An optimal scheme for fast rate fault detection based on multirate sampled data

Multirate systems are abundant in industry. In this paper, the problem studied is designing a residual generator for fault detection based on multirate sampled data. The key new feature of such a residual generator is that it operates at a fast rate for prompt fault detection. The design is based on optimizing a performance index to obtain an optimal parity space based residual generator. The lifting technique is used to convert the time-varying multirate design problem into a time-invariant one with a causality constraint for implementability. A procedure for computing an explicit optimal, causal solution is proposed. The advantages of this design are shown through an example.     

Message logging: pessimistic, optimistic, causal, and optimal

Message-logging protocols are an integral part of a popular technique for implementing processes that can recover from crash failures. All message-logging protocols require that, when recovery is complete, there be no orphan processes, which are surviving processes whose states are inconsistent with the recovered state of a crashed process. We give a precise specification of the consistency property “no orphan processes”. From this specification, we describe how different existing classes of message-logging protocols (namely optimistic, pessimistic, and a class that we call causal) implement this property. We then propose a set of metrics to evaluate the performance of message-logging protocols, and characterize the protocols that are optimal with respect to these metrics. Finally, starting from a protocol that relies on causal delivery order, we show how to derive optimal causal protocols that tolerate f overlapping failures and recoveries for a parameter f (1⩽f⩽n)     

An optimal high capacity reversible data hiding scheme using move to front coding for LZW codes

Reversible Data hiding techniques reduce transmission cost as secret data is embedded into a cover image without increasing its size in such a way that at the receiving end, both secret data and the cover image can be extracted and recovered, respectively, to their original form. To further reduce the transmission cost, the secret data can be embedded in the compression codes by some popular reversible data hiding schemes. One of the popular and important reversible data hiding method is high- performance data-hiding Lempel–Ziv–Welch (HPDH-LZW) scheme which hides the secret data in LZW codes. In this paper, the HPDH-LZW scheme is modified in order to increase its hiding capacity and compression ratio. First, the proposed work modifies the Move to Front (MTF) encoding technique to hide the secret data and also to increase the similarity among the element of the cover media. Then, LZW encoding technique is applied on the resultant cover data to obtain LZW codes, which are used to hide further secret data. Experimental results show that the proposed scheme has significantly increased the data hiding capacity and have good embedding and extraction speed in comparison to other state of the art schemes.

     

Dynamic data auditing scheme for big data storage

When users store data in big data platforms,the integrity of outsourced data is a major concern for data owners due to the lack of direct control over the data.However,the existing remote data auditing schemes for big data platforms are only applicable to static data.In order to verify the integrity of dynamic data in a Hadoop big data platform,we presents a dynamic auditing scheme meeting the special requirement of Hadoop.Concretely,a new data structure,namely Data Block Index Table,is designed to support dynamic data operations on HDFS(Hadoop distributed file system),including appending,inserting,deleting,and modifying.Then combined with the MapReduce framework,a dynamic auditing algorithm is designed to audit the data on HDFS concurrently.Analysis shows that the proposed scheme is secure enough to resist forge attack,replace attack and replay attack on big data platform.It is also efficient in both computation and communication.     

A bandwidth and effective hit optimal cache scheme for wireless data access networks with client injected updates

In this paper, we propose an optimal cache replacement policy for data access applications in wireless networks where data updates are injected from all the clients. The goal of the policy is to increase effective hits in the client caches and in turn, make efficient use of the network bandwidth in wireless environment. To serve the applications with the most updated data, we also propose two enhanced cache access policies making copies of data objects strongly consistent. We analytically prove that a cache system, with a combination of our cache access and replacement policy, guarantees the optimal number of effective cache hits and optimal cost (in terms of network bandwidth) per data object access. Results from both analysis and extensive simulations demonstrate that the proposed policies outperform the popular Least Frequently Used (LFU) scheme in terms of both effective hits and bandwidth consumption. Our flexible system model makes the proposed policies equally applicable to applications for the existing 3G, as well as upcoming LTE, LTE Advanced and WiMAX wireless data access networks.     

LQG optimal scalar static output feedback

An eigenproblem-based technique is given for computing the stabilizing scalar static output feedback gain that minimizes the standard linear quadratic performance index in the presence of a Gaussian white-noise disturbance. The method adapts recent work on eigenproblem-based computation of _∞-norms.     

An optimal method for data clustering

An algorithm for optimizing data clustering in feature space is studied in this work. Using graph Laplacian and extreme learning machine (ELM) mapping technique, we develop an optimal weight matrix W for feature mapping. This work explicitly performs a mapping of the original data for clustering into an optimal feature space, which can further increase the separability of original data in the feature space, and the patterns points in same cluster are still closely clustered. Our method, which can be easily implemented, gets better clustering results than some popular clustering algorithms, like k-means on the original data, kernel clustering method, spectral clustering method, and ELM k-means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database).     

A scheme for privacy-preserving data dissemination

An adequate level of trust must be established between prospective partners before an interaction can begin. In asymmetric trust relationships, one of the interacting partners is stronger. The weaker partner can gain a higher level of trust by disclosing private information. Dissemination of sensitive data owned by the weaker partner starts at this moment. The stronger partner can propagate data to others, who may then choose to spread data further. The proposed scheme for privacy-preserving data dissemination enables control of data by their owner (such as a weaker partner). It relies on the ideas of bundling sensitive data with metadata, an apoptosis of endangered bundles, and an adaptive evaporation of bundles in suspect environments. Possible applications include interactions among patients and healthcare providers, customers and businesses, researchers, and suppliers of their raw data. They will contribute to providing privacy guarantees, which are indispensable for the realization of the promise of pervasive computing.     

Earthnet's coordination scheme for AVHRR data

Abstract

Earthnet's plan for a coordinated Advanced Very High Resolution Radiometer (AVHRR) data service in Europe, as outlined by Fusco and Muirhead (1987), is beginning to take'shape. Three High Resolution Picture Transmission (HRPT) stations are contributing to the data acquisition (providing coverage from West Africa to the Middle East and over the North Pole), the AVHRR catalogue is on-line and an autonomous workstation for AVHRR acquisition, processing and archiving to optical disc is in an advanced state of development.     

An efficient workload-based data layout scheme for multidimensional data

Physical data layout is a crucial factor in the performance of queries and updates in large data warehouses. Data layout enhances and complements other performance features such as materialized views and dynamic caching of aggregated results. Prior work has identified that the multidimensional nature of large data warehouses imposes natural restrictions on the query workload. A method based on a “uniform” query class approach has been proposed for data clustering and shown to be optimal. However, we believe that realistic query workloads will exhibit data access skew. For instance, if time is a dimension in the data model, then more queries are likely to focus on the most recent time interval. The query class approach does not adequately model the possibility of multidimensional data access skew. We propose the affinity graph model for capturing workload characteristics in the presence of access skew and describe an efficient algorithm for physical data layout. Our proposed algorithm considers declustering and load balancing issues which are inherent to the multidisk data layout problem. We demonstrate the validity of this approach experimentally.     

Integration of recurrent convolutional neural network and optimal encryption scheme for intrusion detection with secure data storage in the cloud

Data communication security is growing day after day with the proliferation of cloud computing. It is primarily because of the few security constraints and challenges occurring in the cloud environment during data transmission. Existing research has shown that the intrusion detection system (IDS) centered on the cloud is more complicated. In this article, we address the above issues by proposing an attention‐based recurrent convolutional neural network (RCNN). This proposed RCNN is used to detect whether the text data are intrusion or nonintrusion. The nonintrusion text information is then used for further processing and encrypted using a two‐way encryption scheme. We introduce the elliptical curve cryptography (ECC) approach to increase the security‐level performance of nonintrusion data. Moreover, the integration of ECC with the modified flower pollination algorithm (MFP‐ECC) creates the two‐way encryption scheme, and it is used to produce an optimal private key. The encrypted data are then stored in a cloud environment by steganography and the data with the sensitive information are replaced by some other text, thus providing security to the data at rest. The proposed MFP‐ECC approach shows maximum breaking time results and can also withstand different classical attacks when compared with other methods. As a result, the proposed intrusion detection and secure data storage mechanism is highly secured and it is never affected by any kinds of conspiracy attacks.     

An optimal compact sixth-order finite difference scheme for the Helmholtz equation

In this paper, we present an optimal compact finite difference scheme for solving the 2D Helmholtz equation. A convergence analysis is given to show that the scheme is sixth-order in accuracy. Based on minimizing the numerical dispersion, a refined optimization rule for choosing the scheme’s weight parameters is proposed. Numerical results are presented to demonstrate the efficiency and accuracy of the compact finite difference scheme with refined parameters.     

Self and static interference mitigation scheme for coexisting wireless networks

High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.     

具有优化对照度的灰度图像的可视分存方案

在可视分存方案（VCS）中,其解密过程通过人的视觉系统完成。对照度是VCS中重要的研究主题。现有复制设备提供了反色复制的基本功能,黑白图像的全黑反色方案（PBVCS）通过叠加一定数目的分存图像可以精确重构密图,从而为解决VCS重构图像质量问题提供了一种新的途径。给出了灰度图像的反色方案不能直接使用已有的全黑反色PBVCS来构造的原因和存在的问题,进而给出一般灰度图像的反色(k,n)-VCS,该方案的有效性被证明,使用我们的灰度反色VCS,对分存图执行有限次反色和叠加操作可正确重构密图。     

离散非线性零和博弈的事件驱动最优控制方案

在求解离散非线性零和博弈问题时,为了在有效降低网络通讯和控制器执行次数的同时保证良好的控制效果,本文提出了一种基于事件驱动机制的最优控制方案.首先,设计了一个采用新型事件驱动阈值的事件驱动条件,并根据贝尔曼最优性原理获得了最优控制对的表达式.为了求解该表达式中的最优值函数,提出了一种单网络值迭代算法.利用一个神经网络构建评价网.设计了新的评价网权值更新规则.通过在评价网、控制策略及扰动策略之间不断迭代,最终获得零和博弈问题的最优值函数和最优控制对.然后,利用Lyapunov稳定性理论证明了闭环系统的稳定性.最后,将该事件驱动最优控制方案应用到了两个仿真例子中,验证了所提方法的有效性.     

基于启发式的静态中断数据竞争检测方法

提出一种面向中断驱动型嵌入式软件的启发式静态数据竞争检测方法,并开发了原型工具H-RaceChecker.给定软件的源代码或目标程序,H-RaceChecker能够自动推断中断优先级状态、中断使能状态和内存访问状态等信息,在此基础上识别出每个程序点处可能的数据竞争,进而通过启发式精化策略对原始分析结果进行危险程度排序,提高人工确认结果的效率.实验验证了该方法的有效性.     

A computational model for static data flow machines

Computer architects have been constantly looking for new approaches to design high-performance machines. Data flow and VLSI offer two mutually supportive approaches towards a promising design for future super-computers. When very high speed computations are needed, data flow machines may be relied upon as an adequate solution in which extremely parallel processing is achieved.

This paper presents a formal analysis for data flow machines. Moreover, the following three machines are considered: (1) MIT static data flow machine; (2) TI's DDP static data flow machine; (3) LAU data flow machine.

These machines are investigated by making use of a reference model. The contributions of this paper include: (1) Developing a Data Flow Random Access Machine model (DFRAM), for first time, to serve as a formal modeling tool. Also, by making use of this model one can calculate the time cost of various static data machines, as well as the performance of these machines. (2) Constructing a practical Data Flow Simulator (DFS) on the basis of the DFRAM model. Such DFS is modular and portable and can be implemented with less sophistication. The DFS is used not only to study the performance of the underlying data flow machines but also to verify the DFRAM model.     

The ATSR data processing scheme developed for the EODC

Precise global measurements of sea surface temperature (SST) are of great importance for climate research and our ability to model the ocean/ atmosphere. The ATSR instrument is an Announcement of Opportunity experimental package on the ERS-1 satellite, and it is designed to measure global SST with the accuracy levels (better than 0·5 K) that are required by modern climate models. The ATSR instrument's ability to meet its demanding performance objectives depends critically upon a number of novel design features. The way in which these features enable ATSR to achieve its measurement objectives are outlined, and the main tasks of the data processing scheme developed for the U.K. Earth Observation Data Centre are described, including in particular, the ways in which the telemetry data are decoded, the brightness temperature images are geolocated, and the scientific products are derived.     

Voltage control for static var compensator using novel optimal nonlinear PI

Static var compensators (SVCs) are used for voltage and reactive power control in power systems. In this paper, we will consider the structure of SVCs that mainly consists of Y -connection of mechanically-switched capacitor (MSC) and delta connection of thyrister-controlled reactor (TCR). First, the control model of SVC was established in this paper. Then, a novel optimal nonlinear voltage controller for SVCs is proposed. The proposed SVC voltage controller consists of a nonlinear function and a conventional PI controller. The improved simplex method (SPX) is presented to adjust and optimize the parameters of the nonlinear PI controller in real-time, and to make transient state response procedure of SVC optimum. The integration of time multiplied absolute error (ITAE) is adopted as the optimized objective function of SPX. Simulation and engineering application results show that the proposed voltage control method is able to track reference voltage value of SVC immediately. It also demonstrates that the whole SVC control system can synthetically compensate reactive power.