A Constructive Globally Convergent Adaptive Speed Observer For Port‐Hamiltonian Mechanical Systems with Non‐Holonomic Constraints

This paper presents an adaptive speed observer for general port‐Hamiltonian mechanical systems with non‐holonomic constraints in the presence of unknown friction forces and constant disturbances. Unlike the observers recently reported in the literature, which have been designed either under the assumptions of no friction and the absence of disturbances or for a specific class of mechanical systems with the requirement of an explicit solution of certain Partial Differential Equations (PDEs) that cannot be derived a priori, this observer proposes a design that obviates the solution of PDEs and ensures global convergence for general mechanical systems with k‐non‐holonomic constraints. The observer is totally constructive and given by explicit expressions. The simulation results testify to the effectiveness and the robust features of the developed observer.     

Self-adapting recovery nets for policy-driven exception handling in business processes

In this paper, we propose Self-Adapting Recovery Net (SARN), an extended Petri net model, for specifying exceptional behavior in business processes. SARN adapts the structure of the underlying Petri net at run time to handle exceptions while keeping the Petri net design easy. The proposed framework caters for the specification of high-level recovery policies that are incorporated either with a single task or a set of tasks, called a Recovery Region. These recovery policies are generic directives that model exceptions at design time together with a set of primitive operations used at run time to handle the occurrence of exceptions. We identified a set of recovery policies that are useful and commonly needed in many practical situations. A tool has been developed to illustrate the viability of the proposed exception handling technique. B. Medjahed’s work is supported by a grant from the University of Michigan’s OVPR.     

Learning disabilities and visual-motor skills; comparing assessment from a hapto-virtual reality tool and Bender-Gestalt test

Previous investigations conducted on post-secondary adult students with learning disabilities (LD) suggest that deficits in visual-motor skills contribute to difficulties in written expression which impact academic achievement. Intervention strategies for individuals with LD include assistive computer-based technologies (ATs) to compensate for or maximize performance. However, research fails to assess the impact of ATs on performance, learning, and motivation of students with LD. Also, one of the limitations of ATs is that they cannot be used for assessment and training and there are very few methods to assess or train visual-motor skills in this population. The present study explores the usefulness of a hapto-visual virtual reality motor skills assessment (MSA) device for visual-motor functioning in adults with and without LD. This is a preliminary step of developing an intervention to improve impaired visual-motor skills in adults with LD. A sample of 22 male and female university students with and without LD had their visual-motor skills pretested using a standard paper-and-pencil Bender-Gestalt (BG) test and were compared according to their performance on the MSA tool. We hypothesized that our LD participants’ performance would be significantly lower than our control participants on the VR task in terms of number of errors and speed. Results showed that participants without LD performed better and more rapidly on the VR task than participants with LD. There were no correlations between the BG and MSA performance. We did not find significant differences between the groups on the Bender-Gestalt scores, previous experience with video game, arousal, and mood. Our results suggest that a novel 3D virtual reality tool such as the MSA can potentially discriminate motor function of people with and without LD; however, the difference between both may also be due to a lack of problem-solving ability in LD.     

Partial symmetry breaking by local search in the group

The presence of symmetry in constraint satisfaction problems can cause a great deal of wasted search effort, and several methods for breaking symmetries have been reported. In this paper we describe a new method called Symmetry Breaking by Nonstationary Optimisation, which interleaves local search in the symmetry group with backtrack search on the constraint problem. It can be tuned to break each symmetry with an arbitrarily high probability with high runtime overhead, or as a lightweight but still powerful method with low runtime overhead. It has negligible memory requirement, it combines well with static lex-leader constraints, and its benefit increases with problem hardness.     

Efficient deployment quality analysis for intrusion detection in wireless sensor networks

The intrusion detection in a Wireless Sensor Network is defined as a mechanism to monitor and detect any intruder in a sensing area. The sensor deployment quality is a critical issue since it reflects the cost and detection capability of a wireless sensor network. The quality of deterministic deployment can be determined sufficiently by a rigorous analysis before the deployment. However, when random deployment is required, determining the deployment quality becomes challenging. In the intrusion detection application, it is necessary to define more precise measures of sensing range, transmission range, and node density that impact overall system performance. The major question is centred around the quality of intrusion detection in WSN, how we can guarantee that each point of the sensing area is covered by at least one sensor node, and what a sufficient condition to guarantee the network connectivity? In this paper, we propose an appropriate probabilistic model which provides the coverage and connectivity in k-sensing detection of a wireless sensor network. We have proved the capability of our approach using a geometric analysis and a probabilistic model.     

Structural, optical and electrical properties of zinc oxide thin films deposited by a spray pyrolysis technique

Zinc oxide (ZnO) thin films were deposited on glass substrates by spray pyrolysis technique decomposition of zinc acetate dihydrate in an ethanol solution with 30 mL of deposition rate, the ZnO thin films were deposited at two different temperatures: 300 and 350 ℃. The substrates were heated using the solar cells method. The substrate was R217102 glass, whose size was 30 × 17.5 × 1 mm³. The films exhibit a hexagonal wurtzite structure with a strong (002) preferred orientation. The higher value of crystallite size is attained for sprayed films at 350 ℃, which is probably due to an improvement of the crystallinity of the films at this point. The average trans mittance of obtain films is about 90%-95%, as measured by a UV-vis analyzer. The band gap energy varies from 3.265 to 3.294 eV for the deposited ZnO thin film at 300 and 350 ℃, respectively. The electrical resistivity measured of our films are in the order 0.36 Ω·cm.     

Opportunistic Channel Selection Strategy for Better QoS in Cooperative Networks with Cognitive Radio Capabilities

Mission-oriented MANETs are characterized by implicit common group objectives which make inter-node cooperation both logical and feasible. We propose new techniques to leverage two optimizations for cognitive radio networks that are specific to such contexts: opportunistic channel selection and cooperative mobility. We present a new formal model for MANETs consisting of cognitive radio capable nodes that are willing to be moved (at a cost). We develop an effective decentralized algorithm for mobility planning, and powerful new Altering and fuzzy based techniques for both channel estimation and channel selection. Our experiments are compelling and demonstrate that the communications infrastructure-specifically, connection bit error rates-can be significantly improved by leveraging our proposed techniques. In addition, we find that these cooperative/opportunistic optimization spaces do not trade-off significantly with one another, and thus can be used simultaneously to build superior hybrid schemes. Our results have significant applications in high-performance mission-oriented MANETs, such as battlefield communications and domestic response & rescue missions.     

Mean-based error measures for intermittent demand forecasting

To compare different forecasting methods on demand series, we require an error measure. Many error measures have been proposed, but when demand is intermittent some become inapplicable because of infinities, some give counter-intuitive results, and there is no agreement on which is best. We argue that almost all known measures rank forecasters incorrectly on intermittent demand series. We propose several new error measures with almost no infinities, and with correct forecaster ranking on several intermittent demand patterns. We call these ‘mean-based’ error measures because they evaluate forecasts against the (possibly time-dependent) mean of the underlying stochastic process instead of point demands.