Journal ArticleDOI
Orthogonal, exactly periodic subspace decomposition
Darian Muresan,T.W. Parks +1 more
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TLDR
The detection and estimation of machine vibration multiperiodic signals of unknown periods in white Gaussian noise is investigated and the concept of exactly periodic signals is introduced.Abstract:
The detection and estimation of machine vibration multiperiodic signals of unknown periods in white Gaussian noise is investigated. New estimates for the subsignals (signals making up the received signal) and their periods are derived using an orthogonal subspace decomposition approach. The concept of exactly periodic signals is introduced. This in turn simplifies and enhances the understanding of periodic signals.read more
Citations
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Nested Periodic Matrices and Dictionaries: New Signal Representations for Period Estimation
TL;DR: A new class of techniques to identify periodicities in data that target the period estimation directly rather than inferring the period from the signal's spectrum, obtaining several advantages over the traditional spectrum estimation techniques such as DFT and MUSIC.
Journal ArticleDOI
Cyclo-period estimation for discrete-time cyclo-stationary signals
TL;DR: This paper presents a new method, named as the variability method, to estimate the cyclo-period of a discrete-time Cyclo-stationary signal, essentially based on the time-varying correlation and/or theTime-varies mean, whose estimators are associated with some statistics of blocked signals.
Journal ArticleDOI
Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis
TL;DR: The analysis results of rolling bearing signals show that APMD has excellent ability to identify and extract PCs and is a valid method for rolling bearing fault diagnosis.
Journal ArticleDOI
Searching microsatellites in DNA sequences: approaches used and tools developed
TL;DR: A comparative evaluation of the relative efficiency of some microsatellite search tools with their default settings is performed, finding tools based on stochastic approaches more popular due to their simplicity and added ornamental features.
Proceedings ArticleDOI
Maximum-likelihood period estimation from sparse, noisy timing data
TL;DR: Two new algorithms are presented which represent different compromises between computational and statistical efficiency, having very low computational complexity while attaining good statistical accuracy.
References
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Journal ArticleDOI
Fundamentals of statistical signal processing: estimation theory
TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Book
Statistical signal processing : detection, estimation, and time series analysis
TL;DR: In this article, the authors introduce Rudiments of Linear Algebra and Multivariate Normal Theory, and introduce Neyman-Pearson Detectors and Maximum Likelihood Estimators.
Journal ArticleDOI
Matched subspace detectors
TL;DR: The generalized likelihood ratio (GLR) is the uniformly most powerful invariant detector and the utility of this finding is illustrated by solving a number of problems for detecting subspace signals in subspace interference and broadband noise.
Journal ArticleDOI
Signal processing applications of oblique projection operators
R.T. Behrens,Louis L. Scharf +1 more
TL;DR: The authors show how oblique projections can be used to separate signals from structured noise, damped or undamped interfering sinusoids, and narrow-band noise, and to interpolate missing data samples as a special case of removing impulse noise.