Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

Guidelines for evaluating the ease of reconfiguration of manufacturing systems

This paper presents guidelines for evaluating the ease of reconfiguration of manufacturing systems. Based reconfigurability measurement tools proposed in the past [1], [2]and a reconfiguration process model, the paper proposes a method that can be used to assess different system characteristics. After the guideline is presented, an example of how the method can be applied to a batch processing system is given. It is found that the proposed method can be applied to batch processing systems based on ISA S88 batch server commercial software. ©2008 IEEE.