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  • Free Spectral Images database?

    - by Hani
    I am working on a project "object detection using multi spectral imaging", but i am finding troubles because i dont have images to start testing my ideas. Now i am working with the hardware. Please Does any one knows a database for any spectral imaging(faces, flowers,..etc) such that i can test my ideas for classification until i finish the hardware.

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  • Spectral Reconstruction

    - by Hani
    I have a small system which consist of: Led Clusters, camera(RGB or grayscale) and an object to be detected. I am emitting a light from the LED clusters (ex: yellow). After emitting light on the object, I am capturing an image for the object from the camera. I want to get the spectral image of the object from the captured image. Please if any one knows the algorithm or a code for this purpose(grayscale or RGB camera), tell me. Thanks.....

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  • Rapid spectral analysis of audio file using Python 2.6?

    - by Ephemeralis
    What I want to do is to have a subroutine that analyses every 200 milliseconds of a sound file which it is given and spits out the frequency intensity value (from 0 to 1 as a float) of a specific frequency range into an array which I later save. This value then goes on to be used as the opacity value for a graphic which is supposed to 'strobe' to the audio file. The problem is, I have never ventured into audio analysis before and have no clue where to start. I have looked pymedia and scipy/numpy thinking I would be able to use FFT in order to achieve this, but I am not really sure how I would manipulate this data to end up with the desired result. The documentation on the SpectrAnalyzer class of pymedia is virtually non-existant and the examples on the website do not actually work with the latest release of the library - which isn't exactly making my life easier. How would I go about starting this project? I am at a complete loss as to what libraries I should even be using.

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  • Trying to make a universe [on hold]

    - by caters
    I am wanting to program a universe so that it starts with a big bang and atoms form and then molecules form and stars start to form and planets start to form and then moons around those planets. I have a few questions. If 400 IPMUs(In Program Mass Units) = 1 solar mass than how would I calculate the number of IPMUs for a spectral class of star given the range of solar masses for a main sequence star in that spectral class? How can I have planets not look like stars? Since whether it is a subdwarf, main sequence star, subgiant, giant, bright giant, supergiant, or hypergiant is mainly dependent on the radius and luminosity how can I have the radius and luminosity independent of the mass?

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  • NET Math Libraries

    - by JoshReuben
    NET Mathematical Libraries   .NET Builder for Matlab The MathWorks Inc. - http://www.mathworks.com/products/netbuilder/ MATLAB Builder NE generates MATLAB based .NET and COM components royalty-free deployment creates the components by encrypting MATLAB functions and generating either a .NET or COM wrapper around them. .NET/Link for Mathematica www.wolfram.com a product that 2-way integrates Mathematica and Microsoft's .NET platform call .NET from Mathematica - use arbitrary .NET types directly from the Mathematica language. use and control the Mathematica kernel from a .NET program. turns Mathematica into a scripting shell to leverage the computational services of Mathematica. write custom front ends for Mathematica or use Mathematica as a computational engine for another program comes with full source code. Leverages MathLink - a Wolfram Research's protocol for sending data and commands back and forth between Mathematica and other programs. .NET/Link abstracts the low-level details of the MathLink C API. Extreme Optimization http://www.extremeoptimization.com/ a collection of general-purpose mathematical and statistical classes built for the.NET framework. It combines a math library, a vector and matrix library, and a statistics library in one package. download the trial of version 4.0 to try it out. Multi-core ready - Full support for Task Parallel Library features including cancellation. Broad base of algorithms covering a wide range of numerical techniques, including: linear algebra (BLAS and LAPACK routines), numerical analysis (integration and differentiation), equation solvers. Mathematics leverages parallelism using .NET 4.0's Task Parallel Library. Basic math: Complex numbers, 'special functions' like Gamma and Bessel functions, numerical differentiation. Solving equations: Solve equations in one variable, or solve systems of linear or nonlinear equations. Curve fitting: Linear and nonlinear curve fitting, cubic splines, polynomials, orthogonal polynomials. Optimization: find the minimum or maximum of a function in one or more variables, linear programming and mixed integer programming. Numerical integration: Compute integrals over finite or infinite intervals, over 2D and higher dimensional regions. Integrate systems of ordinary differential equations (ODE's). Fast Fourier Transforms: 1D and 2D FFT's using managed or fast native code (32 and 64 bit) BigInteger, BigRational, and BigFloat: Perform operations with arbitrary precision. Vector and Matrix Library Real and complex vectors and matrices. Single and double precision for elements. Structured matrix types: including triangular, symmetrical and band matrices. Sparse matrices. Matrix factorizations: LU decomposition, QR decomposition, singular value decomposition, Cholesky decomposition, eigenvalue decomposition. Portability and performance: Calculations can be done in 100% managed code, or in hand-optimized processor-specific native code (32 and 64 bit). Statistics Data manipulation: Sort and filter data, process missing values, remove outliers, etc. Supports .NET data binding. Statistical Models: Simple, multiple, nonlinear, logistic, Poisson regression. Generalized Linear Models. One and two-way ANOVA. Hypothesis Tests: 12 14 hypothesis tests, including the z-test, t-test, F-test, runs test, and more advanced tests, such as the Anderson-Darling test for normality, one and two-sample Kolmogorov-Smirnov test, and Levene's test for homogeneity of variances. Multivariate Statistics: K-means cluster analysis, hierarchical cluster analysis, principal component analysis (PCA), multivariate probability distributions. Statistical Distributions: 25 29 continuous and discrete statistical distributions, including uniform, Poisson, normal, lognormal, Weibull and Gumbel (extreme value) distributions. Random numbers: Random variates from any distribution, 4 high-quality random number generators, low discrepancy sequences, shufflers. New in version 4.0 (November, 2010) Support for .NET Framework Version 4.0 and Visual Studio 2010 TPL Parallellized – multicore ready sparse linear program solver - can solve problems with more than 1 million variables. Mixed integer linear programming using a branch and bound algorithm. special functions: hypergeometric, Riemann zeta, elliptic integrals, Frensel functions, Dawson's integral. Full set of window functions for FFT's. Product  Price Update subscription Single Developer License $999  $399  Team License (3 developers) $1999  $799  Department License (8 developers) $3999  $1599  Site License (Unlimited developers in one physical location) $7999  $3199    NMath http://www.centerspace.net .NET math and statistics libraries matrix and vector classes random number generators Fast Fourier Transforms (FFTs) numerical integration linear programming linear regression curve and surface fitting optimization hypothesis tests analysis of variance (ANOVA) probability distributions principal component analysis cluster analysis built on the Intel Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage). Product  Price Update subscription Single Developer License $1295 $388 Team License (5 developers) $5180 $1554   DotNumerics http://www.dotnumerics.com/NumericalLibraries/Default.aspx free DotNumerics is a website dedicated to numerical computing for .NET that includes a C# Numerical Library for .NET containing algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, ports from Fortran to C# of LAPACK, BLAS and EISPACK, respectively. Linear Algebra (CSLapack, CSBlas and CSEispack). Systems of linear equations, eigenvalue problems, least-squares solutions of linear systems and singular value problems. Differential Equations. Initial-value problem for nonstiff and stiff ordinary differential equations ODEs (explicit Runge-Kutta, implicit Runge-Kutta, Gear's BDF and Adams-Moulton). Optimization. Unconstrained and bounded constrained optimization of multivariate functions (L-BFGS-B, Truncated Newton and Simplex methods).   Math.NET Numerics http://numerics.mathdotnet.com/ free an open source numerical library - includes special functions, linear algebra, probability models, random numbers, interpolation, integral transforms. A merger of dnAnalytics with Math.NET Iridium in addition to a purely managed implementation will also support native hardware optimization. constants & special functions complex type support real and complex, dense and sparse linear algebra (with LU, QR, eigenvalues, ... decompositions) non-uniform probability distributions, multivariate distributions, sample generation alternative uniform random number generators descriptive statistics, including order statistics various interpolation methods, including barycentric approaches and splines numerical function integration (quadrature) routines integral transforms, like fourier transform (FFT) with arbitrary lengths support, and hartley spectral-space aware sequence manipulation (signal processing) combinatorics, polynomials, quaternions, basic number theory. parallelized where appropriate, to leverage multi-core and multi-processor systems fully managed or (if available) using native libraries (Intel MKL, ACMS, CUDA, FFTW) provides a native facade for F# developers

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  • what is the best mid/high-end class audio/music creation audio sound card?

    - by Chris
    Hello, I have a computershop myself, and I repair computers. But one of the things I really don't know (yet) is the performace od audio cards for music creation with midi. I have searched and searched and came up with some good reviews, but after browsing for a couple of hours I could't see the trees trough the forrest :-D (it's a dutch expression) At one moment I thought the M-Audio - Delta 1010LT would be a good PCIe card, later on I read that this card was released years ago. (but that could be false information) Also any personal expierence would be great, but not necessairy. I have searched a few cards, and I hope someone can help me make a choice for a friend of mine. He's buget is between $100 and $350 I know there are audio cards from $ 500 - $1850,- this is just too expensive. The following specs are crucial: ASIO Midi Mic in minimal 5.1, 7.1 recommended it's not for airplay, but just to compose music at home. using Ableton and midi keyboard. 1. M-Audio - Delta 1010LT: 8 x 8 analog I/O 2 mic preamps or line inputs S/PDIF digital I/O (coaxial) with 2-channel PCM SCMS copy protection control digital I/O supports surround-encoded AC-3 and DTS pass-through 1 x 1 MIDI I/O directly drive up to 7.1 surround (bass management software included) software controlled 36-bit internal DSP digital mixing/routing +4dbu/-10dBV operation individually switched in software word clock I/O for sample accurate device synchronization 2. RME HDSP 9632: * Stereo Analog Ein- und Ausgang, symmetrisch*, 24-Bit/192kHz, > 110 dB SNR * Optionale Erweiterungsboards mit je 4 symmetrischen Ein- und Ausgängen * Alle analogen I/Os voll 192 kHz-fähig, also keine Reduzierung der Kanalzahl * 1 x ADAT Digital In/Out, 96 kHz-fähig (S/MUX) * 1 x SPDIF Digital In/Out, 192 kHz-fähig * 1 x Breakout Kabel für koaxialen SPDIF-Betrieb* * Also bis zu 16 Ein-und Ausgänge gleichzeitig nutzbar! * 1 x Stereo Kopfhörerausgang, parallel zum analogen Ausgang, aber eigene Pegelanpassung * 1 x MIDI I/O für 16 Kanäle Hi-Speed MIDI über Breakout Kabel * DIGICheck, RMEs einzigartiges Meter- und Analysetool mit Spectral Analyser, Professionelle Level Meter 2/8/16-Kanalig, Vector Audio Scope und diversen weiteren Analysefunktionen * HDSP Meter Bridge: Frei skalierbare Levelmeter mit Peak- und RMS Berechnung in Hardware * TotalMix: 512-Kanal Mischer mit 40 Bit interner Auflösung 3. EMU 1212M (1212 M) PCIe: * Top kwaliteit convertors 24-bit/192kHz convertors. * Hardware gestuurde effecten. * DSP zero-latency hardware mixen en monitoring. * Analoge en digitale I/O plus MIDI. * EMU Production Tools Software Bundle - Cakewalk SONAR , Steinberg Cubase LE, Ableton Live E-MU Edition **EMU 1212M PCI-e inputs/outputs:** * 2 balanced jack inputs. * 2 balanced jack outputs. * 24-bit/192kHz ADAT I/O. * 24-bit/192kHz Coaxiale S/PDif I/O switchable to AES/EBU. * MIDI I/O. 4. M-Audio Audiophile 192: - Up to 24-bit/192kHz audio - 2 balanced analog inputs (1/4” TRS) - 2 balanced analog outputs (1/4” TRS) - S/PDIF digital I/O (coaxial RCA connectors) with 2-channel PCM - SCMS copy protection control - Digital I/O supports surround-encoded AC-3 and DTS pass-through - Direct hardware input monitoring via separate balanced 1/4” TRS monitor outputs - Software routing of inputs and outputs - Digital I/O can be routed to/from external effects - 16-channel MIDI I/O - ASIO, WDM, GSIF 2 and Core Audio driver support for compatibility with most applications - 64-bit driver support for Windows - PCI 2.2 compatibility - Apple G5 compatible - Incompatible exceptions - Includes Ableton Live Lite music production software, so you can make music right away - Works with other Delta cards Technical Specifcations: - Compatibility - ASIO - WDM - GSIF 2 - Core Audio

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  • image processing algorithm in MATLAB

    - by user261002
    I am trying to reconstruct an algorithm belong to this paper: Decomposition of biospeckle images in temporary spectral bands Here is an explanation of the algorithm: We recorded a sequence of N successive speckle images with a sampling frequency fs. In this way it was possible to observe how a pixel evolves through the N images. That evolution can be treated as a time series and can be processed in the following way: Each signal corresponding to the evolution of every pixel was used as input to a bank of filters. The intensity values were previously divided by their temporal mean value to minimize local differences in reflectivity or illumination of the object. The maximum frequency that can be adequately analyzed is determined by the sampling theorem and s half of sampling frequency fs. The latter is set by the CCD camera, the size of the image, and the frame grabber. The bank of filters is outlined in Fig. 1. In our case, ten 5° order Butterworth11 filters were used, but this number can be varied according to the required discrimination. The bank was implemented in a computer using MATLAB software. We chose the Butter-worth filter because, in addition to its simplicity, it is maximally flat. Other filters, an infinite impulse response, or a finite impulse response could be used. By means of this bank of filters, ten corresponding signals of each filter of each temporary pixel evolution were obtained as output. Average energy Eb in each signal was then calculated: where pb(n) is the intensity of the filtered pixel in the nth image for filter b divided by its mean value and N is the total number of images. In this way, en values of energy for each pixel were obtained, each of hem belonging to one of the frequency bands in Fig. 1. With these values it is possible to build ten images of the active object, each one of which shows how much energy of time-varying speckle there is in a certain frequency band. False color assignment to the gray levels in the results would help in discrimination. and here is my MATLAB code base on that : clear all for i=0:39 str = num2str(i); str1 = strcat(str,'.mat'); load(str1); D{i+1}=A; end new_max = max(max(A)); new_min = min(min(A)); for i=20:180 for j=20:140 ts = []; for k=1:40 ts = [ts D{k}(i,j)]; %%% kth image pixel i,j --- ts is time series end ts = double(ts); temp = mean(ts); ts = ts-temp; ts = ts/temp; N = 5; % filter order W = [0.00001 0.05;0.05 0.1;0.1 0.15;0.15 0.20;0.20 0.25;0.25 0.30;0.30 0.35;0.35 0.40;0.40 0.45;0.45 0.50]; N1 = 5; for ind = 1:10 Wn = W(ind,:); [B,A] = butter(N1,Wn); ts_f(ind,:) = filter(B,A,ts); end for ind=1:10 imag_test1{ind}(i,j) =sum((ts_f(ind,:)./mean(ts_f(ind,:))).^2); end end end for i=1:10 temp_imag = imag_test1{i}(:,:); x=isnan(temp_imag); temp_imag(x)=0; temp_imag=medfilt2(temp_imag); t_max = max(max(temp_imag)); t_min = min(min(temp_imag)); temp_imag = (temp_imag-t_min).*(double(new_max-new_min)/double(t_max-t_min))+double(new_min); imag_test2{i}(:,:) = temp_imag; end for i=1:10 A=imag_test2{i}(:,:); B=A/max(max(A)); B=histeq(B); figure,imshow(B) colorbar end but I am not getting the same result as paper. has anybody has aby idea why? or where I have gone wrong? Refrence Link to the paper

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