A semi-definite programming (SDP) method for designing IIR sharp cut-off digital filters using frequency-response masking

IEEE International Symposium on Circuits and Systems Proceedings, Vancouver, Canada, 23-26 May 2004This paper studies the design of frequency response masking (FRM) filters with infinite duration impulse response (IIR) model and masking sub-filters. They are useful in realizing sharp cutoff digital filters with low passband delays. The designs of the model and masking filters are carried out by means of semidefinite programming (SDP) and model order reduction. Design results show that low complexity FRM filters with low passband delay can be obtained.published_or_final_versio

The theory and design of a class of perfect reconstruction modified DFT filter banks with IIR filters

The 47th Midwest Symposium on Circuits and Systems Conference, Salt Lake City, Utah, USA, 25-28 July 2004This paper proposes a theory and design method for a class of PR causal-stable modified discrete Fourier transform (MDFT) filter bank (FB) with IIR filters. The prototype filter of the MDFT FB is assumed to have identical denominator in order to simplify the PR condition. A new model reduction technique is proposed for deriving a nearly PR (NPR) MDFT FB from a PR MDFT FB with FIR prototype filter. With these NPR IIR MDFT FBs as initial guess, PR IIR MDFT FBs with very good frequency characteristics can be obtained by solving a constrained nonlinear optimisation problem. Because the location of the poles can be approximately determined through model reduciton, the efficiency and reliability of the design method is significantly improved. Design examples are given to demonstrate the effectiveness of the proposed method.published_or_final_versio

Factorization of a class of perfect reconstruction modified DFT filter banks with IIR filters

This paper proposed a new factorization of a class of perfect reconstruction (PR) causal-stable modified discrete Fourier transform (MDFT) filter bank (FB) with IIR filters, whose prototype filter has identical denominator in their polyphase components. This factorization technique, which is based on the lifting scheme, is also complete for the PR FIR MDFT FB. It can be applied to convert a nearly PR MDFT FBs to a structural PR system, which is very useful to their multiplier-less realization because the PR property in these structural FBs is unaffected by coefficient quantization. Therefore, it is possible to employ canonical signed digits (CSD) or sum of powers of two coefficients to approximate the coefficients in the factored form without changing the PR property. © 2005 IEEE.published_or_final_versio

On the theory and design of a class of PR causal-stable IIR non-uniform recombination cosine modulated filter banks

This paper studies the theory and design of a class of perfect reconstruction (PR) causal-stable nonuniform recombination cosine modulated filter banks (RN CMFBs) with IIR filters. It is based on the RN CMFB previously proposed by one of the author. A PR FIR RN CMFB of similar specification is first designed. The prototype filters of the CMFBs are then model reduced to obtain a nearly PR (NPR) IIR RN CMFB by modifying a model reduction technique proposed by Brandenstein and Unbehauen. With these NPR IIR RN CMFBs as initial guess, PR IIR RN CMFB with very good frequency characteristics can be obtained readily by solving a constrained nonlinear optimisation problem using for example the function fmincom from MATLAB. Design results show that the proposed method is very effective in designing PR RN IIR CMFBs with good frequency characteristics and different system delays. © 2005 IEEE.published_or_final_versio

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

Design and implementation of computationally efficient digital filters

Ph.DDOCTOR OF PHILOSOPH

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Randomized sampling and multiplier-less filtering

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 151-153).This thesis considers the benefits of randomization in two fundamental signal processing techniques: sampling and filtering. The first part develops randomized non-uniform sampling as a method to mitigate the effects of aliasing. Randomization of the sampling times is shown to convert aliasing error due to uniform under-sampling into uncorrelated shapeable noise. In certain applications, especially perceptual ones, this form of error may be preferable. Two sampling structures with are developed in this thesis. In the first, denoted simple randomized sampling, non-white sampling processes can be designed to frequency-shape the error spectrum, so that its power is minimized in the band of interest. In the second model, denoted filtered randomized sampling, a pre-filter, post-filter, and the sampling process can be designed to further frequency-shape the error to improve performance. The thesis develops design techniques using parametric binary process models to optimize the performance of randomized non-uniform sampling. In addition, a detailed second-order error analysis, including performance bounds and results from simulation, is presented for each type of sampling. The second part of this thesis develops randomization as a method to improve the performance of multiplier-less FIR filters. Static multiplier-less filters, even when carefully designed, result in frequency distortion as compared to a desired continuous-valued filter. Replacing each static tap with a binary random process is shown to mitigate this distortion, converting the error into uncorrelated shapeable noise. As with randomized sampling, in certain applications this form of error may be preferable. This thesis presents a FIR Direct Form I randomized multiplier-less filter structure denoted binary randomized filtering (BRF). In its most general form, BRF incorporates over-sampling combined with a tapped delay-line that changes in time according to a binary vector process.(cont)The time and tap correlation of the binary vector process can be designed to improve the error performance. The thesis develops design techniques using parametric binary vector process models to do so. In addition, a detailed second-order error analysis, including performance bounds, error scaling with over-sampling, and results from simulation, is presented for the various forms of BRF.by Sourav R. Dey.Ph.D

Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression

Volatility is a key parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving- Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.Open Acces

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc