Abstract
Ethernet networks are becoming increasingly popular in industrial computercontrolled systems, as they allow for a single network protocol at both the higher and the lower levels of an industrial communication infrastructure. Despite the introduction in the early 90s of a full-duplex operating mode, numerous industrial Ethernet networks still operate in heterogeneous environments, with Ethernet Switching Hubs interconnecting both independent node stations and industrial Ethernet Repeater Hubs. Among node stations interconnected by a Repeater Hub, the network still operates in the traditional shared Ethernet mode; that is, collisions are solved by means of a probabilistic contention resolution algorithm i.e., the medium access is inherently non-deterministic. In this paper, it is analyzed an enhanced collision resolution algorithm for shared Ethernet networks, referred as high priority Binary Exponential Backoff (h-BEB). Such algorithm allows the coexistence of Ethernet standard devices together with modified (real-time) devices in the same network segment. Both the analytical and the simulation timing analysis show that the h-BEB algorithm guarantees a maximum access delay that is significantly smaller than for the case of standard Ethernet stations. Such enhanced collision resolution algorithm enables the traffic separation between standard and modified (real-time) stations, and is therefore able to guarantee a real-time communication behavior in unconstrained traffic environments.

Abstract
Muscle relaxant drugs are currently given during surgical operations. The design of controllers for the automatic control of neuromuscular blockade benefits from an individual tuning of the controller to the characteristics of the patient. A novel approach to the characterization of the neuromuscular blockade response induced by an initial bolus at the beginning of anaesthesia is proposed. This approach is based on the statistical analysis of the data using principal components and Walsh-Fourier spectral analysis. These methods provide information about the patients dynamics, allowing the on-line autocalibration of the controller, using multiple linear regression techniques. Observed and simulated data are used to compare different approaches to the characterization of the bolus response.

Abstract

Abstract
Replicated time series are a particular type of repeated measures, which consist of time-sequences of measurements taken from several subjects (experimental units). We consider independent replications of count time series that are modelled by first-order integer-valued autoregressive processes, INAR(1). In this work, we propose several estimation methods using the classical and the Bayesian approaches and both in time and frequency domains. Furthermore, we study the asymptotic properties of the estimators. The methods are illustrated and their performance is compared in a simulation study. Finally, the methods are applied to a set of observations concerning sunspot data.

Abstract
The particle swarm optimisation algorithm is proposed as a new method to design a model-based predictive greenhouse air temperature controller subject to restrictions. Its performance is compared with the ones obtained by using genetic and sequential quadratic programming algorithms to solve the constrained optimisation air temperature control problem. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-point tracking and minimisation of the control effort over a prediction horizon of I h with 1-min sampling period, for a greenhouse located in the north of Portugal. Since the controller must be able to predict the greenhouse environmental conditions over the specified time interval, it is necessary to use mathematical models that describe the greenhouse climate, as well as to predict the outside weather. These requirements are met by using auto regressive models with exogenous inputs and time series auto-regressive models to simulate the inside and outside climate conditions, respectively. These models have time variant parameters and so, recursive identification techniques are applied to estimate their values in real-time. The models employ data from the climate inside and outside the greenhouse, as well as from the control inputs. Simulations with the proposed methodology to design the model-based predictive air temperature controller are presented. The results indicate a better efficiency of the particle swarm optimisation algorithm as compared with the efficiencies obtained with a genetic algorithm and a sequential quadratic programming method.

Abstract