Managing cohort movement of mobile sensors via GPS-free and compass-free node localization

A critical problem in mobile ad hoc wireless sensor networks is each node’s awareness of its position relative to the network. This problem is known as localization. In this paper, we introduce a variant of this problem, directional localization, where each node must be aware of both its position and orientation relative to its neighbors. Directional localization is relevant for applications that require uniform area coverage and coherent movement. Using global positioning systems for localization in large scale sensor networks may be impractical in enclosed spaces, and might not be cost effective. In addition, a set of pre-existing anchors with globally known positions may not always be available. In this context, we propose two distributed algorithms based on directional localization that facilitate the collaborative movement of nodes in a sensor network without the need for global positioning systems, seed nodes or a pre-existing infrastructure such as anchors with known positions. Our first algorithm, GPS-free Directed Localization (GDL) assumes the availability of a simple digital compass on each sensor node. We relax this requirement in our second algorithm termed GPS- and Compass-free Directed Localization (GCDL). Through experimentation, we demonstrate that our algorithms scale well for large numbers of nodes and provide convergent localization over time, despite errors introduced by motion actuators and distance measurements. In addition, we introduce mechanisms to preserve swarm formation during directed sensor network mobility. Our simulations confirm that, in a number of realistic scenarios, our algorithms provide for a mobile sensor network that preserves its formation over time, irrespective of speed and distance traveled. We also present our method to organize the sensor nodes in a polygonal geometric shape of our choice even in noisy environments, and investigate the possible uses of this approach in search-and-rescue type of missions.     

Ultra light-weight and high-resolution X-ray mirrors using DRIE and X-ray LIGA techniques for space X-ray telescopes

We are developing novel ultra light-weight and high-resolution X-ray micro pore optics for space X-ray telescopes. In our method, curvilinear micro pore structures are firstly fabricated by silicon deep reactive ion etching (DRIE) or X-ray LIGA processes. Secondly, side walls of the micro structures are smoothed by magnetic field assisted finishing and/or hydrogen annealing techniques for high reflectivity mirrors. Thirdly, to focus parallel X-ray lights from astronomical objects, these structures are elastically or plastically bent into a spherical shape. Fourthly, the bent structures are stacked to form a multi-stage X-ray telescope. In this paper, we report on fabrication and X-ray reflection tests of silicon and nickel X-ray mirrors using the DRIE and LIGA processes, respectively. For the first time, X-ray reflections were confirmed on both of the mirrors. Estimated rms roughnesses were 5 nm and 3 nm for the silicon and nickel mirrors, respectively.     

Evidential reasoning using extended fuzzy Dempster–Shafer theory for handling various facets of information deficiency

This work investigates the problem of combining deficient evidence for the purpose of quality assessment. The main focus of the work is modeling vagueness, ambiguity, and local nonspecificity in information within a unified approach. We introduce an extended fuzzy Dempster–Shafer scheme based on the simultaneous use of fuzzy interval‐grade and interval‐valued belief degree (IGIB). The latter facilitates modeling of uncertainties in terms of local ignorance associated with expert knowledge, whereas the former allows for handling the lack of information on belief degree assignments. Also, generalized fuzzy sets can be readily transformed into the proposed fuzzy IGIB structure. The reasoning for quality assessment is performed by solving nonlinear optimization problems on fuzzy Dempster–Shafer paradigm for the fuzzy IGIB structure. The application of the proposed inference method is investigated by designing a reasoning scheme for water quality monitoring and validated through the experimental data available for different sampling points in a water distribution network. © 2011 Wiley Periodicals, Inc.     

Reconfigurable distributed storage for dynamic networks

This paper presents a new algorithm for implementing a reconfigurable distributed shared memory in an asynchronous dynamic network. The algorithm guarantees atomic consistency (linearizability) in all executions in the presence of arbitrary crash failures of the processing nodes, message delays, and message loss. The algorithm incorporates a classic quorum-based algorithm for read/write operations, and an optimized consensus protocol, based on Fast Paxos for reconfiguration, and achieves the design goals of: (i) allowing read and write operations to complete rapidly and (ii) providing long-term fault-tolerance through reconfiguration, a process that evolves the quorum configurations used by the read and write operations. The resulting algorithm tolerates dynamism. We formally prove our algorithm to be correct, we present its performance and compare it to existing reconfigurable memories, and we evaluate experimentally the cost of its reconfiguration mechanism.     

A queue based mutual exclusion algorithm

A new elegant and simple algorithm for mutual exclusion of N processes is proposed. It only requires shared variables in a memory model where shared variables need not be accessed atomically. We prove mutual exclusion by reformulating the algorithm as a transition system (automaton), and applying simulation of automata. The proof has been verified with the higher-order interactive theorem prover PVS. Under an additional atomicity assumption, the algorithm is starvation free, and we conjecture that no competing process is passed by any other process more than once. This conjecture was verified by model checking for systems with at most five processes.     

Platform‐independent profiling in a virtual execution environment

Virtual execution environments, such as the Java virtual machine, promote platform‐independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform‐specific metrics, such as the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platform‐dependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling‐context‐sensitive program execution statistics. We explore the use of platform‐independent profiling metrics in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java‐based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage by statically instrumenting the core runtime libraries and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code. Copyright © 2008 John Wiley & Sons, Ltd.     

Structural reliability assessment based on probability and convex set mixed model

This paper investigates the reliability assessment of structures exhibiting both stochastic and bounded uncertainties by using a probability and convex set mixed model. The safety measure of a structure is quantified by a reliability index defined by a nested minimization problem. An iterative procedure is developed for seeking the worst-case point and the most probable failure point in the standard uncertainty space. Numerical examples are given to demonstrate the applicability of the probability and convex set mixed model representation in the structural reliability assessment, as well as to illustrate the validity and effectiveness of the proposed numerical method.     

Complete discriminant evaluation and feature extraction in kernel space for face recognition

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.

Development of ‘UR-System 2’: a training system for recovery of motor function of plegic upper limb after stroke

We developed a new training system, called ‘UR-System 2’, for the recovery of motor function of a plegic upper limb after stroke. UR-System 2, which was redesigned based on knowledge gained from clinical tests using UR-System 1, was newly equipped with a function for testing the motor function of patients, such as isolated movement. In order to verify the effectiveness of UR-System 2, we conducted the following procedures: we verified the safety of training in healthy persons; we gathered standard data on pronation and supination of healthy forearms during tests; and, we verified that the test function was effective in evaluating the recovery effect of isolated movement in two hemiplegic patients.     

Implementation of a Gaussian process-based machine learning grasp predictor

With the goal of advancing the state of automatic robotic grasping, we present a novel approach that combines machine learning techniques and physical validation on a robotic platform to develop a comprehensive grasp predictor. After collecting a large grasp sample set (522 grasps), we first conduct a statistical analysis of the predictive ability of grasp quality metrics that are commonly used in the robotics literature. We then apply principal component analysis and Gaussian process (GP) algorithms on the grasp metrics that are discriminative to build a classifier, validate its performance, and compare the results to existing grasp planners. The key findings are as follows: (i) several of the existing grasp metrics are weak predictors of grasp quality when implemented on a robotic platform; (ii) the GP-based classifier significantly improves grasp prediction by combining multiple grasp metrics to increase true positive classification at low false positive rates; (iii) The GP classifier can be used generate new grasps to improve bad grasp samples by performing a local search to find neighboring grasps which have improved contact points and higher success rate.