A class of M-Channel linear-phase biorthogonal filter banks and their applications to subband coding

This correspondence presents a new factorization for linearphase biorthogonal perfect reconstruction (PR) FIR filter banks. Using this factorization, we propose a new family of lapped transform called the generalized lapped transform (GLT). Since the analysis and synthesis filters of the GLT are not restricted to be the time reverses of each other, they can offer more freedom to avoid blocking artifacts and improve coding gain in subband coding applications. The GLT is found to have higher coding gain and smoother synthesis basis functions than the lapped orthogonal transform (LOT). Simulation results also demonstrated that the GLT has significantly less blocking artifacts, higher peak signal-tonoise ratio (PSNR), and better visual quality than the LOT in image coding. Simplified GLT with different complexity/performance tradeoff is also studied. © 1999 IEEE.published_or_final_versio

Emission characteristics of nonmethane hydrocarbons from private cars and taxis at different driving speeds in Hong Kong

Vehicular emissions are the major sources of a number of air pollutants including nonmethane hydrocarbons (NMHCs) in urban area. The emission composition and emission factors of NMHCs from vehicles are currently lacking in Hong Kong. In this study, speciation and emission factors of NMHCs emitted from gasoline-fuelled private cars and liquefied petroleum gas (LPG)-fuelled taxis at different driving speeds were constructed using a chassis dynamometer. Large variations in the contributions of individual NMHC species to total emission were observed for different private cars at different driving speeds. The variations of individual NMHC emissions were relatively smaller for taxis due to their relatively homogeneous year of manufacture and mileages. Incomplete combustion products like ethane, ethene and propene were the major component of both types of vehicles, while unburned fuel component was also abundant in the exhausts of private cars and taxis (i.e. i-pentane and toluene for private car, and propane and butanes for taxi). Emission factors of major NMHCs emitted from private cars and taxis were estimated. High emission factors of ethane, n-butane, i/n-pentanes, methylpentanes, trimethylpentanes, ethene, propene, i-butene, benzene, toluene and xylenes were found for private cars, whereas propane and i/n-butanes had the highest values for taxis. By evaluating the effect of vehicular emissions on the ozone formation potential (OFP), it was found that the contributions of olefinic and aromatic hydrocarbons to OFP were higher than that from paraffinic hydrocarbons for private car, whereas the contributions of propane and i/n-butanes were the highest for taxis. The total OFP value was higher at lower speeds (≤50 km h-1) for private cars while a minimum value at driving speed of 100 km h-1 was found for taxis. At the steady driving speeds, the total contribution of NMHCs emitted from LPG-fuelled taxis to the OFP was much lower than that from gasoline-fuelled private cars. However, at idling state, the contribution of NMHCs from LPG-fuelled vehicles to OFP was comparable to that from gasoline-fuelled vehicles. The findings obtained in this study can be used to mitigate the air pollution caused by vehicles in highly dense urban areas. © 2011 Elsevier Ltd

A rational subdivision scheme using cosine-modulated wavelets

This paper proposes a rational subdivision scheme using cosine-modulated wavelets. Subdivision schemes constructed from iterated filter banks can be used to generate wavelets and limit functions for multiresolution analysis. The proposed subdivision scheme is based on a kind of nonuniform filter banks called recombination nonuniform filterbanks (RN FB). It is shown that if the component FBs in a RNFB are wavelet FBs, then the necessary condition for convergence to limit functions in the subdivision scheme is also satisfied. Therefore, the design of different rational subdivision schemes is considerably simplified. An efficient RNFB, called RN cosine modulated FBs (CMFB), constructed from uniform CMFBs and cosinemodulated transmultiplexers (TMUX) are further investigated. Using a design technique for designing RN CMFB and cosine modulated wavelets (CMW) previously reported by the authors, very smooth limit functions can be generated from the rational subdivision scheme. A design example is given to illustrate the proposed method.published_or_final_versio

Wordlength determination algorithms for hardware implementation of linear time invariant systems with prescribed output accuracy

This paper proposes two novel algorithms for optimizing the hardware resources in finite wordlength implementation of linear time invariant systems. The hardware complexity is measured by the exact internal wordlength used for each intermediate data. The first algorithm formulates the design problem as a constrained optimization, from which an analytic closed-form solution of the internal wordlengths subject to a prescribed output accuracy can be determined by the Lagrange multiplier method. The second algorithm is based on a discrete optimization method called the Marginal Analysis method, and it yields the desired wordlengths in integer values. Both approaches are found to be very effective and they are well-suited to large scale systems such as software radio receivers. Design examples show that the proposed algorithms offer better results and a lower design complexity than conventional methods. © 2005 IEEE.published_or_final_versio

A new Kalman filter-based algorithm for adaptive coherence analysis of non-stationary multichannel time series

IEEE International Symposium on Circuits and Systems, Island of Kos, Greece, 21-24 May 2006This paper proposes a new Kalman filter-based algorithm for multichannel autoregressive (AR) spectrum estimation and adaptive coherence analysis with variable number of measurements. A stochastically perturbed k -order difference equation constraint model is used to describe the dynamics of the AR coefficients and the intersection of confidence intervals (ICI) rule is employed to determine the number of measurements adaptively to improve the timefrequency resolution of the AR spectrum and coherence function. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for non-stationary signals. © 2006 IEEE.published_or_final_versio

On Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction

This paper studies the problem of adaptive kernel selection for multivariate local polynomial regression (LPR) and its application to smoothing and reconstruction of noisy images. In multivariate LPR, the multidimensional signals are modeled locally by a polynomial using least-squares (LS) criterion with a kernel controlled by a certain bandwidth matrix. Based on the traditional intersection confidence intervals (ICI) method, a new refined ICI (RICI) adaptive scale selector for symmetric kernel is developed to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of multidimensional signals. The resulting multivariate LPR method called the steering-kernel-based LPR with refined ICI method (SK-LPR-RICI) is applied to the smoothing and reconstruction problems in noisy images. Simulation results show that the proposed SK-LPR-RICI method has a better PSNR and visual performance than conventional LPR-based methods in image processing. © 2010 The Author(s).published_or_final_versio

A Huber recursive least squares adaptive lattice filter for impulse noise suppression

This paper proposes a new adaptive filtering algorithm called the Huber Prior Error-Feedback Least Squares Lattice (H-PEF-LSL) algorithm for robust adaptive filtering in impulse noise environment. It minimizes a modified Huber M-estimator based cost function, instead of the least squares cost function. In addition, the simple modified Huber M-estimate cost function also allows us to perform the time and order recursive updates in the conventional PEF-LSL algorithm so that the complexity can be significantly reduced to O(M), where M is the length of the adaptive filter. The new algorithm can also be viewed as an efficient implementation of the recursive least M-estimate (RLM) algorithm recently proposed by the authors [1], which has a complexity of O(M 2). Simulation results show that the proposed H-PEF-LSL algorithm is more robust than the conventional PEF-LSL algorithm in suppressing the adverse influence of the impulses at the input and desired signals with small additional computational cost.published_or_final_versio

On the performance analysis of the least mean M-estimate and normalized least mean M-estimate algorithms with Gaussian inputs and additive Gaussian and contaminated Gaussian noises

This paper studies the convergence analysis of the least mean M-estimate (LMM) and normalized least mean M-estimate (NLMM) algorithms with Gaussian inputs and additive Gaussian and contaminated Gaussian noises. These algorithms are based on the M-estimate cost function and employ error nonlinearity to achieve improved robustness in impulsive noise environment over their conventional LMS and NLMS counterparts. Using the Price's theorem and an extension of the method proposed in Bershad (IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-34(4), 793-806, 1986; IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(5), 636-644, 1987), we first derive new expressions of the decoupled difference equations which describe the mean and mean square convergence behaviors of these algorithms for Gaussian inputs and additive Gaussian noise. These new expressions, which are expressed in terms of the generalized Abelian integral functions, closely resemble those for the LMS algorithm and allow us to interpret the convergence performance and determine the step size stability bound of the studied algorithms. Next, using an extension of the Price's theorem for Gaussian mixture, similar results are obtained for additive contaminated Gaussian noise case. The theoretical analysis and the practical advantages of the LMM/NLMM algorithms are verified through computer simulations. © 2009 Springer Science+Business Media, LLC.published_or_final_versionSpringer Open Choice, 01 Dec 201

A robust quasi-newton adaptive filtering algorithm for impulse noise suppression

This paper studies the problem of robust adaptive filtering in impulse noise environment using the Quasi-Newton (QN) adaptive filtering algorithm. An M-estimate based cost function is minimized instead of the commonly used mean square error (MSE) to suppress the adverse effect of the impulse noise on the filter coefficients. In particular, a new robust quasi-Newton (R-QN) algorithm using the self-scaling variable metric (SSV) method for unconstrained optimization is studied in details. Simulation results show that the R-QN algorithm is more robust to impulse noise in the desired signal than the RLS algorithm and other QN algorithm considered. Its initial convergence speed and tracking ability to sudden system change are also superior to those of the quasi-Newton algorithm proposed in [1].published_or_final_versio

Direction finding with partly calibrated uniform linear arrays

A new method for direction finding with partly calibrated uniform linear arrays (ULAs) is presented. It is based on the conventional estimation of signal parameters via rotational invariance techniques (ESPRIT) by modeling the imperfections of the ULAs as gain and phase uncertainties. For a fully calibrated array, it reduces to the conventional ESPRIT algorithm. Moreover, the direction-of-arrivals (DOAs), unknown gains, and phases of the uncalibrated sensors can be estimated in closed form without performing a spectral search. Hence, it is computationally very attractive. The Cramér-Rao bounds (CRBs) of the partly calibrated ULAs are also given. Simulation results show that the root mean squared error (RMSE) performance of the proposed algorithm is better than the conventional methods when the number of uncalibrated sensors is large. It also achieves satisfactory performance even at low signal-to-noise ratios (SNRs). © 2011 IEEE.published_or_final_versio