关于RFID核心关键技术对其应用的制约的研究

从RFID硬件、软件、制造、信息安全等各个方面入手,对影响RFID技术发展因素进行分析并提出一些建设性的建议,各个方面研究可以看出RFID技术发展有三大制约,它们是价格、技术以及信息安全,如果能突破性解决这些问题,那么RFID的应用将更进一步。     

Low‐voltage and high‐transmittance blue‐phase liquid‐crystal device with slanted electrodes

Abstract— A low‐voltage (～10 V_rms) and high‐transmittance (～90%) polymer‐stabilized blue‐phase liquid‐crystal (BPLC) device with a slanted‐electrodes structure is proposed. Unlike the vertical‐field‐switching (VFS) mode in which oblique incident light and a vertical field are employed, the proposed device utilizes normal incident light and an oblique field. The slanted electrodes generate a strong and uniform oblique electric field, which contributes in obtaining low voltage and high transmittance. Moreover, no couple films or prism sheets are needed, which helps to enhance the optical efficiency and simplify of the device structure. This device has great potential application for emerging BPLC displays and photonic devices.     

A propositional probabilistic logic with discrete linear time for reasoning about evidence

The aim of the paper is to present a sound, strongly complete and decidable probabilistic temporal logic that can model reasoning about evidence. The formal system developed here is actually a solution of a problem proposed by Halpern and Pucella (J Artif Intell Res 26:1–34, 2006).     

Detection of heterogeneous samples based on loaded generalized inner product method

Non-homogeneity detector (NHD) is known for improving the detection performance of space time adaptive processing (STAP) in heterogeneous environment. To mitigate the finite sample effect in the generalized inner product (GIP) based NHD, a new type of GIP detector based on diagonal loading (LGIP) is proposed in this paper. We derive the theoretical mean value of LGIP detector based on random matrix theory. Moreover, an approximation of the theoretical mean value is provided for practical considerations. We then apply the derived theoretical mean value of LGIP to the detection of heterogeneous samples. Finally, by theoretical analysis and numerical simulations, we show the effectiveness of the proposed detector.     

Using column generation to solve parallel machine scheduling problems with minmax objective functions

In this paper we consider the parallel machine scheduling problem of minimizing an objective function of the minmax type, like maximum lateness, subject to release dates, deadlines, and/or generalized precedence constraints. We use a destructive strategy to compute a lower bound. Here we test the feasibility of a decision problem by applying column generation to compute a bound on the number of machines that we need to feasibly accommodate all jobs. After having derived the lower bound, we try to find a matching upper bound by identifying a feasible schedule with objective function value equal to this lower bound. Our computational results show that our lower bound is so strong that this is almost always possible. We are able to solve problems with up to 160 jobs and 10 machines in 10 minutes on average.     

Finding a robust assignment of flights to gates at Amsterdam Airport Schiphol

In this paper we investigate the gate assignment problem as it appears at Amsterdam Airport Schiphol (AAS). Currently, the gate planners spend many hours on adjusting the automatically generated planning during the day of operation to make it proof against small deviations from the schedule. To alleviate this problem, we aim at finding a robust solution, given the planned arrivals and departures for the next day. We present a completely new integer linear programming formulation that is based on so-called gate plans. Each gate plan consists of a subset of the flights that can be assigned to a single gate of the corresponding type; gates with identical characteristics are aggregated in gate types. The gate assignment problem then boils down to selecting the best subset of gate plans such that each flight belongs to one selected gate plan, and such that the number of selected gate plans for a certain type of gate is equal to the number of gates of this type. In the first phase, we solve the LP-relaxation through column generation, and we describe specific features to find a very good solution to the ILP quickly. This solution is then handed to the planners at AAS in order to assign gate plans to physical gates. This consists of a number of relatively small problems that can be solved by hand and in which additional operational constraints can be incorporated. We also present the possibility of directly assigning flights to physical gates using the column generation formulation, where we then take into account other criteria as well. Computational results with real-life data provided by AAS are promising and indicate that the algorithm is able to solve real-life instances within rather small running times.     

Integrating local action elements for action analysis

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial–temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.     

Extracting trust information from security system of a service

The issue of trust is a research problem in emerging open environments, such as ubiquitous networks. Such environments are highly dynamic and they contain diverse number of services and autonomous entities. Entities in open environments have different security needs from services. Trust computations related to the security systems of services necessitate information that meets needs of each entity. Obtaining such information is a challenging issue for entities. In this paper, we propose a model for extracting trust information from the security system of a service based on the needs of an entity. We formally represent security policies and security systems to extract trust information according to needs of an entity. The formal representation ensures an entity to extract trust information about a security property of a service and trust information about whole security system of the service. The proposed model is applied to Dental Clinic Patient Service as a case study with two scenarios. The scenarios are analyzed experimentally with simulations. The experimental evaluation shows that the proposed model provides trust information related to the security system of a service based on the needs of an entity and it is applicable in emerging open environments.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

Retrospective adaptive prefetching for interactive Web GIS applications

A major task of a Web GIS (Geographic Information Systems) system is to transfer map data to client applications over the Internet, which may be too costly. To improve this inefficient process, various solutions are available. Caching the responses of the requests on the client side is the most commonly implemented solution. However, this method may not be adequate by itself. Besides caching the responses, predicting the next possible requests from a client and updating the cache with responses for those requests together provide a remarkable performance improvement. This procedure is called “prefetching” and makes caching mechanisms more effective and efficient. This paper proposes an efficient prefetching algorithm called Retrospective Adaptive Prefetch (RAP), which is constructed over a heuristic method that considers the former actions of a given user. The algorithm reduces the user-perceived response time and improves user navigation efficiency. Additionally, it adjusts the cache size automatically, based on the memory size of the client’s machine. RAP is compared with four other prefetching algorithms. The experiments show that RAP provides better performance enhancements than the other methods.