SMART PARTIAL LEAST-SQUARES

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2007, Article ID 60949, 13 pagesdoi:10.1155/2007/60949Research ArticleEqualization of Loudspeaker and Room Responses UsingKautz Filters: Direct Least Squares DesignMatti Karjalainen and Tuomas PaateroD[r]

EURASIP Journal on Applied Signal Processing 2003:6, 555–564c 2003 Hindawi Publishing CorporationA Methodology for Rapid Prototyping Peak-ConstrainedLeast-Squares Bit-Serial Finite Impulse ResponseFilters in FPGAsAlex CarreiraDepartment of Electrical and Computer Engineering, University of C[r]

Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2008, Article ID 271984, 14 pagesdoi:10.1155/2008/271984Research ArticleNLOS Identification and Weighted Least-Squares Localizationfor UWB Systems Using Multipath Channel Statistics˙Ismail G¨uvenc¸, Chi[r]

then this cost function is optimized versus the channel ma-trix. In the next step, the derived equation is implementediteratively by applying the matrix inversion lemma. Finally,the derived iterative algorithm is combined with DDA tobe implemented as a blind MIMO channel tracking algo-rithm.The rest[r]

EURASIP Journal on Applied Signal Processing 2004:13, 1993–2001c 2004 Hindawi Publishing CorporationNon-Data-Aided Feedforward Carrier FrequencyOffset Estimators for QAM Constellations:A Nonlinear Least-Squares ApproachY. Wa n gDepartment of Electrical Engineering, Texas A&M U[r]

For the past decade, the design of variable fractional-delay(VFD) digital filters became an important topic in digitalsignal processing due to their wide applications in signalprocessing and communication systems such as comb filterdesign, sample rate conversion, tunable modulator andacoustic system [[r]

in CDMA systems), then the rays overlap. When this situa-tion occurs, it results in significant errors in the estimation ofthe time and amplitude of arrival of the prompt ray. Figure 1illustrates the combined impulse response of a two-ray chan-nel using a conventional pulse shape in a CDMA IS-95 sys-[r]

is in the thousands andkis much smaller, you can savemost of the computer time in later runs of PROC SYSLIN by reusing the SSCP matrix rather thanrecomputing it.The SYSLIN procedure can process TYPE=CORR, COV, UCORR, UCOV, or SSCP data sets.TYPE=CORR and TYPE=COV data sets, usually created by the CO[r]

atiment Euler (A.202),Avenue Georges Lemaitre 4, B-1348 Leuven-Heverlee, BelgiumReceived 27 September 2007; Accepted 27 September 2007Copyright © 2007 Nicola Mastronardi et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, d[r]

that, added to the current approximation,give the next approximation. In the context of least-squares, the matrix [α], equal toone-half times the Hessian matrix, is usually called the curvature matrix.Equation (15.5.3), the steepest descent formula, translates toδal= constant × βl(15.5[r]

For situations where the stationarity of the time series is in question, the VARMAX procedureprovides tests to aid in determining the presence of unit roots and cointegration. These tests includethe following: Dickey-Fuller tests Johansen cointegration test for nonstationary vector processes of in[r]

4i + UiVới mẫu số liệu trên, bằng phần mềm EVIEWS, ta ước lượng được và được kết quả sau:Bảng báo cáo 1 Dependent Variable: GDPMethod: Least SquaresDate: 01/19/13 Time: 13:45Sample: 2005:1 2012:2Included observations: 30Variable Coefficient Std. Error t-Statistic Prob. C -46.56951 10.49795 -4[r]

whole is non-convex, making the search for globaloptimal difficult. Note that the root of the optimiza-tion difficulty for S3VMs is the non-convex propertyof the second term in the objective function. We willpropose a novel approach which can deal with thisproblem. We introduce an efficient approximati[r]

We usually also need to know the accuracy with which parameters are de-termined by the data set. In other words, we need to know the likely errors ofthe best-fit parameters.Finally, it is not uncommon in fitting data to discover that the merit functionis not unimodal, with a single minimum. In some ca[r]

strength versus number of steps from a purely statistical point of view is not observed.This is probably due to the choice of starting stress for each test which was very variablebecause each test covered a different value of R compared to the prior test.Conventional S–N tests conducted at 420 Hz on[r]

Dependent Variable: PRICE Method: Least Squares Date: 06/30/11 Time: 13:34 Sample: 1 85 Included observations: 85 Variable Coefficient Std. Error t-Statistic Prob.

5.3 ratval evaluate a rational function5.7 dfridr numerical derivative by Ridders’ method5.8 chebft fit a Chebyshev polynomial to a function5.8 chebev Chebyshev polynomial evaluation5.9 chder derivative of a function already Chebyshev fitted5.9 chint integrate a function already Chebyshev fitted5.10 ch[r]

5.3 ratval evaluate a rational function5.7 dfridr numerical derivative by Ridders’ method5.8 chebft fit a Chebyshev polynomial to a function5.8 chebev Chebyshev polynomial evaluation5.9 chder derivative of a function already Chebyshev fitted5.9 chint integrate a function already Chebyshev fitted5.10 ch[r]

◦.For each crosstalk cancellation system, various inverse filterlengths ranging from 50 to 400 samples with an interval of 50are tested. Generally, the crosstalk cancellation performanceis not quite sensitive to the delay value; however, anoptimal delay value is selected for each method separatelyso[r]

more accurate [79, p. 68].) The above algorithm is also more stable, that is,accumulated round-off errors will not cause it to diverge [79, p. 68].In Section 4.3 we introduced the Gram–Schmidt method for performing theorthonormal transformation F. In the three ensuing sections, we shall detail thism[r]