I am new to this Post. Kindly let me know if I make any mistake the way I am asking question.
My question can be clearly understandable when one can go through these links where I have explained what I am looking for.
https://www.dropbox.com/s/5nhmxqgveytl0ba/Comb%20FIR%20Filter%20Explanation.jpg
(And)
https://www.dropbox.com/s/5nhmxqgveytl0ba/Comb%20FIR%20Filter%20Explanation.jpg
The major point will be "How Can I create such a 'Variable FIFO buffer' whcih depends on input period & Filter sampling frequency 'Fs'?
If any one out there has any Idea or soultion for such a problem, Then I will be very glad to know and it will help me a lot in my project here.
Sincerely
Prashant
Maybe I don't fully understand what it is you're looking for here but I think you already have all the information that you need.
Fs = 1000; %% or whatever your sampling rate is to begin with.
Order = 3; %% Or whatever order you decide to work with.
Buffer_length = (Order/2)*Fs; % this will be the amount of data in each buffer
If you're looking for someone to give you anything more than this you're going to have to provide additional information and show what you're tried to do so far.
Related
I am new to the Neuroscience topic and I am supposed to baseline correct some EEG data that I have already epoched. I have been googling to try to find out how to do this on Matlab but I am not sure what the concept is. Can anyone help? I know it's a very general question but I am not sure what to ask. If someone can point me in the right direction or even tell me how to do it, I'd be very thankful.
You have to subtract the mean of the first samples of every channel from the whole length of the channel. Typically the baseline window is 100ms or more before the event.
I'm asking this here because I didn't find anything online.
I would be interested in how Google Quick Draw works, specifically:
1) How does it output the answer - does it have a giant output vector with a probability for each type of drawing?
2) How does it read the data - I see they've implemented some sort of order aware input system, but does that mean that they input positions of the interpolated lines that users draw? This is problematic because it's variable length - how did they solve it?
3) And, finally, which training algorithm are they using? The data grows each time someone draws something new, or do they just feed it into the algorithm when it's created?
If you know any papers on this or by miracle you work at Google and/or can explain how it works, I would be really greatful. :)
I am using Gurobi to run a MIQP (Mixed Integer Quadratic Programming) with linear constraints in Matlab. The solver is very slow and I would like your help to understand whether I can do something about it.
These are the lines which I use to launch the problem
clear model;
clear params;
model.A=[Aineq; Aeq];
model.rhs=[bineq; beq];
model.sense=[repmat('<', size(Aineq,1),1); repmat('=', size(Aeq,1),1)];
model.Q=Q;
model.obj=c;
model.vtype=type;
model.lb=total_lb;
model.ub=total_ub;
params.MIPGap=10^(-1);
result=gurobi(model,params);
This is a screenshot of the output in the Matlab window.
Question 1: It is the first time I am trying to run a MIQP and I would like to have your advice to understand what I can do to improve performance. Let me tell what I have tried so far:
I cheated by imposing params.MIPGap=10^(-1). In this way the phase of node exploration is made shorter. What are the cons of doing this?
I have big-M coefficients and I have tied them to the smallest possible values.
I have tried setting params.ScaleFlag=2; params.ObjScale=2 but it makes things slower
I have changed params.method but it does not seem to help (unless you have some specific recommendation)
I have increase params.Threads but it does not seem to help
Question 2 (minor): Why do I get a negative objective in the root simplex log? How can the objective function be negative?
Without having the full model here, there is not much on advise to give. Tight Big-M formulations are important, but you said, you checked them already. Sometimes splitting them up might help, but this is a complex field.
What might give great benefits for some problems is using the Gurobi parameter tuning tool. So try to export your model and feed the tuning tool with it. It automatically tries different of the hundreds of tuning parameters and might give some nice results.
Regarding the question about negative objectives in the simplex logs, I can think of a couple of possible explanations. First, note that the negative objective values occur in the presence of dual infeasibilities in the dual simplex run. In such a case, I'm not sure exactly what the primal objective values correspond to. Second, if you have a MIQP with products of binaries in the objective, Gurobi may convexify the objective in a way that makes it possible for a negative objective to appear in the reformulated model even when the original model must have a nonnegative objective in any feasible solution.
I'm writing code, that executes MLE. At each step, I get gradient at one point and then move along it to another point. But I have problem with determination of magnitude of the move. How to determine the best magnitude for good convergence? Can you give me an advice how to avoid other pitfalls, such as presence of several maximums?
Regarding the presence of several maxima: this issue will occur when dealing with a function that is not convex. It can be partially solved by multi-start optimization, which essentially means that you run the simulation multiple times in order to find as many maxima as possible and then selecting the 'highest' maximum from among them. Note that this does not guarantee global optimality, as the global optimum might be hard to reach (i.e. the local optima have a larger domain of attraction).
Regarding the optimal step size for convergence: you might want to look at back-tracking linesearch. A short explanation of it can be found in the answer to this question
We might be able to give you more specific help if you could give us some code to look at, as jkalden already pointed out.
Sorry about this noob question, because I never work with matlab and signal processing before.
Here is what I want to do: I have a fixed length of byte array X, now I want to encode it to a sound file, I also want this process to be reversible, which means the sound can be converted back to X with no error. I searched online, and found the following code:
M = 16;
x = randint(5000,1,M);
y=modulate(modem.qammod(M),x);
My question is that, is QAM the best way to do this? and how to use it? A little bit code example will be really appreciated, Thank you!
update#1: I tried to output y by sound(y), but matlab does not allow me to do so, it says I can only output floating numbers. How can I solve this? Thank you!
If you need to transmit over the air, you have quiet a lot of work in front of you I think. The most difficult problem to solve in a telecommunications system is often synchronization, meaning that your receiver will have to know where the QAM symbols are placed in time. This is not easy. If you choose to go ahead I agree with mtrw that you should try dsp.stackexchange.com.
Try for example to imaging a simple modulation scheme where each bit is converted to a short piece of sine with the frequency depending on whether the bit is one or zero. How would you go about decoding this on the receiver end? You need to detect the onset of the first bit and have some self maintaining clock running for synchronization on the receiver to find bits in case they do not change, aka a PLL (Phase Locked Loop). This could possibly be made easier by using manchester coding, but you would still have to do quite a lot to get it running.
As you see, there are no easy solutions when you leave the save Matlab harbor :-)
Best regards