In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.
The synchronization of chaos is a well-known topic which attracted the attention of the scientific community in the last two decades. However, the robustness of the synchronous state has been not widely studied, especially considering real cases in which the effects introduced by the physical channel through which chaotic circuits interact, may deeply influence the quality of synchronization and even the onset of it. In this paper, the synchronization of two chaotic circuit coupled through a non– ideal channel is investigated. In particular, the effects of channels introducing a frequency–independent or frequency–dependent time–delay are investigated. Furthermore, two different design strategies to obtain a linear compensation block able to compen- sate the considered channel effects are presented and the recovery of the synchronous state is discussed.
Self-organizing systems arise in many different fields. In this work we analyze data from social and biological systems. A central question is to demonstrate the presence of the determinism in time-series extracted from such systems that appear apparently not correlated but that are two good benchmarks for the study of complexity in real systems. We will apply the Kaplan test and we will define an order parameter for the biological data to characterize the complexity of the system.
In this work, the collective behavior of Artemia Salina is studied both experimentally and theoretically. Several experiments have been designed to investigate the Artemia motion under different environment conditions. From the results of such experiments, a strategy to control the direction of motion of an Artemia population, by exploiting their sensitivity to light, has been derived and then implemented.