Browsing by Subject "Time Frequency and Spectral Analysis"
Now showing items 120 of 59

An Adaptive OptimalKernel TimeFrequency Representation
(19951001)Timefrequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals. Representations with signal dependent kernels can overcome this ... 
Applications of Adaptive Time Frequency Representations to Underwater Acoustic Signal Processing
(19911101)The authors describe the application of an adaptive optimal kernel (AOK) timefrequency representation to the processing of underwater acoustic data. The optimal kernel is a signaldependent radially Gaussian function. ... 
Beyond Time Frequency Analysis: Energy Densities in One and Many Dimensions
(19940401)Given a unitary operator <i>A</i> representing a physical quantity of interest, we employ concepts from group representation theory to define two natural signal energy densities for <i>A</i>. The first is invariant to ... 
Beyond Time Frequency Analysis: Energy Densities in One and Many Dimensions
(19980901)Given a unitary operator A representing a physical quantity of interest, we employ concepts from group representation theory to define two natural signal energy densities for A. The first is invariant to A and proves ... 
Blind Quadratic and Time Frequency Based Detectors from Training Data
(19950120)Timefrequency based methods, particularly quadratic (Cohen'sclass) representations, are often considered for detection in applications ranging from sonar to machine monitoring. We propose a method of obtaining nearoptimal ... 
A Canonical Covariance Based Method for Generalized Joint Signal Representations
(19960420)Generalized joint signal representations extend the scope of joint timefrequency representations to a richer class of nonstationary signals. Cohen's marginalbased generalized approach is canonical from a distributional ... 
Covariant Time Frequency Representations Through Unitary Equivalence
(19960301)We propose a straightforward characterization of all quadratic timefrequency representations covariant to an important class of unitary signal transforms (namely, those having two continuousvalued parameters and an ... 
Data Driven Signal Detection and Classification
(19970120)In many practical detection and classification problems, the signals of interest exhibit some uncertain nuisance parameters, such as the unknown delay and Doppler in radar. For optimal performance, the form of such parameters ... 
Decomposition of seismic signals via timefrequency representations
(19960115)In this paper we discuss the use of a timefrequency representation, the Wigner distribution, for the decomposition and characterization of seismic signals. The advantage of the Wigner distribution over other representations, ... 
Design of Training Data Based Quadratic Detectors with Application to Mechanical Systems
(19960120)Reliable detection of engine knock is an important issue in the design and maintenance of high performance internal combustion engines. Cost considerations dictate the use of vibration signals, measured at the engine ... 
Diverging moments and parameter estimation
(20040115)Heavy tailed distributions enjoy increased popularity and become more readily applicable as the arsenal of analytical and numerical tools grows. They play key roles in modeling approaches in networking, finance, hydrology ... 
Enhanced Pseudo Power Signatures for Nonstationary Signal Classification: The Projector Approach
(19990501)The classification of nonstationary signals of unknown duration is of great importance in areas like oil exploration, moving target detection, and pattern recognition. In an earlier work, we provided a solution to this ... 
Enhanced signatures for event classification: The projector approach
(19981001)The classification of nonstationary signals of unknown duration is of great importance in areas like oil exploration, moving target detection, and pattern recognition. In an earlier work, we provided a solution to this ... 
Equivalence of Generalized Joint Signal Representations of Arbitrary Variables
(19961220)Joint signal representations (JSRs) of arbitrary variables generalize timefrequency representations (TFRs) to a much broader class of nonstationary signal characteristics. Two main distributional approaches to JSRs of ... 
Generalized Joint Signal Representations and Optimum Detection
(19960120)Generalized joint signal representations (JSRs) extend the scope of joint timefrequency representations (TFRs) to a richer class of nonstationary signals, but their use, just as in the case of TFRs, has been primarily ... 
Hybrid Linear / Bilinear TimeScale Analysis
(19990101)We introduce a new method for the timescale analysis of nonstationary signals. Our work leverages the success of the "timefrequency distribution series / crossterm deleted representations" into the timescale domain ... 
Improved TimeFrequency Filtering of SignalAveraged Electrocardiograms
(19950115)A recently proposed timefrequency filtering technique has shown promising results for the enhancement of signalaveraged electrocardiograms. This method weights the shorttime Fourier transform (STFT) of the ensembleaveraged ... 
Integral Transforms Covariant to Unitary Operators and their Implications for Joint Signal Representations
(19960601)Fundamental to the theory of joint signal representations is the idea of associating a variable, such as time or frequency, with an operator, a concept borrowed from quantum mechanics. Each variable can be associated ... 
Joint Distributions of Arbitrary Variables Made Easy
(19981001)In this paper, we propose a simple framework for studying certain distributions of variables beyond timefrequency and timescale. When applicable, our results turn the theory of joint distributions of arbitrary variables ... 
A Limitation of the Kernel Method for Joint Distributions of Arbitrary Variables
(19960201)Recently, Cohen has proposed a construction for joint distributions of arbitrary physical quantities, in direct generalization of joint timefrequency representations. Actually this method encompasses two approaches, one ...