Pipelined Two-Operand Modular Adders

Pipelined two-operand modular adder (TOMA) is one of basic components used in digital signal processing (DSP) systems that use the residue number system (RNS). Such modular adders are used in binary/residue and residue/binary converters, residue multipliers and scalers as well as within residue processing channels. The design of pipelined TOMAs is usually obtained by inserting an appriopriate number of latch layers inside a nonpipelined TOMA structure. Hence their area is also determined by the number of latches and the delay by the number of latch layers. In this paper we propose a new pipelined TOMA that is based on a new TOMA, that has the smaller area and smaller delay than other known structures. Comparisons are made using data from the very large scale of integration (VLSI) standard cell library

A Comparative Performance of Discrete Wavelet Transform Implementations Using Multiplierless

Using discrete wavelet transform (DWT) in high-speed signal-processing applications imposes a high degree of care to hardware resource availability, latency, and power consumption. In this chapter, the design aspects and performance of multiplierless DWT is analyzed. We presented the two key multiplierless approaches, namely the distributed arithmetic algorithm (DAA) and the residue number system (RNS). We aim to estimate the performance requirements and hardware resources for each approach, allowing for the selection of proper algorithm and implementation of multi-level DAA- and RNS-based DWT. The design has been implemented and synthesized in Xilinx Virtex 6 ML605, taking advantage of Virtex 6’s embedded block RAMs (BRAMs)

A study on adaptive filtering for noise and echo cancellation.

The objective of this thesis is to investigate the adaptive filtering technique on the application of noise and echo cancellation. As a relatively new area in Digital Signal Processing (DSP), adaptive filters have gained a lot of popularity in the past several decades due to the advantages that they can deal with time-varying digital system and they do not require a priori knowledge of the statistics of the information to be processed. Adaptive filters have been successfully applied in a great many areas such as communications, speech processing, image processing, and noise/echo cancellation. Since Bernard Widrow and his colleagues introduced adaptive filter in the 1960s, many researchers have been working on noise/echo cancellation by using adaptive filters with different algorithms. Among these algorithms, normalized least mean square (NLMS) provides an efficient and robust approach, in which the model parameters are obtained on the base of mean square error (MSE). The choice of a structure for the adaptive filters also plays an important role on the performance of the algorithm as a whole. For this purpose, two different filter structures: finite impulse response (FIR) filter and infinite impulse response (IIR) filter have been studied. The adaptive processes with two kinds of filter structures and the aforementioned algorithm have been implemented and simulated using Matlab.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .J53. Source: Masters Abstracts International, Volume: 44-01, page: 0472. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

An adaptive-order rational Arnoldi method for model-order reductions of linear time-invariant systems

AbstractThis work proposes a model reduction method, the adaptive-order rational Arnoldi (AORA) method, to be applied to large-scale linear systems. It is based on an extension of the classical multi-point Padé approximation (or the so-called multi-point moment matching), using the rational Arnoldi iteration approach. Given a set of predetermined expansion points, an exact expression for the error between the output moment of the original system and that of the reduced-order system, related to each expansion point, is derived first. In each iteration of the proposed adaptive-order rational Arnoldi algorithm, the expansion frequency corresponding to the maximum output moment error will be chosen. Hence, the corresponding reduced-order model yields the greatest improvement in output moments among all reduced-order models of the same order. A detailed theoretical study is described. The proposed method is very appropriate for large-scale electronic systems, including VLSI interconnect models and digital filter designs. Several examples are considered to demonstrate the effectiveness and efficiency of the proposed method

Speech enhancement using auditory filterbank.

This thesis presents a novel subband noise reduction technique for speech enhancement, termed as Adaptive Subband Wiener Filtering (ASWF), based on a critical-band gammatone filterbank. The ASWF is derived from a generalized Subband Wiener Filtering (SWF) equation and reduces noises according to the estimated signal-to-noise ratio (SNR) in each auditory channel and in each time frame. The design of a subband noise estimator, suitable for some real-life noise environments, is also presented. This denoising technique would be beneficial for some auditory-based speech and audio applications, e.g. to enhance the robustness of sound processing in cochlear implants. Comprehensive objective and subjective tests demonstrated the proposed technique is effective to improve the perceptual quality of enhanced speeches. This technique offers a time-domain noise reduction scheme using a linear filterbank structure and can be combined with other filterbank algorithms (such as for speech recognition and coding) as a front-end processing step immediately after the analysis filterbank, to increase the robustness of the respective application.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .G85. Source: Masters Abstracts International, Volume: 44-03, page: 1452. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

On Optimal Input Design for Feed-forward Control

This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are analytically solved and fundamental properties of the optimal input signal solution are presented. The optimal input signal contains feed-forward control and depends of the noise model and transfer function of the system in a specific way. Next, we show how to apply the partial correlation approach to closed loop optimal experiment design to the general feed-forward problem. A framework for optimal input signal design for feed-forward control is presented and numerically evaluated on a temperature control problem

Design and multiplierless realization of digital synthesis filters for hybrid-filter-bank A/D converters

This paper studies the optimal least squares and minimax design and realization of digital synthesis filters for hybrid-filter-bank analog-to-digltal converters (HFB ADCs) to meet a given spurious-free dynamic range (SFDR). The problem for designing finite-impulse-response synthesis filters is formulated as a second-order cone-programming problem, which is convex and allows linear and quadratic constraints such as peak aliasing error to be incorporated. The fixed coefficients of the designed synthesis filters are efficiently implemented using sum-of-power-of-two (SOPOT) coefficients, while the internal word length used for each intermediate data is minimized using geometric programming. The main sources of error are analyzed, and a new formula of SFDR in terms of these errors is derived. The effects of component variations of analog analysis filters on the HFB ADC are also addressed by means of two new robust HFB ADC design algorithms based on stochastic uncertainty and worst case uncertainty models. Design results show that the proposed approach offers more flexibility and better performance than conventional methods in achieving a given SFDR and that the robust design algorithms are more robust to parameter uncertainties than the nominal design in which the uncertainties are not taken into account. © 2009 IEEE.published_or_final_versio