We examine the problem of planning a path through a low dimensional continuous state space subject to upper bounds on several additive cost metrics. For the single cost case, previously published research has proposed constructing the paths by gradient descent on a local minima free value function. This value function is the solution of the Eikonal partial differential equation, and efficient algorithms have been designed to compute it. In this paper we propose an auxiliary partial differential equation with which we can evaluate multiple additive cost metrics for paths which are generated by value functions; solving this auxiliary equation adds little more work to the value function computation. We then propose an algorithm which generates paths whose costs lie on the Pareto optimal surface for each possible destination location, and we can choose from these paths those which satisfy the constraints. The procedure is practical when the sum of the state space dimension and number of cost metrics is roughly six or below.

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We present a technique for synchronizing clocks over a wireless link between a pair of resource constrained nodes. A Kalman filter is used as a pre-processor to a PI controller to mitigate the effects of packet-losses and attenuate noise spikes. This Kalman filter directly tracks the skew, which is the rate of change of offset, between a pair of nodes. The PI controller accepts this skew as input and disciplines the clock on the follower node. Experimental results demonstrate the performance of this technique over a single hop. In the future, this technique can be extended to multihop systems and improve determinism in networked embedded systems.

We describe a highly practical program specializer for Java programs. The specializer is powerful, because it specializes optimistically, using (potentially transient) constants in the heap; it is precise, because it specializes using data structures that are only partially invariant; it is deployable, because it is hidden in a JIT compiler and does not require any user annotations or offline preprocessing; it is simple, because it uses existing JIT compiler ingredients; and it is fast, because it specializes programs in under 1s.These properties are the result of (1) a new algorithm for selecting specializable code fragments, based on a notion of influence; (2) a precise store profile for identifying constant heap locations; and (3) an efficient invalidation mechanism for monitoring optimistic assumptions about heap constants. Our implementation of the specializer in the Jikes RVM has low overhead, selects specialization points that would be chosen manually, and produces speedups ranging from a factor of 1.2 to 6.4, comparable with annotation-guided specializers.