Yeng Chai Soh

Robust Kalman filtering for uncertain discrete-time systems

This paper is concerned with the problem of a Kalman filter design for uncertain discrete-time systems. The system under consideration is subjected to time-varying norm-bounded parameter uncertainty in both the state and output matrices. The problem addressed is the design of a linear filter such that the variance of the filtering error is guaranteed to be within a certain bound for all admissible uncertainties. Furthermore, the guaranteed cost can be optimized by appropriately searching a scaling design parameter. >

Brief paper: Optimal linear estimation for systems with multiple packet dropouts

This paper is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with multiple packet dropouts. Based on a packet dropout model, the optimal linear estimators including filter, predictor and smoother are developed via an innovation analysis approach. The estimators are computed recursively in terms of the solution of a Riccati difference equation of dimension equal to the order of the system state plus that of the measurement output. The steady-state estimators are also investigated. A sufficient condition for the convergence of the optimal linear estimators is given. Simulation results show the effectiveness of the proposed optimal linear estimators.

Letters: Ensemble of online sequential extreme learning machine

Liang et al. [A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006), 1411-1423] has proposed an online sequential learning algorithm called online sequential extreme learning machine (OS-ELM), which can learn the data one-by-one or chunk-by-chunk with fixed or varying chunk size. It has been shown [Liang et al., A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006) 1411-1423] that OS-ELM runs much faster and provides better generalization performance than other popular sequential learning algorithms. However, we find that the stability of OS-ELM can be further improved. In this paper, we propose an ensemble of online sequential extreme learning machine (EOS-ELM) based on OS-ELM. The results show that EOS-ELM is more stable and accurate than the original OS-ELM.

Robust Kalman filtering for uncertain systems

Abstract This paper studies the problem of Kalman filter design for uncertain systems. The system under consideration is subjected to time-varying norm-bounded parameter uncertainties in both the state and measurement matrices. The problem we address is the design of a state estimator such that the covariance of the estimation error is guaranteed to be within a certain bound for all admissible uncertainties. A Riccati equation approach is proposed to solve the above problem. Furthermore, a suboptimal covariance upper bound can be computed by a convex optimization.

Analysis and design of impulsive control systems

Some sufficient conditions for asymptotic stability of impulsive control systems with impulses at fixed times have recently been presented. In this paper, we derive some less conservative conditions for asymptotic stability of such impulsive control systems and the results are used to design impulsive control for a class of nonlinear systems. The class of nonlinear systems considered is also extended.

A Novel Rate Control Scheme for Low Delay Video Communication of H.264/AVC Standard

This paper presents a novel rate control scheme for low delay video communication of H.264/AVC standard. A switched mean-absolute-difference (MAD) prediction scheme is introduced to enhance the traditional temporal MAD prediction model, which is not suitable for predicting abrupt MAD fluctuations. Our new model could reduce the MAD prediction error by up to 69%. Furthermore, an accurate linear rate-quantization (R-Q) model is also formulated to describe the relationship between the total amount of bits for both texture and nontexture information and the quantization parameter (QP), so that the negative effect caused by the inaccurate estimation of nontexture bits is removed. By exploring the relationship between peak signal-to-noise ratio and QP value, the proposed linear R-Q model could further optimize QP calculation at the macroblock level. When compared with the rate control scheme JVT-G012 which is adopted by the latest JVT H.264/AVC reference model JM9.8, the proposed rate control algorithm could reduce the mismatch between actual bits and target ones by up to 75%. To meet the low delay requirement, the buffer is better controlled to prevent overflowing and underflowing. The average luminance PSNR of reconstructed video is increased by up to 1.13 dB at low bit rates, and the subjective video quality is also improved

Region-of-Interest Based Resource Allocation for Conversational Video Communication of H.264/AVC

Due to the complexity of H.264/AVC, it is very challenging to apply this standard to design a conversational video communication system. This problem is addressed in this paper by using region-of-interest (ROI) based bit allocation and computational power allocation schemes. In our system, the ROI is first detected by using the direct frame difference and skin-tone information. Several coding parameters including quantization parameter, candidates for mode decision, the number of referencing frames, accuracy of motion vectors and the search range of motion estimation are adaptively adjusted at the macroblock (MB) level according to the relative importance of each MB. Subsequently, the encoder could allocate more resources such as bits and computational power to the ROI, and the decoding complexity is also optimized at the encoder side by utilizing an ROI based rate-distortion-complexity (R-D-C) cost function. The encoder is thus simplified and decoding-friendly, and the overall subjective visual quality can also be improved.

Brief Design and analysis of discrete-time robust Kalman filters

In this paper, the problem of finite and infinite horizon robust Kalman filtering for uncertain discrete-time systems is studied. The system under consideration is subject to time-varying norm-bounded parameter uncertainty in both the state and output matrices. The problem addressed is the design of linear filters having an error variance with an optimized guaranteed upper bound for any allowed uncertainty. A novel technique is developed for robust filter design. This technique gives necessary and sufficient conditions to the design of robust quadratic filters over finite and infinite horizon in terms of a pair of parameterized Riccati equations. Feasibility and convergence properties of the robust quadratic filters are also analyzed.

Robust pole assignment

Abstract This paper presents new theorems on the theory of interval matrix inequalities and the theory of polynomials with interval roots, and applies them to the problem of robust pole-placement. We formulate optimization problems and derive convergent iterative algorithms which allow the designer to find controllers that place closed-loop poles within desired intervals for plants with unknown-but-bounded parameter uncertainties. The algorithms are computationally reasonable and provide a useful addition to currently existing control CAD tools.

Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.