sampling

问题 I am doing a study to between profilers mainly instrumenting and sampling. I have came up with the following info: sampling: stop the execution of program, take PC and thus deduce were the program is instrumenting: add some overhead code to the program so it would increment some pointers to know the program If the above info is wrong correct me. After this I was looking at the time of execution and some said that instrumenting takes more time than sampling! is this correct? if yes why is that

问题 I get an error, when I set the sampling rate to 44100 for the AudioRecord object. When it's 22050 it works fine. 02-16 10:45:45.099 24021-24021/com.vlad.jackcomms E/AudioRecord﹕ frameCount 1024 < minFrameCount 1792 02-16 10:45:45.099 24021-24021/com.vlad.jackcomms E/AudioRecord-JNI﹕ Error creating AudioRecord instance: initialization check failed. 02-16 10:45:45.099 24021-24021/com.vlad.jackcomms E/android.media.AudioRecord﹕ Error code -20 when initializing native AudioRecord object. 02-16 10

问题 I am trying to extract amplitude information from a sound loaded from a URL using the Web Audio API instantaneously (not in real time), which will likely require the OfflineAudioContext . I am expecting to obtain something along the lines of an array containing the amplitude of the sound every t seconds for the duration of the sound (with size depending on the duration of the sound, divided by t ). Unfortunately, documentation is sparse at this point, and I'm unsure of how to proceed. How can

问题 I would like to perform a frame based analysis on the following curve Which expresses the relation between time and concentration (x-axis: time in minutes; y-axis: concentration in Mbq): For the above curve I would like to perform frame based sampling by splitting the curve into 19 frames: 19 frames: 4 frames : Each of 15 seconds time interval 2 frames : Each of 30 seconds time interval 2 frames : Each of 60 seconds time interval 11 frames : Each of 200 seconds time interval I have written

问题 I am writing a ray tracer and I wish to fire rays from a point p into a hemisphere above that point according to some distribution. 1) I have derived a method to uniformly sample within a solid angle (defined by theta) above p Image phi = 2*pi*X_1 alpha = arccos (1-(1-cos(theta))*X_2) x = sin(alpha)*cos(phi) y = sin(alpha)*sin*phi z = -cos(alpha) Where X is a uniform random number That works and Im pretty happy with that. But my question is what happens if I do not want a uniform distribution

问题 I have an R function that provides the 95% confidence Interval for the ncp (non-centrality parameter) of a t distribution. Via simulation in R, is it possible to show that in the long-run the CIs from this R function capture a given TRUE ncp (here "2" same as input t ) 95% of the time? (I appreciate any ideas as to how to do this) CI.ncp <- function(t, N){ f <- function (ncp, alpha, q, df) { abs(suppressWarnings(pt(q = t, df = N - 1, ncp, lower.tail = FALSE)) - alpha) } sapply(c(0.025, 0.975)

问题 This is kind of a basic question which might sound too obvious to many of you , but I am getting confused so bad. Here is what a Quora user says. Now It is clear to me what a Sampling rate is - The number of samples you take of a sound signal (in one second) is it's sampling rate. Now my doubt here is - This rate should have nothing to do with the quantisation , right? About bit-depth , Is the quantisation dependant on bit-depth? As in 32-bit (2^32 levels) and 64-bit (2^64 levels). Or is it

问题 I have an array of samples at 75 Hz, and I want to store them at 128 Hz. If it was 64 Hz and 128 Hz it was very simple, I would just double all samples. But what is the correct way if the samplerates are not a fraction of eachother? 回答1: When you want to avoid Filtering then you can: handle signal as set of joined interpolation cubics curves but this point is the same as if you use linear interpolation. Without knowing something more about your signal and purpose you can not construct valid

问题 I'd like to sample from indices of a 2D Numpy array, considering that each index is weighted by the number inside of that array. The way I know it is with numpy.random.choice however that does not return the index but the number itself. Is there any efficient way of doing so? Here is my code: import numpy as np A=np.arange(1,10).reshape(3,3) A_flat=A.flatten() d=np.random.choice(A_flat,size=10,p=A_flat/float(np.sum(A_flat))) print d 回答1: You could do something like: import numpy as np def wc

问题 I have a vector with five items. my_vec <- c("a","b","a","c","d") If I want to re-arrange those values into a new vector (shuffle), I could use sample(): shuffled_vec <- sample(my_vec) Easy - but the sample() function only gives me one possible shuffle. What if I want to know all possible shuffling combinations? The various "combn" functions don't seem to help, and expand.grid() gives me every possible combination with replacement, when I need it without replacement. What's the most efficient