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18

L1magic. This is the toolbox associated with the original paper. CompSens. This looks like it's in C, but you could possibly call it with mex -- not sure. Model-based compressive sensing toolbox. Most of the code is plain Matlab code Each folder in the package consists of a CS recovery algorithm based on a particular signal model, and a script that ...


16

OK, let's try to derive the best: $$ \begin{array}{lcl} y[n] &=& \alpha x[n] + (1 - \alpha) y[n - 1] \\ &=& \alpha x[n] + (1 - \alpha) \alpha x[n-1] + (1 - \alpha)^2 y[n - 2]\\ &=& \alpha x[n] + (1 - \alpha) \alpha x[n-1] + (1 - \alpha)^2 \alpha x[n-2] + (1 - \alpha)^3 y[n - 3]\\ \end{array} $$ so that the coefficient of $x[n-m]$ is ...


16

There's a nice discussion of this problem in Embedded Signal Processing with the Micro Signal Architecture, roughly between pages 63 and 69. On page 63, it includes a derivation of the exact recursive moving average filter (which niaren gave in his answer), $$ H(z) = { 1 \over{N} } { 1 - z^{-N} \over { 1 - z^{-1} } }. $$ For convenience with respect to ...


9

There is no analytic solution for $\alpha$ being a scalar (I think). Here is a script that gives you $\alpha$ for a given $K$. If you need it online you can build a LUT. The script finds the solution that minimizes $$ \int_{0}^{\pi} dw \quad \left|H_1(jw) - H_2(jw)\right|^2 $$ where $H_1$ is the FIR frequency response and $H_2$ is the IIR frequency ...


7

You can think of linear-least squares in single dimension. The cost function is something like $a^{2}$. The first derivative (Jacobian) is then $2a$, hence linear in $a$. The second derivative (Hessian) is $2$ - a constant. Since the second derivative is positive, you are dealing with convex cost function. This is eqivalent to positive definite Hessian ...


7

I suppose I am answering off-topic here then, but for L1-optimization approaches, I find YALL1 (http://yall1.blogs.rice.edu/) and SPGL1 (http://www.cs.ubc.ca/~mpf/spgl1/) very useful and efficient packages. TFOCS (http://cvxr.com/tfocs/) is probably a bit harder to use, but should be quite flexible. There is also CVX (http://cvxr.com/cvx/) which makes it ...


6

As you have already pointed out in your question, it is not possible (without using optimization methods) to compute an exact L2 solution for the frequency domain design problem of IIR filters due to the non-linear relationship between the filter coefficients and the error function. There is, however, a method which can come close and which transforms the ...


6

Keep in mind, L1 is not the only approach to compressive sensing. In our research, we've had better success with Approximate Message Passing (AMP). I am defining "success" as lower error, better phase transitions (ability to recover with fewer observations), and lower complexity (both memory and cpu). The Approximate Message Passing algorithm establishes ...


5

There's a whole area of signal processing dedicated to optimal filtering. In pretty much every case I've seen the filtering problem is formulated with a convex cost function. Here's a freely available book on the subject - Sophocles J. Orfanidis - Optimum Signal Processing.


5

You may also want to check the Matlab UNLocBox: http://unlocbox.sourceforge.net There are 4 compressive sensing scripts on the demo page: http://unlocbox.sourceforge.net/doc/demos/index.php


5

I found the following in Charles Therrien's "Discrete Random Signals and Statistical Signal Processing" in one of the Appendicies. Say you have the function $Q(a)$ you wish to minimize such that $C(a)=0$, where $C(a)$ may be complex valued and $a$ may be a complex vector. The constraint really represents two real-valued constraints. $$C_r(a)=0,\qquad C_i(a)...


5

It's a bandpass filter, which is fairly obvious. FIR or IIR is hard to tell without phase. A FIR design would likely to have assumed linear phase, and omitted the phase response. and a package probably wouldn't calculate it, like in an IIR design, so I would tend towards a FIR. The band goes to .5 which implies whoever designed the filter was taught unit ...


5

The Frobenius Norm has multiple equivalent definitions – the useful for error measure is probably this one: $$\left\|M\right\|_\mathrm F = \sqrt{\sum_{p\in M}\left\lvert p\right\rvert^2}$$ That's a root square over all pixels. Root mean squares are very useful cost functions, as they describe the power of a signal.


4

the solution for a sparse recovery problem is given by: $$\text{min} ||x||_0$$ $$\text{s.t} \hspace{2mm} y = Ax$$ The definition of $||x||_0$ is no. of non-zero entries in $x$. This is also called the sparsity of the vector. i.e., we are asking for the sparsest solution $x$, that satisfies $y = Ax$. Consider the simplest case where $...


4

As has been referenced in the comments already, you're describing a pulse-amplitude modulation (PAM) signal constellation. The problem, as you've framed it, seems to suggest the AWGN vector channel (where symbols are described using discrete values $s_1$, $s_2$, and so on), in contrast to the waveform channel, where symbols are expressed using waveforms that ...


4

I am sorry I cannot comment your answer due to my low reputation. Gini and your suggested sparsity ratio ($l_1(x)/l_2(x)$) both give me the same value for $\lambda$. But The problem I still see is that I cannot take into account how well the vector is solving the equation $Ax-y$. I would like to combine the residuum $l_1(A\hat{x}-y)$ and the sparsity $l_1(\...


4

That's a trick which you will also find in a DSP context, that's why I choose to provide an answer here. It is related to the Wirtinger derivative, and you can find more details about it in this answer over at math.SE. In practice this trick is often used to compute the extremum (minimum or maximum) of a real-valued function depending on a complex variable (...


3

The quadratic surface is determined by the autocorrelation matrix of the data, which is always positive definite or positive semi-definite. This means that any stationary point is always a minimum. In the worst case, this minimum is not unique if the matrix is singular, but it can never be a saddle point.


3

For large intensities / large "bins", i.e. "areas for which events are counted and accumulated", Poisson processes lead to nearly Gaussian distributed individual values -- basically, without trying to derive this, I think that's the application of the CLT on a lot of realization of a point process. EDIT: Shameless plug: I really really like the wikipedia ...


3

So, you have a bunch of datapoints of the form (x,y), and considering all of those datapoints together, you have the vectors $x$ and $y$. To do a curve fit, you would like to solve the equation $Aw=y$ for the column vector $w$, which holds the coefficients of your curve fit polynomial. These coefficients are also the weights of the basis vectors in the ...


3

I stumbled upon this old question and I would like to share my solution. As mentioned in other answers, there is no analytical solution, but the function to be minimized behaves nicely and the optimal value of $\alpha$ can be found easily with a few Newton iterations. There is also a formula to check the optimality of the result. The impulse response of the ...


3

Based in experimental tests with k in range (2 to 100) the best fit (sum squared error) gives a relation of alfa = 1/k^0.865 being k number of samples for MovAvg filter


3

This really reminds me of the problem of beat induction in music signals, in which you have to infer a latent periodic grid from note onset measurements. The grid is "latent" - it's not because a track is at 120 BPM that it strictly has a quarter note every 500ms - the composer can choose to phrase the melody with half notes, then eighth notes... And then ...


3

In order to be able to choose an optimal value for the delay $\Delta$ it's important to understand how the system works. The purpose of the delay is to decorrelate the desired signal $s(n)$ and the signal component $s(n-\Delta)$ at the input of the adaptive filter. This means that $\Delta$ must be chosen such that the autocorrelation $R_{ss}(k)$ of $s(n)$ is ...


3

Hi: I'll try to answer as briefly as possible and only with respect to statistics. not dsp. In statistics, if you have a nice pdf such as the normal distribution, then maximizing the likelihood is equivalent to minimizing the sum of squares of the residuals ( often called errors ). In other cases, where you either have a complicated distribution ( maybe ...


3

SQP is a method for solving smooth (objective and constraint functions are at least twice differentiable) constrained nonlinear optimization problems. It solves a series of quadratic programming problems to converge to a solution to the Karush-Kuhn-Tucker conditions for the constrained optimization problem. IRLS is a method for solving unconstrained ...


3

Their intention is to solve: $$ \arg \min_{ J \left( x, y \right) } \sum_{x, y} \left\| J \left( x, y \right) - 255 \right\|_{2}^{2} + \lambda \sum_{x, y} \left\| \nabla J \left( x, y \right) - \nabla I \left( x, y \right) \right\|_{2}^{2} $$ Where $ I \left( x, y \right) $ is the input image and $ J \left( x, y \right) $ is the output image. They state ...


3

This simply reflects the load asymmetry of real world applications. On most networks, traffic is dominated by content streaming, which is a download activity. Netflix alone accounts for 15% of the world's entire Internet traffic.


3

There are few options: Stephen Boyd, Lieven Vandenberghe - Convex Optimization. This is the classic in this field. Very well written book. Also have a look on other papers of Boyd on similar subjects such as the The Alternating Direction Method of Multipliers (ADMM). They also have a great MOOC Course Stanford Online CVX 101 - Convex Optimization. Amir Beck ...


3

The problem is given by: $$\begin{equation} \arg \min_{X} \frac{1}{2} \sum_{k} {\left\| {T}_{k} {X}_{:, k} - {Y}_{:, k} \right\|}_{2}^{2} + \lambda {\left\| G X \right\|}_{2, 1} \\ = \arg \min_{X} \frac{1}{2} \sum_{k} {\left\| {T}_{k} {X}_{:, k} - {Y}_{:, k} \right\|}_{2}^{2} + \lambda \sum_{l} {\left\| G {X}_{:, l} \right\|}_{2} \end{equation}$$ In the ...


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