This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effectiveness of the proposed algorithm for EMG signal measurement was validated by a portable EMG system developed as a part of the EU research project and EMG raw measurement sets. Examples of removing the parasitic interferences are presented for each stage of signal processing. Finally, it is shown that the proposed processing of EMG signals enables cleaning of the EMG signal with minimal loss of the diagnostic content.

The reduction or elimination of selected torque and current harmonics is widely used in multilevel inverter fed induction motor drives. A wide range of methods are employed to achieve this goal. Those modes of operation allow the avoidance of certain torque and current harmonics that may lead to disturbance and even mechanical and electrical resonance. In this paper, the selective harmonic elimination technique (SHE-PWM) is employed to reduce the chosen torque harmonics in a three-level NPC inverter-fed induction motor drive. The proposed method is compared with the synchronized carrier pulse width modulation technique (SCPWM). For both schemes of control simulation, a model is proposed using Simulink software. Furthermore, laboratory stand experiments are carried out to verify the correctness of torque harmonics formation.

No abstract

No abstract

No abstract