Error analysis and complexity optimization for the multiplier-less FFT-like transformation (ML-FFT)

This paper studies the effect of the signal round-off errors on the accuracies of the multiplier-less Fast Fourier Transform-like transformation (ML-FFT). The idea of the ML-FFT is to parameterize the twiddle factors in the conventional FFT algorithm as certain rotation-like matrices and approximate the associated parameters inside these matrices by the sum-of-power-of-two (SOPOT) or canonical signed digits (CSD) representations. The error due to the SOPOT approximation is called the coefficient round-off error. Apart from this error, signal round-off error also occurs because of insufficient wordlengths. Using a recursive noise model of these errors, the minimum hardware to realize the ML-FFT subject to the prescribed output bit accuracy can be obtained using a random search algorithm. A design example is given to demonstrate the effectiveness of the proposed approach.published_or_final_versio

A rate-constrained adaptive quantization scheme for wavelet pyramid image coding

Conference theme: Connecting the WorldIt is well known that orthogonal wavelet transform with filters of nonlinear phase gives poor visual results in low bit rate image coding. Biorthogonal wavelet is a good substitute, which is, however essentially nonorthogonal. A greedy steepest descend algorithm is proposed to design an adaptive quantization scheme based on the actual statistics of the input image. Since the L2 norm of the quantization error is not preserved through the nonorthogonal transform, a quantization error estimation formula considering the characteristic value of the reconstruction filters is derived to incorporate the adaptive quantization scheme. Computer simulation results demonstrate significant SNR gains over standard coding technique, and comparable visual improvements.published_or_final_versio

A new algorithm of tracking time-varying channels in impulsive noise environment using a robust Kalman filter

2005 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2005), Hong Kong, 13-16 December 2005This paper proposes a new algorithm for tracking time-varying channels in impulsive noise environment using a robust Kalman filter. It employs a simple dynamical model of the channel, where the changes in the impulse response coefficients are due entirely to the innovations of the Kalman filter. This reduces the arithmetic complexity, while offering reasonable good performance. The robust Kalman filter is used to restrain the adverse effect of impulsive noise and provide estimates of the covariance matrices of the state and measurement noises. The noisy channel estimates from the Kalman filter can be used to estimate the parameters of the channel coefficients when they are assumed to follow an AR model. Finally, the two processes can be coupled together to further improve the performance. Simulation results show that the new algorithm gives more stable performance than the conventional methods under impulsive noise environment. © 2005 IEEE.published_or_final_versio

Finite state lattice vector quantization for wavelet-based image coding

IEEE International Symposium on Circuits and Systems, Hong Kong, China, 9-12 June 1997It is well known that there exists strong energy correlation between various subbands of a real-world image. A new powerful technique of Finite State Vector Quantization (FSVQ) has been introduced to fully exploit the self-similarity of the image in wavelet domain across different scales. Lattices in RN have considerable structure, and hence, Lattice VQ offers the promise of design simplicity and reduced complexity encoding. The combination of FSVQ and LVQ gives rise to the so-called FSLVQ, which is proved to be successful in exploiting the energy correlation across scales and simple enough in implementation.published_or_final_versio

A novel entropy-constrained adaptive quantization scheme for wavelet pyramid image coding

The orthogonal wavelet transform with filters of nonlinear phase gives poor visual results in low bit rate image coding. The biorthogonal wavelet is a good substitute, which is, however essentially nonorthogonal. A greedy steepest descent algorithm is proposed to design an adaptive quantization scheme based on the actual statistics of the input image. Since the L2 norm of the quantization error is not preserved through the nonorthogonal transform, a quantization error estimation formula considering the characteristic value of the reconstruction filters is derived to incorporate the adaptive quantization scheme. Computer simulation results demonstrate significant SNR gains over standard coding techniques, and comparable visual improvements.published_or_final_versio

A new robust kalman filter algorithm under outliers and system uncertainties

This paper proposes a new robust Kalman filter algorithm under outliers and system uncertainties. The robust Kalman filter of Durovic and Kovacevic is extended to include unknown-but-bounded parameter uncertainties in the state or observation matrix. We first formulate the robust state estimation problem as an M-estimation problem, which leads to an unconstrained nonlinear optimization problem. This is then linearized and solved iteratively as a series of linear least-squares problem. These least-squares problems, subject to the bounded system uncertainties using the robust least squares method proposed by A. Ben-Tal and A. Nemirovski. Simulation results show that the new algorithm leads to a better performance than the conventional algorithms under outliers as well as system uncertainties. © 2005 IEEE.published_or_final_versio

Model-based multirate Kalman filtering approach for optimal two-dimensional signal reconstruction from noisy subband systems

Conventional synthesis filters in subband systems lose their optimality when additive noise (due, for example, to signal quantization) disturbs the subband components. The multichannel representation of subband signals is combined with the statistical model of input signal to derive the multirate state-space model for the filter bank system with additive subband noises. Thus the signal reconstruction problem in subband systems can be formulated as the process of optimal state estimation in the equivalent multirate state-space model. Incorporated with the vector dynamical model, a 2-D multirate state-space model suitable for 2-D Kalman filtering is developed. The performance of the proposed 2-D multirate Kalman filter can be further improved through adaptive segmentation of the object plane. The object plane is partitioned into disjoint regions based on their spatial activity, and different vector dynamical models are used to characterize the nonstationary object-plane distributions. Finally, computer simulations with the proposed 2-D multirate Kalman filter give favorable results. ©1998 Society of Photo-Optical instrumentation Engineers.published_or_final_versio

Design of complex-valued variable digital filters and its application to the realization of arbitrary sampling rate conversion for complex signals

The 47th Midwest Symposium on Circuits and Systems Conference, Salt Lake City, Utah, USA, 25-28 July 2004This paper studies the design of complex-valued variable digital filters (CVDFs) and their applications to the efficient arbitrary sample rate conversion for complex signals in software radio receivers. The design of CVDFs using either the minimax or least squares criteria is formulated as a convex optimization problem and solved using the second order cone programming (SOCP) or semidefmite programming (SDP). In addition, linear and convex quadratic inequality constraints can be readily incorporated. Design examples are given to demonstrate the effectiveness of the proposed approach.published_or_final_versio

On the minimax design of passband linear-phase variable digital filters using semidefinite programming

Variable digital filters (VDFs) are useful to the implementation of digital receivers because its frequency characteristics such as fractional delays and cutoff frequencies can be varied online. In this letter, it is shown that the optimal minimax design of VDFs with passband linear-phase can be formulated and solved as a semi-definite programming (SDP) problem, which is a powerful convex optimization method. In addition, other objective functions, such as least squares, and linear and convex quadratic inequality constraints can readily be incorporated. Design examples using a variable fractional delay (VFD) and a variable cutoff frequency (VCF) FIR filters are given to demonstrate the effectiveness of the proposed approach. © 2004 IEEE.published_or_final_versio

Do unexpected land auction outcomes bring new information to the real estate market?

Land and real estate are intrinsically related but generally traded in two different markets. Vacant land, being a major "raw material" for development of real estate, is traded by developers who actively manage development risk for profit. Real estate, being a long lived final product, is traded by end-users or investors for use or investment in the secondary market. This study examines price discovery between the two markets. The key question is whether land transactions, in the form of public auctions, convey any new information to the secondary real estate market. Our results suggest unexpected land auction outcomes have both market-wide and local effects on real estate prices. However, the impacts are asymmetric. We found that lower than expected land auction prices have a significant negative market-wide and local impact on real estate prices while higher than expect land auction prices have little or no impact. © Springer Science + Business Media, LLC 2009.published_or_final_versionSpringer Open Choice, 01 Dec 201