DIGITAL SIGNAL PROCESSING LABORATORY USING MATLAB SOLUTION MANUAL

Image ProcessingMy- Ha Le, Ph.Dhalm@hcmute.edu.vnAugust 31, 2015Reading• Golzalez, Digital Image Processing (2nd Edition)• Golzalez, Digital Image Processing Using MATLAB• Richard Szeliski, Computer Vision: Algorithms and Applications, September 3, 2010 draf[r]

• When the speech is interfered by a linear bandpass channel, the frequency response ofwhich is shown in Fig. 27.3, a translation of the cepstral clusters is observed, as shown inFig. 27.4(b).• When the speech is corrupted by Gaussian white noise of 15 dB SNR, a shrinkage of thecepstral vectors resu[r]

transformscanbeimplementedintheformoffilterbankstructures.Itiswellknownthatthetime-frequencyenergydistributionofsignalsoftenchangeswithtime.Thus,inthissense,theconventionallineartime-frequencytransformparadigmisfundamentallymismatchedtomanysignalsofinterest.Amoreflexibleandaccurateapproachisobtainedif[r]

[20] de Queiroz, R.L., Nguyen, T.Q. and Rao, K.R., The generalized lapped orthogonal transforms,Electron. Lett., 30, 107, Jan. 1994.[21] de Queiroz, R.L. and Rao, K.R., On orthogonal transforms of images using paraunitary filterbanks,J. Visual Commun. Image Representation, 6(2), 142–153, June[r]

−∞p(, θ )q− , θd(26.3)are carried out, using a convolving function q (of one variable) whose exact choice will have animportant influence on the appearance of the final image. Second, our estimate f∗of f is obtainedby backprojection as follows:f∗(r, φ) =π0[p ∗Yq](r cos(θ − φ),θ)dθ.(26.4[r]

30.1 IntroductionSignalreceptionhasnumerousapplicationsincommunications,radar,sonar,andgeoscienceamongothers.However,theadverseeffectsofnoiseintheseapplicationslimittheirutility.Hence,thequestfornewandimprovednoiseremovaltechniquesisanongoingresearchtopicofgreatimportanceinavastnumberofapplicationso[r]

decomposition. This case does have a lower first-order entropy than its conventional counterpart.c1999 by CRC Press LLCFIGURE 36.6: A four-level octave band decomposition using finite field filter banks.Finite field filter banks are still in their early phases of study. As a result of the constrai[r]

Tkc. We will assumethat˜dk= Ak+N, then the notation in Eq. (31.9) can be written in the compact form,d = Xc + e(31.11)by defining d =˜d1,...,˜dLTand by making the obvious associations with Eq. (31.9). Note thatthe parameter L determines the number of rows of the time varying matrix X. Therefore, ch[r]

(s) =√2α/(s + α) driven by an independent stationary whitenoise source. The weighted superposition of a continuum of such processeshas an effective spectrumSx(ω) =∞0Sα(ω) f (α) dα,(73.6)where the weights f(α)correspond to the density of poles or, equivalently, relaxation times. If anunnormalizable,[r]

,ω2,ω3) ≡ 0; equivalently, Cxxx(i, k) ≡0 and Cxxxx(i,k,l)≡ 0. This forms a basis for testing Gaussianity of a given measurement record.When {x(t)} is linear (i.e., it obeys (16.1)), then using (16.5) and (16.10),|Bxxx(ω1,ω2)|2Sxx(ω1)Sxx(ω1)Sxx(ω

brief introduction is given in the appendix of this chapter. Computationally simple IVMs for arraysignal processing appeared in [10, 11]. These methods perform poorly in difficult scenarios involvingclosely spaced DOAs and correlated signals.More recently, the combined Instrumental Variable