Movassaghi, S, Maleki, B, Smith, DB & Abolhasan, M 2017, 'Biologically inspired self-organization and node-level interference mitigation amongst multiple coexisting wireless body area networks', Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017, International Wireless Communications and Mobile Computing Conference, IEEE, Valencia, Spain, pp. 1221-1226.View/Download from: Publisher's site
© 2017 IEEE. This paper presents a node-level self-organizing interference avoidance scheme (SIAC) between multiple coexisting wireless body area networks (WBANs) that incorporates self-organization and smart spectrum allocation. It follows a biologically inspired approach based on the theory of pulse-coupled oscillators for self-organization. The proposed scheme makes three major contributions as compared to the current literature. Firstly, it considers node-level interference for internetwork interference mitigation rather than considering each WBAN as a whole. Secondly, it allocates synchronous and parallel transmission intervals for interference avoidance in an optimal manner and dynamically adapts to changes in their coexistence. Finally, it achieves collision-free, self-organized communication with only information of the firing signal of each WBAN and does not require a global coordinator to manage its communications. It operates on a nodes traffic priority, signal strength, and density of sensors in a WBAN. Simulation results show that our proposal achieves a fast convergence time despite the little information it receives. Moreover, SIAC is shown to be robust to variations in signal strength, number of coexisting WBANs and number of sensor nodes within each WBAN.
Maleki, B, Ebrahimnezhad, H, Xu, M & He, X 2015, 'Hand Gesture Recognition for a Virtual Mouse Application Using Geometric Feature of Finger's Trajectories', Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, International Conference on Internet Multimedia Computing and Service, ACM, China.View/Download from: Publisher's site
We aim to enable a computer to comprehend and perform the mouse functions by analyzing a video with hand motions. For this purpose, dynamic gestures are captured by a web cam and are recognized as pre-defined gestures which are used to suggest mouse functions. The proposed algorithm initially detects the hand. Then, it tracks fingertips' trajectories within a frame sequence. Finally, hand gestures are recognized through computing a set of proposed geometric features of fingers' trajectories and comparing with our collected gestures dataset. In this paper, four types of descriptors are defined for a dynamic gesture. Each descriptor includes different number of features, which compose a feature vector with 135 dimensions. Different classification algorithms (e.g. KNN, LDA, Naïve Bayes and SVM) are applied to compare the detection results. The minimal misclassification error rate (MCR) reaches about 4% (i.e. Correct Recognition rate of 96%). Furthermore, we applied Principle Component Analysis (PCA) to reduce the number of features. With 30 dimensional features (principle components), LDA classifier can achieve about 0.09% misclassification error rate.