Design And Simulation Of Heterogeneous

Complex control systems are heterogeneous from both an implementation and a modeling perspective. Design and simulation environments for such systems need to integrate different component interaction styles, like differential equations, discrete events, state machines, dataflow networks, and real-time scheduling. This paper motivates the use of Ptolemy II software environment for modeling and simulation of heterogeneous control systems. Ptolemy II advocates a component-based design methodology, and hierarchically integrates multiple models of computation, which can be used to capture different design perspectives. A Furuta pendulum control system is used as a motivating example. After designing a three-mode hybrid controller under idealized assumptions, implementation effects, like real-time scheduling and network protocols, are taken into consideration to achieve a more realistic simulation. The 3D animation package in Ptolemy II helps designers to visualize the control results. In this process of refining the design, components modeled in early phases can be reused. Copyright 2001 IFAC Keywords: Simulators, real-time computers, embedded systems 1

Channel Normalisation

Human auditory perception is perfectly capable to deal with time-invariant linear filter effects, such as those introduced by telephone handsets and telephone channels. We compared two different schemes for modeling human auditory time-frequency masking: RASTA filtering and the dynamic cepstrum representation (DCR). We used a small set of context-independent phone hidden Markov models for a recognition task of connected digit strings over the telephone. We found that RASTA filtering outperformed the Gaussian DCR approach, despite the fact that RASTA represents a more crude approximation of human forward masking. Our results may be influenced by the choice of the mel-frequency cepstral representation that we used. The superiour performance of the RASTA technique may also be explained by the fact that the frequency response of the RASTA filter is better matched to the region of modulation frequencies where human auditory perception is most sensitive

PARTICLE METROPOLIS HASTINGS USING LANGEVIN DYNAMICS

Particle Markov Chain Monte Carlo (PMCMC) samplers allow for routine inference of parameters and states in challenging nonlinear problems. A common choice for the parameter proposal is a simple random walk sampler, which can scale poorly with the number of parameters. In this paper, we propose to use log-likelihood gradients, i.e. the score, in the construction of the proposal, akin to the Langevin Monte Carlo method, but adapted to the PMCMC framework. This can be thought of as a way to guide a random walk proposal by using drift terms that are proportional to the score function. The method is successfully applied to a stochastic volatility model and the drift term exhibits intuitive behaviour