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21
Multichannel Blind Deconvolution: Fir Matrix Algebra And Separation Of Multipath Mixtures
, 1996
"... A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and mat ..."
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Cited by 86 (0 self)
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A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and matrix algorithms for use in multichannel /multipath problems. Using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework. Techniques developed for equalization may be applied to source separation and vice versa. Potential applications of these results lie in neural networks with feedforward memory connections, wideband array processing, and in problems with a multiinput, multioutput network having channels between each source and sensor, such as source separation. Particular applications of FIR polynomial matrix alg...
Eigenvector Algorithm For Blind Equalization
 International Signal Processing Workshop on Higher Order Statistics
, 1993
"... This paper introduces a new algorithm for blind equalization which uses a set of cost functions. Each of them guarantees a closed form solution of the equalization problem and approximates the ideal MSE (mean square error) solution. On the basis of an iterative process the best approximation is sele ..."
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Cited by 16 (3 self)
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This paper introduces a new algorithm for blind equalization which uses a set of cost functions. Each of them guarantees a closed form solution of the equalization problem and approximates the ideal MSE (mean square error) solution. On the basis of an iterative process the best approximation is selected. Application of this algorithm is not limited to linear equalizers operating at symbol rate. As a possible generalization to include other areas (such as system identification, decisionfeedback equalization or fractionally spaced equalization), an extension to fractional tap space equalizers is outlined.
Solar wind control of auroral zone geomagnetic activity, Geophys
 Res. Lett
, 1981
"... Abstract. An empirical analysis of solar windmagnetosphere energy coupling functions is reported. Using the technique of linear prediction filtering with 2.5 minute data, we examine the relationship of auroral zone geomagnetic activity to solar wind power input functions which depend on the solar ..."
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Cited by 9 (2 self)
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Abstract. An empirical analysis of solar windmagnetosphere energy coupling functions is reported. Using the technique of linear prediction filtering with 2.5 minute data, we examine the relationship of auroral zone geomagnetic activity to solar wind power input functions which depend on the solar wind quantities VB 2, VBs, or V2Bs. In this analysis a least squares prediction filter or impulse response function which relates a solar wind power function to an auroral zone geomagnetic index is designed directly from the data. We find that the computed impulse response functions have the characteristics of a low pass filter with a time delay which may be dependent on the strength of the energy input. While the AL index is reasonably well related to the solar wind energy functions, the AU index shows a substantially poorer relationship. In addition, high frequency variations of the auroral indices and some substorm expansions are not predictable with solar wind information alone, suggesting that internal magnetospheric processes partially control the AL index. We also find that the e parameter which depends on VB • ' in the solar wind has a poorer relationship to auroral zone geomagnetic activity than a power parameter having a VB, solar wind dependence.
Polynomial matrix whitening and application to the multichannel blind deconvolution problem
 IEEE Conference on Military Communications
"... ..."
Intraframe and interframe coding of speech spectral parameters
, 1996
"... Most low bit rate speech coders employ linear predictive coding (LPC) which models the shortterm spectral information within each speech frame as an allpole lter. In this thesis, we examine various methods that can e ciently encode spectral parameters for every 20 ms frame interval. Line spectral ..."
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Most low bit rate speech coders employ linear predictive coding (LPC) which models the shortterm spectral information within each speech frame as an allpole lter. In this thesis, we examine various methods that can e ciently encode spectral parameters for every 20 ms frame interval. Line spectral frequencies (LSF) are found to be the most e ective parametric representation for spectral coding. Product code vector quantization (VQ) techniques such as split VQ (SVQ) and multistage VQ (MSVQ) are employed in intraframe spectral coding, where each frame vector is encoded independently from other frames. Depending on the product code structure, \transparent coding " quality isachieved for SVQ at 26{28 bits/frame and for MSVQ at 25{27 bits/frame. Because speech is quasistationary, interframe coding methods such as predictive SVQ (PSVQ) can exploit the correlation between adjacent LSF vectors. Nonlinear PSVQ (NPSVQ) is introduced in which a nonparametric and nonlinear predictor replaces the linear predictor used in PSVQ. Regardless of predictor type, PSVQ garners a performance gain of 5{7 bits/frame over SVQ. By interleaving intraframe SVQ with
Exploratory spectral analysis of hydrological time series
 Journal of Stochastic Hydrology and Hydraulics
, 1995
"... Current methods of estimation of the univariate spectral density are reviewed and some improvements are suggested. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements rather than competes with the popular ARIMA model ..."
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Cited by 1 (1 self)
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Current methods of estimation of the univariate spectral density are reviewed and some improvements are suggested. It is suggested that spectral analysis may perhaps be best thought of as another exploratory data analysis (EDA) tool which complements rather than competes with the popular ARIMA model building approach. A new diagnostic check for ARMA model adequacy based on the nonparametric spectral density is introduced. Two new algorithms for fast computation of the autoregressive spectral density function are presented. A new style of plotting the spectral density function is suggested. Exploratory spectral analysis of a number of hydrological time series is performed and some interesting periodicities are suggested for further investigation. The application of spectral analysis to determine the possible existence of long memory in riverflow time series is discussed with long riverflow, treering and mud varve series. A comparison of the estimated spectral densities suggests the ARMA models fitted previously to these datasets adequately describe the low frequency component. The software and data used in this paper are available by anonymous ftp from fisher.stats.uwo.ca in the directory pub\mhts.
Cynomolgus and Rhesus Monkey Visual Figments Application of Fourier Transform Smoothing and Statistical Techniques to the Determination of Spectral Parameters
"... ABSTRACT Microspectrophotometric measurements were performed on 217 photoreceptors from cynom*us, Macaca fascicularis, and rhesus, M. mulatto, monkeys. The distributions of cell types, for rods and blue, green, and red ..."
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ABSTRACT Microspectrophotometric measurements were performed on 217 photoreceptors from cynom*us, Macaca fascicularis, and rhesus, M. mulatto, monkeys. The distributions of cell types, for rods and blue, green, and red
L1 [Spä76, Abd80, RR73b]. ≤ 80 [CM66].
, 2013
"... Version 2.42 Title word crossreference (a, a ′ ) [SR73, SR73]. (a, b) [SMD71, SR73]. (a, bγ) [SR73]. (a, bγ − γ) [SR73]. (a, bσ) [SMD71]. (a, bσ − σ) [SMD71]. (a, γ) [SR73]. (a, γ − γ) [SR73]. + [AI79]. 0 [Fia73, MT78]. 1 [Fia73, MT78, RCL75]. ..."
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Version 2.42 Title word crossreference (a, a ′ ) [SR73, SR73]. (a, b) [SMD71, SR73]. (a, bγ) [SR73]. (a, bγ − γ) [SR73]. (a, bσ) [SMD71]. (a, bσ − σ) [SMD71]. (a, γ) [SR73]. (a, γ − γ) [SR73]. + [AI79]. 0 [Fia73, MT78]. 1 [Fia73, MT78, RCL75].