k-Multi-preference query over road networks

Nowadays, the road network has gained more and more attention in the research area of databases. Existing works mainly focus on standalone queries, such as k-nearest neighbor queries over a single type of objects (e.g., facility like restaurant or hotel). In this paper, we propose a k-multi-preference (kMP) query over road networks, involving complex query predicates and multiple facilities. In particular, given a query graph, a kMP query retrieves of the top-k groups of vertices (of k facility types) satisfying the label constraints and their aggregate distances are the smallest. A naïve solution to this problem is to enumerate all combinations of vertices with k possible facility types and then select the one with the minimum sum distance. This method, however, incurs rather high computation cost due to exponential possible combinations. In addition, the existing solutions to other standalone queries are for a single type of facilities and cannot be directly used to answer kMP queries. Therefore, in this paper, we propose an efficient approach to process a kMP query, which utilizes an index with bounded space and reduces the computation cost of the shortest path queries. We also design effective pruning techniques to filter out false alarms. Through our extensive experiments, we demonstrate the efficiency and effectiveness of our proposed solutions.     

An efficient algorithm for partially matched services in internet of services

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This large-scale information exchange results in new interactions between things and people. Unlike traditional web services, internet of services is highly dynamic and continuously changing due to constant degrade, vanish and possibly reappear of the devices, this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides’ leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.     

Switching adaptive learning control for nonlinearly parameterized systems with disturbance of unknown periods

In this paper, a logic‐based switching adaptive learning control mechanism is proposed for a class of nonlinearly parameterized systems with disturbance of unknown periods. The switching algorithms include two parts: one is to stabilize the nonlinearly parameterized uncertainties, the other is to learn the periodic bounded disturbance. An adaptive control method with fully saturated periodic adaptation law is presented to take advantage of the periodic and bounded property of the disturbance. It is shown that under the proposed control designs, the asymptotic convergence is ensured irrespective of initial conditions with all the signals in the closed‐loop system bounded. An illustrative example is given to show the validity of the switching adaptive learning control. Copyright © 2014 John Wiley & Sons, Ltd.     

Federating Policy-Driven Autonomous Systems: Interaction Specification and Management Patterns

Ubiquitous systems and applications involve interactions between multiple autonomous entities—for example, robots in a mobile ad-hoc network collaborating to achieve a goal, communications between teams of emergency workers involved in disaster relief operations or interactions between patients’ and healthcare workers’ mobile devices. We have previously proposed the Self-Managed Cell (SMC) as an architectural pattern for managing autonomous ubiquitous systems that comprise both hardware and software components and that implement policy-based adaptation strategies. We have also shown how basic management interactions between autonomous SMCs can be realised through exchanges of notifications and policies, to effectively program management and context-aware adaptations. We present here how autonomous SMCs can be composed and federated into complex structures through the systematic composition of interaction patterns. By composing simpler abstractions as building blocks of more complex interactions it is possible to leverage commonalities across the structural, control and communication views to manage a broad variety of composite autonomous systems including peer-to-peer collaborations, federations and aggregations with varying degrees of devolution of control. Although the approach is more broadly applicable, we focus on systems where declarative policies are used to specify adaptation and on context-aware ubiquitous systems that present some degree of autonomy in the physical world, such as body sensor networks and autonomous vehicles. Finally, we present a formalisation of our model that allows a rigorous verification of the properties satisfied by the SMC interactions before policies are deployed in physical devices.     

Robust flat filtering DSP based control of the boost converter

The article deals with the design and implementation of a flat filter tracking digital controller for a boost dc-dc power converter. A highly perturbed switched boost converter circuit is shown to be efficiently controlled, in a trajectory tracking task for its non-minimum phase output, by means of a suitable linear filter, here addressed as a flat filter. Flat filtering is a natural robust version of generalized proportional integral control (GPIC) by which the effects of arbitrary time varying exogenous disturbances, unknown endogenous nonlinearities and un-modeled dynamics can be jointly attenuated in a conceptually similar fashion to observer-based active disturbance rejection control (ADRC) and algebraic identification based model free control (MFC) but: a) without using extended state observers and b) respecting the original system order in a time-varying simplified model while avoiding algebraic estimation techniques. The proposed control technique based on the TMS320F28335 digital signal processor chip is tested by means of realistic simulations and experimental setup.     

无人机编队多分辨率指控建模分析

从功能角度对无人机进行建模,分别描述了无人机编队在高分辨率和低分辨率下模型功能及各功能模块之间的信息交互关系。首先分析了无人机个体单元的组成结构,从体系结构的角度分析了各功能模型,从控制论的角度分析了各组成结构之间的信息交互关系。然后分析了编队队形保持算法,并分析了队形控制信息交互处理流程。     

A secure and high-performance multi-controller architecture for software-defined networking

Controllers play a critical role in software-defined networking (SDN). However, existing single-controller SDN architectures are vulnerable to single-point failures, where a controller’s capacity can be saturated by flooded flow requests. In addition, due to the complicated interactions between applications and controllers, the flow setup latency is relatively large. To address the above security and performance issues of current SDN controllers, we propose distributed rule store (DRS), a new multi-controller architecture for SDNs. In DRS, the controller caches the flow rules calculated by applications, and distributes these rules to multiple controller instances. Each controller instance holds only a subset of all rules, and periodically checks the consistency of flow rules with each other. Requests from switches are distributed among multiple controllers, in order to mitigate controller capacity saturation attack. At the same time, when rules at one controller are maliciously modified, they can be detected and recovered in time. We implement DRS based on Floodlight and evaluate it with extensive emulation. The results show that DRS can effectively maintain a consistently distributed rule store, and at the same time can achieve a shorter flow setup time and a higher processing throughput, compared with ONOS and Floodlight.     

Portability of executable service-oriented processes: metrics and validation

A key promise of process languages based on open standards, such as the Web Services Business Process Execution Language, is the avoidance of vendor lock-in through the portability of processes among runtime environments. Despite the fact that today various runtimes claim to support this language, every runtime implements a different subset, thus hampering portability and locking in their users. It is our intention to improve this situation by enabling the measurement of the portability of executable service-oriented processes. This helps developers to assess their implementations and to decide if it is feasible to invest in the effort of porting a process to another runtime. In this paper, we define several software quality metrics that quantify the degree of portability of an executable, service-oriented process from different viewpoints. When integrated into a development environment, such metrics can help to improve the portability of the outcome. We validate the metrics theoretically with respect to measurement theory and construct validity using two validation frameworks. The validation is complemented with an empirical evaluation of the metrics using a large set of processes coming from several process libraries.     

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.     

Massively parallel Lucas Kanade optical flow for real-time video processing applications

This paper deals with dense optical flow estimation from the perspective of the trade-off between quality of the estimated flow and computational cost which is required by real-world applications. We propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on GPU. It leads to very high performance even on large image formats such as 4 K (3,840 × 2,160) resolution. In order to assess the interest of eFOLKI, we first present a comparative study with currently available GPU codes, including local and global methods, on a large set of data with ground truth. eFOLKI appears significantly faster while providing quite accurate and highly robust estimated flows. We then show, on four real-time video processing applications based on optical flow, that eFOLKI reaches the requirements both in terms of estimated flows quality and of processing rate.