Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling

Efficient Aging-Aware SRAM Failure Probability Calculation via Particle Filter-Based Importance Sampling

Efficient Failure Rate Prediction for SRAM Cells via Gibbs Sampling

An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability

The estimation of system failure probabilities may be a difficult task when the values involved are very small, so that sampling-based Monte Carlo methods may become computationally impractical, especially if the computer codes used to model the system response require large computational efforts, both in terms of time and memory. This paper proposes a modification of an algorithm proposed in l...

Estimation in hidden Markov models via efficient importance sampling

CHENG-DER FUH and INCHI HU 1 Graduate Institute of Statistics, National Central University, Chongli, Taiwan, Republic of China and Institute of Statistical Science, Academia Sinica, Nakang, Taipei 115, Taiwan, Republic of China. E-mail: [email protected] 2 Department of Information and Systems Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Honh Ko...

An Efficient Target Tracking Algorithm Based on Particle Filter and Genetic Algorithm

In this paper, we propose an efficient hybrid Particle Filter (PF) algorithm for video tracking by employing a genetic algorithm to solve the sample impoverishment problem. In the presented method, the object to be tracked is selected by a rectangular window inside which a few numbers of particles are scattered. The particles’ weights are calculated based on the similarity between feature vecto...