Scheduler-based state estimation over multiple channels networks

We investigate the remote state estimation problem for networked systems over parallel noise-free communication channels. Due to limited network capabilities in practical network environments, communication schedulers are implemented at the transmit side of each subchannel to promote resource efficiency. Specifically, the processed signals are transmitted only when it is necessary to provide the real-time measurements to the remote estimator. The recursive approximate minimum mean-square error estimator is established to restore the state vector of a target plant by utilizing the scheduled transmission signals. All the information coming from the individual subchannels, even if no measurement is sent, will contribute to improve the estimation performance in an analytical form. Finally, a numerical example is given to illustrate the effectiveness of the main results.     

多描述可分级编码的研究进展

多媒体及Internet的迅速发展促使视频编码不仅要有较好的容错性能,而且要根据网络带宽和接收设备的特性而自适应地进行调整和传输。多描述编码可以提高视频传输的容错性能但不具有网络自适应性;可分级编码能够根据网络带宽和接收设备的特性而自适应地进行调整和传输但不具有容错性能。综合了多描述编码和可分级编码优势的多描述可分级编码,则不但具有网络带宽的自适应性,同时具有很好的容错性能。在分析了多描述编码和可分级编码的各自局限性的基础上,重点介绍了多描述可分级编码的产生和几种实现策略。     

基于Ad hoc网络的联合多描述分层编码新方法*

提出了一种基于Ad hoc网络中视频传输的自适应插入过渡帧的联合多描述分层编码(AFI-MDLC)新方法.该方法根据相邻帧的相对运动自适应地插入过渡帧,然后将视频序列分成两个描述子,描述子独立分层编码后,生成各自的基本层和增强层,每个描述子具有独立的预测环路.多路径传输策略用于AFI-MDLC数据传输,同时利用ARQ反馈信道状态,实时调整传输策略.实验结果袁明,该方法与以往方法相比,能够有效地缓解误匹配问题,帮助解码器快速从随机错误或突发错误中恢复,提供更加稳定的重建视频质量.     

3D网格的分层多描述编码方法

结合分层编码与多描述编码的优势,提出三维网格分层多描述编码（LMDC）方法：先对3D网格进行几何分解,得到一个粗糙网格和细化网格所需的连通性信息,采用分层编码思想,粗糙网格作为基本层,而将细化信息作为增强层。同时采用基于顶点分裂树的多描述编码方法对基本层加以保护,保证基本层在差错信道中的有效传输。采用分层多描述编码对3D模型进行编码的方法,非常适合于带宽受限和多路径传输的异构网络。实验证明,该方法能获得较高的压缩率,并在有丢包的情况下能有效地保护并恢复出可接受的基本层网格。     

State of charge estimation for lithium-ion batteries: An adaptive approach

State of charge (SoC) estimation is of key importance in the design of battery management systems. An adaptive SoC estimator, which is named AdaptSoC, is developed in this paper. It is able to estimate the SoC in real time when the model parameters are unknown, via joint state (SoC) and parameter estimation. The AdaptSoC algorithm is designed on the basis of three procedures. First, a reduced-complexity battery model in state-space form is developed from the well-known single particle model (SPM). Then a joint local observability/identifiability analysis of the SoC and the unknown model parameters is performed. Finally, the SoC is estimated simultaneously with the parameters using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.     

A new state estimation method for high-mix semiconductor manufacturing processes

In semiconductor manufacturing upstream processes may affect the wafer substrate in a manner that alters performance in downstream operations, and the context within which a process is run may fundamentally change the way the process behaves. Incorporating these influences into a control method ultimately leads to better predictability and improved control performance, because one lot of a specific product may take a very different processing path through the fabrication facility than the next lot of that same product. This paper provides a new method for state estimation in a high-mix manufacturing scenario, based on a random walk model. This model, combined with a moving window approach and least squares solution, provides better estimates for simulated processes with a high-mix of tools and products with many low-runners as compared to alternative methods. An approach combining the Kalman filter and the least squares solution is also developed, with improved results in some cases. In the case of manufacturing data, we modify the model parameters and the weights on processing contexts to get better results.     

基于低频预测的图像多描述编码及其差错隐藏

为了提高图像、视频在丢包或误码环境下的传输性能,提出了基于低频预测的图像多描述编码方法。根据小波系数的独特特点将原始图像小波变换后的系数分为低频,次高频,高频三部分,对各部分进行不同方式的分组;采用低频预测的预处理和后处理方案,实现描述间的冗余插入;实现了差错隐藏算法以提高传输的鲁棒性。仿真结果表明,系统的抗误码能力大为提高,且可提供灵活冗余插入以适应各种网络传输状况。     

State estimation for linear systems with observations partially corrupted by noise

This paper exploits the fact that any row vector of the observability matrix applied for transforming the state converts the latter to the new state component in the form of some derivative of the output component. Using the same but appropriately chosen vectors for transforming the system with the observation not fully corrupted by white noise we can accurately determine some state components. These vectors create the basis for the l-dimensional subspace of transformation vectors to the new accurately determinable state components. Using this basis the state transformation is constructed which in one step converts the singular linear filtering problem to a nonsingular one with state dimension decreased by l.     

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.     

一种标准兼容的多描述图像编码分析及改进

多描述编码被证明是用于非优先级网络中传输多媒体数据的有效方法,而现有很多算法与图像标准不兼容,分析了一种与标准兼容的多描述图像编码算法,该算法可以提供灵活冗余插入,以提高丢包或误码环境下的图像传输性能。通过深入分析该算法在不同比特率下的表现,发现其在低比特率端性能与较高比特率下的表现迥异,结合区分对待的思想来改善了这一缺点,并利用JPEG2000的ROI特性证明其有效性。     

On-line state and parameter estimation of EPDM polymerization reactors using a hierarchical extended Kalman filter

A hierarchical extended Kalman filter (EKF) design is proposed to estimate unmeasured state variables and key kinetic parameters in a first principles model of a continuous ethylene–propylene–diene polymer (EPDM) reactor. The estimator design is based on decomposing the dynamic model into two subsystems by exploiting the triangular model structure and the different sampling frequencies of on-line and laboratory measurements directly related to the state variables of each subsystem. The state variables of the first subsystem are reactant concentrations and zeroth-order moments of the molecular weight distribution (MWD). Unmeasured state variables and four kinetic parameters systematically chosen to reduce bias are estimated from frequent and undelayed on-line measurements of the ethylene, propylene, diene and total polymer concentrations. The state variables of the second subsystem are first-order moments of the MWD. Given state and parameters estimates from the first subsystem EKF, the first-order moments and three non-stationary parameters added to the model for bias reduction are estimated from infrequent and delayed laboratory measurements of the ethylene and diene contents and number average molecular weight of the polymer. Simulation tests show that the hierarchical EKF generates satisfactory estimates even in the presence of measurement noise and plant/model mismatch.     

Box particle filtering for nonlinear state estimation using interval analysis

In recent years particle filters have been applied to a variety of state estimation problems. A particle filter is a sequential Monte Carlo Bayesian estimator of the posterior density of the state using weighted particles. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate weights to these particles at each iteration. If the imprecision, i.e. bias and noise, in the available information is high, the number of particles needs to be very large in order to obtain good performances. This may give rise to complexity problems for a real-time implementation. This kind of imprecision can easily be represented by interval data if the maximum error is known. Handling interval data is a new approach successfully applied to different real applications. In this paper, we propose an extension of the particle filter algorithm able to handle interval data and using interval analysis and constraint satisfaction techniques. In standard particle filtering, particles are punctual states associated with weights whose likelihoods are defined by a statistical model of the observation error. In the box particle filter, particles are boxes associated with weights whose likelihood is defined by a bounded model of the observation error. Experiments using actual data for global localization of a vehicle show the usefulness and the efficiency of the proposed approach.     

面向ZigBee的视频多描述编码及其传输性能仿真

针对视频码流在ZigBee等不可靠低速无线网络传输时易发生编码信息包丢失而导致接收端无法正确解码这一问题,提出一种基于3D-SPIHT视频多描述编码方案。与传统多描述编码如MDSQ相比,该方案创建的各描述质量平衡,并能通过去除视频帧间冗余来提高压缩比;与国际标准（H.264等）相比,该方案具有算法简单、能够在任意码流截断处解码、码率可连续分级和无明显块效应等优点。实验表明,该编码方案能够较好地应用于ZigBee多信道网络,并在较大压缩比的情况下仍能获取质量较好的重建图像,具有较高的应用价值。     

无线Ad hoc网络上的自适应多描述编码*

为了在带宽较小、拓扑实时变化、易受外界影响的无线Ad hoc网络中实行视频的可靠通信,提出了一种自适应多相变换多描述编码策略。通过不同的相位点来产生多种描述,利用OLSR路由协议中的Hop和ETX来动态改变各描述的量化步长,以适应实时变化的Ad hoc网络。仿真实验证明,与传统的单描述编码（如H.264/AVC）相比,自适应多相变换多描述编码在提高信噪比、降低丢包率和平均失真方面比传统方式均有明显改善。仿真实验证明了所提出方案传输视频是优越的、可行的。