entropy

#include <cv.h> #include <highgui.h> #include <iostream> #include <cmath> #include <cstdlib> #include <fstream> using namespace std; typedef struct histBundle { double rCh[256]; double gCh[256]; double bCh[256]; }bundleForHist; bundleForHist getHistFromImage (IplImage* img, int numBins) { float range[] = { 0, numBins }; float *ranges[] = { range }; bundleForHist bfh; CvHistogram *hist = cvCreateHist (1, &numBins, CV_HIST_ARRAY, ranges, 1); cvClearHist (hist); IplImage* imgRed = cvCreateImage(cvGetSize(img), 8, 1); IplImage* imgGreen = cvCreateImage(cvGetSize(img), 8, 1); IplImage* imgBlue =

This article states that Despite the fact that the Mersenne Twister is an extremely good pseudo-random number generator, it is not cryptographically secure by itself for a very simple reason. It is possible to determine all future states of the generator from the state the generator has at any given time, and either 624 32-bit outputs, or 19,937 one-bit outputs are sufficient to provide that state. Using a cryptographically-secure hash function, such as SHA-1, on the output of the Mersenne Twister has been recommended as one way of obtaining a keystream useful in cryptography. But there are no

I'm building a binary classification tree using mutual information gain as the splitting function. But since the training data is skewed toward a few classes, it is advisable to weight each training example by the inverse class frequency. How do I weight the training data? When calculating the probabilities to estimate the entropy, do I take weighted averages? EDIT: I'd like an expression for entropy with the weights. State-value weighted entropy as a measure of investment risk. http://www56.homepage.villanova.edu/david.nawrocki/State%20Weighted%20Entropy%20Nawrocki%20Harding.pdf Robert Harvey

I am using skimage library for most of image analysis work. I have an RGB image and I intend to extract texture features like entropy , energy , homogeneity and contrast from the image. Below are the steps that I am performing: from skimage import io, color, feature from skimage.filters import rank rgbImg = io.imread(imgFlNm) grayImg = color.rgb2gray(rgbImg) print(grayImg.shape) # (667,1000), a 2 dimensional grayscale image glcm = feature.greycomatrix(grayImg, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4]) print(glcm.shape) # (256, 256, 1, 4) rank.entropy(glcm, disk(5)) # throws an error since entropy

I'm principally interested in the implementation of SecRandomCopyBytes on iOS , if it differs from the OS X implementation. (I would presume that it does, since a mobile device has more and more readily available sources of entropy than a desktop computer.) Does anyone have information on: Where SecRandomCopyBytes gets entropy from? What rate it can generate good random numbers? Will it block, or fail immediately if not enough entropy is available? Is it FIPS 140-2 compliant, or has it been included in any other official certification? The documentation does not cover these points. I've only

I have an embedded system. What I would like for it to do when it powers up or otherwise resets, is to generate a unique ID, so that on different restarts a different unique ID is generated with high probability. It does not have access to a real-time clock, but it does have access to an ADC and a UART. I am wondering if there is a decent way to gather entropy from these sources to generate a unique ID. I am vaguely familiar with Yarrow . Is there a good way to use this? Unfortunately I do not have any noise sources of predictable characteristics; the ADC is connected to a number of relatively