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The single biometric system may be inadequate for passive authentication either because of noise in data samples or because of unavailability of a sample at a given time. In order to overcome the limitation of the single biometric, a multiple biometric modalities are used. In this paper, palmprint and iris are integrated in order to construct an efficient multibiometric recognition system based on matching score level fusion and by using the technique of sum of score. This system tries to improve the recognition results of single biometric systems. The features of Iris and palmprint are extracted using Log Gabor filter. The Hamming Distance is used for matching of Iris or palmprint feature vector. We have evaluated the proposed scheme and we have compared it with the results of the single biometric using the same data. The experimental results showed that the designed system achieves an excellent recognition rate and provides more security than single biometric system.

Background: The need for a diagnosis today, becomes a necessity for variable speed AC drives in several industrial applications. An important research axis is oriented towards monitoring the state of the converter supplying the electric motor. Indeed, the voltage source inverter is likely to have switching faults. Therefore, an emergency stop of the motor drive must be done. Objective: After reviewing related patents and works, the objective of this paper is to identify the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. Methods: The proposed approach is a simple threshold fault classification method applied to fault diagnosis of a direct torque control (DTC) induction motor drive using the stator Concordia mean current vector. With a fault occurrence, a localization domain consisting of seven patterns is constructed. Results: Simulated results on 1.5-kW induction motor drive show the effectiveness of the proposed approach with a good classification performance. Conclusion: The classification performance of our simple diagnosis system is acceptable for one fault occurrence compared to others methods. Faulty switch detection and identification is completed within a few periods of current. Using intelligent technique should improve classification performances for multiple faults occurrence.

Background: The study of induction motor behavior during abnormal conditions due to the presence of faults and the possibility to diagnose these abnormal conditions has been a challenging topic for many electrical machine researchers. Objective: Direct Torque Control (DTC) is applied to the control of an induction motor in healthy and an open circuit fault in the PWM three phase voltage fed inverter. Neural DTC is developed and used to improve the dynamic behavior of the drive system under faulty switch occurrence. Methods: The validity of the proposed control scheme is tested under normal conditions and switching fail in the Voltage Source Inverter (VSI) caused by an open circuit. Through a simulation testing of an induction motor drive system at different speed references, a comparison between basic DTC and Neural DTC is performed. Results: Simulated results on 1.5-kW induction motor drive show the performance of the proposed control in normal and faulty cases. The stator current, flux, torque, and speed at different references are determined and compared in the above techniques using MATLAB-SIMULINK. Conclusion: A Neural Network (NN) DTC control system under an open switch fault is proposed without the need for classical switching table. The use of hybrid intelligent techniques aims to improve the DTC performances in case of multiple faults occurrence.