Decision making and uncertainty management in a 3D reconstruction system

This paper presents a control structure for a general-purpose image understanding system. It addresses the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is done by an independent subsystem that uses Bayesian networks and utility theory to compute marginal value of information and selects the algorithm with the highest value of information. It is shown that the knowledge base can be acquired using learning techniques and the value-driven approach to the selection of vision algorithms leads to performance gains.     

Mode-locking optimization with a real-time feedback system in a Nd:yttrium lithium fluoride laser cavity

We present a control system, which allows an automatic optimization of the pulse train stability in a mode-locked laser cavity. In order to obtain real-time corrections, we chose a closed loop approach. The control variable is the cavity length, mechanically adjusted by gear system acting on the rear cavity mirror, and the controlled variable is the envelope modulation of the mode-locked pulse train. Such automatic control system maintains the amplitude of the mode-locking pulse train stable within a few percent rms during the working time of the laser. Full implementation of the system on an Nd:yttrium lithium fluoride actively mode-locked laser is presented.