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About 10,000 papers for “Statistical signal processing”

Statistical Signal Processing

Georgios B. Giannakis · ˜The œelectrical engineering handbook/Electrical engineering handbook series/Electrical engineering handbook · 2009

Cited by 2

Statistical Signal Processing

Nandini Kannan, Debasis Kundu · Encyclopedia of Statistical Sciences · 2005

Abstract Statistical signal processing is an important area of research with extensive applications in the fields of communication theory, array processing, seismology, and medical diagnosis. The goal of signal processing is to recover characteristics of the underlying process from observed data. The random nature of the signals underscores the need for statistical techniques in model formulation, estimation, and data analysis. A brief discussion of two models that have been widely studied in the signal processing literature is provided. In addition, different methods of estimation are presented, and some unique characteristics of these models are highlighted.

Cited by 0

Statistical graph signal processing

Shi, Enbing · DR-NTU (Nanyang Technological University) · 2023

This study provides an insight into the application of different filters in Graph Signal Processing (GSP) on different datasets. First, a comprehensive overview of GSP-related concepts is given, including the derivation of graph signals, the computation of Laplace matrices, and their application in various practical scenarios. Next, we discuss in detail the latest GSP filter design methods, covering various linear and non-linear methods. We review graph filtering, graph signal sampling, graph signal compression/reconstruction, graph neural networks, etc., and compare and analyse the advantages and limitations of each type of filter from a theoretical perspective. Four models, linear regression (LR), linear regression graph (LRG), kernel regression (KR) and kernel regression graph (KRG), were selected to process different data sets and the effects of training sample variation and noise interference on the prediction accuracy (i.e., normalised mean square error) of the models were investigated in depth. Through simulation experiments, we found that the performance of all models improved as the number of training samples increased. In some cases, the KR and KRG models outperformed the LR and LRG models, which only capture linear relationships, due to their ability to capture non-linear relationships in the data. In contrast, in noisy environments, the LRG and KRG models have higher robustness in handling additive white Gaussian noise due to the superiority of their Gaussian noise model design. In addition, we observed that the normalised mean square error of all models were approximately stable when the training sample size reached a certain level, which may mean that the performance of the models had reached their limits and further increasing the training data size may not significantly improve the performance.

Cited by 0

Studies In Statistical Signal Processing

Formf Afove, Thmas Kailath, Peiroilmin Organlzanon Name(s et al. · 1990

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Cited by 0

STATISTICAL METHODS FOR SIGNAL PROCESSING

Alfred O. Hero · 2005

This set of notes is the primary source material for the course “Estimation, filtering and detection” taught by Prof. Alfred Hero over the period 1998-2004 at the University of Michigan Ann Arbor. The course pack is the property of Alfred O. Hero III and is copyright protected. Author can be reached at Dept. EECS, University of Michigan, Ann Arbor, MI 48109-2122 Tel: 734-763-0564. email hero@eecs.umich.edu; http://www.eecs.umich.edu/~hero/.

Cited by 6

Studies in Statistical Signal Processing.

T. Kailath · 1984

Abstract : The primary objective of our research is to develop efficient and numerically stable algorithms for nonstationary signal processing problems by understanding and exploiting special structures, both deterministic and stochastic, in the problems. We also strive to establish and broaden links with related disciplines, such as cascade filter synthesis, scattering theory, numerical linear algebra, and mathematical operator theory for the purpose of cross fertilization of ideas and techniques. These explorations have led to new results both in estimation theory and in these other fields, e.g., to new orthogonal cascade digital filter structures, new algorithms for triangular and QR factorization of structured matrices and new techniques for stability testing. For several years, the guiding principle in these studies has been the concept of (Toeplitz-oriented) displacement structure (Kailath, Kung and Morf, (1979)), which generalized and subsumed our earlier work on fast (Chandrasekhar) control and estimation algorithms for state-space models (Morf, Sidhu and Kailath, (1974)). Several authors have since picked up these ideas in a number of fields. A notable such work is a recent book by Heinig and K. Rost of East Germany, entitled 'Algebraic Methods for Toeplitz-Like Matrices and Operators'.

Cited by 0

Wavelet-Based Statistical Signal Processing

Using Hidden Markov, Edicsnumbers:. Wavelets, Statisticalmodeling Revised:august Fax:(){ January · 2008

Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the statistical dependencies and nonGaussian statistics encountered with real-world signals.

Cited by 0

Statistical Signal Processing in Neuroscience

Karim Oweiss, José C. Prı́ncipe, Don H. Johnson · EURASIP Journal on Advances in Signal Processing · 2010

When the three of us decided to coedit a special issue on Statistical Signal Processing in Neuroscience, we implicitly knew that the submitted papers would span a wide field, the field measured in coordinate system with disparate axes: engineering-oriented or scientific; analysis of single neuron, populations or aggregate activity recordings; and known signal processing techniques or novel ones “invented” for the neuroscience application. We were not disappointed. The six papers chosen for this special issue represent the best of the submitted papers. The papers span a wide variety of very challenging problems. Some focus on basic estimation theory for reconstructing sensory stimuli from spike trains, while others focus on the prediction of seizure activity from EEG signals in clinical applications. Two papers are inspired by recent striking advances in Brain Machine Interface technology that is aimed at improving our basic understanding of brain function, and possibly restoring, replacing, or augmenting it when it is damaged. The paper by Lazar and Pnevmatikakis, “Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds”, employs a fresh idea—time encoding machines—inspired by neural systems that holds promise for new signal processing applications. This paper explores how to optimally decode population responses created within the time-machine encoding framework. Darmanjian and Principe’s paper, “Spatial-temporal clustering of neural data using linked-mixtures of hidden Markov models”, looks at HMM decoding of new populations using unsupervised hierarchical clustering for brainmachine interface (BMI) applications, focusing on motor control. Next, the paper by Filho et al., “Magnitude squared of coherence to detect imaginarymovement”, analyzes ERP data using cross-spectral analysis on a large scale, again for a motor BMI application. In contrast, Krusienski’s paper “A Method for visualizing independent spatio-temporal patterns of brain activity” uses a new spatio-temporal analysis of EEG data to tease out the basic independent patterns from which the measurements were constructed. Continuing along the axis of using modern algorithms, Liu, Pang and Wang explore in their paper “Epileptic seizure prediction by a system of particle filter associated with a neural network” using statistical bayesian filters to analyze EEG data. Finally, Hu et al. propose using a realistic nonlinear model rather than a linearized version to analyze multivoxel behavior in fMRI images in their paper “Nonlinear analysis of the BOLD signal.” We hope you enjoy these articles as much as we did reviewing them.

Cited by 8Open access

Statistical Techniques for Signal Processing.

S. A. Kassam · 1983

Abstract : The area of nonlinear edge-preserving robust smoothing was one area of focus for our research. In this area a dissertation was completed. We have been able to give deterministic and statistical characterizations of the performance of some useful types of nonlinear filters which may be thought of as arising from the classical robust estimates of location (L- and M- estimates), and we have demonstrated their applicability in image processing. We are continuing to obtain new results in this area in our current work. In the area of nonparametric detection the case of narrowband signals in noise has been studied. We have established the natural counterparts of the sign-detection schemes for this class of signals. This material is currently being prepared for publication in a technical journal. A paper on quantization of data in narrowband signal detection was also published during the last grant year. On the subject of optimum quantization of data for signal detection (hypothesis testing) a comprehensive exposition has been written for publication as a chapter in a book to be published next year. These results on statistical optimization of quantization in detection systems are of considerable interest for digital implementations. Currently being revised for publication also is a paper on optimum quantization in matched filtering and smoothing of data. Finally, a paper on multi-input robust Wiener smoothing was also published during the last grant year.

Cited by 0

Statistical Signal Processing for Graphs

E Acar, D Dunlavy, T Kolda et al. · Arizona State University Library Digital Repository (Arizona State University) · 2015

abstract: Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.

Cited by 0Open access