Info

This question is locked. 请重新打开它进行编辑或回答。

Explanation of the matlab code

16 次查看（过去 30 天）
Jolini
Jolini 2013-6-16
Locked: Rik 2024-7-9
Hi I foun this 'extract' of a matlab code on the internet. Can someone explain to me line by line whats happening? Im REALLY in need of help.
*
% ------------- % This is code to make the edge detecting filter % ----%
function filter=gaussfilt(N)
% calculate alpha so the filter fills all N points
alpha=N;
first=-(1-N/2)*exp(-(1-N/2)^2/alpha);
count=0;
while first<.1*(-(1530/4000*N-N/2)*exp(-(1530/4000*N-N/2)^2/alpha))
count=count+1;
alpha=N*500*count;
first=-(1-N/2)*exp(-(1-N/2)^2/alpha);
end
for n=1:N
filter(n)=-(n-N/2)*exp(-(n-N/2)^2/alpha); % d/dt of a gaussian
end
filter=filter/sum(abs(filter)); % normalization
return
  8 个评论
显示 6更早的评论
Md Ibrahim
Md Ibrahim 2020-10-18
编辑：Walter Roberson 2023-11-9
vmax=8;
vmin=-vmax;
del=(vmax-vmin)/L;
part=vmin:del:vmax; % level are between vmin and vmax with difference of del
code=vmin-(del/2):del:vmax+(del/2); % Contain Quantized values
[ind,q]=quantiz(s,part,code); % Quantization process
% ind contain index number and q contain quantized values
l1=length(ind);
l2=length(q);
for i=1:l1
if(ind(i)~=0) % To make index as binary decimal so started from 0 to N
ind(i)=ind(i)-1;
end
i=i+1;
end
for i=1:l2
if(q(i)==vmin-(del/2)) % To make quantize value in between the levels
q(i)=vmin+(del/2);
end
end
subplot(3,1,3);
stem(q);grid on; % Display the Quantize values
title('Quantized Signal');
ylabel('Amplitude--->');
xlabel('Time--->');
Murthy
Murthy 2023-1-12
% Load the voice signal [voice, fs] = audioread('stomp.mp3');
% Add sinusoidal interference at 1500Hz t = (0:length(voice)-1)/fs; sin_interference = 0.1*sin(2*pi*1500*t); corrupted_voice = voice , sin_interference;
% Design FIR null filter n = 100; % Filter length f = [0 0.7 0.75 1]; % Normalized frequency bands a = [1 1 0 0]; % Amplitude at each band b = firpm(n, f, a);
% Filter the corrupted signal to recover the original voice signal recovered_voice = filter(b, 1, corrupted_voice);
% Play the original, corrupted, and recovered voice signals sound(voice, fs); pause(1); sound(corrupted_voice, fs); pause(10); sound(recovered_voice, fs); pause(50) % Plot the original, corrupted, and recovered voice signals figure; subplot(3,1,1); plot(voice); title('Original Voice Signal'); subplot(3,1,2); plot(corrupted_voice); title('Corrupted Voice Signal'); subplot(3,1,3); plot(recovered_voice); title('Recovered Voice Signal');
% Plot the frequency response of the null filter figure; freqz(b, 1); title('Frequency Response of Null Filter');

This question is locked.

采纳的回答

Muthu Annamalai
Muthu Annamalai 2013-6-19
Your code calculates a filter kernel. I haven't run the code, but, it seems to me a kind of Gaussian filter, with some normalizations.
It could be rewritten like,
function filter=gaussfilt(N)
% calculate alpha so the filter fills all N points
alpha=N;
first=-(1-N/2)*exp(-(1-N/2)^2/alpha);
count=0;
% find the standard deviation for the Gaussian
while first<.1*(-(1530/4000*N-N/2)*exp(-(1530/4000*N-N/2)^2/alpha))
count=count+1;
alpha=N*500*count;
first=-(1-N/2)*exp(-(1-N/2)^2/alpha);
end
% calculate the filter kernel
n=1:N
filter =-(n-N/2).*exp(-(n-N/2).^2/alpha);
%normalize kernel & return
HTH
  2 个评论
Jolini
Jolini 2013-6-20
Hi. Thank you for the reply. Yes, its a Gaussian filter. I anyhow made some changes and I've finally understood it. I'm having trouble in finding peaks (maximum) of an enveloped signal. I've posted the question in MATLAB.. Would you be able to go through it and possibly help me out??
This is the link.
http://www.mathworks.us/matlabcentral/answers/79586-finding-peaks-of-a-signal
Thanx in advance
Usama
Usama 2023-4-7
编辑：Walter Roberson 2023-11-9
H1_y= (I./(2*pi.*((X-r*cos((n-1)*2*pi/N)).^2+(Y-r*sin((n-1)*2*pi/N)).^2))).*(X-r*cos((n-1)*2*pi/N)).*...
(cos((n-1)*2*pi/N));

更多回答（5 个）

varshini rebala
varshini rebala 2015-3-19
Can any one explain me what is happing in this code ?
% Selected Mapping (SLM)is one of the techniques used for % peak-to-average power ratio (PAPR) reduction in OFDM systems. % In this .m file it is simulated for a QPSK modulated, 64-subband % OFDM symbols.
clc clear
load ofdm_100000 % this is a .mat file containing 100000 QPSK modulated, % 64-element OFDM symbols. It is available with the % partial_transmit_sequence.m file, previously submitted % by the auther, for free download at the 'file exchange' % web page.
NN=10000; % the test is achieved on 10000 OFDM symbols only. It is % possible to use all of the 100000 symbols, but it will % take more time. N=64; % number of subbands L=4; % oversampling factor C=16; % number of OFDM symbol candidates
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % phase factor matrix [B] generation p=[1 -1 j -j]; % phase factor possible values randn('state', 12345); B=randsrc(C,N,p); % generate N-point phase factors for each one of the % C OFDM candidates %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=1:NN % calculate papr of original ofdm time_domain_signal=abs(ifft([ofdm_symbol(i,1:32) zeros(1,(L-1)*N) ofdm_symbol(i,33:64)])); meano=mean(abs(time_domain_signal).^2); peako=max(abs(time_domain_signal).^2); papro(i)=10*log10(peako/meano);
% B*ofdm symbol
p=[];
for k=1:C
p=[p; B(k,:).*ofdm_symbol(i,:)];
end
% Transform Pi to Time Domain and find paprs
for k=1:C
pt(k,:)=abs(ifft([p(k,1:32) zeros(1,(L-1)*N) p(k,33:64)]));
papr(k)=10*log10(max(abs(pt(k,:)).^2)/mean(abs(pt(k,:)).^2));
end
% find papr_min
papr_min(i)=min(papr);
end
figure [cy,cx]=ccdf(papro,0.1); semilogy(cx,cy) % CCDF of PAPR of the original OFDM hold on [cy,cx]=ccdf(papr_min,0.1); semilogy(cx,cy,'r') % CCDF of the modified OFDM (by SLM)
Sombaran Gupta
Sombaran Gupta 2015-4-13
Can anyone explain me from the third line till the end of this code..?? a=imread('kmeans.jpg'); imshow(255-rgb2gray(a)); b=reshape(double(255-rgb2gray(a)),[],1); [idx,c]=kmeans(b,2,'emptyaction','singleton'); Fin_a=reshape(idx,[size(rgb2gray(a))]); [ind1, ind2]=find(Fin_a(:,:)==2); [ind3, ind4]=find(Fin_a(:,:)==1); figure(); imshow(255-rgb2gray(a)); hold on if(length(ind1)<length(ind3)) scatter(ind2,ind1,'g'); else scatter(ind4,ind3,'g');a end
Tanzila Minhaj
Tanzila Minhaj 2019-9-29
编辑：Walter Roberson 2023-11-9
Can anyone help me understanding the code?
function [sigma] = constitutive_Armstrong_Frederick (e, E, sigma_y, H_k, H_nl)
% The function reconstructs the trend of the stress-strain curve
% according to the constitutive link of Armstrong & Frederick
% e = deformation vector
% E = Young's modulus [Pa]
% sigma_y = yield stress [Pa]
% H_k = instant kinematic work hardening parameter [Pa]
% H_nl = hardening parameter by Armstrong & Frederick [Pa]
% Data initialization
sigma (1) = 0;
alpha (1) = 0;
state (1) = 0;
for n = 1: length (e) -1
% Stress calculation by elastic prediction
sigma_e = sigma (n) + E * (s (n + 1) -e (n));
% Increased effort assessed in the elastic prediction phase
dsigma_e = sigma_e-sigma (n);
% Purified elastic prediction of back stress
sigma_e_tilde = sigma_e-alpha (n);
% Evaluation of the yield function to less than a tolerance
fi (n) = abs (sigma (n) -alpha (n)) - sigma_y;
tol = 0.001;
if abs (fi (n)) <toll
fi (n) = 0;
end
% Recognition of the elastic / plastic phase by the function of
% yield and the direction of deformation
if fi (n) == 0 && (e (n + 1) -e (n)) * (e (n) -e (n-1))> 0
% Plastic phase
state (n + 1) = 1;
% Sign of tensions (+ = traction, - = compression)
sign = sign (dsigma_e);
% Increase in the plastic dlambda multiplier
dlambda = (a * sigma_e_tilde-sigma_y) / (a * (E + H_k) -H_nl * alpha (n));
% Increase in plastic deformation
de_pl = a * dlambda;
% Increase in back stress
Dalfa = H_k * de_pl-H_nl * abs (de_pl) * alpha (n);
% Back stress
alpha (n + 1) = alpha (n) + Dalfa;
% Current voltage
sigma (n + 1) = sign * sigma_y alpha + (n + 1);
else
% Elastic phase
state (n + 1) = 0;
sigma (n + 1) = sigma_e;
de_pl = 0;
alpha (n + 1) = alpha (n);
end
end
end
Ayshath Afra
Ayshath Afra 2020-4-5
Can anyone help me understanding the code?
How to obtain the read cbir function
Arun das H A
Arun das H A 2023-11-7
clear all;
clf('reset');
cam = webcam(); % Create a webcam object
right = imread('RIGHT1.jpg');
left = imread('LEFT1.jpg');
noface = imread('no_face1.jpg');
straight = imread('STRAIGHT1.jpg');
detector = vision.CascadeObjectDetector(); % Create a face detector using Viola-Jones
detector1 = vision.CascadeObjectDetector('EyePairSmall'); % Create an eyepair detector
while true
vid = snapshot(cam); % Get a snapshot from the webcam
vid = rgb2gray(vid); % Convert to grayscale
img = flip(vid, 2); % Flip the image horizontally
bbox = step(detector, img); % Creating bounding boxes using detector
if ~isempty(bbox)%if face exists
biggest_box = 1;
for i = 1:size(bbox, 1)%find the biggest face
if bbox(i, 3) > bbox(biggest_box, 3)
biggest_box = i;
end
end
faceImage = imcrop(img, bbox(biggest_box, :)); % Extract the face from the image
bboxeyes = step(detector1, faceImage); % locations of he eye pair using detector
subplot(2,2,1), subimage(img); hold on; % Display full image
for i = 1:size(bbox,1)%draw all the regions that contain face
rectangle('position', bbox(i, :), 'lineWidth', 2, 'edgeColor', 'y');
end
subplot(2,2,3), subimage(faceImage); % Display face image
if ~isempty(bboxeyes)%check if eyepair is available
biggest_box_eyes = 1;
for i = 1:size(bboxeyes, 1)%find the biggest eye pair
if bboxeyes(i, 3) > bboxeyes(biggest_box_eyes, 3)
biggest_box_eyes = i;
end
end
bboxeyeshalf = [bboxeyes(biggest_box_eyes, 1), bboxeyes(biggest_box_eyes, 2), bboxeyes(biggest_box_eyes, 3)/3, bboxeyes(biggest_box_eyes, 4)];%resize the eyepair width in half
eyesImage = imcrop(faceImage, bboxeyeshalf(1, :)); % Extract the half eyepair from the face image
eyesImage = imadjust(eyesImage); % Adjust contrast
r = bboxeyeshalf(1, 4) / 4;
[centers, radii, metric] = imfindcircles(eyesImage, [floor(r - r/4) floor(r + r/2)], 'ObjectPolarity', 'dark', 'Sensitivity', 0.93); % Hough Transform
[M, I] = sort(radii, 'descend');
eyesPositions = centers;
subplot(2,2,2), subimage(eyesImage); hold on;
viscircles(centers, radii,'EdgeColor','b');
if ~isempty(centers)
pupil_x = centers(1);
disL = abs(0 - pupil_x);%disatance from left edge to center point
disR = abs(bboxeyes(1,3)/3 - pupil_x);%distance right edge to center point
subplot(2,2,4);
if disL > disR + 16
subimage(right);
elseif disR > disL
subimage(left);
else
subimage(straight);
end
end
end
else
subplot(2,2,4);
subimage(noface);
end
end
  1 个评论
Walter Roberson
Walter Roberson 2023-11-9
@Arun das H A
I do not understand how this code will help anyone to understand how the posted gaussfilt function works??

This question is locked.

类别

在 Help Center 和 File Exchange 中查找有关 Test and Measurement 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by