□ Phase demodulation using Hilbert transform □ Extracting instantaneous amplitude, phase, frequency □ Method 3: Using FFT to compute convolution □ Multiplication of polynomials and linear convolution □ Representing single variable polynomial functions Polynomials, convolution and Toeplitz matrices.□ Computation of power of a signal - simulation and verification □ Reconstructing the time domain signal from the frequency domain samples □ Representing the signal in frequency domain using FFT Obtaining magnitude and phase information from FFT.Interpreting FFT results - complex DFT, frequency bins and FFTShift.Hand-picked Best books on Communication Engineering Note: There is a rating embedded within this post, please visit this post to rate it. Rate this post: ( 3 votes, average: 3.67 out of 5) Baum-Welch algorithm helps us to find the unknown parameters of a HMM. In learning problems, we attempt to find the various parameters (transition probabilities, emission probabilities) of the HMM, given the observation. Learning problems involve parametrization of the model. What is the probability of fourth die being loaded, given the above sequence ? Forward-backward algorithm to our rescue. What is the most likely sequence of die (hidden states) given the above sequence ? Such problems are addressed by Viterbi decoding. Forward algorithm is applied for such evaluation problems. Given the model of the dishonest casino, what is the probability of obtaining the above sequence ? This is a typical evaluation problem in HMMs. In the dishonest casino, the gambler rolls the following numbers: Figure 2: Sample Observations 1. The algorithms will be explained in detail in the future articles. Let’s briefly discuss the different problems and the related algorithms for HMMs.
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