Dependency mining-based causal message logging

In fault-tolerant computing, the approach of causal message logging provides on-demand stable logging and enables the independent recovery of nodes. It imposes the requirement that the dependency between non-deterministic events needs to be known for nodes. The dependency information is disseminated through messages. A central problem in traditional causal message logging is that if the message logging progress of nodes cannot be effectively tracked, some redundant dependency information will be piggybacked on the messages. In this paper, a dependency mining based approach is proposed. It tries to detect the message logging progress of nodes only from the dependency between non-deterministic events, without needing to piggyback an additional dependency vector or dependency matrix on each message.     

Robust stability for uncertain genetic regulatory networks with interval time-varying delays

This paper addresses the problem of robust stability of uncertain genetic regulatory networks (GRNs) with interval time-varying delays. We derive some new delay-range-dependent and delay-derivative-dependent/independent stability criteria by employing some free-weighting matrices and linear matrix inequalities. In our analysis, we carefully consider the relationship between the time-varying state delays and their bounds when calculating the upper bound of the derivative of Lyapunov functional. We hence show that, the rigorous requirement of other literatures that the time-derivatives of time-varying delays must be smaller than one is abandoned in the proposed scheme. The new criteria are applicable to both fast and slow time-varying delays. Finally, four numerical examples are presented to illustrate the effectiveness and the less conservativeness of the developed results.     

基于LPC2131的实时时钟控制设计

实时时钟在工业系统中具有良好的应用前景.本系统以微控制器LPC2131为核心控制器,控制内部实时时钟,实现对秒、分、小时等各个时间寄存器的准确操作,通过串口将采集到的数据发送到上位机.本文详细给出硬件设计总体框图、设计原理和软件实现的方法,得出了实验结果.这种实时时钟的控制方法,很容易应用到现代工业以及各种智能化应用系...     

高端磁盘阵列体系结构分析

本文介绍了两种市场主流高端磁盘阵列的体系结构,阐述了软件对提高存储服务器性能的作用,比较了两种高端磁盘阵列,最后总结了高端磁盘阵列所采用技术并预测未来发展趋势.     

Lapped transform‐based codec for frame‐memory reduction in super‐quality LCD overdrive

Abstract— Overdrive is commonly used to reduce the liquid‐crystal response time and motion blur in liquid‐crystal displays (LCDs). However, overdrive requires a large frame memory in order to store the previous frame for reference. In this paper, a lapped transform‐based codec (LTC) is proposed to reduce the frame memory needed for LCD overdrive. In the latest literature, a directional prediction‐based codec (DPC) employs eight directional predictions, which takes up a large percentage of the computational complexity of the codec and does not consider the de‐correlation of the inter‐blocks. Therefore, the LTC first uses the lapped transform to decompose the correlation of the inter‐blocks in the YUV color space. A hadamard transform is then used for energy compaction. The reordered coefficients are pre‐quantized and encoded using the proposed adaptive bit‐plane coding (ABPC) method for a simple hardware implementation. The simulation results show that the proposed LTC significantly improves the DPC in both subjective and objective performance and outperforms the block truncation coding (BTC) and adaptive multi‐level BTC (AM‐BTC), which have been described in the literature.     

A practical design of content protection for H.264/AVC compressed videos by selective encryption and fingerprinting

Digital Rights Management (DRM) of videos is an important issue nowadays. Considering that H.264/AVC videos will be widely used in various applications, we propose a practical design, which combines the methodologies of selective encryption and fingerprinting, for effective DRM of H.264/AVC streaming videos. A selective encryption scheme is first presented to scramble the video content by encrypting a small amount of data in the compressed bit-stream. The scrambled video is H.264-compliant to reduce the complexity of decoder since it can still be played without triggering errors in the decoding process. A fingerprinting scheme is then introduced to provide further protection. We extract a reasonable amount of data from the video and embed the watermark acting as the fingerprint of the video recipient. To acquire the high-quality video for viewing, the user has to decrypt the video obtained from a video server and then combine it with the watermarked data provided from a user information server. The resulting viewable video is thus fingerprinted to deter the user from illegally redistributing the content. Experimental results will demonstrate the feasibility of the proposed approach.     

Manifold elastic net: a unified framework for sparse dimension reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al., Ann Stat 32(2):407–499, 2004), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: (1) the local geometry of samples is well preserved for low dimensional data representation, (2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, (3) the projection matrix of MEN improves the parsimony in computation, (4) the elastic net penalty reduces the over-fitting problem, and (5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.     

Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

This paper presents a novel algorithm based on generalized opposition-based learning (GOBL) to improve the performance of differential evolution (DE) to solve high-dimensional optimization problems efficiently. The proposed approach, namely GODE, employs similar schemes of opposition-based DE (ODE) for opposition-based population initialization and generation jumping with GOBL. Experiments are conducted to verify the performance of GODE on 19 high-dimensional problems with D = 50, 100, 200, 500, 1,000. The results confirm that GODE outperforms classical DE, real-coded CHC (crossgenerational elitist selection, heterogeneous recombination, and cataclysmic mutation) and G-CMA-ES (restart covariant matrix evolutionary strategy) on the majority of test problems.     

Next generation session management for 3D teleimmersive interactive environments

In the recent past we have seen a boom of distributed interactive multimedia environments which use multiple correlated media sensors, multi-view displays, and advanced haptic-audio-visual user interfaces for teleimmersive gaming, business meetings and other collaborative activities. However, when we investigate the emerging teleimmersive environments closer, we realize that their overall session management, including end-to-end session setup, establishment and run-time management are not appropriate for the new demands that these environments present. These environments are cyber-physical rooms that demand (a) large scale of multi-sensory devices across geographically-distributed locations and (b) interaction with each other in synchronous and real-time manner. To deal with the new teleimmersive demands, we present a new session management design with (a) session initiation protocol(s) that understand media correlations, (b) view-based multi-stream topology establishment among multiple parties, (c) efficient, light-weight and distributed session monitoring with querying and debugging capabilities, (d) flexible view-based session adaptation with efficient topology adjustments, and (e) light-weighted and consistent session tear-down protocols. The presented design of the next generation session management protocols, services, algorithms and data structures is based on our extensive experiences with building 3D teleimmersive interactive systems, experimenting with high impact teleimmersive applications and deploying such environments at various venues.     

Membership evaluation and feature selection for fuzzy support vector machine based on fuzzy rough sets

A fuzzy support vector machine (FSVM) is an improvement in SVMs for dealing with data sets with outliers. In FSVM, a key step is to compute the membership for every training sample. Existing approaches of computing the membership of a sample are motivated by the existence of outliers in data sets and do not take account of the inconsistency between conditional attributes and decision classes. However, this kind of inconsistency can affect membership for every sample and has been considered in fuzzy rough set theory. In this paper, we develop a new method to compute membership for FSVMs by using a Gaussian kernel-based fuzzy rough set. Furthermore, we employ a technique of attribute reduction using Gaussian kernel-based fuzzy rough sets to perform feature selection for FSVMs. Based on these discussions we combine the FSVMs and fuzzy rough sets methods together. The experimental results show that the proposed approaches are feasible and effective.