The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Guo Shi-jian - One of the best experts on this subject based on the ideXlab platform.
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The technology and application of wideband real-time Spectrum Analysis
Signal Processing, 2012Co-Authors: Guo Shi-jianAbstract:The technology of wideband real-time Spectrum Analysis focuses on the effective discovery,capture and detection of signal with the large bandwidth and the complex modulation.This paper first Analysis the challenge of wideband realtime Spectrum Analysis(RTSA),then highlights the conception of technology of wideband real-time Spectrum Analysis,general system architecture,basic principle and key technologies.At the third section of the paper,some applications are provided. Finally,with the deepening application on signal monitoring and detection,and with supplement of some new theoretical methods,wideband real-time Spectrum Analysis should be promoting in wide area.
Hossein Hassani - One of the best experts on this subject based on the ideXlab platform.
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Univariate Singular Spectrum Analysis
Singular Spectrum Analysis, 2018Co-Authors: Hossein Hassani, Rahim MahmoudvandAbstract:A concise description of univariate Singular Spectrum Analysis (SSA) is presented in this chapter. A step-by-step guide for performing filtering, forecasting as well as forecasting interval using univariate SSA and associated R codes is also provided. After reading this chapter, the reader will be able to select two basic, but very important, choices of SSA: window length and number of singular values. The similarity and dissimilarity between SSA and principal component Analysis (PCA) is also briefly deliberated.
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Applications of Singular Spectrum Analysis
Singular Spectrum Analysis, 2018Co-Authors: Hossein Hassani, Rahim MahmoudvandAbstract:This chapter presents three main applications of using Singular Spectrum Analysis (SSA): change point detection, gap filling/missing value imputation, and filtering/denoising. A concise description of the main idea along with technical background with various practical illustrations with associated R codes are given in this chapter. Both univariate and multivariate SSA R codes are provided for filtering and missing values imputation.
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Multivariate Singular Spectrum Analysis
Singular Spectrum Analysis, 2018Co-Authors: Hossein Hassani, Rahim MahmoudvandAbstract:When multiple time series are observed, we are usually interested in the internal structure of each, and at the same time their joint structure, or the dependency among series. Accordingly, the second chapter of this book is dedicated to this vital concept. In this chapter, the basic univariate Singular Spectrum Analysis (SSA) is extended in a fairly obvious way to the multivariate case and transition from univariate SSA to multivariate with emphasis on intuition and applications are deliberated. The related multivariate SSA codes along with several practical examples are also presented.
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Predicting daily exchange rate with singular Spectrum Analysis
Nonlinear Analysis: Real World Applications, 2010Co-Authors: Hossein Hassani, Abdol S. Soofi, Anatoly ZhigljavskyAbstract:This paper uses univariate and multivariate singular Spectrum Analysis for predicting the value and the direction of changes in the daily pound/dollar exchange rate. In prediction of daily pound/dollar rate, we use the rescaled and bootstrapped daily euro/dollar rate as a guidepost for the singular Spectrum Analysis method. We use the random walk model as a benchmark to evaluate performances of the singular Spectrum Analysis as a prediction method. Empirical results show that the forecast based on the multivariate singular Spectrum Analysis compares favorably to the forecast of the random walk model both for predicting the value and the direction of changes in the daily pound/dollar exchange rate. We compared the prediction results based on an error correction model in the context of a restricted vector autoregressive model and compared them with the prediction results by a random walk as well as by those of singular Spectrum and multiple singular Spectrum models and found that the VEC results are inferior.
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Singular Spectrum Analysis: Methodology and Comparison
2007Co-Authors: Hossein HassaniAbstract:Abstract: In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series Analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA tech-nique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com-pared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above. Key words: ARAR algorithm, Box-Jenkins SARIMA models, Holt-Winter algorithm, singular Spectrum Analysis (SSA), USA monthly accidental deaths series. 1
Adnanul Haq - One of the best experts on this subject based on the ideXlab platform.
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B-spline enhanced time-Spectrum Analysis
Signal Processing, 2005Co-Authors: Roger A. Green, Adnanul HaqAbstract:A new technique for the time-Spectrum Analysis of non-stationary signals is presented. The proposed technique smoothly fits a system's time-varying spectral coefficients using the combined methods of Fourier Analysis and B-splines. The resulting algorithm is efficient and generally effective. Algorithm assumptions and limitations are identified; performance is explored using simulated data. Provided certain conditions are met, the algorithm degenerates into the well-known cases of the simple and averaged periodograms. Methods are presented to calculate knot spacing based on the frequency and geometric properties of the ensuing time-Spectrum curve. Near real-time capabilities are also discussed. Finally, the method is compared with other time-Spectrum Analysis techniques such as the evolutionary periodogram (EP).
Roger A. Green - One of the best experts on this subject based on the ideXlab platform.
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B-spline enhanced time-Spectrum Analysis
Signal Processing, 2005Co-Authors: Roger A. Green, Adnanul HaqAbstract:A new technique for the time-Spectrum Analysis of non-stationary signals is presented. The proposed technique smoothly fits a system's time-varying spectral coefficients using the combined methods of Fourier Analysis and B-splines. The resulting algorithm is efficient and generally effective. Algorithm assumptions and limitations are identified; performance is explored using simulated data. Provided certain conditions are met, the algorithm degenerates into the well-known cases of the simple and averaged periodograms. Methods are presented to calculate knot spacing based on the frequency and geometric properties of the ensuing time-Spectrum curve. Near real-time capabilities are also discussed. Finally, the method is compared with other time-Spectrum Analysis techniques such as the evolutionary periodogram (EP).
Vairis Shtrauss - One of the best experts on this subject based on the ideXlab platform.
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Spectrum Analysis and synthesis of relaxation signals
Signal Processing, 1997Co-Authors: Vairis ShtraussAbstract:Abstract The paper is devoted to Spectrum Analysis and synthesis of monotonie long-time-interval and wide-frequency-band relaxation signals by nonlinear phase FIR digital filters with the logarithmic sampling (Shtrauss, 1995). Expressions for frequency responses and filter algorithms are derived for the basic integral transforms related to Spectrum Analysis and synthesis. It is shown that most of the problems of Spectrum Analysis and synthesis may be solved by the filters designed for the Fourier and Heviside-Carson sine transforms, or by using coefficients from these filters. Peculiarities of filter design by the identification method are considered. A complex optimization criterion is proposed minimizing the square error of output signal and controlling over the filter coefficients. In order to expand the argument range and the sampling rate of output sequence, filter banks with filters having the shifted impulse responses are built up for the Fourier and Heviside-Carson sine transforms. Performance of the designed filters is evaluated. Simulation results with noisy and noise-free signals are presented.
