Improved Frequency-selective Filters

This paper gives an account of some techniques for designing recursive frequency-selective filters which can be applied to data sequences of limited duration which may be nonstationary. The designs are based on the Wiener-Kolmogorov theory of signal extraction which employs a statistical model of the processes generating the data. The statistical model may be regarded as an heuristic device which is designed with a view to ensuring that the resulting signal-extraction filters have certain preconceived properties.Signal extraction, Linear filtering, Filter design, Trend estimation, Frequency-domain analysis

IIR approximation of FIR filters via discrete-time vector fitting

We present a novel technique for approximating finite-impulse-response (FIR) filters with infinite-impulse-response (IIR) structures through extending the vector fitting (VF) algorithm, used extensively for continuous-time frequency-domain rational approximation, to its discrete-time counterpart called VFz. VFz directly computes the candidate filter poles and iteratively relocates them for progressively better approximation. Each VFz iteration consists of the solutions of an overdetermined linear equation and an eigenvalue problem, with real-domain arithmetic to accommodate complex poles. Pole flipping and maximum pole radius constraint guarantee stability and robustness against finite-precision implementation. Comparison against existing algorithms confirms that VFz generally exhibits fast convergence and produces highly accurate IIR approximants. © 2008 IEEE.published_or_final_versio

Algorithms and architectures for the multirate additive synthesis of musical tones

In classical Additive Synthesis (AS), the output signal is the sum of a large number of independently controllable sinusoidal partials. The advantages of AS for music synthesis are well known as is the high computational cost. This thesis is concerned with the computational optimisation of AS by multirate DSP techniques. In note-based music synthesis, the expected bounds of the frequency trajectory of each partial in a finite lifecycle tone determine critical time-invariant partial-specific sample rates which are lower than the conventional rate (in excess of 40kHz) resulting in computational savings. Scheduling and interpolation (to suppress quantisation noise) for many sample rates is required, leading to the concept of Multirate Additive Synthesis (MAS) where these overheads are minimised by synthesis filterbanks which quantise the set of available sample rates. Alternative AS optimisations are also appraised. It is shown that a hierarchical interpretation of the QMF filterbank preserves AS generality and permits efficient context-specific adaptation of computation to required note dynamics. Practical QMF implementation and the modifications necessary for MAS are discussed. QMF transition widths can be logically excluded from the MAS paradigm, at a cost. Therefore a novel filterbank is evaluated where transition widths are physically excluded. Benchmarking of a hypothetical orchestral synthesis application provides a tentative quantitative analysis of the performance improvement of MAS over AS. The mapping of MAS into VLSI is opened by a review of sine computation techniques. Then the functional specification and high-level design of a conceptual MAS Coprocessor (MASC) is developed which functions with high autonomy in a loosely-coupled master- slave configuration with a Host CPU which executes filterbanks in software. Standard hardware optimisation techniques are used, such as pipelining, based upon the principle of an application-specific memory hierarchy which maximises MASC throughput

Symbol Synchronization for SDR Using a Polyphase Filterbank Based on an FPGA

This paper is devoted to the proposal of a highly efficient symbol synchronization subsystem for Software Defined Radio. The proposed feedback phase-locked loop timing synchronizer is suitable for parallel implementation on an FPGA. The polyphase FIR filter simultaneously performs matched-filtering and arbitrary interpolation between acquired samples. Determination of the proper sampling instant is achieved by selecting a suitable polyphase filterbank using a derived index. This index is determined based on the output either the Zero-Crossing or Gardner Timing Error Detector. The paper will extensively focus on simulation of the proposed synchronization system. On the basis of this simulation, a complete, fully pipelined VHDL description model is created. This model is composed of a fully parallel polyphase filterbank based on distributed arithmetic, timing error detector and interpolation control block. Finally, RTL synthesis on an Altera Cyclone IV FPGA is presented and resource utilization in comparison with a conventional model is analyzed

On Vector Fitting methods in signal/power integrity applications

This conference proceedings appears in: Lecture Notes in Engineering and Computer Science. Open-access online version: http://www.iaeng.org/publication/IMECS2010/Vector Fitting (VF) has been applied to reformulate traditional system identification techniques by introducing a partial-fraction basis to avoid ill-conditioned calculation in broadband system identifications. Because of the reliable and versatility of VF, many extensions and applications have been proposed, for example, the macromodeling of linear structures in signal/power integrity analyses. In this paper, we discuss the macromodeling framework and some main features in VF in terms of data, algorithms and models. Finally, an alternative P-norm approximation criterion is proposed to enhance the macromodeling process.postprintThe International MultiConference of Engineers and Computer Scientists (IMECS 2010), Hong Kong, 17-19 March 2010. In Proceedings of the International MultiConference of Engineers and Computer Scientists, 2010, v. 2, p. 1407-141

A decade of vector fitting development: Applications on signal/power integrity

This issue also has title: IAENG transactions on engineering technologies, volume 5: Special Edition of the International MultiConference of Engineers and Computer Scientists 2009International MultiConference of Engineers and Computer Scientists 2010, Hong Kong, China, 17-19 March 2010Vector Fitting (VF) has been introduced as a partial-fraction basis response fitting methodology for over a decade. Because of its reliability and versatility, VF has been applied and extended to a number of areas. In this book chapter, we will discuss the applications of VF in the context of macromodeling of linear structures in signal/power integrity analyses. We will also discuss main features of VF along three directions: data, algorithms and models. Two practical examples are given to demonstrate the merits of VF. An alternative P-norm approximation criterion is proposed to enhance the accuracy of the macromodeling process. © 2010 American Institute of Physics.published_or_final_versio

Modeling of the head-related transfer functions for reduced computation and storage

The synthesis of three-dimensional sound via headphones generally requires the implementation of rather complex filters based on the head-related transfer functions (HRTFs), direction-specific transfer functions which simulate the transformation of sound pressure between a sound source and the eardrums of the listener. Current implementations generally rely on FIR filtering techniques, resulting in high computational complexity. The main objective of this work was to develop a set of computationally efficient filters which would be capable of emulating the head-related transfer functions. To accomplish this objective, a modification of conventional system modeling techniques through the application of psychoacoustic principles has been applied to the design of low-order IIR filters, resulting in the reduction of computation and storage requirements without significantly sacrificing perceptual performance. Results presented will include objective measurements based on a critical band distance measure and subjective measurements based on sound localization tests

Adaptive Background Modeling with Temporal Feature Update for Dynamic Foreground Object Removal

In the study of computer vision, background modeling is a fundamental and critical task in many conventional applications. This thesis presents an introduction to background modeling and various computer vision techniques for estimating the background model to achieve the goal of removing dynamic objects in a video sequence. The process of estimating the background model with temporal changes in the absence of foreground moving objects is called adaptive background modeling. In this thesis, three adaptive background modeling approaches were presented for the purpose of developing \teacher removal algorithms. First, an adaptive background modeling algorithm based on linear adaptive prediction is presented. Second, an adaptive background modeling algorithm based on statistical dispersion is presented. Third, a novel adaptive background modeling algorithm based on low rank and sparsity constraints is presented. The design and implementation of these algorithms are discussed in detail, and the experimental results produced by each algorithm are presented. Lastly, the results of this research are generalized and potential future research is discussed

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide