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Basically, there are 2 parts to this problem Step 1 : Find the slope between the last 2 samples. The slope is simply the difference between the last 2 samples. $$m = x[n]-x[n-1];$$ Step 2 : You need to extrapolate to find the next sample based on the hypothesis that the next sample will be colinear with the 2 previous samples. $x[n+1] = x[n] + m$ $x[n+1]... 1 If you assume that the samples lie on a line then the current sample is related to the previous sample by $$x[n]=x[n-1]+c\tag{1}$$ with some unknown constant$c$. If there are two past samples given, then$c$is simply determined by the difference between these two samples: $$c= x[n-1]-x[n-2]\tag{2}$$ Combining$(1)$and$(2)$gives$$x[n]=2x[n-1]-x[n-2]\tag{... 1 You'd avoid having this problem by detecting whether FM is present before demodulating it. The reason is simple: FM demodulation throws away a lot of the info that's still in the RF signal – most prominently, the actual received power – and that info is what you'd want to use to squelch. That can (unless there's other types of transmissions than audio FM ... 0 You can possibly generate the C++ code from the Faust DSP and start from there? Possibly by copy/paste the DSP code in the Faust Web IDE (https://faustide.grame.fr/) and export in C++: The IDE shows the SVG for the signal flow digram: 1 to obtain the desired ideal filter. That's like asking: how do design the ideal car? There is no such a thing: The reason why there are so many different car models, is because people have very different needs and requirements. Each car model represents a different trade off between size, cost, efficiency, loadability, towing ability, aesthetics, ... 4 The answer can be given without being at all specific to filters. In fact, one has to answer that in terms of engineering in general: Whenever you have alternative solutions, the only measure of quality that really matters, is fulfillment of the requirements. So, you'll have to know what you'll use that filter for, and evaluate how well the alternatives ... 0 For early image preprocessing tasks, normalization is not mandatory, as long as they only add a common multiplication factors for all images. Indeed, one is mostly interested in relative "importance" of features, or in detection, localization. As much as "integer" pixel values are somewhat arbitrary, multiplying them a constant often does ... 1 The answer is simple, the Sobel Filter is a composition of Lows Pass Filter (LPF) and High Pass Filter (HPF). The composition is done by convolution. Now, indeed the LPF presented above$ {\left[ 1, 2, 1 \right]}^{T} \$ has amplification in the DC value (Its sum is 4 so the amplification is 4). Yet it is convolved with an HPF filter which rejects the DC ...

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Digital Filters as dedicated pieces of hardware are quite rate these days. Most filtering just happens on general purpose CPUs such as ARM core and may be partially accelerated by specific instruction sets or co-processor (e.g. NEON). Your smart phone or your laptop do a lot of digital filtering every day. Dedicated filter hardware is typically part of a ...

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I'm not an expert on the LMS algorithm. Perhaps you should add a link to the algorithm description so we can help you. However, I have adapted a lot of algorithms to fixed-point implementation, so I think I can help you. I'm gonna give you a few pointers : 1 - If your algorithm is not recursive, quantization errors can affect the accuracy of your results but ...

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