With the rapid growth of some novel services (e.g. video services), the demand for network bandwidth increases dramatically, which induces an intensive desire in allocating network bandwidth with high flexibility and ...
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作者：
Cajigas, I.Malik, W. Q.Brown, E. N.Harvard Univ
Massachusetts Gen Hosp Sch Med Dept Anesthesia & Crit Care Boston MA 02114 USA MIT
Harvard Mit Div Hlth Sci & Technol Cambridge MA 02139 USA MIT
Dept Brain & Cognit Sci Cambridge MA 02139 USA
Over the last decade there has been a tremendous advance in the analytical tools available to neuro-scientists to understand and model neural function. In particular, the point process - generalized linear model (PP-G...
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Over the last decade there has been a tremendous advance in the analytical tools available to neuro-scientists to understand and model neural function. In particular, the point process - generalized linear model (PP-GLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT - an open source neural spike train analysis toolbox for Matlabe (R). By adopting an object-oriented programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of pen-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems. (C) 2012 Elsevier B.V. All rights reserved.
Establishing measures for local stationarity is an open problem in the field of time-frequency analysis. One promising theoretical measure, known as the spread, provides a means for quantifying potential correlation b...
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Establishing measures for local stationarity is an open problem in the field of time-frequency analysis. One promising theoretical measure, known as the spread, provides a means for quantifying potential correlation between signal elements. In this paper we investigate the issue of generalizing techniques developed by the authors to better estimate the spread of a signal. Existing techniques estimate the spread as the rectangular region of support of the associated expected ambiguity function oriented parallel to the axes. By applying Radon Transform techniques we can produce a parameterized model which describes the orientation of the region of support providing tighter estimates of the signal spread. Examples are provided that illustrate the enhancement of the new method.
This paper addresses time-frequency (TF) analysis from a statistical signalprocessing perspective, with the goal of developing a general statistical methodology for TF analysis. We review earlier work on statistical ...
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This paper addresses time-frequency (TF) analysis from a statistical signalprocessing perspective, with the goal of developing a general statistical methodology for TF analysis. We review earlier work on statistical models for nonstationary stochastic signals, including frequency modulated locally stationary processes, which have covariance functions which yield nonnegative Wigner distributions. For such processes, time-frequency spectra may be defined without invoking `local-' or `quasi-' stationarity. These results are extended to include general time-varying linear systems and their associated time-frequency spectra. Any time-varying linear system driven by white noise has associated with it a nonnegative time-frequency spectrum. The bilinear class of time-frequency distributions are estimators of this time-frequency spectrum, as are adaptive methods such as positive time-frequency distributions and adaptive multitaper spectrograms. An analysis of the statistical properties of these estimators, including moments and distributional properties, is reviewed.
An architecture is presented for front-end processing in a wideband array system which samples real signals. Such a system may be encountered in cellular telephony, radar, or low SNR digital communications receivers. ...
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An architecture is presented for front-end processing in a wideband array system which samples real signals. Such a system may be encountered in cellular telephony, radar, or low SNR digital communications receivers. The subbanding of data enables system data rate reduction, and creates a narrowband condition for adaptive processing within the subbands. The front-end performs passband filtering, equalization, subband decomposition and adaptive beamforming. The subbanding operation is efficiently implemented using a prototype lowpass finite impulse response (FIR) filter, decomposed into polyphase form, combined with a Fast Fourier Transform (FFT) block and a bank of modulating postmultipliers. If the system acquires real inputs, a single FFT may be used to operate on two channels, but a channel separation network is then required for recovery of individual channel data. A sequence of steps is described based on data transformation techniques that enables a maximally efficient implementation of the processing stages and eliminates the need for channel separation. Operation count is reduced, and several layers of processing are eliminated.
This paper presents arithmetic implementations which use binary redundant numbers based on carry-save representations. It is well-known that constant-time addition, in which the execution delay is independent of opera...
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This paper presents arithmetic implementations which use binary redundant numbers based on carry-save representations. It is well-known that constant-time addition, in which the execution delay is independent of operand length, is feasible only if the result is expressed in a redundant representation. Carry-save based formats are one type of a redundant representation which can lead to highly efficient implementations of arithmetic operations. In this paper, we discuss two specific carry-save formats that lead to particularly efficient realizations. We illustrate these formats, and the `equal-weight grouping' (EWG) mechanism wherein bits having the same weight are grouped together during an arithmetic operation. This mechanism can reduce the area and delay complexity of an implementation. We present a detailed comparison of implementations based on these two carry-save formats including measurements from VLSI cell layouts. We then illustrate the application of these VLSI cells for multi-operand additions in fast parallel multipliers. Finally, we also indicate the relationship with previous results.
We present a matrix decomposition that can be used to derive features from processes that are described by discrete-time, time-frequency representations. These include, among others, electrocardiograms, brain wave sig...
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We present a matrix decomposition that can be used to derive features from processes that are described by discrete-time, time-frequency representations. These include, among others, electrocardiograms, brain wave signals, seismic signals, vibration and shock signals, speech signals for voice recognition, and acoustic transient signals. The new decomposition is based on a transformation of the basis vectors of the singular value decomposition (SVD) which we call transformed singular value decomposition or TSVD. The transformed basis vectors are obtained by forming linear combinations of the original SVD basis vectors in a way such that the means of the transformed vectors are extrema of each other. The TSVD basis vectors are used to identify concentrations of energy density in the discrete-time, time-frequency representation by time and frequency descriptors. That is, descriptors such as the location in time, the spread in time, the location in frequency and the spread in frequency for each principal concentration of energy density can be obtained from the TSVD terms in the matrix decomposition series. Several examples are presented which illustrate the application of the new matrix decomposition for deriving principal time and frequency features from the discrete-time, time-frequency representations of nonstationary processes. Two of the examples illustrate how the derived time and frequency features can be used to classify individual short duration transient signals into respective classes, that is,: (1) automatically classify sonar signals as belonging to one of ten classes, and (2) automatically classify heartbeat signals as belonging to one of two people.
The proceedings contains 58 papers from the conference of SPIE: advanced signalprocessingalgorithms, Architectures, and implementations VIII. The topics discussed include: blind channel identification and extraction...
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The proceedings contains 58 papers from the conference of SPIE: advanced signalprocessingalgorithms, Architectures, and implementations VIII. The topics discussed include: blind channel identification and extraction of more sources than sensors;blind channel estimation for CDMA systems with orthogonal modulation;blind equalization and source separation with MSK inputs and adaptive blind channel estimation by least-squares smoothing for CDMA.
The aim of the paper is to estimate the contribution of the polarization diversity in high frequency (3 - 30 MHz) direction finding systems. We first describe the peculiarities of H.F. propagation and the resulting si...
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ISBN:
(纸本)0819416207
The aim of the paper is to estimate the contribution of the polarization diversity in high frequency (3 - 30 MHz) direction finding systems. We first describe the peculiarities of H.F. propagation and the resulting signal model involved in computer simulations. Next, we analyze the behavior of some particular direction finding systems using linear and circular geometries and polarization diversity. Some algorithms (non linear frequential analysis, M.U.S.I.C.) are tested in several conditions (narrowband or broadband signals, polarization filtering reiterated or no, sub-sampling). Theoretical and experimental results show that polarization diversity based upon the knowledge of the antenna complex responses improves greatly the efficiency of direction finding.
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