464 research outputs found

    MDL-BASED JOINT DENOISING AND COMPRESSION OF INTRACORTICAL SIGNALS

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    Intra-cortical signals are usually affected by high levels of noise (0 dB SNR is not uncommon) either due to the recording equipment or to magnetical and electrical couplings between surrounding sources and the recording system. Besides from hindering effective exploitation of the information content in the signals, noise also influences the bandwidth needed to transmit them, which is a problem especially when a large number of channels are to be recorded. In this paper we propose a novel technique for joint denoising and compression of intra-cortical signals based on the Minimum Description Length principle (MDL). This method was tested on simulated signals and the results showed that the proposed technique achieves improvements in SNR (up to .6 dB over MNML for very noisy signals) and compression ratios greater than alternative denoising/compression methods

    Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings

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    ECG Signal Compression Using Discrete Wavelet Transform

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    Wavelet Based Speech Strategy in Cochlear Implant

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    Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition

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    Title on author’s file: Classification of mechanomyogram signal using wavelet packet transform and singular value decomposition for multifunction prosthesis control2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

    Drift Removal in Plant Electrical Signals via IIR Filtering Using Wavelet Energy

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Plant electrical signals often contains low frequency drifts with or without the application of external stimuli. Quantification of the randomness in plant signals in a stimulus-specific way is hindered because the knowledge of vital frequency information in the actual biological response is not known yet. Here we design an optimum Infinite Impulse Response (IIR) filter which removes the low frequency drifts and preserves the frequency spectrum corresponding to the random component of the unstimulated plant signals by bringing the bias due to unknown artifacts and drifts to a minimum. We use energy criteria of wavelet packet transform (WPT) for optimization based tuning of the IIR filter parameters. Such an optimum filter enforces that the energy distribution of the pre-stimulus parts in different experiments are almost overlapped but under different stimuli the distributions of the energy get changed. The reported research may popularize plant signal processing, as a separate field, besides other conventional bioelectrical signal processing paradigms.This work was supported by EU FP7 project PLants Employed As SEnsor Devices (PLEASED), EC grant agreement number 296582

    Wavelet Theory

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    The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior
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