The constant v/f control method is one of the most common speed control methods for Induction motors (IMs). In this paper the performance of constant v/f control method is improved by full compensation of the stator resistance voltage drop by the injection of low frequency boost voltage to achieve the rated torque speed characteristic at any speed below rated speed. Also simple frequency compensation based on estimation of air-gap power and a linear motor torque speed approximation is introduced. The dynamic performance of IM for proposed system is studied by MATLAB/SIMULINK under different load and speed variations. Further the proposed system is compared with the previous work. The simulation results show that the speed accuracy of the proposed method is improved effectively, even at low speed.

The constant v/f control method is one of the most common speed control methods for Induction motors (IMs). In this paper the performance of constant v/f control method is improved by full compensation of the stator resistance voltage drop by the injection of low frequency boost voltage to achieve the rated torque speed characteristic at any speed below rated speed. Also simple frequency compensation based on estimation of air-gap power and a linear motor torque speed approximation is introduced. The dynamic performance of IM for proposed system is studied by MATLAB/SIMULINK under different load and speed variations. Further the proposed system is compared with the previous work. The simulation results show that the speed accuracy of the proposed method is improved effectively, even at low speed.

In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto-regressive model. Two algorithms are implemented for auto-regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.