An asynchronous scale decomposition for biomedical signals

Can, Azime; Sejdić, Ervin; Chaparro, Luıs F.

doi:10.1109/spmb.2011.6120107

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Non-uniform samples appear in many signal analysis applications as a result of signal-dependent sampling algorithms [1], [2], [3], [4], [5]. The density of the samples drawn by any of the signal-dependent sampling algorithms increases with the signal's activity in time.…”

Section: Introductionmentioning

confidence: 99%

Discrete prolate spheroidal sequence based filter banks for the analysis of nonstationary signals

Can-Cimino

Chaparro

Sejdić

2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Filter banks are often used in the analysis and the synthesis of signals. By using cascade or parallel connected filters, multi-resolution analysis of a signal in different sub-bands is possible. A method based on the filter banks using discrete prolate spheroidal sequences (DPSS) is proposed in this paper. DPSS are solutions to an energy maximization problem in a limited bandwidth context; their time-frequency concentration aspect is exploited in the proposed design. A filter bank is derived using DPSS sequences and their modulated counter parts and is utilized in the analysis of non-stationary signals. The designed filter-bank achieves a concise representation of a non-stationary signal by maximizing the spectral energy in each sub-band.

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Section: Introductionmentioning

confidence: 99%

Discrete prolate spheroidal sequence based filter banks for the analysis of nonstationary signals

Can-Cimino

Chaparro

Sejdić

2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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“…Such representation allows us to obtain a signal decomposition that generalizes the Haar wavelet representation [26]. As we will show, this is accomplished by latticing the time-frequency plane choosing fixed frequency ranges and allowing the time-windows to be set by the signal in each of these frequency ranges.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Representation and Processing of Nonstationary Signals : A Time-Frequency Framework

Chaparro

Sejdić

Can

et al. 2013

IEEE Signal Process. Mag.

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N onstationarity relates to the variation over time of the statistics of a signal. Therefore, signals from practical applications that are realizations of nonstationary processes are difficult to represent and to process. In this article, we provide a comprehensive discussion of the asynchronous representation and processing of nonstationary signals using a time-frequency framework. Power consumption and type of processing imposed by the size of the devices in many applications motivate the use of asynchronous, rather than conventional synchronous, approaches. This leads to the consideration of nonuniform, signal-dependent level-crossing (LC) and asynchronous sigma delta modulator (ASDM)-based sampling. Reconstruction from

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An asynchronous scale decomposition for biomedical signals

Cited by 2 publications

References 15 publications

Discrete prolate spheroidal sequence based filter banks for the analysis of nonstationary signals

Discrete prolate spheroidal sequence based filter banks for the analysis of nonstationary signals

Asynchronous Representation and Processing of Nonstationary Signals : A Time-Frequency Framework

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