signal-processing

问题 I am new to matlab and signal processing. I have wrote the below posted code. what i could not understand is, the time soecification section at the beginning of the code. I do not why do we need sampling when specifying an interval or time duration, i think it suffice to specify something like the following : t = (0: 0.2: 1.0) for an example, why do i need some thing like sampling to plot such as stationary signal. another question is, this code gives me an error saying paranthesis imbalance

问题 I'm writing a C# application which gets accelerometer data continuously at a rate of ~100Hz from a Wii remote. This data is stored in a list (one list for each axis) as it arrives. I have a Timer, which fires every one second,(so by the time it fires the list contains~100 elements), it then applies a low pass filter to the list (using the Signal Processing Math.NET Neodym library), and writes the data to a file, and clears all the list for the next batch of data. Now the problem is, the

问题 I'm trying to transfer some data through audio port of android device, I'm using FSK (Frequency Shift Keying) I'm using two different frequencies to represent 1 and 0. Is there any android library or sample code to FSK decode captured audio data? 回答1: You might want to check out this https://code.google.com/p/audio-analyzer-for-android/source/browse/README General tips: Make sure to apply a window function. Select a sample frequency that makes the fft transform hit your two frequencies as

问题 Disclaimer: I'm not a signal processing expert. I'm writing a function that takes a 1D array and performs Fast Fourier Transform on it. Here's how it works: If the array's size is not a power of two, pad it with 0s at the end so that its size becomes a power of two. Perform FFT on the padded array and store the results in an array x . Downsample the complex array x to match the length of the original non-padded array. Return x . I'm having trouble with step 3 . If I omit step 3 and perform

问题 I am trying to estimate the pose of a camera by scanning two images taken from it, detecting features in the images, matching them, creating the fundamental matrix, using the camera intrinsics to calculate the essential matrix and then decompose it to find the Rotation and Translation. Here is the matlab code: I1 = rgb2gray(imread('1.png')); I2 = rgb2gray(imread('2.png')); points1 = detectSURFFeatures(I1); points2 = detectSURFFeatures(I2); points1 = points1.selectStrongest(40); points2 =

问题 I need to denoise a signal. I tried to denoise it with savgol_filter but it result in loosing singularities in the signal. In order to denoise and keep singularities i tried to use wavelet transform, wavelet thresholding and inverse wavelet transform but i didn't succeed. Does someone know how to use wavelet denoising ? here is a text file with signal datas import numpy as np from matplotlib import pyplot as plt from scipy.signal import savgol_filter import pywt def readSignal(nomFichier, N):

问题 Has anyone tried implementing matlab's filtfilt() function in Java (or at least in C++) ? If you guys have an algorithm, that would be of great help. 回答1: Here is my implementation in C++ of the filtfilt algorithm as implemented in MATLAB. Hope this helps you. 回答2: Allright, I know this question is ancient, but maybe I can be of help to someone else who winds up here wondering what filtfilt actually does. Although it is obvious from the docs that filtfilt does forward-backward (a.k.a. zero

问题 I am having some difficulties in understanding how to decode a Bell 202 encoded signal. How do I do that? I know Bell202 encodes binary data with ones and zeros as 1200 Hz and 2200 Hz. I recorded a radio signal on 145.825 Mhz with a 2Mhz sampling rate when the International Space Station (ISS) transmitted APRS. I uploaded a Python Numpy NPY file here https://drive.google.com/drive/folders/0B-6iySZt3rrjQVdpRHBOc2lyQTQ and it has 4 data streams in it. Don't be confused, I just recorded the same

问题 Trying to understand an fft (Fast Fourier Transform) routine I'm using (stealing)(recycling) Input is an array of 512 data points which are a sample waveform. Test data is generated into this array. fft transforms this array into frequency domain. Trying to understand relationship between freq, period, sample rate and position in fft array. I'll illustrate with examples: ======================================== Sample rate is 1000 samples/s. Generate a set of samples at 10Hz. Input array has

问题 The aim of this post is to properly understand Numerical Fourier Transform on Python or Matlab with an example in which the Analytical Fourier Transform is well known. For this purpose I choose the rectangular function, the analytical expression of it and its Fourier Transform are reported here https://en.wikipedia.org/wiki/Rectangular_function Here the code in Matlab x = -3 : 0.01 : 3; y = zeros(length(x)); y(200:400) = 1; ffty = fft(y); ffty = fftshift(ffty); plot(real(ffty)) And here the