In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

The main purpose of this article is to investigate the impact of teacher's position on students' performance in higher education. A new pedagogical approach based on collaborative learning is used due to the design of a smart learning environment (SLE). This workspace uses, respectively, information and communication technologies (ICT) and radio frequency identification (RFID)-based indoor positioning system in order to examine students' perceptions and the involvement of groups into this smart classroom. The merge of interactive multimedia system, ubiquitous computing and several handheld devices should lead to a successful active learning process. Firstly, we provide a detailed description of the proposed collaborative environment using mainly new technologies and indoor location system serving as a platform for evaluating attention. The research provides an obvious consensus on the teacher's role in assessing classroom attention. We discuss our preliminary results on how teacher's position influences essentially students' participation. Our first experiments show that the integration of novel technologies in the area of higher education is extremely promoting the traditional way of teaching. The smart classroom model has been recommended to support this evolution. As a result, the found results indicate that the teacher's position increases the learner's motivation, engagement and effective learning.

No abstract

This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. The proposed features based on image descriptors are extracted from the T-F representation of EEG signals and are considered and processed as an image using T-F image processing techniques. The proposed features include shape and texture-based descriptors and are able to describe visually the normal and seizure activity patterns observed in T-F images. The results obtained on real EEG data show that T-F image descriptor-based features achieve an overall classification accuracy of up to 98% for 100 EEG segments using one-against-one SVM classifier. The results suggest that the proposed method outperforms those methods, which employ signal features only or combined signal-image features by about 3% for 100 EEG signals.

This paper presents novel time-frequency (t-f) feature extraction approach for the classification of EEG signals for Epileptic seizure activities detection. The proposed features are based on Local Binary Patterns (LBP) descriptor extracted from t-f representation of EEG signals processed as a textured image. Compared to most previous t-f approaches were based only on features derived from the instantaneous frequency and the energies of EEG signals generated from different spectral sub-bands, the proposed t-f features are capable to describe visually the epileptic seizure activity patterns observed in t-f image of EEG signals. The results obtained on real EEG data show that the use of t-f LBP descriptor-based features achieve an overall classification accuracy up to 99% for 150 EEG signals using 2-class SVM classifier. This is confirmed by ROC curve analysis.