Can anyone really help to explain the Weiss procedure of deconvolution process with FFT ?
436554
Dude, you are wasting your time.  Go to a newsgroup for this kind of thing.
734225
i just learned this in class... \nafter you enter your mass matix M,\nyour Dampening Matrix C\nand your Stiffness Matrix K\nyou take [K] - lamda [M]= (b) \nyou can type \n[Evals]=eigs(new)\nThese will also be the square of your natural frequencies.\nyour mode shapes are the eigvectors! fyi
209312
Go to the website in sources and download the sample Matlab programs\n\nWhen you unzip the file, the examples will be divided into chapters based on the book Digital Signal Processing using Matlab V, by Vinay K. Ingle and  John G. Proakis.   Here are the titles to the chapters:\n\n    *  Chapter 2:  Discrete-Time Signals and Systems\n    *  Chapter 3:  The Discrete-Time Fourier Series\n    *  Chapter 4:  The z-Transform\n    *  Chapter 5:  The Discrete Fourier Transform\n    *  Chapter 6:  Digital Filter Structures\n    *  Chapter 7:  FIR Filter Design\n    *  Chapter 8:  IIR Filter Design
730722
put sinx=t\ncosxdx=dt\nI=fcos(pi t)dt with limits -1,+1\nI=(sin(pi t))/pi with limits +1,-1\nI=(sin(pi)-sin(-pi))/pi=0\nthanks & good luck
562495
Well the Discrete Cosine Transform compacts a lot of energy into low frequencies, so the jist of compressing using it is to drop off the high frequency image components until the desired compression is reached.  The reason this works is because broad regions of color and shape are preserved (they are low frequency), so the image is still recognizable.\n\nWith matlab you will probably need the signal processing toolkit (they might include it these days), and you will just use the function dct(x).\n\nhttp://www-ccs.ucsd.edu/matlab/toolbox/signal/dct.html\n\nIf you look at the code sample, you can use that to find the DCT coefficients that have the most energy and drop the rest.  In the example it shows 99% as the threshold, but you can use less if you want more compression.  Then you will run an IDCT to reconstruct the image.  One last thing... before you do all this you need to make sure the input image has been imported into a matlab friendly matrix format.
57773
What kind of enhancement?  Just how much work do you expect us to do?  Also, I'm assuming DCT is equivalent to DFT and you're just asking what to do with the signal's spectrum.
402176
You're right, spectroscopy is the proper method...Below you'll find a link to a quick and simple explanation.
45092
You first have to represent your wave as a function. Let's say your wave has maximum and minimum aplitudes of 2 and -2 respectively, and a period of 10 seconds. The square of your function would be \nx^2 (t)= 2^2.\n\nMathematically, the squared signal is x^2 (t) and the "mean squared" signal is the integral of s^2(t) over an entire period, divided by the period. Finally, the "root mean squared" is the square root of this final result. \n\nIt's hard to put in the integrals and square roots in this text only format, so check out the website.
359570
Browse some of these links for ideas, algorithm descriptions, etc., in the comments.\n\n\nhttp://www.google.com/search?q=%22histogram+equalization%22+%22source+code%22+C%2B%2B
919471
Three Pfund series transitions from n=5 state (to n=1, n=2 and n=3)\n1<--5 @ 95 nm\n2<--5 @ 434 nm\n3<--5 @ 1282 nm
1088014
你很漂亮. ni3 hen3 piao4 liang
949909
So Im guessing your have a transformation matrix.  \n\nYou have an initial state specified by matrix [A] and the linear transform specified by matrix [T].\n\nTo find the state after iteration 1 you multiply\n[A]x[T]=[y1]\n\nIf [y1] was your initial matrix you would multiply it by [T] to get the state after iteration.\n[y1]x[T]=[y2]\nby substitution\n([A] x [T]) x [T] = [y2]\nor \n[A] x [T]^2 = [y2]\n\nso what is T at the nth iteration?\n[A] x [T]^n = [yn]\n\nso what is the limit as n goes to inifinity?\n[A] x lim([T]^n,n->inf) = [y_inf]\n\nNumerical results break down, consider a rotation.  The limit as it goes to infinity is undefined.\nT(1,1)=sin(x)\nT(1,2)=cos(x)\nT(2,1)=-1*cos(x)\nT(2,2)=sin(x)\n\nWhen you square it you get\nT2(1,1)=sin(x)^2-cos(x)^2 = -cos(2*x)\nT2(1,2)=2*cos(x)*sin(x) = sin(2*x)\nT2(2,1)=-2*cos(x)*sin(x) = -sin(2*x)\nT2(2,2)=sin(x)^2-cos(x)^2 = -cos(2*x)\n\nWhen you cube it you get\nT3(1,1)=-sin(3*x)\nT3(1,2)=-cos(3*x)\nT3(2,1)=cos(3*x)\nT3(2,2)=-sin(3*x)\n\nWhen you raise it to the power of 100 (come on MatLab, dont die on me now, darnit) ... ahem.  When you raise it to the power of 10 (?)  aha\nT10(1,1)=-cos(10*x)\nT10(1,2)=sin(10*x)\nT10(2,1)=-sin(10*x)\nT10(2,2)=-cos(10*x)\n\nSee how that works?  Not bad huh?\n\nFor the pure rotation it never ends.\nWhat about a shrinking, a scaling by a matrix whose norm is less than 1? (note if you use 0.9, the norm is 1.8.)\n\nT(i,j)=0.45\nyields\nT2(i,j)=0.4050\nT3(i,j)=0.3645\nT10(i,j)=0.1743\nT1000(i,j)=0.1328*10^-4\n\nIt disappears to zero.\n\nintuitively you can also figure out that for a scaling by a matrix whose norm is larger than one the transform eventually goes to infinity as the limit is taken to infinity.\n\nyou could make a non-trigonometric rotation, like the sin(1/x)  Thats a fun expression.\n\nT(i,i)=sin(1/x) \nT(1,2)=cos(1/x)\nT(2,1)=-1*cos(1/x)\n\nAs x goes to infinity what happens?\nit becomes a reflection\nT_inf(i,i)=0\nT_inf(1,2)=1\nT_inf(2,1)=-1\n\nIm digressing.  Its a fun field with interesting applications.  Weather forecasting, deformation of solids, heat transfer, optimization, all use linear transformations to do their work.\n\nHave fun.
629797
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
379771
http://www.mikeholt.com/mojonewsarchive/EC-HTML/HTML/WyeTransformerCalculations~20030916.htm
979873
originally it was  for finding out about heat dissipation when cooling the links of chains for ships (interesting isn't it) since that time in the 18th century the general math model has been applied to many various problems in a wide range of fields but one of the fav's is the analysis of acoustics and denoted as an FFT for  fast fourier transform.
22183
Consider the case of a uniformly sampled signal x(t). \n\nPower spectral density is a statistic that describes how the energy E of the signal x(t); t=0,1,2,...N-1 defined as \n\nE={sum over t} <|x(t)|^2> is distributed with frequency f. \n\nx(t)= {integral from -.5 to .5 } dZ(f)\n\nwhere dZ(f) is a stochastic measure, and \n\n<dZ(f) dZ*(f)>=S(f)df \n\nwhere S(f) is the power spectral density.
36159
As mechanical engineer this is out of my normal field but felt you deserved an answer of some sort.\n\n"Because of diffraction effects caused by slits or apertures in optical measurement systems, the radiation energy which is directed towards a particular region on a sample will be spread over a larger area than desirable. By employing an apodizing filter in the radiation path in such system, diffraction tails of the system will be reduced. The apodizing filter preferably has a pattern of alternating high transmittance areas and substantially opaque areas where the locally averaged transmittance function is an apodizing function. In the preferred embodiment, the locally averaged transmittance function varies smoothly and monotonically from the periphery to the center of the filter"\n\nI see the theory here, also found on on using an apodizing mirror to stabalise images in a telescope\n\n"I built and used an apodizing screen for my 6" f/8 scope many years \nago. I made it by layering several layers of window screen, each one \nwith a circular cutout of different diameter. The result was an aperture \nmask with a step-wise approximation to a smooth apodizing function. It \nwas claimed to improve contrast for planetary viewing. Anyway, my \nexperience was that it seemed to make the image more stable but if it \nimproved contrast, I couldn't see it. If you do this on-axis, the \ndownside is that your diagonal mirror will now become more of a factor \nin the contrast equation. The bottom line is I wouldn't recommend it."\n\nAlso "An excellent reference for all sorts of technical techniques is\n>"Procedures in Experimental Physics" (also printed under the title\n>"Physical Laboratory Practice") by John Strong.  I don't recall the\n>publisher, but it came out in the 30's.  The bad news it that it's been\n>out of print for decades (and some SOB stole the copy I got from my\n>father!); the good news is that an outfit called Lindsay is reprinting it\n>in paperback.  (Don't have their name/address here in the office; I'll try\n>to remember to look it up tonight.)\n>\n>                            Dr. Kurt Hillig\n\nEvery ATM should have a copy of this great book. Lots of stuff on\nmirror making, figuring, special laps,making flats, prism ,etc.\nThere is nice modifed Draper machine ( upper disk also powered)\nand a homemade diamond cutting wheel to cut small glass blocks.\n( I would like to build this one someday ) A great section on\nkinematical mechanical construction. Yeah this a mid 30,s book but the \ninfo\nstill can be used. No I don't work for Lindsay Publications,<G>\n\nBob Pfaff\n\n\n This book, in paperback form, can be ordered from\n      http://www.amazon.com, on the Web. It can also be ordered\n      through your local bookstore. Amazon prices it at $23.95, and\n      provides the following information:\n\n      Published by Lindsey Publications\n      Publication date: June 1986\n      ISBN: 0917914562"\n\nSorry couldn't find any really decent expplanations, but at least you've got a starting point.