Electromyography (EMG) signals are muscles that enable the identification of human movements without need complex kinematics calculations. Researchers prefer EMG as input to control prosthetic arms and exoskeleton robots. However, proper algorithm classify from raw has been an interesting challenging topic researchers. Various studies have carried out produce EMG-based movement classification g...

Deep learning algorithms have been shown to be powerful in many communication network design problems, including that automatic modulation classification. However, they are vulnerable carefully crafted attacks called adversarial examples. Hence, the reliance of wireless networks on deep poses a serious threat security and operation networks. In this letter, we propose for first time countermeas...

The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to spectrum is becoming increasingly more important. ever-growing wireless traffic increasing scarcity of available warrants efficient management spectrum. At same time, machine learning (ML) ubiquitous found applications in many fi...

this paper aims to propose an effective paroxysmal atrial fibrillation (paf) predictor which is based on the analysis of the heart rate variability (hrv) signal. predicting the onset of paf, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize the risks for the patients. this method consists of four st...