Workshop on Cyclostationary Signals

Gardner, William A.

doi:10.21236/ada266112

Cited by 4 publications

(6 citation statements)

References 53 publications

(90 reference statements)

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“…A cyclostationary signal (as defined in [8]) is one which, after undergoing nonlinear transformations, will contain certain periodicities. Communication signals are not, in general, periodic.…”

Section: Temporal Higher Order (Cyclostationary) Statisticsmentioning

confidence: 99%

Compressive Temporal Higher Order Cyclostationary Statistics

Lim

Wakin

2015

IEEE Trans. Signal Process.

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The application of nonlinear transformations to a cyclostationary signal for the purpose of revealing hidden periodicities has proven to be useful for applications requiring signal selectivity and noise tolerance. The fact that the hidden periodicities, referred to as cyclic moments, are often compressible in the Fourier domain motivates the use of compressive sensing (CS) as an efficient acquisition protocol for capturing such signals. In this work, we consider the class of Temporal Higher Order Cyclostationary Statistics (THOCS) estimators when CS is used to acquire the cyclostationary signal assuming compressible cyclic moments in the Fourier domain. We develop a theoretical framework for estimating THOCS using the low-rate nonuniform sampling protocol from CS and illustrate the performance of this framework using simulated data.

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Section: Temporal Higher Order (Cyclostationary) Statisticsmentioning

confidence: 99%

Compressive Temporal Higher Order Cyclostationary Statistics

Lim

Wakin

2015

IEEE Trans. Signal Process.

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“…Therefore, assuming that changes in the frequency content of the myoelectric signal are negligible within a few cycles, averaging across cycles can reduce the vari ability of the instantaneous median frequency. When doing so, it is assumed that the surface myoelectric signal may be mod eled as a cyclostationary process within these cycles, i.e., a sto chastic process whose first and second moments are marked by a periodic behavior over an indefinite period of time [13]. This assumption is valid in our application within a limited time in terval (i.e., within a limited number of cycles).…”

Section: Improving Estimation Stability: the Importance Of Cyclicmentioning

confidence: 99%

Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions

Bonato

Roy

Knaflitz

et al. 2001

IEEE Trans. Biomed. Eng.

247

160

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Abstract-The time-dependent shift in the spectral content of the surface myoelectricsignal to lowerfrequencieshas proven to be a useful tool for assessinglocalized muscle fatigue. Unfortunately, the technique has been restricted to constant-force, isometric con tractions because of limitations in the processingmethods used to obtain spectral estimates. A novel approach is proposed for cal culating spectral parameters from the surface myoelectric signal during cyclicdynamic contractions. The procedure was developed using Cohen class time-frequency transfonns to define the instan taneous median and mean frequency during cyclic dynamic con tractions. Changes in muscle length, force, and electrode position contribute to the nonstationarity of the surface myoelectricsignal.These factors, unrelated to localized fatigue, can be constrained and isolated for cyclic dynamic contractions, where they are as sumed to be constant for identical phases of each cycle. Estima tion errors for the instantaneous median and mean frequency are calculated from synthesized signals. It is shown that the instan taneous median frequency is affected by an error slightly lower than that related to the instantaneous mean frequency. In addi tion, we present a sample application to surface myoelectricsignals recorded from the first dorsal interosseous muscle during repeti tive abduction/adduction of the indexfinger against resistance. Re sults indicate that the variability of the instantaneous median fre quency is related to the repeatability of the biomechanics of the exercise.

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“…The most widely used techniques in spectrum sensing and identification of opportunities for transmission in Cognitive Radio (CR) are [7]: detection of signal energy [14], matched filters [15] and detection of cyclostationary characteristics [11]. Assuming a scenario in which radio equipment operates without prior knowledge of the transmitted signals, which in principle is desirable for cognitive radio systems [16], then sensing by matched filters becomes unviable [7].…”

Section: Spectrum Sensingmentioning

confidence: 99%

“…The CAF is a timedomain function that allows to identify if a signal exhibits second-order cyclostationarity [11]. A stochastic process X(t) is defined as cyclostationary if its expected value, x(t) , and its autocorrelation function, R x (t, τ), defined as…”

Section: Formal Definitions For Caf and Scdmentioning

confidence: 99%

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Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

Lima

Barros

Silveira

et al. 2014

IEICE Trans. Commun.

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SUMMARYThe evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

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Workshop on Cyclostationary Signals

Cited by 4 publications

References 53 publications

Compressive Temporal Higher Order Cyclostationary Statistics

Compressive Temporal Higher Order Cyclostationary Statistics

Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions

Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

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