Ultrafast non-linear optical properties of Ge20As25Se55 chalcogenide films

The non-resonant third-order non-linear optical properties of amorphous Ge20As25Se55 films were studied experimentally by the method of the femtosecond optical heterodyne detection of optical Kerr effect. The real and imaginary parts of complex third-order optical non-linearity could be effectively separated and their values and signs could be also determined, which were 6.6 x 10(-12) and -2.4 x 10(-12) esu, respectively. Amorphous Ge20As25Se55 films showed a very fast response in the range of 200 fs under ultrafast excitation. The ultrafast response and large third-order non-linearity are attributed to the ultrafast distortion of the electron orbitals surrounding the average positions of the nucleus of Ge, As and Se atoms. The high third-order susceptibility and a fast response time of amorphous Ge20As25Se55 films makes it a promising material for application in advanced techniques especially in optical switching. (c) 2005 Elsevier B.V. All rights reserved.

Non-resonant third-order non-linear optical properties of amorphous GeSe2 film

The real and imaginary parts of third-order susceptibility of amorphous GeSe2 film were measured by the method of the femtosecond optical heterodyne detection of optical Kerr effect at 805 nm with the 80 fs ultra fast pulses. The results indicated that the values of real and imaginary parts were 8.8 x 10(-12) esu and -3.0 x 10(-12) esu, respectively. An amorphous GeSe2 film also showed a very fast response within 200 fs. The ultra fast response and large third-order non-linearity are attributed to the ultra fast distortion of the electron orbits surrounding the average positions of the nucleus of Ge and Se atoms. (c) 2005 Elsevier B.V. All rights reserved.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.