An improved macroblock level rate control algorithm for MPEG-4 video object coding

2005 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2005), Hong Kong, 13-16 December 2005This paper presents an improved macroblock (MB) level rate control algorithm for MPEG-4 object coding. Motivated by the statistical analysis of the coding of MBs and VOPs for typical sequences, a refined quadratic rate-distortion model is proposed in the new MB rate control algorithm. The basic idea is to exploit the statistical coding property of a group of MBs coded by an identical QP rather than an individual MB to do the modeling, thereby producing more stable quantization parameter to improve the estimation and coding performances. In addition, some improved measures which explore the new model for coding the MBs in a VOP are incorporated. Simulation results show that the proposed algorithm can achieve the much smoother bit rate and better picture quality in terms of PSNR than the MPEG-4 VM18 algorithm for the tested sequences. © 2005 IEEE.published_or_final_versio

Improved methods for object-based coding of plenoptic videos

2005 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2005), Hong Kong, 13-16 December 2005Plenoptic videos (PVs) are a class of dynamic image-based representations, where the videos are taken at regularly spaced locations along a line. To yield the better rendering quality in scenes with large depth variations and support the functionalities at the object level for rendering, an object-based coding scheme is employed for the coding of PVs. Upon this object-based coding framework, the paper studies the improved coding methods for the texture and depth coding to achieve better compression efficiency. Experimental results show that considerable improvements in texture coding performance are obtained for both synthetic and real scenes. The improved depth coding quality is also illustrated. © 2005 IEEE.published_or_final_versio

A generalized algorithm for fast two-dimensional angle estimation of a single source with uniform circular arrays

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A semi-discrete model and its approach to a solution for a wide moving jam in traffic flow

This paper investigates the analytical and numerical solutions to wide moving jams in traffic flow. Under the framework of the Lagrange coordinates, a semi-discrete model and a continuum model correlate with each other, in which the former model approaches the latter as the increment ΔM in the former model vanishes. This implies that the solution to a wide moving jam in the latter model, which can be analytically derived using the known theory, can be conceivably taken as an approximation to that of the former model. These results were verified through numerical simulations. Because a detailed understanding of the traffic phase "wide moving jam" is very important for the further development of Kerner's three-phase traffic theory, this study helps to explain the empirical features of traffic breakdown and resulting congested traffic patterns that are observed in real traffic. © 2011 Elsevier B.V. All rights reserved.postprin

Sustained slow-scale oscillation in higher order current-mode controlled converter

Author name used in this publication: Chi K. Tse2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Designing bus routes and frequencies for Tin Shui Wai, Hong Kong

A real bus network design problem for a suburban residential area, Tin Shui Wai, Hong Kong, is investigated. The problem considers bus service from origins inside this area to destinations in the city. The aim is to improve the existing bus network by reducing the number of transfers and total travel time of users. This is achieved by the proposed integrated solution method, which simultaneously solves the route design and frequency setting problems. In the proposed method, a genetic algorithm that tackles the route design problem is hybridized with a neighborhood search heuristic that addresses the frequency setting problem. A new representation scheme and specific genetic operators are developed so that the genetic algorithm can search all possible route structures rather than selecting from an initial set of predefined routes. The proposed method reduces the number of transfers and total travel time by 20.6% and 7.0%, respectively.postprintThe 2010 HKIE Civil Division Conference on Infrastructure Solutions for Tomorrow, Hong Kong, 12-14 April 2010. In Proceedings of the HKIE Civil Division Conference 2010: Infrastructure Solutions for Tomorrow, 201

An artificial bee colony algorithm for the capacitated vehicle routing problem

Session MF-03: Population-based metaheuristics for routing problems - Stream: Metaheuristics - Invited session no. 3This paper introduces an artificial bee colony heuristic for the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. The performance of the heuristic is evaluated on two sets of benchmark instances. A new scheme is also developed to improve the performance of the artificial bee colony heuristic. Computational results show that the heuristic with the new scheme produces good solutions.postprintThe 24th European Conference on Operational Research (EURO 24), Lisbon, Portual, 11-14 July 2010. In Abstract Book of EURO 24, 2010, p. 89, MF-03-

Symbol-timing estimation in space-time coding systems based on orthogonal training sequences

Space-time coding has received considerable interest recently as a simple transmit diversity technique for improving the capacity and data rate of a channel without bandwidth expansion. Most research in space-time coding, however, assumes that the symbol timing at the receiver is perfectly known. In practice, this has to be estimated with high accuracy. In this paper, a new symbol-timing estimator for space-time coding systems is proposed. It improves the conventional algorithm of Naguib et al. such that accurate timing estimates can be obtained even if the over-sampling ratio is small. Analytical mean-square error (MSE) expressions are derived for the proposed estimator. Simulation and analytical results show that for a modest oversampling ratio (such as Q equal to four), the MSE of the proposed estimator is significantly smaller than that of the conventional algorithm. The effects of the number of transmit and receive antennas, the oversampling ratio, and the length of training sequence on the MSE are also examined. © 2005 IEEE.published_or_final_versio

Robust Logistic Principal Component Regression for classification of data in presence of outliers

The Logistic Principal Component Regression (LPCR) has found many applications in classification of high-dimensional data, such as tumor classification using microarray data. However, when the measurements are contaminated and/or the observations are mislabeled, the performance of the LPCR will be significantly degraded. In this paper, we propose a new robust LPCR based on M-estimation, which constitutes a versatile framework to reduce the sensitivity of the estimators to outliers. In particular, robust detection rules are used to first remove the contaminated measurements and then a modified Huber function is used to further remove the contributions of the mislabeled observations. Experimental results show that the proposed method generally outperforms the conventional LPCR under the presence of outliers, while maintaining a performance comparable to that obtained under normal condition. © 2012 IEEE.published_or_final_versionThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 2809-281

Robust recursive eigendecomposition and subspace-based algorithms with application to fault detection in wireless sensor networks

The principal component analysis (PCA) is a valuable tool in multivariate statistics, and it is an effective method for fault detection in wireless sensor networks (WSNs) and other related applications. However, its online implementation requires the computation of eigendecomposition (ED) or singular value decomposition. To reduce the arithmetic complexity, we propose an efficient fault detection approach using the subspace tracking concept. In particular, two new robust subspace tracking algorithms are developed, namely, the robust orthonormal projection approximation subspace tracking (OPAST) with rank-1 modification and the robust OPAST with deflation. Both methods rely on robust M-estimate-based recursive covariance estimate to improve the robustness against the effect of faulty samples, and they offer different tradeoff between fault detection accuracy and arithmetic complexity. Since only the ED in the major subspace is computed, their arithmetic complexities are much lower than those of other conventional PCA-based algorithms. Furthermore, we propose new robust T 2 score and SPE detection criteria with recursive update formulas to improve the robustness over their conventional counterparts and to facilitate online implementation for the proposed robust subspace ED and tracking algorithms. Computer simulation and experimental results on WSN data show that the proposed fault detection approach, which combines the aforementioned robust subspace tracking algorithms with the robust detection criteria, is able to achieve better performance than other conventional approaches. Hence, it serves as an attractive alternative to other conventional approaches to fault detection in WSNs and other related applications because of its low complexity, efficient recursive implementation, and good performance. © 2012 IEEE.published_or_final_versio