c++,vector,ascii,frequency-analysis,caesar-cipher

Take your frequency vector and the frequency vector for "typical" English text, and find the cross-correlation. The highest values of the cross-correlation correspond to the most likely shift values. At that point you'll need to use each one to decrypt, and see whether the output is sensible (i.e. forms real...

java,android,fft,frequency-analysis

One of the issues of a DFT is that if your peak is wide and lies over two (or more) bins and it shifts ever so slightly (because of doppler or other reasons), you'll get fluctuations of energy between the two bins. One way is to increase the number of...

Generally, you can use cat to print extra information with an answer: > cat("No of Females = ", nrow(mydf[mydf$Sex == "Female", ])) No of Females = 2 If you want the result as a character string to use elsewhere, it's probably easier to use sprintf or paste: > sprintf("No of...

c++,fft,frequency-analysis,unreal-engine4

I don't see a Fourier transform to be carried out in your code snippet. Any way, using a DFT given N samples at an average sampling frequency R, the frequency corresponding to the bin k is k·R/2N

windows-phone-8,signal-processing,frequency-analysis,goertzel-algorithm

If you know the set of frequencies and the durations, then a set of sliding Goertzel filters is a good start to building a simple demodulator. Comparing and scanning for for a peak difference between these filters is a better decision criteria than just checking for a certain magnitude output.

javascript,audio,web-audio,frequency-analysis

"Displaying the frequency" can mean many things. Actually, my PitchDetect demo DOESN'T use a Fourier Transform - it uses autocorrelation. But that will only give you a single pitch, at high accuracy. If your signal has multiple simultaneous notes - well, that's a hard problem. If you want to see...

c++,filtering,signal-processing,fft,frequency-analysis

You may want to have a look at this answer for an explanation for the effects you are observing. Otherwise, the 'ideal' filter you are trying to achieve is more a mathematical tool than a practical implementation since the rectangular function in the frequency domain (with a zero-transition and infinite...

c++,matlab,fft,fftw,frequency-analysis

If you have a real signal consisting of 1024 samples, the contribution from the 16 frequency bins of interest could be obtained by multiplying the frequency spectrum by a rectangular window then taking the IFFT. This essentially amounts to: filling a buffer with zeros before and after the frequency bins...

Your code is correct as it is. But your signal, once made periodic, is not just a sine wave (there is a discontinuity, because the 1st and last samples of x are the same). You can try removing 1 sample at the end: t=0:dt:maxtime; % time interval in which we...

Instead of parsing the dates in the page, you could download the home page and store it. Then you could come back every day and download the homepage again to see if it changed. This approach would work even for sites that don't publish any dates on their homepage. It...

audio,web-audio,frequency-analysis

1) Is this a problem with how you are displaying the data? i.e. are you displaying every bucket, or grouping them together? 2) If it is a problem with the actual data you are getting and not how you are displaying it, try playing with AnalyserNode.fftSize. https://developer.mozilla.org/en-US/docs/Web/API/AnalyserNode/fftSize This property defaults...

c++,encryption,counter,frequency-analysis

Something like the following would do the trick, though you'd have to do some jiggery pokery to make it suit your own needs. #include <iostream> #include <map> #include <string> int main () { std::string message("some string that you will probably get from some encrypted file"); std::map<std::string,int> occurences; std::string seq(" ");...

python,numpy,fft,frequency-analysis,audio-analysis

Consider the FFT of a single period of a sine wave: >>> t = np.linspace(0, 2*np.pi, 100) >>> x = np.sin(t) >>> f = np.fft.rfft(x) >>> np.round(np.abs(f), 0) array([ 0., 50., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,...

matlab,function,plot,fft,frequency-analysis

fft returns spectrum as complex numbers. In order to analyze it you have to use its absolute value or phase. In general, it should look like this (let's assume that t is vector containing time and y is the one with actual signal, N is the number of samples): fY...