Multirate Signal Processing: Theory and Applications
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This article covers the theory and applications of multirate signal processing, including noble identity z-transform, interpolation, and perfect reconstruction systems. It also provides references to further reading and offers solved assignments and essays on the topic.
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Multirate1 Multirate signal processing By (Name) (Course) (Professor’s Name) (Institution) (State) (Date)
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Multirate2 Question.1 Noble identity z- transform for down sampling is given by; Ck[n]=δu∨n[n]=1 K∑ k=0 k−1 e j2kπn K Xk[n]={x[n]k[n] 0k≠n=Ck[n]x[n] Xk[z]=∑ n Xk[n]z−n=1 K∑ n ∑ k=0 k−1 e j2πkn Kx[n]z−n=1 K∑ k=0 k−1 ∑ n x[n](e j2πkn Kz)−n ¿1 K∑ k=0 k−1 x(¿e −j2πkn Kz)¿ Thus Xk(z)=Y(zk) Y(z)=Xk(z 1 k)=1 K∑ k=0 k−1 x(e¿¿−j2πkn Kz 1 k)¿ Given; y[n]=(↓3){h[n]∗x[n]}The Z-transform Y (Z) is given as; Y(Z)=(↓3){H(Z)∗X(Z)}ButH(Z)=1−z−1 1−z−3 Substituting, we get;Y(Z)=(↓3){1−z−1 1−z−3∗X(Z)}taking inverse z transform y[n]=1 1−z−1∗{(↓3)(1−z−1∗x[n])}=h1[n]∗{(↓3)(h2[n]∗x[n])} Hence the process can efficiently be implemented as shown. Question.2 The discrete time Fourier transform of a signal is given by; X(w)=∑ −∞ ∞ x(n)e−jwn=∑ −π 12 π 12 e−jwn Butw=π/12
Multirate3 y1(w)=1 6π∑ −π/12 π/12 e−jwn=1 6πe−jπ/12n |π/12 −π/12 y2(w)=2 π∑ −π/12 π/12 e−jwn=2 πe−jπ/12n |π/12 −π/12 y1() Question.3 For interpolation, the cutoff frequency¿2∗π/4 It takes into interpretation that after the interpolation procedure the signal is made up of (L–1) zero coefficients, and the decimation progression infers that only one out of every M samples is needed at the productivity of the converter. To ensure the system is more resourceful, the low- pass filter in is substituted with a bank of filters organized in parallel. The sampling-rate π/12−π/12
Multirate5 Channel 1 z−2x2 2=z−2 Channel 2 z−1xz−1x2 2=z−2 The transfer function z−2+z−2 z−2z−2=2z2 2z2>0 Thus, the system is perfectly reconstructive Question.5 Determine the transfer function, The ratio between the output and the input h(z)=Y(z) X(z) The systems comprise of an expander (L) and compressor (M) X(z)h(z)=Y(z) For the blocks in series, the transfer function is given by the product of their values h(z)=9z−3
Multirate6 Reference Bandyopadhyay, B., & Janardhanan, S. (2006).Discrete-time Sliding Mode Control: a Multirate Output Feedback Approach. Berlin Heidelberg, Springer-Verlag. Available from: http://dx.doi.org/10.1007/11524083.[Accessed Date: 3rdMay 2018]. Dolecek, G. J. (2018).Advances in Multirate Systems. Cham, Springer. Fliege, N. (2005).Multirate digital signal processing: multirate systems, filter banks, wavelets. Chichester, Wiley Harris, F. (2008).Multirate signal processing for communication systems. Upper Saddle River, N.J., Prentice Hall PTR. Proakis, J. G., & Manolakis, D. G. (2014).Digital signal processing. Harlow, Essex, Pearson. SPA (Conference: Institute of Electrical and Electronics Engineers). (2015).SPA 2015: Signal Processing, Algorithms, Architectures, Arrangements, and Applications: conference proceedings: Poznan, 23-25th September 2015. Available from: http://ieeexplore.ieee.org/servlet/opac?punumber=7360272. [Accessed Date: 3rdMay 2018] Stranneby, D. (2001).Digital signal processing: DSP and applications. Oxford, Newnes. Available from:http://site.ebrary.com/id/10186537. [Accessed Date: 3rdMay 2018] Yaroslavsky, L. (2011).Digital Signal Processing in Experimental Research Volume 1: Fast Transform Methods in Digital Signal Processing. Sharjah, Bentham Science Publishers. Available from:http://public.eblib.com/choice/publicfullrecord.aspx?p=864334. [Accessed Date: 3rdMay 2018].