A method is presented for detecting impulsive noises in chaotic time series, based on a new nonlinear prediction algorithm. A multi-dimensional trajectory is reconstructed from a time series using delay coordinates. The future value of a point on the trajectory is predicted using a local approximation technique revised by adding the Biweight estimation method and then the prediction error is calculated. Impulsive noises are detected by examining the prediction errors for all points on the trajectory. The proposed method is applied to the time series of the pupil area and the refractive power of the lens in the human eye. The Lyapunov exponent analysis for thses time series is conducted. As a result, it is shown that the proposed method is effective in detecting impulsive noises caused by blinking in these time series.

This paper assesses the performance of the handoff algorithm based on distance and RSSI measurements in a multi-cellular environment by computer simulation. The algorithm performs a handoff if handoff initiation conditions, handoff possible conditions, and handoff selective conditions are met. The performance criteria are based on the average number of handoffs, the crossover points and the average number of outages. Numerical results are presented to demonstrate the feasibility of the algorithm. The performance of the distance-assisted handoff algorithm is compared with that of a conventional algorithm that utilizes signal strength alone. Overall, the distance-assisted algorithm exhibits higher performance in average number of handoffs and the crossover points, yet exhibits a higher number of outages on average than the conventional algorithm.