This paper presents a collision warning system that uses laser radar to measure the distance to a preceding vehicle and issues an audible warning to alert the driver if a safe headway is not maintained. The laser radar system is of the cooperative type in that it detects light reflected from a reflex reflector attached at the rear of other vehicles. With a 10-watt pulsed laser, a maximum detection range of over 100m is obtained. The construction and operation of the collision warning system are described along with the configuration of the optical system used in the laser radar head and the results of detection performance evaluations.

Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.