mathematically by defining an objective function, a measure of performance for any possi-ble movement. The unique trajectory which yields the best performance is determined using dynamic optimization the-ory. In the work presented here, the objective function is the square of the magnitude of jerk (rate

The theory of Optimal Unsupervised Motor Learning shows how a network can discover a reduced-order controller for an unknown nonlinear system by representing only the most significant modes. Here, I extend the theory to apply to command sequences, so that the most significant components discovered

, the overhead of the protocol is quite low, falling to just 1 % of total data packets transmitted for moderate movement rates in a network of 24 mobile hosts. In all cases, the difference in length between the routes used and the optimal route lengths is negligible, and in most cases, route lengths

We study the problem of finding optimal space-time trajectories for two factory cranes or hoists that move along a single overhead track. Each crane is a assigned a sequence of pickups and deliveries at specified locations that must be performed within given time windows. The cranes must

A mobile ad hoc network consists of wireless hosts that may move often. Movement of hosts results in a change in routes, requiring some mechanism for determining new routes. Several routing protocols have already been proposed for ad hoc networks. This paper suggests an approach to utilize

al pli al E of hy

We study the problem of finding optimal space-time trajectories for two fac-tory cranes or hoists that move along a single overhead track. Each crane is assigned a sequence of pickups and deliveries at specified locations that must be performed within given time windows. The cranes must be operated

We study a class of optimal stochastic control problems arising from the control of movements. Exact solutions are first presented for linear cases for both the during- and post-movement control problem, depending on a parameter α> 0. It is found that for the Langevin type equation

distances at vary-ing rates, using only the finger, hand, ur forearm. Based on their performance, we identified the optimal aniplitirde and fre-quency of movement for each limb segment. Then we allowed the siibjects to use the finger, hand, and forearm Iiowever they wished. We demonstrate that the relative

network, an I/O interface, and a custom node controller called MAGIC. The MAGIC chip handles all communication both within the node and among nodes, using hardwired data paths for efficient data movement and a programmable processor optimized for executing protocol operations. The use of the protocol