In this paper a method for footstep detection and gait event identification is presented. A three-axis accelerometer can be mounted on someonepsilas foot to record the acceleration signals produced by their gait. The data can be analysed in real time or after a period of time to identify the gait events and their respective acceleration values. Data analysis consists of signal processing in order to point out some characteristics of the signals. Furthermore an algorithm is proposed for the automatic gait event identification based on these signals.

A Monte Carlo approach for maximum power estimation in CMOS very large scale integration (VLSI) circuits is proposed. The approach is based on the largely unexploited area of statistics known as extreme value theory. Within this framework, it attempts to appropriately model the extreme behavior of the probability distribution of the peak instantaneous power drawn from the power supply bus, in order to yield a close estimate of its maximum possible value. The approach features a relatively small number of necessary input patterns that does not depend on the circuit size, user-specified accuracy, and confidence levels for the final estimate, simplicity in the algorithmic implementation, noniterative single-loop execution, highly accurate simulation-based operation, and easy integration within the design flow of CMOS VLSI circuits. Experimental results establish the above claims and demonstrate the overall efficiency of the proposed approach to address the problem of maximum power estimation.