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Recent content by me2please

  1. M

    Whats the difference between random and stochastic process?

    Random processes All of the Prob. & Stochastic processes books I came across use these 2 terms synonymously. A realisation of a stochastic process or random process is a possible scenario or an outcome. When you refer to a random/stochastic process you refer to something you don't know...
  2. M

    [help] how to prove this equation

    https://en.wikipedia.org/wiki/Cauchy-Schwarz_inequality
  3. M

    subband coding!!!!...pls help me

    Try use search in UL/DL forum for more books.
  4. M

    what is the maning of e^jw in fourier transform?

    Be careful! How do you get from to Then how did you loose "^iQ"? :|
  5. M

    Probability Proof with E(x)

    It's just Markov's ineq \[X \ge 0, \epsilon > 0, P(X \ge \epsilon) \le \frac{E(X)}{\epsilon}\] \[P(X \ge \sqrt \eta) \le \frac{\eta}{\sqrt \eta}\] proof of Markov's ineq can be found in almost all probability books.
  6. M

    question about optimization

    complete clean: x=0 wage w= 2 + 6(0) = 2 satisfaction u = √2 -2(0) = √2 ≈1.4142 complete dirty: x=1 wage w=2 + 6(1) = 8 satisfaction u = √8 -2(1) ≈2.8284 -2 ≈0.8284 a) Is this employe more happier in a complete "clean"job or a complete "dirty"job? Since utility of clean job 1.4142 > utility...
  7. M

    Fingerprint biometrics systems project

    feature extraction, pca, fingerprint, matlab I wouldn't recommend this autocorrelation . This method is hardly used in practice based on its limitations. This method could work with images that are identical (no stretch, bend, etc.) Due to fine details nature of fingerprint image, a little...
  8. M

    Recurrence relation of Bessel Function

    bessel function recurrence relation Use generating function \[g(x,t) = e^{\frac{x}{2}( t-\frac{1}{t})}\] From \[ e^{\frac{x}{2}( t-\frac{1}{t})} = \sum_{=-\infty}^{\infty} J_n (x) t^n\] \[ \frac{\partial}{\partial t} g(x,t) = \frac{1}{2} x (1+ \frac{1}{t^2})e^{\frac{x}{2}( t-\frac{1}{t})} \]...
  9. M

    Zeros of the funcion in Laplace domain

    Padé Approximation (1,1) approximation: \[e^{T_ds} \approx \frac{1- \frac{T_ds}{2}}{1+ \frac{T_ds}{2}}\] (2,2) approximation: \[e^{T_ds} \approx \frac{1- \frac{T_ds}{2}+ \frac{(T_ds)^2}{12}}{1+ \frac{T_ds}{2} + \frac{(T_ds)^2}{12}}\] small delay: \[e^{T_ds} \approx \frac{1}{1+ \frac{T_ds}{2}}\]
  10. M

    Differences betwen Laplace and Fourier Transform

    After G.B. Dantzig presented his Simplex method for solving linear program:
  11. M

    Gabor Filter for Fingerprint Image

    gabor filter code Try this, **broken link removed** The above RaymondThai paper has incorrect equations.
  12. M

    Discrete Time Fourier Transform or Discrete Fourier Transfor

    It's just a normalization factor, so that you get x[n]-(forward)->X[k]-(inverse)->x[n] Therefore, it doesn't matter whether you put 1/N at the forward, at the inverse, or even split it up to 1/sqrt(N) for both forward and inverse.
  13. M

    what is meant by DCT?

    Z. Wang, Harmonic analysis with a real frequency function. I. Aperiodic case, Appl. Math. Comput. 9:53-73(1981)
  14. M

    Help needed Discrete cosine Transform

    1. Theoretically, there is no limitation that the size has to be multiples of 8. You can do the DCT at any arbitrary size. 2. MATLAB wise, dct2() can do the 2D DCT of any arbitrary size also. See https://www.mathworks.com/access/helpdesk_r13/help/toolbox/images/dct2.html
  15. M

    IIR filters and linear phase

    From pp. 298, Discrete-Time Signal Processing by Oppenheim, Schafer, Buck
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