tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File Exchange - type:function product:"Communications System Toolbox"User-contributed code library2015-03-29T07:23:51-04:001101100tag:www.mathworks.com,2005:FileInfo/35822003-06-10T16:07:40Z2015-03-13T21:34:59ZAdaptive FilteringMATLAB files to implement all Adaptive Filtering Algorithms in this book.<p>MATLAB files to implement all Adaptive Filtering Algorithms in the book by Paulo S. R. Diniz, Adaptive Filtering Algorithms and Practical Implementation, Fourth Edition, Springer, New York, 2013.
<br />MATLAB files by: Guilherme O. Pinto, Markus V. S. Lima, Wallace A. Martins, Luiz W. P. Biscainho, and Paulo S. R. Diniz.
<br />This book presents a concise overview of adaptive filtering, covering as many as possible in a unified form that avoids repetition and simplifies notation. It is suitable as a textbook for senior undergraduate or first-year graduate courses in adaptive signal processing and adaptive filters.
<br />The philosophy of the presentation is to expose the material with a solid theoretical foundation, to concentrate on algorithms that really work in a finite-precision implementation, and to provide easy access to working algorithms. Hence, practicing engineers and scientists will also find the book to be an excellent reference.
<br />In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation.
<br />The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes:</p>
<p>- Analytical and simulation examples in Chapters 4, 5, 6 and 10
<br />- Appendix E, which summarizes the analysis of set-membership algorithm
<br />- Updated problems and references</p>
<p>Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more.</p>
<p>Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. </p>
<p>An instructor`s manual, a set of master transparences, and MATLAB codes for all of the algorithms described in the text are also available. Useful to both professional researchers and students, the text includes hundreds of problems, numerous examples, and over 150 illustrations. It is of primary interest to those working in signal processing, communications, and circuits and systems. </p>
<p>It will also be of interest to those working in power systems,networks, learning systems, and intelligent systems.</p>
<p>For book ordering information, please visit: <a href="http://www.mathworks.com/support/books/book48941.html">http://www.mathworks.com/support/books/book48941.html</a></p>Paulo S. R. Dinizhttp://www.mathworks.com/matlabcentral/profile/authors/539355-paulo-s-r-dinizMATLAB 6.0 (R12)Communications System ToolboxControl System ToolboxImage Processing ToolboxNeural Network ToolboxRobust Control ToolboxSignal Processing ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/497562015-02-19T11:43:45Z2015-02-19T11:43:45ZBasic Digital Modulation GUIBasic Digital Modulation GUI<p>This is the Graphical User Interface for performing Digital Modulation like ASK,PSK and FSK.This project can be extended for further digital modulation techniques.</p>Abhishek Bansalhttp://www.mathworks.com/matlabcentral/profile/authors/5152253-abhishek-bansalMATLAB 8.1 (R2013a)Communications System ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/491642015-01-26T07:39:53Z2015-02-08T09:54:17ZPDToolbox_matlabImplementation of some evolutionary dynamics from game theory for multiple populations.<p>Matlab implementation of some evolutionary dynamics from game theory, such as: replicator dynamics, smith dynamics, logit dynamics, and Brown-von Neumann-Nash.</p>Carlos Barretohttp://www.mathworks.com/matlabcentral/profile/authors/1299478-carlos-barretoMATLAB 7.6 (R2008a)Communications System ToolboxStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/227252009-01-19T21:27:59Z2015-01-27T00:15:32ZVariable Precision Integer ArithmeticArithmetic with integers of fully arbitrary size. Arrays and vectors of vpi numbers are supported.<p>Every once in a while, I've wanted to do arithmetic with large integers with magnitude exceeding that which can fit into MATLAB's standard data types. Since I don't have the symbolic toolbox, the simple solution was to write it in MATLAB. I did that in these tools which are entirely written in MATLAB, so there is no need for compiled code.
<br />Arithmetic is simple with the vpi tools.
<br />A = vpi(17)^17
<br />ans =
<br />827240261886336764177</p>
<p>17 + A^17
<br />ans =
<br />39786732894291535047752038041559739510060813980024082
<br />30012867731573722066105737100731556603857745946047229
<br />53759676529121155309750944582301597489457676380805029
<br />59227566911971103003303064782118652210655457390045806
<br />99039190393572334521701109889855832341416056005878848
<br />49943142324389193616484809157960034059531548585473213
<br />36465170635561696613297503569949729314
<br />
<br />There are also a few nice add ons, for example a tool to compute exact binomial coefficients for large arguments, or large factorials, or convert binary numbers with thousands of digits to decimal (vpi) form.</p>
<p>For example, the existing nchoosek function in matlab gets upset for even reasonably small binomial coefficients. </p>
<p>nchoosek(100,50)
<br />Warning: Result may not be exact. Coefficient is greater than 1.000000e+15
<br /> and is only accurate to 15 digits.
<br />> In nchoosek at 66
<br />ans =
<br /> 1.0089e+29</p>
<p>However, nchoosek has no such issues on vpi numbers.</p>
<p>nchoosek(vpi(100),50)
<br />ans =
<br />100891344545564193334812497256</p>
<p>Similarly, the computation of factorial(171) will cause an overflow. While I'll admit that there are many good ways to avoid this problem, the factvpi function has no problems at all.</p>
<p>factorial(171)
<br />ans =
<br /> Inf</p>
<p>factorial(vpi(171))
<br />ans =
<br />12410180702176678234248405241031039926166055775016931
<br />85388951803611996075221691752992751978120487585576464
<br />95950167038705280988985869071076733124203221848436431
<br />04735778899685482782907545415619648521534683180442932
<br />39598173696899657235903947616152278558180061176365108
<br />428800000000000000000000000000000000000000000</p>
<p>There are now GCD and LCM tools, both of which can accept more than two input arguments.</p>
<p>lcm(vpi(123452356),12344332,65364467)
<br />ans =
<br />3557547184310976844988</p>
<p>I've also put in some tools that can test for primality. For example, the Mersenne prime:</p>
<p>p = vpi(2)^127 - 1
<br />ans =
<br />170141183460469231731687303715884105727</p>
<p>isprime(p)
<br />ans =
<br /> 1</p>
<p>Factoring of vpi numbers is now implemented.</p>
<p>factor(vpi('1234567890123456789'))
<br />ans =
<br /> 3 3 101 3541 3607 3803 27961</p>
<p>Vectors or arrays of vpi numbers work very nicely now.</p>
<p>A = vpi(eye(3))*3 + 1
<br />A =
<br /> 4 1 1
<br /> 1 4 1
<br /> 1 1 4</p>
<p>A^17
<br />ans =
<br /> 5642305908354 5642176768191 5642176768191
<br /> 5642176768191 5642305908354 5642176768191
<br /> 5642176768191 5642176768191 5642305908354</p>
<p>Dozens of other tools are also included. I've even included a tool just for fun that will convert a number into a readable text version of it as a large integer.</p>
<p>vpi2english(vpi('12000000110022987'))
<br />ans =
<br />twelve quadrillion, one hundred ten million, twenty two thousand, nine hundred eighty seven</p>
<p>For those Project Euler solvers around, vpi makes many of the problems easy to solve.</p>
<p>Addenda - Ben Petschel has graciously given me code for unique and sortrows operations on vpi arrays. Many thanks to Ben!</p>John D'Erricohttp://www.mathworks.com/matlabcentral/profile/authors/869215-john-d-erricoMATLAB 7.5 (R2007b)Communications System ToolboxControl System ToolboxSymbolic Math ToolboxMATLAB6446falsetag:www.mathworks.com,2005:FileInfo/491422015-01-24T05:27:47Z2015-01-24T05:27:47ZFastfood kernel expansionsCode for paper: Fastfood - Approximating Kernel Expansions in Loglinear Time, ICML'13.<p>This function provides an implementation of the Fastfood kernel expansions [1]. We provide two verisons of Walsh-Hadamard transform in Fastfood. One is the Matlab built-in function fwht, the other is based on Spiral WHT package.
<br />Reference:
<br />[1] Q. Le, T. Sarlos, and A. Smola. Fastfood - Approximating Kernel Expansions in Loglinear Time. ICML, 2013.</p>Ji Zhaohttp://www.mathworks.com/matlabcentral/profile/authors/6097505-ji-zhaoMATLAB 8.3 (R2014a)Communications System ToolboxSignal Processing Toolboxfalsetag:www.mathworks.com,2005:FileInfo/465662014-05-10T12:13:31Z2015-01-15T07:17:21ZLTE Cell Search (from A/D samples to PBCH MIB decoding)Decode TDD/FDD LTE PBCH MIB message from 1.92Msps A/D samples.<p>Play with LTE signal (especially China TD-LTE) captured by rtl-sdr dongle! The set of scripts can decode LTE PBCH MIB message from 1.92Msps A/D samples. Offline captured IQ samples or live capturing are both supported.
<br />Use this program to see LTE signals around you. Have fun!</p>Xianjun Jiaohttp://www.mathworks.com/matlabcentral/profile/authors/535033-xianjun-jiaoMATLAB 8.1 (R2013a)Communications System ToolboxSignal Processing ToolboxStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/333812011-10-19T23:19:21Z2015-01-03T05:52:50ZJSONlab: a toolbox to encode/decode JSON files in MATLAB/OctaveJSONlab is an open-source JSON/UBJSON encoder and decoder (parser) for MATLAB and Octave.<p>** JSONLAB v1.0 (Optimus - Final) is released on 01/02/2015.**
<br />JSONlab is a component of the "iso2mesh" toolbox (<a href="http://iso2mesh.sf.net">http://iso2mesh.sf.net</a>). For the latest information regarding JSONlab, please visit its homepage at <a href="http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab">http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab</a></p>
<p>JSON (JavaScript Object Notation) is a highly portable, human-readable and "fat-free" text format to represent complex and hierarchical data. It is as powerful as XML, but less verbose. JSON format is widely used for data-exchange in applications, and is essential for the wild success of Ajax and Web2.0.</p>
<p>UBJSON (Universal Binary JSON) is a binary JSON format, specifically optimized for compact file size and better performance while keeping the semantics as simple as the text-based JSON format. Using the UBJSON format allows to wrap complex binary data in a flexible and extensible structure, making it possible to process complex and large dataset without accuracy loss due to text conversions.</p>
<p>We envision that both JSON and its binary version will serve as part of the mainstream data-exchange formats for scientific research in the future. It will provide the flexibility and generality achieved by other popular general-purpose file specifications, such as HDF5, with significantly reduced complexity and enhanced performance.</p>
<p>JSONlab is a free and open-source implementation of a JSON/UBJSON encoder and a decoder in the native MATLAB language. It can be used to convert a MATLAB data structure (array, struct, cell, struct array and cell array) into JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB data structure. JSONlab supports both MATLAB and GNU Octave (a free MATLAB clone).</p>
<p>JSONlab provides two functions, loadjson.m -- a MATLAB->JSON decoder,
<br />and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and
<br />two equivallent functions -- loadubjson and saveubjson for the binary
<br />JSON. The savejson, loadubjson and saveubjson functions were written by
<br />Qianqian Fang, while the loadjson.m script was derived from the previous works by the following people:</p>
<p>- Nedialko Krouchev: <a href="http://www.mathworks.com/matlabcentral/fileexchange/25713">http://www.mathworks.com/matlabcentral/fileexchange/25713</a>
<br /> date: 2009/11/02
<br />- FranÃ§ois Glineur: <a href="http://www.mathworks.com/matlabcentral/fileexchange/23393">http://www.mathworks.com/matlabcentral/fileexchange/23393</a>
<br /> date: 2009/03/22
<br />- Joel Feenstra: <a href="http://www.mathworks.com/matlabcentral/fileexchange/20565">http://www.mathworks.com/matlabcentral/fileexchange/20565</a>
<br /> date: 2008/07/03</p>
<p>Please find detailed online help at <a href="http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?jsonlab/Doc">http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?jsonlab/Doc</a></p>Qianqian Fanghttp://www.mathworks.com/matlabcentral/profile/authors/1583198-qianqian-fangMATLAB 7.4 (R2007a)Communications System ToolboxSimulink Verification and ValidationMATLABJSONlab is platform independent.falsetag:www.mathworks.com,2005:FileInfo/28392002-12-09T15:39:56Z2014-12-08T22:06:45ZPSK31 Model with Symbol Timing and Carrier RecoveryUpdated version of PSK31 communication standard that is now R2013B compliant.<p>This model implements a communication standard known as PSK31. The transmit portion of the model can either synthesize the PSK31 signal, or use real world signals that were captured as Wave files.
<br />The receiver features include: Automatic Frequency Control, Automatic Gain Control, Symbol Timing recovery with a fractionally spaced Farrow interpolator, Decision Directed Carrier Phase Lock Loop, dbsk or dqpsk demodulation with FEC.
<br />References are provided in the Model.</p>Dick Bensonhttp://www.mathworks.com/matlabcentral/profile/authors/869365-dick-bensonMATLAB 8.4 (R2014b)Communications System ToolboxDSP System ToolboxSignal Processing ToolboxSimulinkStateflowSoundcard support for PC only, but these elements can be removed from the model to run on other systems.falsetag:www.mathworks.com,2005:FileInfo/220742008-11-12T13:21:50Z2014-12-08T22:05:54ZA Synchronized Mil-Std-188-110B Receiver Extends the shipping "188" modem. Features a synchronized 1200 bps / short interleave receiver. <p>This file set includes a top level unsychronized Tx / Rx model and a more detailed synchronized Rx model. The top level model Tx outptut has been tested with a third party reference mil-std-188-110 modem and found to generates compliant waveforms. A variable symbol rate Tx driving the synchronized Rx model was added on 11/21/08.
<br />The Rx model implements the 1200 bps / short (0.6 sec) interleave mode. It is driven by recorded wave files that have varying degrees of timing and carrier errors to test the tracking loops. </p>
<p>The model includes:
<br />1) selectable channel impairments
<br />2) preamble correlation to initiate downstream processing
<br />3) a 20 forward-20 feedback tap Recursive Decision Feedback Equalizer
<br />3) timing recovery loop to track long term clock rate errors
<br />4) carrier recvoery loop to ease the burdon of the equalizer
<br />5) novel timing and phase error detectors
<br />6) received data converted to scrolling text output </p>Dick Bensonhttp://www.mathworks.com/matlabcentral/profile/authors/869365-dick-bensonMATLAB 8.4 (R2014b)Communications System ToolboxDSP System ToolboxSignal Processing ToolboxSimulinkStateflowMATLABWindows audio support. 3716falsetag:www.mathworks.com,2005:FileInfo/326512011-08-23T14:55:54Z2014-12-08T22:04:44ZATSC: From RF to VideoA set of models to process an ATSC RF signal and output an MPEG II video transport stream.<p>This set of files implements a *compliant* ATSC demodulator and decoder that accepts a captured real-world RF ATSC signal as the input and produces an MPEG-II video transport stream as the final output. The models are built in stages demonstrating a top-down design flow leading from RF to the video transport stream.
<br />Please read the ATSC_README.doc in the ATSC_Models.zip file. This document provides details on the models and contains a link to the captured data files as well.
</p>Dick Bensonhttp://www.mathworks.com/matlabcentral/profile/authors/869365-dick-bensonMATLAB 8.4 (R2014b)Communications System ToolboxDSP System ToolboxSignal Processing ToolboxSimulinkfalse