COBRA: Compression of the Basis for PCA Represented Animations

In this paper, we present an extension of dynamic mesh compression techniques based on PCA. Such representation allows very compact representation of moving 3D surfaces; however, it requires some side information to be transmitted along with the main data. The biggest part of this information is the PCA basis, and since the data can be encoded very efficiently, the size of the basis cannot be neglected when considering the overall performance of a compression algorithm.
We present a new work in this area, as none of the papers about PCA based compression really addresses this issue. We will show that for an efficient and accurate encoding there are better choices than even sophisticated algorithms such as LPC.
We will present results showing that our approach can reduce the size of the basis by 90% with respect to direct encoding, which can lead to approximately 25% increase of performance of the compression algorithm without any significant loss of accuracy. Such improvement moves the performance of the PCA encoder beyond the performance of current state of the art dynamic mesh compression algorithms, such as the recently adopted MPEG standard, FAMC.     

Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression

The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.     

基于隐马尔可夫模型的音频自动分类

音频的自动分类,尤其是语音和音乐的分类,是提取音频结构和内容语义的重要手段之一,它在基于内容的音频检索、视频的检索和摘要以及语音文档检索等领域都有重大的应用价值.由于隐马尔可夫模型能够很好地刻画音频信号的时间统计特性,因此,提出一种基于隐马尔可夫模型的音频分类算法,用于语音、音乐以及它们的混合声音的分类.实验结果表明,隐马尔可夫模型的音频分类性能较好,最优分类精度达到90.28%.     

Modified self-organising maps with a new topology and initialisation algorithm

Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement.     

H ∞ control of networked control systems with state quantisation

This article addresses the problem of controller design for networked control systems over digital communication. The systems under consideration are stabilised via state feedback, where the effects of sampled signal, state quantisation, network-induced delay and packet dropout are considered. The proposed delay-dependent stability criteria are formulated in the form of a linear matrix inequality, which ensure asymptotic stability and a prescribed H _∞ performance level for networked control systems with admissible uncertainties. Maximum allowable delay bound of networked control systems is obtained by solving a convex optimisation problem. Furthermore, a numerical example is given to illustrate the effectiveness of the main result.     

Robust fault detection for Markovian jump systems with unreliable communication links

This article addresses the problem of robust fault detection for Markovian jump linear systems with unreliable communication links. In the network communication channel, the effects of signal quantisation and measurement missing, which appears typically in a network environment, are taken into consideration simultaneously. A stochastic variable satisfying the Bernoulli random binary distribution is utilised to model the phenomenon of the measurements missing. The aim is to design a fault detection filter such that, for all unknown input and incomplete measurements, the error between the residual and weighted faults is made as small as possible. A sufficient condition for the existence of the desired fault detection filter is established in terms of a set of linear matrix inequalities. A simulation example is provided to illustrate the effectiveness and applicability of the proposed techniques.     

Networked predictive control of constrained linear systems with input quantisation

This article investigates the problem of stabilising predictive control for constrained systems, wherein communication from the controller to the plant input is through a digital channel subject to quantisation and delay. A novel model with structured norm-bounded uncertainties is proposed to describe control system with input quantisation. Under a multirate scheme, a delay compensation strategy is presented. The networked predictive control synthesis approach is developed by solving a finite receding horizon optimisation problem with free control moves. It is shown that the proposed predictive controller not only efficiently reduces the negative effects of the quantisation and communication delays but also guarantees the closed-loop stability and satisfies constraints. Finally, a simulation example illustrates the effectiveness of the derived method.     

Decentralised event-triggered cooperative control with limited communication

This note studies event-triggered control of Multi-Agent Systems (MAS) with first-order integrator dynamics. It extends previous work on event-triggered consensus by considering limited communication capabilities through strict peer-to-peer non-continuous information exchange. The approach provides both a decentralised control law and a decentralised communication policy. Communication events require no global information and are based only on local state errors; agents do not require a global sampling period or synchronous broadcasting as in sampled-data approaches. The proposed decentralised event-triggered control technique guarantees that the inter-event times for each agent are strictly positive. Finally, the ideas in this note are used to consider the practical scenario where agents are able to exchange only quantised measurements of their states.     

Quantised control of nonlinear systems: analysis of robustness to parameter uncertainty,measurement errors,and exogenous disturbances

We propose a variant of the recently introduced strategy for stabilisation with limited information by D. Liberzon and J.P. Hespanha and analyse its robustness properties. We show that, if the nominal plant can be made input-to-state stable with respect to measurement errors, parameter uncertainty and exogenous disturbances, then this robustness is preserved with this quantised feedback. More precisely, if a sufficient bandwidth is available on the communication network, then the resulting closed loop is shown to be semiglobally input-to-state practically stable.     

Reduced-order filtering for networks with Markovian jumping parameters and missing measurements

The problem of reduced-order H _∞ filters design for Markovian jumping complex networks with polytopic time-varying transition probability matrices is first addressed in this paper, where the dynamic of each node is described by the sector-bounded nonlinearity. For the measurements, both quantisation and packet dropouts are considered, where each node has its own packet dropout rate. By using the mode- and transition probability-dependent Lyapunov function approach, two sufficient conditions are provided to ensure the stochastic stability and the disturbance attenuation performance of the resulting filtering error system. Then, the mode-independent reduced-order filters design method is proposed, and the filter parameters are given explicitly by linear matrix inequality method. Finally, the effectiveness of the theoretic results presented is illustrated via a numerical example which contains performance comparison of different mode-independent reduced-order filters.