The Center for Research in Engineering, Media and Performance (REMAP) at the University of California at Los Angeles (UCLA), has collaborated with the NSF Center for Embedded Networked Sensing (CENS) to deploy a metropolitan scale Wi-Fi mesh network in the Los Angeles State Historic Park near Downtown Los Angeles. We refer to this network as Met-Wi. This will be an extension of the University’s...

This paper examines the application of optimization and control ideas to the formulation of feature-preserving numerical methods, with particular emphasis on the conservative and bound-preserving remap (constrained interpolation) and transport (advection) of a single scalar quantity. We present a general optimization framework for the preservation of physical properties and relate the recently ...

The portion of space that closely surrounds our body parts is termed peripersonal space, and it has been shown to be represented in the brain through multisensory processing systems. Here, we tested whether voluntary actions, such as grasping an object, may remap such multisensory spatial representation. Participants discriminated touches on the hand they used to grasp an object containing task...

ÐCurrent multilevel repartitioning schemes tend to perform well on certain types of problems while obtaining worse results for other types of problems. We present two new multilevel algorithms for repartitioning adaptive meshes that improve the performance of multilevel schemes for the types of problems that current schemes perform poorly while maintaining similar or better results for those pr...

In this paper, we brieey describe REMAP, an approach for the training and estimation of posterior probabilities, and report its application to speech recognition. REMAP is a recursive algorithm that is reminiscent of the Expectation Maximization (EM) 5] algorithm for the estimation of data likelihoods. Although very general, the method is developed in the context of a statistical model for tran...