Task and reference spaces are important communication channels for remote collaboration. However, all existing systems for sharing these spaces have an inherent weakness: they cannot share arbitrary physical and digital objects on arbitrary surfaces. We present IllumiShare, a new cost-effective, light-weight device that solves this issue. It both shares physical and digital objects on arbitrary surfaces and provides rich referential awareness. To evaluate IllumiShare, we studied pairs of children playing remotely. They used IllumiShare to share the task-reference space and Skype Video to share the person space. The study results show that IllumiShare shared the play space in a natural and seamless way. We also found that children preferred having both spaces compared to having only one. Moreover, we found that removing the task-reference space caused stronger negative disruptions to the play task and engagement level than removing the person space. Similarly, we found that adding the task-reference space resulted in stronger positive disruptions.

Sleep is an important part of our lives which affects many life factors such as memory, learning, metabolism and the immune system. Researchers have found correlations between sleep and several diseases such as Chronic Obstructive Pulmonary disease, Chronic Heart Failure, Alzheimer's disease, etc. However, sleep data is mainly recorded and diagnosed in sleep labs or in hospitals for some critical cases with high costs. In this work we develop a non-invasive, wearable neck-cuff system capable of real-time monitoring and visualization of physiological signals. These signals are generated from various sensors housed in a soft neck-worn collar and sent via Bluetooth to a cell phone which stores the data. This data is processed and reported to the user or uploaded to the cloud and/or to a local PC. With this system we are able to monitor people's sleep continuously in a non-invasive and low cost method while at the same time collect a large database for sleep data which may benefit future advances in new findings and possibly enable a diagnosis of other diseases. We show as one of the applications of our system the possible detection of obstructive sleep apnea which is a common sleep disorder.

Hermod is an interactive behavioral synthesis program developed at Stanford University. With the use of a combined control and data flow graph (C /DFG) as an intermediate representation, Hermod generates functional blocks and their interconnection from behavioral descriptions. Hermod supports a menu-driven interface, displaying the control and data flow graph with a set of legitimate timing-cuts and its hardware representation. Emphasizing user participation, the system allows the user to control state partitioning and resource sharing through a graphical interface to explore the maximal design space. Written in an object-oriented language C ++, Hermod generates a hardware representation in several minutes from a behavioral description of practical size on a VAXstation II /GPX.

In an effort to speed up simulation, a novel algorithm,, called incremental simulation evaluates the circuit components that can be affected directly or indirectly by design changes, utilizing the information generated during the previous simulation to reduce the number of component evaluations to a minimum. The authors describe the design and implementation of the incremental algorithm for logic or functional simulation, which substantially improves the run-time performance over existing simulators by using the incremental property of the hardware design process.>

This paper presents a new distributed multifrontal sparse matrix decomposition algorithm suitable for message passing parallel processors. The algorithm uses a nested dissection ordering and a multifrontal distribution of the matrix to minimize interprocessor data dependencies and overcome the communication bottleneck previously reported for sparse matrix decomposition [1]. Distributed multifrontal forward elimination and back substitution algorithms are also provided. Results of an implementation on the Intel iPSC are presented. Up to 16 processors are used to solve systems with as many as 7225 equations. With 16 processors, speedups of 10.2 are observed and the decomposition is shown to achieve 67 percent processor utilization. This work was motivated by the need to reduce the computational bottleneck in the Stanford PISCES [2] device simulator; however, it should be applicable to a wide range of scientific and engineering problems

In this paper we propose a new algorithm for optimal PLA folding based on a graph theoretic formulation. An efficient best-first search (BFS) algorithm is presented which finds a near-optimal PLA folding. The proposed algorithm first constructs the longest paths on the associated disjoint graph generated from the PLA personality matrix, and then extracts the ordered folding sets from the constructed paths. The algorithm is shown to be effective for most test cases.