statistical signal processing algorithms
MIA Sig: multiplex chromatin interaction analysis by signal processing and statistical algorithms
... calling algorithms exist for Hi-C data (Zufferey et ...1D signal track then apply wavelet transformation (Mallat [19]) to smooth the signal while retaining clear change points (Additional file 1: ...
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The Language of Interpersonal Interaction: An Interdisciplinary Approach to Assessing and Processing Vocal and Speech Data
... speech signal processing and statistical text-mining and natural language processing (Busso, Lee, & Narayanan, 2009; Gaut, Steyvers, Imel, Atkins, & Smyth, ...level, signal ...
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Statistical Methods for Signal Processing with Application to Automatic Accent Recognition
... the algorithms of discriminant function, support vector machine, and k-nearest ...Since signal data usually exhibit the feature of High Dimension Low Sample Size, it is crucial in the automatic accent ...
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Array signal processing algorithms for localization and equalization in complex acoustic channels
... of algorithms that incorporate IID and spectral cues have been pro- posed for sound source localization using the HRTF ...ceived signal, and identify the source locations through a pattern matching [72, ...
183
Analysis of Patient-Robot Interaction Using Statistical and Signal Processing Methods.
... learning algorithms can automatically generate models, including rules and patterns, from ...learning algorithms are used to identify functions for mapping inputs to desired outputs based on training ...
170
Adaptive independent sticky MCMC algorithms
... and statistical signal ...(MCMC) algorithms, to sample efficiently from any bounded target probability density function ...of algorithms employs adaptive non-parametric proposal densities, ...
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Statistical Signal Processing of EEG Signals for Lie Detection
... learning algorithms. Through EEG signal processing, we automatically detected brain waves corresponding to different mental activity patterns to uncover the critical items from the non critical ...
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Statistical signal processing techniques for visual post production
... The problem of transfer of statistics is rst addressed. It transpires that this funda- mental process underlies several key problems in lm grading. This can be understood by considering an image as of features samples. ...
137
Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids
... Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired ...conventional ...
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Development of Digital Signal Processing and Statistical Classification Methods for Distinguishing Nasal Consonants
... In this thesis, we summarized the recent work on the topic of identifying between nasal consonants. We have made valuable trials on feature extraction and we have conducted experiments using the most popular ...
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Acoustic Signal Processing Algorithms for Reverberant Environments
... The approach of this thesis is to explore how the intrinsic properties of re- verberation can be exploited to improve acoustic signal processing algorithms. A general approach to soundfield modelling ...
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A Novel Approach for Voice Activity Detection Using Noise Energy from Spectrum
... VAD algorithms operate by taking in a digitized audio signal, processing this signal, extracting particular features from the processed signal, passing the extracted features of the ...
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A Review on Statistical Signal Processing of EEG Signals for Lie Detection
... learning algorithms. Through EEG signal processing, we automatically detected brain waves corresponding to different mental activity patterns to uncover the critical items from the non critical ...
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Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices
... digital signal processing, these techniques are referred to as blind source separation (BSS) and rely on statistical assumptions on the source ...ICA algorithms do not per- mit to introduce ...
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Implementation of Multisensor Data Fusion Algorithm
... (1) signal or image processing; (2) computational and numerical techniques and algorithms; (3) information theoretical, statistical, and probabilistic methods; (4) sensor modeling, sensor ...
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The use of Digital Signal Processing Algorithms for Electrophysiological Diagnostics of Cardiovascular Diseases
... According to these basic cycles, an ECG model (artificial ECG) with specified characteristics: frequency of cardiac contractions (heart rate), average height of RS-waves can be modeled. An example of the ECG for the case ...
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DESIGN AND EVALUATION OF TEST BED SOFTWARE FOR A SMART ANTENNA SYSTEM SUPPORTING WIRELESS COMMUNICATION IN RURAL AREAS. Michael David Panique
... loop algorithms such as least mean squares, sample matrix inversion, and minimum variance are all iterative algorithms which continually refine the beamforming weights over a set number of ...These ...
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A Dataflow based Hardware Design Methodology for Digital Signal Processing Algorithms.
... border processing to keep the 2D filtering ...higher processing throughput than Case 2 if input and output devices use Row Processors efficiently to process data ...
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Optimization and Implementation of the Wavelet Based Algorithms for Embedded Biomedical Signal Processing
... In existing literature, there are several contributions on using ASICs and Field Programmable Gate Arrays (FPGAs), which are a type of PLDs, in wavelet- based processing of biomedical signals, and especially ECG. ...
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Fpga Implementation Of 16-Bit Radix-2 Fft Architecture
... a signal from its original domain to a representation in the frequency domain and vice ...FFT algorithms depend upon the factorization of N, but there are FFTs with O(N log N) complexity for all N, even for ...
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