Wavelet analysis [for signal processing]

As every engineering student knows, any signal can be portrayed as an overlay of sinusoidal waveforms of assorted frequencies. But while classical analysis copes superbly with naturally occurring sinusoidal behavior-the kind seen in speech signals-it is ill-suited to representing signals with discontinuities, such as the edges of features in images. Latterly, another powerful concept has swept applied mathematics and engineering research: wavelet analysis. In contrast to a Fourier sinusoid, which oscillates forever, a wavelet is localized in time-it lasts for only a few cycles. Like Fourier analysis, however, wavelet analysis uses an algorithm to decompose a signal into simpler elements. Here, the authors describe how localized waveforms are powerful building blocks for signal analysis and rapid prototyping-and how they are now available in software toolkits     

用于HRRP超分辨处理的RELAX算法性能分析

针对现有文献中未出现关于RELAX算法超分辨性能的定量讨论或关于RELAX应用的边界条件分析,导致RELAX算法的实际应用十分困难这一问题,在详细分析RELAX算法的超分辨原理的基础上,通过仿真给出了一些关于RELAX实际应用的边界条件及结论,可用于指导RELAX算法在实际散射中心估计中的应用：RELAX超分辨处理对估计散射点个数不敏感;当FFT点数约为要达到真实分辨力所需FFT点数的2倍时,RELAX超分辨处理的重构精确度可满足要求;在保证一定的重构精确度的前提下,RELAX超分辨处理的分辨力最高可以达到实际分辨力的2倍。本文仿真条件下,当RSN=10 dB时,RELAX超分辨处理在一定误差容忍范围内基本可用。     

Predictive transform estimation [image processing]

Minimum mean squared error (MSE) linear predictive transform (LPT) source decoding (or modeling) and Kalman estimation are integrated to yield a unified approach to source modeling and estimation called PT estimation. PT estimation enhances classical Kalman estimation in two ways: first, it directly addresses the source modeling problem of scalar or multidimensional Kalman estimation by integrating an exact minimum MSE LPT decoder with a Kalman estimator; second, it provides a transformation mechanism that inherently leads to significant design and implementation simplifications when the state dimensionality is large. In the specific case of image reconstruction, the design and implementation requirements of 2-D LPT smoother structures are lessened with respect to those of classical 2-D Kalman smoother structures with exactly equivalent performance by factors that approach eight and four, respectively. Simple nonadaptive 2-D LPT smoothers perform quite well when compared with previous adaptive linear minimum MSE estimators     

A Fourier analysis for a fast simulation algorithm [for powerconvertors]

A powerful discrete modeling approach to the simulation of a switching converter that has appeared in power electronics literature over the last several years is briefly reviewed, and some desirable traits for its matrix exponential subroutine are discussed. The key result is a systematic and generic Fourier analysis that operates on the steady-state solution as provided by a discrete model. The Fourier analysis algorithm was tested on a phase-controlled parallel-loaded resonant converter which is useful as a fixed-frequency low-distortion AC source. Three verifications of the algorithm were made: a phasor analysis, an alternate closed-form analysis directed specifically to the problem at hand, and an analysis using experimental data. The performance of the algorithm was good for the case considered     

Moment preserving quantization [signal processing]

The general solution to the moment-preserving (MP) quantizer problem is presented. It is shown that the moment preserving quantizer is related to the Gauss-Jacobi mechanical quadrature, the output levels of the N-level MP quantizer are the N zeros of an Nth degree orthogonal polynomial associated with the input probability distribution function, and the N-1 thresholds of the MP quantizer are related to the Christoffel numbers through the Chebyshev-Markov-Stieltjes separation theorem. The statistical convergence of the MP quantizer is investigated. MP quantizer tables are presented for the uniform, Gaussian, and Laplacian density functions. The moment-preserving quantizer is shown to be related to block truncation coding     

The affordable gigaflop [parallel processing]

Designers of large structures such as oil rigs cannot afford to build a succession of prototypes so they use computers to analyse the structure. However, this takes time and this kind of computer can be very expensive. The author discusses the use of parallel processing, in particular the Inmos T800 transputer. This kind of processor is very powerful, fast and flexible and is also affordable     

Performance of optimum and suboptimum combining at the antennaarray of a W-CDMA system

The performance of an array of antennas combined with a RAKE receiver at the base station of a wideband code division multiple access system with quaternary phase shift keying modulation is investigated for single-user reception. This receiver structure is also known as a multidimensional RAKE receiver. Expressions for the interference correlation matrix are derived, and subsequently, an analysis of the error probability is provided. With regard to antenna weights, the optimum combining linear receiver, which takes into account the spatial distribution of all the users in the system, is compared with the suboptimum combining receiver (SOC), which exploits knowledge of the desired user array vector only. It is shown how the two receivers, ideal in the sense that perfect channel estimation is assumed, achieve very similar performances under realistic conditions. Consequently, the investigated pilot assisted combining receiver, which estimates the channel based on limited overhead, calculates weights according to the SOC strategy by a very simple algorithm     

Eigenvalues of the moment-method matrix and their effect on theconvergence of the conjugate gradient algorithm [EM scattering]

A theory that relates eigenvalues of a continuous operator to those of the moment-method matrix operator is discussed and confirmed by examples. This theory suggests reasons for ill conditioning when certain types of basis and testing functions are used. In addition, the effect of eigenvalue location on the convergence of the conjugate gradient (CG) method is studied. The convergence rate of the CG method is dependent on the eigenvalues of the iteration matrix as well as on the number of eigenvectors of the iteration matrix needed to represent the right side of the equation. These findings explain the previously reported convergence behavior of the CG method when applied to electromagnetic-scattering problems     

Application of signal processing to the analysis of financial data [In the Spotlight]

This article highlights some of the techniques used to represent and predict the main features of price evolution and to classify stock so as to design diversified investment portfolios.     

A new optimal window [signal processing]

An optimal window that can provide the best tradeoff between the peak sidelobe level and the total sidelobe level is presented. Necessary and sufficient conditions are established for the optimal window. A design algorithm is presented, and examples are discussed. The examples demonstrate that the window is capable of much better performance than previously available windows, such as the Hamming window. Relationships between the maximum error and the mean-squared error are explored for the window optimization problem. It is concluded that the minimax and least squares approximations are both fundamentally inefficient. This conclusion is supported by experimental evidence and mathematical analyses