There has been a great deal of material in the area of discrete-time transforms that has been published in recent years. This book does an excellent job of presenting important aspects of such material in a clear manner. The book has 11 chapters and a very useful appendix. Seven of these chapters are essentially devoted to the Fourier series/transform, discrete Fourier transform, fast Fourier transform (FFT), and applications of the FFT in the area of spectral estimation. Chapters 8 through 10 deal with many other discrete-time transforms and algorithms to compute them. Of these transforms, the KarhunenLoeve, the discrete cosine, and the Walsh-Hadamard transform are perhaps the most well-known. A lucid discussion of number theoretic transforms i5 presented in Chapter 11. This reviewer feels that the authors have done a fine job of compiling the pertinent material and presenting it in a concise and clear manner. There are a number of problems at the end of each chapter, an appreciable number of which are challenging. The authors have included a comprehensive set of references at the end of the book. In brief, this book is a valuable contribution to the general areas of signal processing and communications. It can be used for a graduate level course in perhaps two ways. One would be to cover the first seven chapters in great detail. The other would be to cover the whole book by focussing on different topics in a selective manner. This book  by  Elliott  and Rao  is extremely  useful to researchers/engineers who are working  in the areas of signal processing and communications. It i s also an excellent reference book, and hence a valuable addition to one’s library

http://ieeexplore.ieee.org/iel5/5/31347/01457444.pdf

Multirate digital signal processing

The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges

We propose a new model for detail that inherently captures oscillations, a key property that distinguishes textures from individual edges. Inspired by techniques in empirical data analysis and morphological image analysis, we use the local extrema of the input image to extract information about oscillations: We define detail as oscillations between local minima and maxima. Building on the key observation that the spatial scale of oscillations are characterized by the density of local extrema, we develop an algorithm for decomposing images into multiple scales of superposed oscillations.Current edge-preserving image decompositions assume image detail to be low contrast variation. Consequently they apply filters that extract features with increasing contrast as successive layers of detail. As a result, they are unable to distinguish between high-contrast, fine-scale features and edges of similar contrast that are to be preserved. We compare our results with existing edge-preserving image decomposition algorithms and demonstrate exciting applications that are made possible by our new notion of detail.

/pdf/edge-preserving-multiscale-image-decomposition-based-on-55vcw0jf3i.pdf

Edge-preserving multiscale image decomposition based on local extrema

Healthcare big data processing mechanisms: The role of cloud computing

This paper introduces a structure for the compensation of frequency-response mismatch errors in M-channel time-interleaved analog-to-digital converters (ADCs). It makes use of a number of fixed digital filters, approximating differentiators of different orders, and a few variable multipliers that correspond to parameters in polynomial models of the channel frequency responses. Whenever the channel frequency responses change, which occurs from time to time in a practical time-interleaved ADC, it suffices to alter the values of these variable multipliers. In this way, expensive on-line filter design is avoided. The paper includes several design examples that illustrate the properties and capabilities of the proposed structure.

A Polynomial-Based Time-Varying Filter Structure for the Compensation of Frequency-Response Mismatch Errors in Time-Interleaved ADCs

The Farrow structure provides an efficient way to implement sampling rate increase between arbitrary sampling rates. However, in the case of sampling rate decrease the Farrow structure can only implement filters with poor anti-aliasing properties because the transfer zeros are clustered around the integer multiples of the input sampling rate and not around the multiples of the output sampling rate where aliasing components appear. This problem can be overcome by using the transposed Farrow structure with the transfer zeros clustered around the integer multiples of the output sampling rate. The aliasing components are attenuated using the same polynomial function as for the Farrow structure. The main difference is that this polynomial is determined using the output sampling period as the basic interval, instead of the input sampling period. This paper gives overview and compares two alternative implementation forms for the transposed Farrow structure.

Implementation of the transposed Farrow structure

This paper presents an efficient way to implement flexible multirate signal processing systems with high oversampling ratio and adjustable fractional or irrational sampling rate conversion ratio. One application area is a multi-standard communication receiver which should be adjustable for different symbol rates utilized in different systems. The proposed decimation filter consists of parallel CIC (cascaded integrator-comb) filters followed by a linear interpolation filter. The idea is to use two parallel CIC filters to calculate the two needed sample values for linear interpolation. These samples occur just before and after the final output sample. This corresponds to a system where the linear interpolation is done at the higher input sampling rate.

Decimation by irrational factor using CIC filter and linear interpolation

If sampling rate conversion (SRC) is performed between arbitrary sampling rates, then the SRC factor can be a ratio of two very large integers or even an irrational number. An efficient way to reduce the implementation complexity of a SRC system in those cases is to use polynomial-based interpolation filters that mimic digitally the hybrid analogue/digital system. In practice, the sampling rate conversion is approximated with a rational factor. In this case, the hybrid analogue/digital model used to represent the SRC process may be represented by an equivalent discrete-time model. The discrete-time modeling of the rational SRC has been used earlier for the zeroth order interpolation. This paper extends this idea to arbitrary polynomial-based interpolation. Furthermore, this paper derives the relation between various polynomial-based interpolation filters (Farrow structure and its modifications) and polyphase FIR model filters. This paper observes possible applications of these relations, such as filter design, implementation complexity reduction, and response distortion analysis.

Discrete-time modeling of polynomial-based interpolation filters in rational sampling rate conversion

The non-uniform sampling of the signal can be product of an intentional or unintentional process. For example jitter sampling is an unintentional process which is a consequence of time error of sampling circuits. In many DSP applications it is very important to reconstruct the signal from its non-uniformity distributed samples, especially to obtain a uniformly sampled sequence. This paper presents an efficient and accurate technique to obtain an uniform sequence from the non-uniform input. The reconstruction is done by using the transposed Farrow structure. The reconstruction can also be done in a decimating way, reducing the number of samples. The transposed Farrow structure can be designed to satisfy given frequency or time domain requirement according to the application at hand.

Reconstruction of non-uniformly sampled signal using transposed Farrow structure

Decimation by non-integer factor in multistandard radio receivers

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