Adding sense to the Internet of Things

The Internet of Things (IoT) concept is being widely presented as the next revolution toward massively distributed information, where any real-world object can automatically participate in the Internet and thus be globally discovered and queried. Despite the consensus on the great potential of the concept and the significant progress in a number of enabling technologies, there is a general lack of an integrated vision on how to realize it. This paper examines the technologies that will be fundamental for realizing the IoT and proposes an architecture that integrates them into a single platform. The architecture introduces the use of the Smart Object framework to encapsulate radio-frequency identification (RFID), sensor technologies, embedded object logic, object ad-hoc networking, and Internet-based information infrastructure. We evaluate the architecture against a number of energy-based performance measures, and also show that it outperforms existing industry standards in metrics such as network throughput, delivery ratio, or routing distance. Finally, we demonstrate the feasibility and flexibility of the architecture by detailing an implementation using Wireless Sensor Networks and Web Services, and describe a prototype for the real-time monitoring of goods flowing through a supply chain.     

基于Cookie的安全防护技术研究

Cookie的使用方便了人们的网上生活,但同时对用户的许多隐私信息构成了威胁。文章首先介绍了Cookie技术的发展背景、相关概念、应用领域及基本特征,说明了Cookie的工作原理和Cookie结构;其次讲述了常见的Cookie技术的漏洞,Cookie欺骗和Cookie注入的原理和方法;最后介绍了针对Cookie技术的安全防护措施,并详细描述了一种基于Cookie特性分析的系统设计思想及其在VC环境下的实现过程。系统主要包含两个核心模块：Cookie特性分析模块和Cookie安全防护模块。实现了Cookie查看、修改Cookie属性、删除Cookie、禁用Cookie、监视Cookie等功能。系统为普通用户和专业用户提供了不同的防御功能,让用户一定程度上可以更清晰地认识Cookie、提高自身安全意识和防范本地Cookie失窃。     

装甲装备故障预测与健康管理系统技术研究

预测和健康状态管理(PHM)技术是一种先进测试、维修技术;从视情维修保障的背景出发,介绍了装甲装备PHM系统的内涵,进行了装甲装备故障模式影响和危害分析,明确了PHM系统具备的功能,初步构建了基于开放体系的装甲装备PHM系统的技术框架,并对其中的关健技术进行了深入研究,对炮控系统进行故障预测分析,结果和实际应用一致。     

Volterra filter modeling of nonlinear discrete-time system using improved particle swarm optimization

This paper focuses on the identification problem of nonlinear discrete-time systems using Volterra filter series model. Generally, to update the kernels of Volterra model, the most commonly used method is the gradient adaptive algorithm. However, this method probably traps at the local minimum for searching parameter solutions. In this study, a new intelligence swarm computation of the global search is considered. We utilize an improved particle swarm optimization (IPSO) algorithm to design the Volterra kernel parameters. It is somewhat different from the original algorithm due to modifying its velocity updating formula and this can promote the algorithm?s searching ability for solving the optimization problem. Using the IPSO algorithm to minimize the mean square error (MSE) between the actual output and model output, the identification problem for nonlinear discrete-time systems can be fulfilled. Finally, two different kinds of examples are provided to demonstrate the efficiency of the proposed method. Moreover, some examinations including the Volterra model memory size and algorithm initial condition are further considered.     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.     

OpenGL ES 1.1 implementation based on OpenGL

In this paper, we present an efficient way of implementing OpenGL ES 1.1 3D graphics API library for the environments with hardware-supported OpenGL facility, typically as desktop PCs. Although OpenGL ES was started from the existing OpenGL features, it rapidly became the standard 3D graphics library customized for embedded systems through introducing fixed-point arithmetic operations, buffer management with fixed-point data type supports, completely new texture mapping functions and others. Currently, it is the official 3D graphics API for Google Android, Apple iPhone, Sony PlayStation3, etc. In this paper, we achieved improvements on the arithmetic operations for the fixed-point number representation, which is the most characteristic data type for OpenGL ES 1.1. For the conversion of fixed-point data types to the floating-point number representations for the underlying OpenGL, we show the way of efficient conversion processes even with satisfying OpenGL ES standard requirements. We also introduced a specialized memory management scheme to manage the converted data from the buffer containing fixed-point numbers. In the case of texture processing, the requirements in both standards are quite different, and thus we used completely new software-implementations. Our final implementation of OpenGL ES library provides all of more than 200 functions in the standard specification and passed its conformance test, to show its compliance with the standard. From the efficiency point of view, we measured its execution times for several OpenGL ES-specific application programs and achieved remarkable improvements.     

AutoMed: an automated mediator for multi-issue bilateral negotiations

In this paper, we present AutoMed, an automated mediator for multi-issue bilateral negotiation under time constraints. AutoMed elicits the negotiators preferences and analyzes them. It monitors the negotiations and proposes possible solutions for resolving the conflict. We conducted experiments in a simulated environment. The results show that negotiations mediated by AutoMed are concluded significantly faster than non-mediated ones and without any of the negotiators opting out. Furthermore, the subjects in the mediated negotiations are more satisfied with the resolutions than the subjects in the non-mediated negotiations.     

k-Nearest neighbor query processing algorithm for cloaking regions towards user privacy protection in location-based services

Due to the advancement of wireless internet and mobile positioning technology, the application of location-based services (LBSs) has become popular for mobile users. Since users have to send their exact locations to obtain the service, it may lead to several privacy threats. To solve this problem, a cloaking method has been proposed to blur users’ exact locations into a cloaked spatial region with a required privacy threshold (k). With the cloaked region, an LBS server can carry out a k-nearest neighbor (k-NN) search algorithm. Some recent studies have proposed methods to search k-nearest POIs while protecting a user’s privacy. However, they have at least one major problem, such as inefficiency on query processing or low precision of retrieved result. To resolve these problems, in this paper, we propose a novel k-NN query processing algorithm for a cloaking region to satisfy both requirements of fast query processing time and high precision of the retrieved result. To achieve fast query processing time, we propose a new pruning technique based on a 2D-coodinate scheme. In addition, we make use of a Voronoi diagram for retrieving the nearest POIs efficiently. To satisfy the requirement of high precision of the retrieved result, we guarantee that our k-NN query processing algorithm always contains the exact set of k nearest neighbors. Our performance analysis shows that our algorithm achieves better performance in terms of query processing time and the number of candidate POIs compared with other algorithms.     

Rigid body dynamics with a scalable body,quaternions and perfect constraints

In this paper, we present a formulation of the quaternion constraint for rigid body rotations in the form of a standard perfect bilateral mechanical constraint, for which the associated Lagrangian multiplier has the meaning of a constraint force. First, the equations of motion of a scalable body are derived. A scalable body has three translational, three rotational, and one uniform scaling degree of freedom. As generalized coordinates, an unconstrained quaternion and a displacement vector are used. To the scalable body, a perfect bilateral constraint is added, restricting the quaternion to unit length and making the body rigid. This way a quaternion based differential algebraic equation (DAE) formulation for the dynamics of a rigid body is obtained, where the 7×7 mass matrix is regular and the unit length restriction of the quaternion is enforced by a mechanical constraint. Finally, the equations of motion in the form of a DAE are linked to the Newton–Euler equations of motion of a rigid body. The rigid body DAE formulation is useful for the construction of (energy) consistent integrators.     

More than modelling and hiding: towards a comprehensive view of Web mining and privacy

Over the last decade, privacy has been widely recognised as one of the major problems of data collections in general and the Web in particular. This concerns specifically data arising from Web usage (such as querying or transacting) and social networking (characterised by rich self-profiling including relational information) and the inferences drawn from them. The data mining community has been very conscious of these issues and has addressed in particular the inference problems through various methods for “privacy-preserving data mining” and “privacy-preserving data publishing”. However, it appears that these approaches by themselves cannot effectively solve the privacy problems posed by mining. We argue that this is due to the underlying notions of privacy and of data mining, both of which are too narrow. Drawing on notions of privacy not only as hiding, but as control and negotiation, as well as on data mining not only as modelling, but as the whole cycle of knowledge discovery, we offer an alternative view. This is intended to be a comprehensive view of the privacy challenges as well as solution approaches along all phases of the knowledge discovery cycle. The paper thus combines a survey with an outline of an agenda for a comprehensive, interdisciplinary view of Web mining and privacy.