Habib M. Fardoun

A mismatched membership function approach to sampled-data stabilization for T-S fuzzy systems with time-varying delayed signals

Abstract This paper is concerned with the issue of sampled-data stabilization for T-S fuzzy systems with time-varying delays, where the mismatched membership function (MMF) approach is proposed. The superiority of the proposed method lies that delay interval is split into flexible terminals, and newly Lyapunov–Krasovskii function (LKF) is constructed. By employing the Wirtinger inequality and extended Jensen inequality, some sufficient conditions of the sampled-data control for T-S fuzzy systems are established, which are efficiently solved by using standard available numerical packages. Finally, the superiority of achieved result is illustrated by the truck-trailer model.

Virtual Rehabilitation for Multiple Sclerosis Using a Kinect-Based System: Randomized Controlled Trial

Background The methods used for the motor rehabilitation of patients with neurological disorders include a number of different rehabilitation exercises. For patients who have been diagnosed with multiple sclerosis (MS), the performance of motor rehabilitation exercises is essential. Nevertheless, this rehabilitation may be tedious, negatively influencing patients’ motivation and adherence to treatment. Objective We present RemoviEM, a system based on Kinect that uses virtual reality (VR) and natural user interfaces (NUI) to offer patients with MS an intuitive and motivating way to perform several motor rehabilitation exercises. It offers therapists a new motor rehabilitation tool for the rehabilitation process, providing feedback on the patient’s progress. Moreover, it is a low-cost system, a feature that can facilitate its integration in clinical rehabilitation centers. Methods A randomized and controlled single blinded study was carried out to assess the influence of a Kinect-based virtual rehabilitation system on the balance rehabilitation of patients with MS. This study describes RemoviEM and evaluates its effectiveness compared to standard rehabilitation. To achieve this objective, a clinical trial was carried out. Eleven patients from a MS association participated in the clinical trial. The mean age was 44.82 (SD 10.44) and the mean time from diagnosis (years) was 9.77 (SD 10.40). Clinical effectiveness was evaluated using clinical balance scales. Results Significant group-by-time interaction was detected in the scores of the Berg Balance Scale (P=.011) and the Anterior Reach Test in standing position (P=.011). Post-hoc analysis showed greater improvement in the experimental group for these variables than in the control group for these variables. The Suitability Evaluation Questionnaire (SEQ) showed good results in usability, acceptance, security, and safety for the evaluated system. Conclusions The results obtained suggest that RemoviEM represents a motivational and effective alternative to traditional motor rehabilitation for MS patients. These results have encouraged us to improve the system with new exercises, which are currently being developed.

Online Social Networks Impact in Secondary Education

This paper presents and analyzes the potential uses and motivations of online social networks in education, with special emphasis on secondary education. First, we show several previous researches supporting the use of social networking as an educational tool and discuss Edmodo, an educative online social network. The work carried out during two academic years with senior students of primary and secondary schools is also analyzed. This research has allowed us to see the reality of social network use among young people and identify the challenges of its application to education environment.

Interactive Learning Panels

New sensing technologies, as RFID readers, are being incorporated into mobile devices to provide users with new interaction experiences. And, the combinations of these new technologies open up new challenging application scenarios. One of the areas that could exploit this potential is the design of interactive solutions for context-aware applications applied to learning environments. This article describes an m-learning environment that is enriched with new interaction features that are, or may, be provided by actual or future mobile device technologies. The proposed gesture based interface allows users to relate ideas and concepts through the improvement of traditional methods. This environment is based on the reuse of existent physical resources, such as the learning panels used in school classes. For instance maps, historical posters, timelines, and so on. These panels are improved with the low cost and widely used RFID technology that enables students to interact with them through mobile devices, encouraging the interest the students applying the constructivist education theory.

Personalizable smartphone application for detecting falls

A personalizable fall detector system is presented in this paper. It relies on a semisupervised novelty detection technique and has been implemented in a smartphone application. Thus, it has been tested that the algorithm can run comfortably in this kind of devices. Details about the internal structure of the application and a preliminary evaluation are also shown. The main difference with previous approaches relies in the fact that semisupervised techniques only require activities of daily life for its operation. Departures from normal movements are considered as falls. In this way, no simulated falls are needed, except for testing the performance. Therefore, the system can be easily adapted to each user.

Usability Heuristics: Reinventing the Wheel?

Heuristic evaluation is a well-known and widely accepted usability evaluation method. When performing a heuristic evaluation, generic or specific heuristics may be used. But forming heuristic evaluators may be a challenging task. The paper presents a study that evaluates the perception of (novice) evaluators on Nielsen’s usability heuristics. A standard survey was applied in five experiments.

MoNGEL: monotonic nested generalized exemplar learning

In supervised prediction problems, the response attribute depends on certain explanatory attributes. Some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. In this paper, we aim at formalizing the approach to nested generalized exemplar learning with monotonicity constraints, proposing the monotonic nested generalized exemplar learning (MoNGEL) method. It accomplishes learning by storing objects in

Exponential synchronization for a class of impulsive networks with time-delays based on single controller

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