The main purpose of this research is to move the robotic arm (5DoF) in real-time, based on the surface Electromyography (sEMG) signals, as obtained from the wireless Myo gesture armband to distinguish seven hand movements. The sEMG signals are biomedical signals that estimate and record the electrical signals produced in muscles through their contraction and relaxation, representing neuromuscular activities. Therefore, controlling the robotic arm via the muscles of the human arm using sEMG signals is considered to be one of the most significant methods. The wireless Myo gesture armband is used to record sEMG signals from the forearm. In order to analyze these signals, the pattern recognition system is employed, which consists of three main parts: segmentation, feature extraction, and classification. Overlap technique is chosen for segmenting part of the signal. Six time domain features (MAV, WL, RMS, AR, ZC, and SSC) are extracted from each segment. The classifiers (SVM, LDA, and KNN) are employed to enable comparison between them in order to obtain optimum accuracy of the system. The results show that the SVM achieves higher system accuracy at 96.57 %, compared to LDA reaching 96.01 %, and 92.67 % accuracy achieved by KNN.The electrical signal produced through contraction or relaxation of muscles which are ruled by the 3 nervous system are called Electromyography (EMG) signals. This signal depends on the physiological and anatomical characteristic of muscles and is considered to be a complex signal.The surface electromyography (sEMG) are EMG signals that collect the electrical signals of the muscle activity through placing the electrodes on the surface of the skin. Fig. 1 shows the surface electromyography (sEMG) signals that start with the low amplitude, which changes with muscle contraction activity [1].Detection of sEMG signals are useful and improve important methodologies in many applications.Such applications are becoming increasingly in demand, in spheres such as biomedical engineering[2], the robotics arm and automation control systems [3,4].The measurements and precise representations of the sEMG signals depend on the characteristics of the electrodes and their relationship with the skin of the forearm or shoulder, and are affected by the amplifier design, and the transition of the sEMG signals from analogue to digital format [5].A raw sEMG signal has the maximum voltage of (0-2) mV, and a range of frequency approximately between (0-1000) Hz, but the important frequency that contains useful information lies between (20-500) Hz [6]. The sEMG signals can be acquired by positioning surface electrodes on the arm or the shoulder.There are two main types of the electrodes that acquire sEMG signals: needle electrodes (inside the skin) and surface electrodes, with no significant variance between them [7]. There are two types of surface electrodes: wired like Myoware muscle sensor or wireless such as Myo gesture control armband. They differ in features, the most important of which is the sampling rate. All these...
Applying propagation models with good accuracy is an essential issue for increasing the capacity and improving the coverage of cellular communication systems. This work presents an algorithm to calculate total diffraction losses for multiple obstacles objects using Epstein-Peterson approach. The proposed algorithmic procedure to model the diffracting can be integrated with other propagation mechanisms in ray-tracing for the prediction of received signal level in non-line-of-sight environments. This algorithm can be interpreted into software application to scan large areas with a reasonable simulation time.
<span lang="EN-US">The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. </span>
Brain Computer Interface is a technology make a communication with the outside world via brain thoughts. The performance of the BCI system depends on the choice of approaches to process the signals of the human brain at each step. The recording signals of a human brain having bad or small signal to noise ratio (SNR) made brain patterns hard to be distinguished. So, the signal quality need to be enhanced, i.e. enhancing the SNR. The electroencephalogram (EEG) signals are composed of true signal and noise signals so that in order to have high SNR, the EEG signals should be transformed so that the undesired components (noise signal) will be isolated and the true signal will remain.Methods proposed in this paper are for preprocessing, feature extraction and classification of EEG signals (brain signals) recorded from Emotiv EPOC. The raw EEG data is preprocessed to remove noise and then is handled in order to eliminate the artifacts using Principal Component Analysis (PCA), Common Spatial Pattern (CSP), and Common Average Reference(CAR). Power Spectral Density (PSD) is computed from filtered data as a feature. Finally, Support Vector Machine method used to interpret the EEG patterns. The PCA algorithm showed good performance with a value 94.28% compared to other algorithms. KeywordsElectroencephalogram (EEG), Brain Computer Interface (BCI), Emotiv EPOC.
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