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65
Cognitive Radio: BrainEmpowered Wireless Communications
, 2005
"... Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a softwaredefined radio, is defined as an intelligent wireless communication system that is aware of its environment and use ..."
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Cited by 610 (0 self)
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Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a softwaredefined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understandingbybuilding to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: • highly reliable communication whenever and wherever needed; • efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radioscene analysis. 2) Channelstate estimation and predictive modeling. 3) Transmitpower control and dynamic spectrum management. This paper also discusses the emergent behavior of cognitive radio.
Unitary Equivalence: A New Twist On Signal Processing
, 1995
"... Unitary similarity transformations furnish a powerful vehicle for generating infinite generic classes of signal analysis and processing tools based on concepts different from time, frequency, and scale. Implementation of these new tools involves simply preprocessing the signal by a unitary transfo ..."
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Cited by 50 (15 self)
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Unitary similarity transformations furnish a powerful vehicle for generating infinite generic classes of signal analysis and processing tools based on concepts different from time, frequency, and scale. Implementation of these new tools involves simply preprocessing the signal by a unitary transformation, performing standard processing techniques on the transformed signal, and then (in some cases) transforming the resulting output. The resulting unitarily equivalent systems focus on the critical signal characteristics in large classes of signals and, hence, prove useful for representing and processing signals that are not well matched by current techniques. As specific examples of this procedure, we generalize linear timeinvariant systems, orthonormal basis and frame decompositions, and joint timefrequency and timescale distributions, illustrating the utility of the unitary equivalence concept for uniting seemingly disparate approaches proposed in the literature. This work...
Multidimensional QuasiEigenfunction Approximations and Multicomponent AMFM Models
 IEEE TRANS. IMAGE PROC
, 2000
"... We develop multicomponent AMFM models for multidimensional signals. The analysis is cast in a generaldimensional framework where the component modulating functions are assumed to lie in certain Sobolev spaces. For both continuous and discrete LSI systems with AMFM inputs, powerful new approximat ..."
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Cited by 31 (12 self)
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We develop multicomponent AMFM models for multidimensional signals. The analysis is cast in a generaldimensional framework where the component modulating functions are assumed to lie in certain Sobolev spaces. For both continuous and discrete LSI systems with AMFM inputs, powerful new approximations are introduced that provide closed form expressions for the responses in terms of the input modulations. The approximation errors are bounded by generalized energy variances quantifying the localization of the filter impulse response and by Sobolev norms quantifying the smoothness of the modulations. The approximations are then used to develop novel spatially localized demodulation algorithms that estimate the AM and FM functions for multiple signal components simultaneously from the channel responses of a multiband linear filterbank used to isolate components. Two discrete computational paradigms are presented. Dominant component analysis estimates the locally dominant modulations in a signal, which are useful in a variety of machine vision applications, while channelized components analysis delivers a true multidimensional multicomponent signal representation. We demonstrate the techniques on several images of general interest in practical applications, and obtain reconstructions that establish the validity of characterizing images of this type as sums of locally narrowband modulated components.
Measuring timefrequency information content using the Rényi entropies
 IEEE Trans. on Info. Theory
, 2001
"... Abstract—The generalized entropies of Rényi inspire new measures for estimating signal information and complexity in the time–frequency plane. When applied to a time–frequency representation (TFR) from Cohen’s class or the affine class, the Rényi entropies conform closely to the notion of complexity ..."
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Cited by 16 (0 self)
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Abstract—The generalized entropies of Rényi inspire new measures for estimating signal information and complexity in the time–frequency plane. When applied to a time–frequency representation (TFR) from Cohen’s class or the affine class, the Rényi entropies conform closely to the notion of complexity that we use when visually inspecting time–frequency images. These measures possess several additional interesting and useful properties, such as accounting and crosscomponent and transformation invariances, that make them natural for time–frequency analysis. This paper comprises a detailed study of the properties and several potential applications of the Rényi entropies, with emphasis on the mathematical foundations for quadratic TFRs. In particular, for the Wigner distribution, we establish that there exist signals for which the measures are not well defined. Index Terms—Complexity, Rényi entropy, time–frequency analysis, Wigner distribution.
Beyond timefrequency analysis: Energy densities in one and many dimensions
, 1998
"... 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 useful when the effect of A is to be ignored; the second is covariant to A and meas ..."
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Cited by 16 (4 self)
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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 useful when the effect of A is to be ignored; the second is covariant to A and measures the “A ” content of signals. We also consider joint densities for multiple operators and, in the process, provide an alternative interpretation of Cohen’s general construction for joint distributions of arbitrary variables.
Generalized Evolutionary Spectral Analysis and the Weyl Spectrum of Nonstationary Random Processes
, 1997
"... The evolutionary spectrum (ES) is a "timevarying power spectrum" of nonstationary random processes. Starting from an innovations system interpretation of the ES, we introduce the generalized evolutionary spectrum (GES) as a novel family of timevarying power spectra. The GES contains the ..."
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Cited by 14 (5 self)
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The evolutionary spectrum (ES) is a "timevarying power spectrum" of nonstationary random processes. Starting from an innovations system interpretation of the ES, we introduce the generalized evolutionary spectrum (GES) as a novel family of timevarying power spectra. The GES contains the ES and the recently introduced transitory evolutionary spectrum as special cases. We consider the problem of finding an innovations system for a process characterized by its correlation function, and we discuss the connection between GES analysis and the class of underspread processes. We furthermore show that another special case of the GES, a novel timevarying power spectrum that we call Weyl spectrum, has substantial advantages over all other members of the GES family. The properties of the Weyl spectrum are discussed, and its superior performance is verified experimentally for synthetic and realdata processes. This work was supported by FWF Grants P10012 OPH and S7001MAT. 1 Introduction S...
ModulationScale Analysis for Content Identification
 IEEE Transactions on Signal Processing
, 2004
"... For nonstationary signal classification, e.g., speech or music, features are traditionally extracted from a timeshifted, yet short data window. For many applications, these shortterm features do not efficiently capture or represent longer term signal variation. Partially motivated by human auditio ..."
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Cited by 13 (0 self)
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For nonstationary signal classification, e.g., speech or music, features are traditionally extracted from a timeshifted, yet short data window. For many applications, these shortterm features do not efficiently capture or represent longer term signal variation. Partially motivated by human audition, we overcome the deficiencies of shortterm features by employing modulationscale analysis for longterm feature analysis. Our analysis, which uses timefrequency theory integrated with psychoacoustic results on modulation frequency perception, not only contains shortterm information about the signals, but also provides longterm information representing patterns of time variation. This paper describes these features and their normalization. We demonstrate the effectiveness of our longterm features over conventional shortterm features in contentbased audio identification. A simulated study using a large data set, including nearly 10 000 songs and requiring over a billion audio pairwise comparisons, shows that modulationscale features improves content identification accuracy substantially, especially when time and frequency distortions are imposed. Index TermsAudio fingerprinting, audio identification, audio retrieval, auditory classification, content identification, feature extraction, feature normalization, longterm features, modulation features, modulation scale, modulation spectrum, pattern recognition, shortterm features, 2D features.
Techniques of EMG signal analysis: detection, processing, classification and Applications
, 2006
"... Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of t ..."
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Cited by 13 (0 self)
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Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.
Optimizing TimeFrequency Kernels for Classification
, 2001
"... In many pattern recognition applications, features are traditionally extracted from standard timefrequency representations (TFRs). This assumes that the implicit smoothing of, say, a spectrogram is appropriate for the classification task. Making such assumptions may degrade classification performa ..."
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Cited by 12 (1 self)
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In many pattern recognition applications, features are traditionally extracted from standard timefrequency representations (TFRs). This assumes that the implicit smoothing of, say, a spectrogram is appropriate for the classification task. Making such assumptions may degrade classification performance. In general, any timefrequency classification technique that uses a singular quadratic TFR (e.g., the spectrogram) as a source of features will never surpass the performance of the same technique using a regular quadratic TFR (e.g., Rihaczek or WignerVille). Any TFR that is not regular is said to be singular. Use of a singular quadratic TFR implicitly discards information without explicitly determining if it is germane to the classification task. We propose smoothing regular quadratic TFRs to retain only that information that is essential for classification. We call the resulting quadratic TFRs classdependent TFRs. This approach makes no a priori assumptions about the amount and type of timefrequency smoothing required for classification. The performance of our approach is demonstrated on simulated and real data. The simulated study indicates that the performance can approach the Bayes optimal classifier. The realworld pilot studies involved helicopter fault diagnosis and radar transmitter identification.