What is the use of MATLAB and how can we learn it?Please suggest any book or ebook?
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MATLAB is a numerical computing environment and programming language. Created by The MathWorks, MATLAB allows easy matrix manipulation, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs in other languages. Although it specializes in numerical computing, an optional toolbox interfaces with the Maple symbolic engine, making it a full computer algebra system. It is used by more than one million people in industry and academia. The costs are $2000 for a commercial license without any toolboxes, and US$100 for an academic license with a limited set of Toolboxes.\n\nFollowing link has very good tutorials...\n\nhttp://web.cecs.pdx.edu/~gerry/MATLAB/masterOutline.html\n\nIntroduction to Computer Programming with MATLAB\nhttp://www.phon.ucl.ac.uk/courses/spsci/matlab/
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look here:\n\nhttp://en.wikipedia.org/wiki/Wavelet
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http://scienceworld.wolfram.com/
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Prolog or Lisp. Machine learning\nMore detail please.\nI'm more for Java.\nBut most likely you are looking for the toolkit From MATLAB(tm) the first link.
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You need the software: TI-Connect. I think it is a free download from education.ti.com.
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Singular value decomposition and principal component analysis\n\n\nMichael E. Wall, Andreas Rechtsteiner, Luis M. Rocha\nModeling, Algorithms, and Informatics Group (CCS-3)\nLos Alamos National Laboratory, MS B256\nLos Alamos, New Mexico 87545, USA\n\n\n\nCitation: Wall, Michael E., Andreas Rechtsteiner, Luis M. Rocha."Singular value decomposition and principal component analysis". in A Practical Approach to Microarray Data Analysis. D.P. Berrar, W. Dubitzky, M. Granzow, eds. pp. 91-109, Kluwer: Norwell, MA (2003). LANL LA-UR-02-4001.\n\nAlso available in the arXiv.org e-Print archive and in Adobe Acrobat (.pdf) format. \n\nAbstract.\n\nThis chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aim is to provide definitions, interpretations, examples, and references that will serve as resources for understanding and extending the application of SVD and PCA to gene expression analysis.\n\nKeywords: bioinformatics, computational biology, linear algebra, data mining, singular value decomposition, principal component analysis, gene expression analysis, SVD, PCA, microarray analysis techniques.\n\n1. Introduction\nOne of the challenges of bioinformatics is to develop effective ways to analyze global gene expression data. A rigorous approach to gene expression analysis must involve an up-front characterization of the structure of the data. In addition to a broader utility in analysis methods, singular value decomposition (SVD) and principal component analysis (PCA) can be valuable tools in obtaining such a characterization. SVD and PCA are common techniques for analysis of multivariate data, and gene expression data are well suited to analysis using SVD/PCA. A single microarray  For simplicity, we use the term microarray to refer to all varieties of global gene expression technologies.\n\n\nClose experiment can generate measurements for thousands, or even tens of thousands of genes. Present experiments typically consist of less than ten assays, but can consist of hundreds (Hughes et al., 2000). Gene expression data are currently rather noisy, and SVD can detect and extract small signals from noisy data. \nThe goal of this chapter is to provide precise explanations of the use of SVD and PCA for gene expression analysis, illustrating methods using simple examples. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the mathematical relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aims are 1) to provide descriptions and examples of the application of SVD methods and interpretation of their results; 2) to establish a foundation for understanding previous applications of SVD to gene expression analysis; and 3) to provide interpretations and references to related work that may inspire new advances.\n\nIn section 1, the SVD is defined, with associations to other methods described. A summary of previous applications is presented in order to suggest directions for SVD analysis of gene expression data. In section 2 we discuss applications of SVD to gene expression analysis, including specific methods for SVD-based visualization of gene expression data, and use of SVD in detection of weak expression patterns. Some examples are given of previous applica
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http://kr.cs.ait.ac.th/~radok/math/mat7/step15.htm go here and you will find your Answer\nYou also can read -\n*Numerical Methods of Engineers\n -Steven C. Chapra\n -Raymond P. Canale
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A few that come to mind at Euler, Maxima and Octave. Unfortunately, I don't think any of the free packages come close to Mathematica or Matlab, but maybe one of them will still meet your needs.
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The last link was the most informative.\nThe function is as follows:\n(x' y')T = [(1 1)T (1 -1)T] (x y)T\n\nIf you multiply the basis vectors by this matrix you get:\n(0 2)T for the (1 -1)T vector and\n(-1 -5)T for the (-3 2)T vector.\n\n(0, 2) = c1 * (1, -1) + c2 * (-3, 2)\nso:\n0 = c1 - 3 * c2\n2 = - c1 + 2 * c2\nc1 = -6\nc2 = -2\n\nlikewise:\n(-1, -5) = c3 * (1, -1) + c4 * (-3, 2)\nc3 = 17 and c4 = 6\n\nM = [(-6 -2)T (17 6)T]
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Try the following link:\n\nhttp://www.cs.bham.ac.uk/~axk/EC_tut2.ppt
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Matlab, Gauss, S+ and probably a lot more.\n\nIn a pinch, you can also use MS-Excel.\nDownload this pdf file from Cliff Ragsdale´s book site:\nhttp://swlearning.com/quant/ragsdale/chapter12.exe\nIt actually works better than you´d think.\n\nOr go to http://swlearning.com/quant/ragsdale/fourth_edition/ragsdale.html\npick previous edition and student resource.
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http://www.convertit.com/Go/ConvertIt/Reference/AMS55.ASP?Res=150\n\nhttp://www.math.sfu.ca/~cbm/aands/subj.htm\n\nhttp://clusterfie.epn.edu.ec/ibernal/html/CURSOS/AbrilAgosto06/Metodos/Libros/Fundamentals%20Of%20Numerical%20Computing%20-%201997%20-Wiley%20%20(By%20Laxxuss).pdf
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you can use text file output format , for transfering data between softwares . but how can you import it to matlab .. idon't know, but it shoult have an input file format . try to find it out.
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if u have \n\nt=1:2:1000\n\nS =10sin30t\n\nplot(S)\n\nFs=fft(S,512)\n\nplot(Fs) \n\n\nso if u have any signal it will be stored in vector on the matlab .. like .. X= 155x1 matrix size\n\njust convert the X by\n\nXftt= fft(X)\nor\nXfft2=ffft2(X)\nplot(Xfft)\npause\nplot (Xfft)\n\n\nand anything u want u can use matlab help it is v cool \n\n\ni wish i could help u
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Check out Markov Chain's.  You will find many uses for matrices.
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here is a simple program algo\n\nprogram quad\nbegin\nvar\n     declare variables;\nget variable values of a,b,c for equation ax2+bx+c=0\nd=)b*b)-4*a*c;\nif(d<0)\nbegin\n\n   root are imaginary;\nend\nelse\n\n    root1=b(sqrt(d))/2a;\n    root2=-b(sqrt(d))/2a;\n ( note : formula i am not sure make changes as per required)\nprint the value of root1 and root2;\nend
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Here's a link to Colorado Tech, Professor Santiago's Digital Signal Processing website.  There is a lot of useful information, sample Matlab code, and links under "Tools and Resources".  Much of this is for processing audio "wav" files, but the principles apply to RF or video processing as well.\n\nUnder "Tools and Resources" => M-Files => Basic Applications you can find example Matlab code for white noise filtering, reverb, upsampling, and downsampling.  \n\nUsing these examples, you could come up with a nice project doing audio signal processing.  Audio is fun because you can play with music and listen to the results.  The frequency is low enough that you can process the signals on your computer - you won't need specialized hardware like you would for real-time processing of high-frequency signals.\n\nAlso, I got your e-mail and sent you an example project, designing a digital filter using the Kaiser Window technique.