Download Digital Signal Processing with Matlab Examples, Volume 3: by Jose Maria Giron-Sierra PDF

By Jose Maria Giron-Sierra

This is the 3rd quantity in a trilogy on smooth sign Processing. the 3 books supply a concise exposition of sign processing themes, and a advisor to aid person functional exploration according to MATLAB programs.

This publication comprises MATLAB codes to demonstrate all of the major steps of the speculation, delivering a self-contained advisor compatible for self sufficient examine. The code is embedded within the textual content, assisting readers to place into perform the information and techniques discussed.

The e-book essentially makes a speciality of filter out banks, wavelets, and pictures. whereas the Fourier remodel is enough for periodic signs, wavelets are improved for different circumstances, resembling short-duration indications: bursts, spikes, tweets, lung sounds, and so forth. either Fourier and wavelet transforms decompose signs into elements. additional, either also are invertible, so the unique signs might be recovered from their elements. Compressed sensing has emerged as a promising proposal. one of many meant purposes is networked units or sensors, that are now changing into a truth; hence, this subject is additionally addressed. a range of experiments that show photo denoising purposes also are incorporated. within the curiosity of reader-friendliness, the longer courses were grouped in an appendix; additional, a moment appendix on optimization has been extra to complement the content material of the final chapter.

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Read or Download Digital Signal Processing with Matlab Examples, Volume 3: Model-Based Actions and Sparse Representation PDF

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Extra info for Digital Signal Processing with Matlab Examples, Volume 3: Model-Based Actions and Sparse Representation

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The radar measures the distance y from the radar to the body. Body state variables are chosen such that x1 is altitude, x2 is velocity, and x3 is the ballistic coefficient. 148) Fig. 22 Falling body example g y x1 L 44 1 Kalman Filter, Particle Filter and Other Bayesian Filters where ρ is air density and k is a constant. 81 m/s2 is gravity acceleration. 150) Notice the two nonlinearities of the example: the drag, and the square root. A simple Euler discretization is chosen for the motion equations.

110) B(q −1 ) = b1 q −1 + b2 q −2 + . . 111) C(n, q −1 ) = 1 + (K 1 (n − 1) + a1 ) q −1 + (K 2 (n − 1) + a2 )q −2 + . . 112) In the case of a stationary Kalman filter, C(n, q −1 ) would be C( q −1 ). The Kalman filter could be seen as a model that can be given in ARMAX format. Likewise, the ARMAX model could be interpreted as a Kalman filter. 5 Numerical Issues It was soon noticed, in the first aerospace applications of the Kalman filter, that the programs may suffer from numerical instability.

This has been shown in the previous subsection. Actually, many applications involving tracking or positioning of vehicles or objects incur in this situation. In two dimensions, the tangent to a curve f (x) can be obtained using the first derivative d f (x)/d x. A generalization of the first derivative is the gradient of a scalar function of several variables. A larger generalization is the ‘Jacobian’, which can be described as the first derivative of a vector function of several variables. Here is the expression of a Jacobian: ⎛ ∂f 1 ∂x1 ⎜ ⎜ ∂ f2 ⎜ ∂x1 ∂f = ⎜ Jf = ⎜...

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