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Journal articles on the topic 'Statistical Signal Processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

Ray, W. D., Edward J. Wegman, and James G. Smith. "Statistical Signal Processing." Journal of the Royal Statistical Society. Series A (General) 148, no. 1 (1985): 63. http://dx.doi.org/10.2307/2981518.

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2

Vosvrda, Miloslav S. "Discrete random signals and statistical signal processing." Automatica 29, no. 6 (November 1993): 1617. http://dx.doi.org/10.1016/0005-1098(93)90033-p.

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3

Ottersten, Björn, Torsten Söderström, and Bo Wahlberg. "Statistical signal processing and control." Automatica 30, no. 1 (January 1994): 9. http://dx.doi.org/10.1016/0005-1098(94)90224-0.

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4

Woolfson, M. S. "Book Review: Discrete Random Signals and Statistical Signal Processing." International Journal of Electrical Engineering & Education 30, no. 1 (January 1993): 94. http://dx.doi.org/10.1177/002072099303000125.

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5

Liu, Shin Ta. "Nonlinear Signal Processing: A Statistical Approach." Technometrics 48, no. 1 (February 2006): 148–49. http://dx.doi.org/10.1198/tech.2006.s355.

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6

Caby, Errol C. "An Introduction to Statistical Signal Processing." Technometrics 48, no. 4 (November 2006): 572–73. http://dx.doi.org/10.1198/tech.2006.s436.

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7

Riedi, Rudolf H. "An Introduction to Statistical Signal Processing." Journal of the American Statistical Association 101, no. 475 (September 2006): 1317. http://dx.doi.org/10.1198/jasa.2006.s133.

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8

Kameoka, Hirokazu. "1. Music and Statistical Signal Processing." Journal of The Institute of Image Information and Television Engineers 71, no. 7 (2017): 452–56. http://dx.doi.org/10.3169/itej.71.452.

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9

Potters, Marc, and William Bialek. "Statistical mechanics and visual signal processing." Journal de Physique I 4, no. 11 (November 1994): 1755–75. http://dx.doi.org/10.1051/jp1:1994219.

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10

Gruber, Marvin H. J. "Statistical Digital Signal Processing and Modeling." Technometrics 39, no. 3 (August 1997): 335–36. http://dx.doi.org/10.1080/00401706.1997.10485128.

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11

Praus, P., and J. Štěpánek. "Statistical signal processing in Raman spectroscopy." Journal of Molecular Structure 294 (March 1993): 243–46. http://dx.doi.org/10.1016/0022-2860(93)80360-8.

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12

Barrera Alvarez, Jayro Lazaro, and Fidel Ernesto Hernandez Montero. "Classification of MPSK Signals through Eighth-Order Statistical Signal Processing." IEEE Latin America Transactions 15, no. 9 (2017): 1601–7. http://dx.doi.org/10.1109/tla.2017.8015041.

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13

Sengupta, Sailes K., and Steven M. Kay. "Fundamentals of Statistical Signal Processing: Estimation Theory." Technometrics 37, no. 4 (November 1995): 465. http://dx.doi.org/10.2307/1269750.

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14

Hero, A., H. Messer, J. Goldberg, D. J. Thomson, M. G. Amin, G. Giannakis, A. Swami, et al. "Highlights of statistical signal and array processing." IEEE Signal Processing Magazine 15, no. 5 (1998): 21–64. http://dx.doi.org/10.1109/79.708539.

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15

Gustafsson, F. "STATISTICAL SIGNAL PROCESSING APPROACHES TO FAULT DETECTION." IFAC Proceedings Volumes 39, no. 13 (2006): 24–35. http://dx.doi.org/10.3182/20060829-4-cn-2909.00004.

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16

Brockwell, A. E., Robert E. Kass, and A. B. Schwartz. "Statistical Signal Processing and the Motor Cortex." Proceedings of the IEEE 95, no. 5 (May 2007): 881–98. http://dx.doi.org/10.1109/jproc.2007.894703.

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17

Sengijpta, Sailes K. "Fundamentals of Statistical Signal Processing: Estimation Theory." Technometrics 37, no. 4 (November 1995): 465–66. http://dx.doi.org/10.1080/00401706.1995.10484391.

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18

Popescu, Theodor D. "Introduction to statistical signal processing with applications." Control Engineering Practice 4, no. 10 (October 1996): 1484. http://dx.doi.org/10.1016/s0967-0661(96)90047-7.

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19

Prasad, K. Venkatesh. "Fundamentals of statistical signal processing: Estimation theory." Control Engineering Practice 2, no. 4 (August 1994): 728. http://dx.doi.org/10.1016/0967-0661(94)90195-3.

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20

Praus, P., J. Štěpánek, P. Mojzeš, and J. Bok. "Statistical signal processing in multichannel Raman spectroscopy." Journal of Molecular Structure 348 (March 1995): 285–88. http://dx.doi.org/10.1016/0022-2860(95)08644-b.

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21

Gustafsson, F. "Statistical signal processing approaches to fault detection." Annual Reviews in Control 31, no. 1 (January 2007): 41–54. http://dx.doi.org/10.1016/j.arcontrol.2007.02.004.

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22

Courcier, Thierry, Patrick Pittet, Paul G. Charette, Vincent Aimez, and Guo Neng Lu. "BQJ Photodetector Signal Processing." Key Engineering Materials 605 (April 2014): 91–94. http://dx.doi.org/10.4028/www.scientific.net/kem.605.91.

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We propose a signal processing method for the CMOS Buried Quad Junction (BQJ) photodetector employed for multi-label fluorescence detection. It serves to quantify label components in an arbitrary mixture with improved signal-to-noise ratio. The proposed method includes least squares optimization and statistical data preprocessing based on Principal Component Analysis (PCA). The method was applied to the BQJ as well as to Buried Double Junction (BDJ) and Buried Triple Junction (BTJ) detectors. The obtained results show that BQJ case achieves best accuracy in label quantification compared to BDJ and BTJ detectors in any tested configurations. The statistical data preprocessing approach was also evaluated: 5dB SNR improvements for an example case of two-label mixture (Green-Red excitation with optical power over 28pW).

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23

Doucet, A., and Xiaodong Wang. "Monte Carlo methods for signal processing: a review in the statistical signal processing context." IEEE Signal Processing Magazine 22, no. 6 (November 2005): 152–70. http://dx.doi.org/10.1109/msp.2005.1550195.

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24

Lima, Robson Rosserrani, Augusto Santiago Cerqueira, and Paulo Fernando Ribeiro. "A Statistical Signal Processing Approach to Islanding Detection." Learning and Nonlinear Models 21, no. 1 (February 28, 2023): 60–76. http://dx.doi.org/10.21528/lnlm-vol21-no1-art5.

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The integration of distributed generation (DG) sources in the electric energy systems may bring new problems that need attention, one of these problems is the occurrence of unintentional islanding. Islanding is a condition in which part of the distribution network is disconnected from the system, and consumer units are still powered by one or more DGs, which can cause damage to equipment and pose risks to the safety of technicians. This paper shows an islanding detection method (IDM) in Power Systems with DG based on statistical signal processing. We used a MathWorks Simulink model of a grid-connected 250 kW photovoltaic (PV) array to simulate the behavior of the three-phase voltage signal in the point of common coupling (PCC) under the nominal operation, islanding condition, and fault condition using different load compositions. Principal Component Analysis (PCA) was used to extract the transitory events from the voltage signals, and then we used second-, third-, and fourth-order cumulants to generate features and the best ones were selected using the Fisher’s Discriminant Ratio (FDR). A Radial Basis Function Network (RBFN) makes the classification of the events. We found that, for this setup, we can achieve detection rates of 99% for both islanding condition detection and fault occurrence classification, no matter the power mismatch between the load and the DG.

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25

Woodman, Ronald F. "Coherent radar imaging: Signal processing and statistical properties." Radio Science 32, no. 6 (November 1997): 2373–91. http://dx.doi.org/10.1029/97rs02017.

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26

Yoshii, Kazuyoshi, and Katsutoshi Itoyama. "1.Recent Progress of Statistical Audio Signal Processing." Journal of the Institute of Image Information and Television Engineers 69, no. 2 (2015): 111–16. http://dx.doi.org/10.3169/itej.69.111.

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27

Zoubir, Abdelhak, Mats Viberg, and Bin Yang. "Special section on Statistical Signal and Array Processing." Signal Processing 90, no. 5 (May 2010): 1335–37. http://dx.doi.org/10.1016/j.sigpro.2010.01.014.

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28

Praus, P., J. Bok, and J. Štěpánek. "Adaptation of Raman spectrometer to statistical signal processing." Czechoslovak Journal of Physics 41, no. 12 (December 1991): 1161–70. http://dx.doi.org/10.1007/bf01613536.

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29

Garth, L. M., and H. V. Poor. "Detection of non-Gaussian signals: a paradigm for modern statistical signal processing." Proceedings of the IEEE 82, no. 7 (July 1994): 1061–95. http://dx.doi.org/10.1109/5.293163.

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30

Qi, Yang, Taichu Shi, and Ben Wu. "Wideband Mixed Signal Separation Based on Photonic Signal Processing." Telecom 2, no. 4 (November 2, 2021): 413–29. http://dx.doi.org/10.3390/telecom2040024.

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The growing needs for high-speed and secure communications create an increasing challenge to the contemporary framework of signal processing. The coexistence of multiple high-speed wireless communication systems generates wideband interference. To protect the security and especially the privacy of users’ communications requires stealth communication that hides and recovers private information against eavesdropping attacks. The major problem in interference management and stealth information recovery is to separate the signal of interest from wideband interference/noise. However, the increasing signal bandwidth presents a real challenge to existing capabilities in separating the mixed signal and results in unacceptable latency. The photonic circuit processes a signal in an analog way with a unanimous frequency response over GHz bandwidth. The digital processor measures the statistical patterns of the signals with sampling rate orders of magnitude smaller than the Nyquist frequency. Under-sampling the signals significantly reduces the workload of the digital processor while providing accurate control of the photonic circuit to perform the real-time signal separations. The wideband mixed signal separation, based on photonic signal processing is scalable to multiple stages with the performance of each stage accrued.

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31

Istomin, Andrey, and Egor Demidchenko. "DIGITAL PROCESSING OF THE ELECTROMYOGRAM SIGNAL." Modern Technologies and Scientific and Technological Progress 2020, no. 1 (June 16, 2020): 111–12. http://dx.doi.org/10.36629/2686-9896-2020-1-111-112.

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As a result of the study of physiological processes occurring in the human hand, data were obtained that are subject to analysis and statistical processing in the environment for solving engineering and scientific problems of Matlab

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32

Borisov, Evgeniy A., and Aleksey S. Zhabin. "Statistical Signal Processing Method in the Laser Rangefinder’s Photodetector." Vestnik MEI 5, no. 5 (2018): 146–51. http://dx.doi.org/10.24160/1993-6982-2018-5-146-151.

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33

Crouse, M. S., R. D. Nowak, and R. G. Baraniuk. "Wavelet-based statistical signal processing using hidden Markov models." IEEE Transactions on Signal Processing 46, no. 4 (April 1998): 886–902. http://dx.doi.org/10.1109/78.668544.

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34

Frery, Alejandro C., and Francisco Cribari-Neto. "Foreword: Special section on statistical image and signal processing." Brazilian Journal of Probability and Statistics 23, no. 2 (December 2009): 105–6. http://dx.doi.org/10.1214/09-bjps016.

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35

Vacek, Michael, and Ivan Prochazka. "Single photon laser altimeter simulator and statistical signal processing." Advances in Space Research 51, no. 9 (May 2013): 1649–58. http://dx.doi.org/10.1016/j.asr.2012.11.021.

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36

Blum, Rick S. "Robust image fusion using a statistical signal processing approach." Information Fusion 6, no. 2 (June 2005): 119–28. http://dx.doi.org/10.1016/j.inffus.2003.12.001.

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37

Chepuri, Sundeep Prabhakar, and Geert Leus. "Sparse Sensing for Statistical Inference." Foundations and Trends® in Signal Processing 9, no. 3-4 (2016): 233–386. http://dx.doi.org/10.1561/2000000069.

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38

Popiński, Waldemar. "Statistical View on Phase and Magnitude Information in Signal Processing." Artificial Satellites 47, no. 3 (January 1, 2012): 127–36. http://dx.doi.org/10.2478/v10018-012-0018-6.

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Statistical View on Phase and Magnitude Information in Signal ProcessingIn this work the problem of reconstruction of an original complex-valued signalot,t= 0, 1, …,n- 1, from its Discrete Fourier Transform (DFT) spectrum corrupted by random fluctuations of magnitude and/or phase is investigated. It is assumed that the magnitude and/or phase of discrete spectrum values are distorted by realizations of uncorrelated random variables. The obtained results of analysis of signal reconstruction from such distorted DFT spectra concern derivation of the expected values and bounds on variances of the reconstructed signal at the observation moments. It is shown that the considered random distortions in general entail change in magnitude and/or phase of the reconstructed signal expected values, which together with imposed random deviations with finite variances can blur the similarity to the original signal. The effect of analogous random amplitude and/or phase distortions of a complex valued time domain signal on band pass filtration of distorted signal is also investigated.

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39

Tulyakova, N. O., and O. M. Trofymchuk. "Modified algorithms for signal nonlinear trend detection." Radiotekhnika, no. 206 (September 24, 2021): 137–51. http://dx.doi.org/10.30837/rt.2021.3.206.13.

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There is a problem of nonlinear (abrupt) signal trend detection in many digital signals processing practical applications. In particular, in the field of biomedical signals processing, the actual task is the elimination of abrupt signal baseline distortions caused by the patient's movements. For processing such signals containing edges and other discontinues, linear filtering based on discrete Fourier or cosine transforms leads to significant smoothing of a signal. Median type algorithms related to nonlinear stable (robust) filters are successfully applied for filtering such signals, in particular, high efficiency is provided by median hybrid filters with finite impulse response (FIR). The article considers simple algorithms of the class of FIR-median hybrid filters used for signal nonlinear trend detection. It is proposed to modify these algorithms by replacing the operation of finding the median of the data in the sliding filter window with the calculation of their myriad, as well as adding weights (number of duplications) to certain window elements. Statistical estimates of filter efficiency according to the mean square error (MSE) criterion for test signals like “step” and “ramp” edges, and triangular peak and parabola have been obtained. The high efficiency of the investigated nonlinear filters for the listed test signals types and the improvements achieved as a result of the proposed filter modifications are shown based on the analysis of the filter output signals and statistical estimates of their quality. Some examples of processing biomedical signals of electroencephalograms which illustrate good quality of noise suppression and signal abrupt changes preservation, and motion artifacts removal without large signal distortions are given.

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40

Ksendzuk, A. V., and A. A. Kanatchikov. "SPACEBORNE SAR SIGNAL DETECTION AND PARAMETER ESTIMATION IN SPACE TRACKING AND SURVEILLANCE SYSTEM MODELING." Issues of radio electronics, no. 3 (March 20, 2019): 31–35. http://dx.doi.org/10.21778/2218-5453-2019-3-31-35.

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The development of information support of the space tracking and surveillance system is a highly topical task, the solution of which will help to increase the effectiveness of monitoring space objects. Development and practical application of the software for Spaceborne synthetic aperture radar (SAR) signal detection and parameter estimation described and analyzed. Architecture of the software is described, processing results in the simulation mode (comparison of different processing methods) and real SAR satellite signals processing mode analyzed. In simulation mode detection and parameter estimation methods compared statistically as averaged estimation error as a function of the signal‑to‑noise ratio. Estimator statistical characteristics – bias, variation, error histogram – derived and analyzed.

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41

Wu, Hao, Yongqiang Cheng, and Hongqiang Wang. "Isometric Signal Processing under Information Geometric Framework." Entropy 21, no. 4 (March 27, 2019): 332. http://dx.doi.org/10.3390/e21040332.

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Information geometry is the study of the intrinsic geometric properties of manifolds consisting of a probability distribution and provides a deeper understanding of statistical inference. Based on this discipline, this letter reports on the influence of the signal processing on the geometric structure of the statistical manifold in terms of estimation issues. This letter defines the intrinsic parameter submanifold, which reflects the essential geometric characteristics of the estimation issues. Moreover, the intrinsic parameter submanifold is proven to be a tighter one after signal processing. In addition, the necessary and sufficient condition of invariant signal processing of the geometric structure, i.e., isometric signal processing, is given. Specifically, considering the processing with the linear form, the construction method of linear isometric signal processing is proposed, and its properties are presented in this letter.

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42

Shen, Tao, Yukari Nagai, M. Udayakumar, K. Narasimhan, R. K. Arvind Shriram, N. Mohanraj, and V. Elamaran. "Automated Genomic Signal Processing for Diseased Gene Identification." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1254–61. http://dx.doi.org/10.1166/jmihi.2019.2726.

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Genomic signal processing (GSP) is the engineering discipline for the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Statistical Computations on DNA Sequences is one of key areas in which GSP can be applied. In this paper, we apply DSP tools on trinucleotide repeat disorders (too many copies of a certain nucleotide triplet in the DNA) to classify any gene sequence into diseased/non-diseased state. Intially, we collected the Gene sequences responsible for trinucleotide repeat disorders from NCBI. Then, we applied GSP techniques to convert the given gene sequence into an indicator sequence, and furthermore we apply Fast Fourier transforms (FFTs) and Discrete Wavelet Transforms (DWTs), followed by statistical feature extraction and the obtained statistical features, fed into an Artificial Neural Network to predict the state of the input genomic sequence.

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43

Nishimura, Yoshihiro, Sasamoto Akira, and Takayuki Suzuki. "Study of Signal Processing for EMAT." Materials Science Forum 670 (December 2010): 345–54. http://dx.doi.org/10.4028/www.scientific.net/msf.670.345.

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EMAT using a magnetostrictive effect was employed to detect flaws in the sample with scaled surface. Chromium molybdenum steel samples were annealed from 600 °C to 900°C for two hours to eight hours and subjected to EMAT to survey its signal properties. The signal output was found to have too much noise to identify internal flaws and to reconstruct flaw images in a computer. This study proposes spectrum analysis methods and statistical methods based on the noise probability to decrease this noise.

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44

Jeyaraj, Pandia Rajan, and Edward Rajan Samuel Nadar. "Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal." Multidimensional Systems and Signal Processing 31, no. 4 (February 17, 2020): 1299–316. http://dx.doi.org/10.1007/s11045-020-00710-7.

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45

LI, XIAOLI, and R. DU. "MONITORING MACHINING PROCESSES BASED ON DISCRETE WAVELET TRANSFORM AND STATISTICAL PROCESS CONTROL." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 03 (September 2004): 299–311. http://dx.doi.org/10.1142/s0219691304000548.

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This paper presents a new method to monitor machining processes based on a combination of discrete wavelet transform (DWT) and statistical process control (SPC), called a multi-scale statistical approach. First, DWT is applied to decompose the sensor signal onto different scales. Next, the detection limits are formed for each decomposed signal components, called the sub-signals, using Shewhart control charts. Finally, by inverse wavelet transform of the threshold crossing points of the sub-signals, malfunctions can be detected. Based on a test on the tool condition monitoring in turning using acoustic emission (AE) signal, it is shown that the new method is effective and robust.

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46

Volosyuk, V. K., V. V. Pavlikov, and S. S. Zhyla. "Statistical Synthesis of Signal Processing Algorithms in Chopper Radiometric System." Физические основы приборостроения 3, no. 3 (September 15, 2014): 86–103. http://dx.doi.org/10.25210/jfop-1403-086103.

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47

Chong-Yung Chi. "Fourier series based nonminimum phase model for statistical signal processing." IEEE Transactions on Signal Processing 47, no. 8 (1999): 2228–40. http://dx.doi.org/10.1109/78.774766.

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48

Sala-Alvarez, J., and G. Vazquez-Grau. "Statistical reference criteria for adaptive signal processing in digital communications." IEEE Transactions on Signal Processing 45, no. 1 (1997): 14–31. http://dx.doi.org/10.1109/78.552202.

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49

Benveniste, A., M. Basseville, A. S. Willsky, K. C. Chou, and R. Nikoukhah. "Multiscale Statistical Signal Processing and Random Fields on Homogeneous Trees." IFAC Proceedings Volumes 25, no. 15 (July 1992): 405–10. http://dx.doi.org/10.1016/s1474-6670(17)50666-7.

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50

Zeng, Xuexing, Jinchang Ren, Zheng Wang, Stephen Marshall, and Tariq Durrani. "Copulas for statistical signal processing (Part I): Extensions and generalization." Signal Processing 94 (January 2014): 691–702. http://dx.doi.org/10.1016/j.sigpro.2013.07.009.

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