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Both von Neumann’s NAND multiplexing, based on a massive duplication of imperfect devices and randomized imperfect interconnects, and reconfigurable architectures have been investigated to come up with solutions for integrations of highly unreliable nanometre-scale devices. In this paper, we review these two techniques, and present a defect- and fault-tolerant architecture in which von Neumann’s NAND multiplexing is combined with a massively reconfigurable architecture. The system performance of this architecture is evaluated by studying its reliability, i.e. the probability of system survival. Our evaluation shows that the suggested architecture can tolerate a device error rate of up to 10−2, with multiple redundant components; the structure is efficiently robust against both permanent and transient faults for an ultra-large integration of highly unreliable nanometre-scale devices.

The Distributed ASCI Supercomputer (DAS) is a homogeneous wide-area distributed system consisting of four cluster computers at different locations. DAS has been used for research on communication software, parallel languages and programming systems, schedulers, parallel applications, and distributed applications. The paper gives a preview of the most interesting research results obtained so far in the DAS project.

Recognizing and understanding surgical high-level tasks from sensor readings is important for surgical workflow analysis. Surgical high-level task recognition is also a challenging task in ubiquitous computing because of the inherent uncertainty of sensor data and the complexity of the operating room environment. In this paper, we present a framework for recognizing high-level tasks from low-level noisy sensor data. Specifically, we present a Markov-based approach for inferring high-level tasks from a set of low-level sensor data. We also propose to clean the noisy sensor data using a Bayesian approach. Preliminary results on a noise-free dataset of ten surgical procedures show that it is possible to recognize surgical high-level tasks with detection accuracies up to 90%. Introducing missed and ghost errors to the sensor data results in a significant decrease of the recognition accuracy. This supports our claim to use a cleaning algorithm before the training step. Finally, we highlight exciting research directions in this area.