International Journal of Electrical and Computer Engineering 3:2 2008 Compression and Filtering of Random Signals under Constraint of Variable Memory
We study a new technique for optimal data compression subject to conditions of causality and different types of memory. The technique is based on the assumption that some information about compressed data can be obtained from a solution of the associated problem without constraints of causality and memory. This allows us to consider two separate problem related to compression and de- compression subject to those constraints. Their solutions are given and the analysis of the associated errors is provided. Keywords—stochastic signals, optimization problems in signal processing.
One of the key challenges in nanoelectronics design is the decreasing reliability due to radiation induced single-event upsets. Without detailed device level simulations or physical experimentation, circuit level models can generate misleading reliability information. We present the results from a screening experiment to identify significant parameters in circuit level SEU simulations. We show that cell supply voltage, sizing parameters, and transient waveform descriptions have an important impact on design and should therefore be considered with care in circuit level designs. Larger variations in parameters can lead to soft error rate estimates that vary by more than 4 orders of magnitude, even small variations can lead to 15times variation in soft error rate estimation for a design. We present our methodology for screening and a ranking based on significance of several parameters involved in soft error simulation at the SPICE level.