algorithm optimizes the dictionary vectors with respect to the average nonlinear approximation error, i.e., the error resulting when keeping a fixed number n of expansion coefficients but not necessarily the first n coefficients. Using signals from a Gaussian, autoregressive process with correlation factor 0.95, it is demonstrated that for established signal expansions like the Karhunen-Loeve transform, the lapped orthogonal transform, and the biorthogonal 7/9 wavelet, it is possible to improve the approximation capabilities by up to 30% by fine tuning of the expansion vectors     

On denoising and best signal representation

We propose a best basis algorithm for signal enhancement in white Gaussian noise. The best basis search is performed in families of orthonormal bases constructed with wavelet packets or local cosine bases. We base our search for the “best” basis on a criterion of minimal reconstruction error of the underlying signal. This approach is intuitively appealing, because the enhanced or estimated signal has an associated measure of performance, namely, the resulting mean-square error. Previous approaches in this framework have focused on obtaining the most “compact” signal representations, which consequently contribute to effective denoising. These approaches, however, do not possess the inherent measure of performance which our algorithm provides. We first propose an estimator of the mean-square error, based on a heuristic argument and subsequently compare the reconstruction performance based upon it to that based on the Stein (1981) unbiased risk estimator. We compare the two proposed estimators by providing both qualitative and quantitative analyses of the bias term. Having two estimators of the mean-square error, we incorporate these cost functions into the search for the “best” basis, and subsequently provide a substantiating example to demonstrate their performance     

Complete Gabor transformation for signal representation

Properties of the Gabor transformation used for image representation are discussed. The properties can be expressed in matrix notation, and the complete Gabor coefficients can be found by multiplying the inverse of the Gabor (1946) matrix and the signal vector. The Gabor matrix can be decomposed into the product of a sparse constant complex matrix and another sparse matrix that depends only on the window function. A fast algorithm is suggested to compute the inverse of the window function matrix, enabling discrete signals to be transformed into generalized nonorthogonal Gabor representations efficiently. A comparison is made between this method and the analytical method. The relation between the window function matrix and the biorthogonal functions is demonstrated. A numerical computation method for the biorthogonal functions is proposed.     

The phasor representation of the OFDM signal in the SFN networks

The multipath is a cause of frequency notches leading to signal fading. In order to get insight into this effect the connections between individual path parameters and frequency envelope of the OFDM signal are needed. To this aim the phasor representation of the OFDM signal is proposed. The phasor representation exposes the regularity related within different paths of the signal allowing for insight into the structure of multipath OFDM channel. In this work we analyze paths with time delays also accessing OFDM symbol guard interval.     

Electromagnetic pulse signal representation using orthonormalLaguerre sequences

The notion of representing discrete-time electromagnetic pulse (EMP) signals using orthonormal Laguerre sequences is introduced. The motivation for doing so is that EMP signals and Laguerre sequences are both characterized by decaying exponentials. It is shown a that very efficient Laguerre representation of EMP signals is possible using this approach     

The internal dynamics and small signal analysis of BARITT diodes

A “two-carrier” computer simulation showing the transient response of BARITT diodes to an applied current step is presented. The internal dynamics of the diode in terms of the time evolution of the electric field, electron and hole densities as well as both conduction and displacement currents are discussed. The dynamics of the solution reveals the formation of a low field region at the injecting contact, a result which is not obtainable from “one-carrier” models of BARITT diodes. A small signal analysis is performed on the steady state solution and demonstrates an increased differential negative resistance (DNR) for increased device length at a given doping, and an increased DNR coupled with a wider frequency bandwidth for a decrease in carrier lifetime at the injecting junction.     

Efficient design of orthonormal wavelet bases for signal representation

The efficient representation of a signal as a linear combination of elementary "atoms" or building blocks is central to much signal processing theory and many applications. Wavelets provide a powerful, flexible, and efficiently implementable class of such atoms. In this paper, we develop an efficient method for selecting an orthonormal wavelet that is matched to a given signal in the sense that the squared error between the signal and some finite resolution wavelet representation of it is minimized. Since the squared error is not an explicit function of the design parameters, some form of approximation of this objective is required if conventional optimization techniques are to be used. Previous approximations have resulted in nonconvex optimization problems, which require delicate management of local minima. In this paper, we employ an approximation that results in a design problem that can be transformed into a convex optimization problem and efficiently solved. Constraints on the smoothness of the wavelet can be efficiently incorporated into the design. We show that the error incurred in our approximation is bounded by a function that decays to zero as the number of vanishing moments of the wavelet grows. In our examples, we demonstrate that our method provides wavelet bases that yield substantially better performance than members of standard wavelet families and are competitive with those designed by more intricate nonconvex optimization methods.     

一种获得交流信号真有效值方法

提出一种基于数字采样任意波形的交流信号真有效值计算方法。该算法通过等间隔采样获得信号一定时长的样本数据并对样本数据进行逐点平方,依据信号的最低频率分量和精度要求设计合适的数字低通滤波器,通过对信号样本平方序列进行低通滤波并对结果开方就可获得信号的真有效值。经过仿真分析和实际测量表明,该算法结果和波形无关,能够满足频率及波形随机变化的应用要求。     

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...     

缝纫机专用交流伺服系统设计

提出了一种基于DSP交流伺服系统。交流伺服系统是缝纫机控制系统的核心部分,因此详细研究交流伺服控制系统对整个系统将有非常重要的意义。重点介绍了硬件电路设计及软件模块设计。通过实践应用表明,本设计是一种简单而高效的控制系统,可以应用于各种工业缝纫机控制系统。     

The AICE-CRT and digital signal processing algorithms: The complex case

The Chinese remainder theorem is a fundamental technique widely employed in digital signal processing for designing fast algorithms for computing convolutions. Classically, it has two versions. One is over a ring of integers and the second is over a ring of polynomials with coefficients defined over a field. In our previous papers, we developed an extension to this well-known theorem for the case of a ring of polynomials with coefficients defined over a finite ring of integers. The objective was to generalize number-theoretictransforms, which turn out to be a special case of this extension. This paper focuses on the extension of the Chinese remainder theorem for processing complex-valued integer sequences. Once again, the present work generalizes the complex-number-theoretic transforms. The impetus for this work is provided by the occurrence of complex integer sequences in digital signal processing and the desire to process them using exact arithmetic.This paper was completed in the fall of 1992 when H. Krishna was a visiting professor at the Department of Electrical Engineering, I.I.T. Delhi, N. Delhi, 110016, India.     

利用改进的稀疏表示处理FBG传感信号

基于 压缩感知(CS)的正交匹配追 踪 ( OMP ) 算法,须以稀疏度 确定 为先验条件, 在 实际 应用 中稀疏度 不 易 确定 的情况下, 本文 提出了 稀疏度确 定方法和 二次正交匹配追踪 (TOMP)算法。 先 引入熵权法 采用 多指标融合并结合饱和值点法确定稀疏度 , 然后利用所提 方 法 对实验信号进行重构 。 实验 仿真结果表明: 与同类算法相比,本文所提 TOMP 算法增加 0.1s 运行时间降低了 12～ 22% 的重构误差,更好折中处理了重构误差和时间;与不同类算法相比,本文 所提方法重构的信号信噪比(SNR)最大可提升 22 dB ,且均方根误差(RMSE)降低 0.7,因此去噪效果更优。     

On the detection of chipless RFID through signal space representation

paper presents a novel approach to model and represent chipless radio-frequency identification (RFID) frequency signatures. The approach involves the geometrical representation of chipless RFID frequency signatures in a signal space. A small set of orthonormal basis functions is derived using singular value decomposition in order to represent the 2 ^b possible tag signatures of a b-bit chipless tag. Each tag signature is represented as a point in an L-dimensional signal space, and minimum distance detection is used to extract the information bit sequence of the tag. Detection error probability is also examined through analytical derivations and Monte Carlo simulation. A set of 3-bit tags were fabricated to validate the approach. Experimental results show that the new approach is capable of accurately detecting information contained in chipless RFID tags. This approach offers a solid mathematical framework for developing novel detection methods for chipless tags.     

Orthonormal-basis partitioning and time-frequency representation of cardiac rhythm dynamics

Although a number of time-frequency representations have been proposed for the estimation of time-dependent spectra, the time-frequency analysis of multicomponent physiological signals, such as beat-to-beat variations of cardiac rhythm or heart rate variability (HRV), is difficult. We thus propose a simple method for 1) detecting both abrupt and slow changes in the structure of the HRV signal, 2) segmenting the nonstationary signal into the less nonstationary portions, and 3) exposing characteristic patterns of the changes in the time-frequency plane. The method, referred to as orthonormal-basis partitioning and time-frequency representation (OPTR), is validated using simulated signals and actual HRV data. Here we show that OPTR can be applied to long multicomponent ambulatory signals to obtain the signal representation along with its time-varying spectrum.     

Automotive signal diagnostics using wavelets and machine learning

In this paper, we describe an intelligent signal analysis system employing the wavelet transformation in the solution of vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments representing physical events or states based on wavelet multi-resolution analysis. Second, by applying the segmentation result of the leading signal to the other signals, the detailed properties of each segment, including inter-signal relationships, are extracted to form a feature vector. Finally, a fuzzy intelligent system is used to learn diagnostic features from a training set containing feature vectors extracted from signal segments at various vehicle states. The fuzzy system applies its diagnostic knowledge to classify signals as abnormal or normal. The implementation of the system is described and experiment results are presented     

基于稀疏表达的微弱信号提取及检测方法

首先简要介绍了稀疏表达模型以及其常用的求解方法;然后讨论了一种基于稀疏表达的微弱信号提取及检测方法;在此基础上,为了减少时间与空间复杂度,开发了一种快速求解算法;最后给出信号的提取结果以及定性与定量分析,并用大量的实验结果证明讨论的基于稀疏表达的微弱信号提取及检测方法的顽健性与自适应能力。     

The representation of nonlinear systems by exhaustive linearization

Some established results in functional analysis yield a simple formula for representing physical systems exactly in terms of their linear behaviors. Although the formula has been exploited to solve bifurcation problems in elasticity and hydrodynamics, its use in system theory remains to be developed.