Noncoherent receivers are attractive for pulsed UWB systems due to the implementation simplicity. To alleviate the noise effect in detecting UWB PPM signals, this letter proposes a simple yet flexible weighted noncoherent receiver structure, which adopts a square-law integrator multiplied with a window function.
標(biāo)簽： implementation Noncoherent attractive simplicity
上傳時間： 2013-12-01
上傳用戶：wys0120
1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if A = 1 2 3 then B = 3 2 1 4 5 6 6 5 4 7 8 9 9 8 7 Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen both A and reverse(A). 2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output: Enter k>0: 100 144 is the smallest fibonacci number greater than or equal to 100. It is the 12th fibonacci number.
標(biāo)簽： dimensions arbitrary function reverse
上傳時間： 2016-04-16
上傳用戶：waitingfy
The BeeStack Application Development Guide describes how to develop an application for BeeStack, including discussions on major considerations for commercial applications. This document is intended for software developers who write applications for BeeStack-based products using Freescale development tools. It is assumed the reader is a programmer with at least rudimentary skills in the C programming language and that the reader is already familiar with the edit/compile/debug process.
標(biāo)簽： BeeStack Application Development application
上傳時間： 2016-04-17
上傳用戶：lijianyu172
aiNet application is a very powerful and a very simple tool for solving the problems which are usually solved with artificial neural networks (ANN). All possible tests we had run proved that the results obtained with aiNet are at least as good as the results obtained with some other ANNs. Let us state some of aiNet抯 features. (c) aiNet 1995-1997
標(biāo)簽： very application powerful problems
上傳時間： 2014-01-16
上傳用戶：wang5829
This book is for the experience and not the same level of the design process so prepared by the staff, of course, the reader should at least be able to prepare a simple C language program. On the C language are learning readers, this book is any C language tutorial excellent supporting materials, to be able to answer all relevant questions.
標(biāo)簽： the experience prepared process
上傳時間： 2013-12-20
上傳用戶：jcljkh
GloptiPoly 3: moments, optimization and semidefinite programming. Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of Moments (GPM), an infinite-dimensional optimization problem which can be viewed as an extension of the classical problem of moments [8]. From a theoretical viewpoint, the GPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to cite a few. In addition, and despite a rather simple and short formulation, the GPM has a large number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For an account of various methodologies as well as some of potential applications, the interested reader is referred to [1, 2] and the nice collection of papers [5].
標(biāo)簽： optimization semidefinite programming GloptiPoly
上傳時間： 2016-06-05
上傳用戶：lgnf
Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial di?erential equations.
標(biāo)簽： introductory Numerical Computing textbook
上傳時間： 2016-07-04
上傳用戶：思琦琦
Toolbox for Numerical Computing with MATLAB （by Cleve Moler). Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial differential equations.
標(biāo)簽： Numerical Computing MATLAB with
上傳時間： 2014-01-01
上傳用戶：guanliya
this directory contains the following: * The acdc algorithm for finding the approximate general (non-orthogonal) joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices. [acdc.m] * The acdc algorithm for finding the same for a set of Symmetric matrices. [acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version [acdc.m], rather than the Symmetric version[acdc_sym] is preferable. * A function that finds an initial guess for acdc by applying hard-whitening followed by Cardoso s orthogonal joint diagonalizer. Note that acdc may also be called without an initial guess, in which case the initial guess is set by default to the identity matrix. The m-file includes the joint_diag function (by Cardoso) for performing the orthogonal part. [init4acdc.m]
標(biāo)簽： approximate directory algorithm the
上傳時間： 2014-01-17
上傳用戶：hanli8870
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another. KEYWORDS: Total least squares, generalized total least squares, software implementation.
標(biāo)簽： approximate The modeling problems
上傳時間： 2013-12-20
上傳用戶：15071087253
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