Abstract
A new technique for designing filters with long time constants in the discrete time domain is presented. The F&H (filter and hold) methodology halts the state of a continuous time filter every T seconds resulting in a filter implementation with time constants that can be controlled in three distinct ways: by the sampling period T, the duty cycle k = t/T or the time constant of the continuous time filter prototype. The final filter can be constructed from a typical Gm-C technique with very low power consumption.

Abstract
A sensor based on a Sagnac topology in reflection is described. The sensor uses the properties of its frequency response in order to obtain a self-referenced signal proportional to the measured signal. The sensor is a versatile structure for the monitoring of static and dynamic physical parameters, which require a small number of optical components and simple signal processing.

Abstract
A procedure for parameter estimation of multicomponent Polynomial Phase Signals is presented. This scheme, while restricted to high SNR's, has the advantage of being extremely simple. It is also insensitive to the equal-coefficient identifiability problem of HAF (High-order Ambiguity Function) based methods. It poses, however, some restrictions on the component amplitudes. Its performance in noise is investigated, and confirmed with several examples.

Abstract
The failure of the traditional definition of instantaneous frequency (IFt) in the multicomponent case has been often reported, We will determine the reasons for failure of this definition, That will enable us to understand and integrate all previously reported cases in a simple unified theory. As a direct consequence, we will be able to extrapolate and predict the behavior of the traditional definition for any type of multicomponent signal.

Abstract
We present the architecture of a parallel programming system, the di_pSystem. Our target machine consists of clusters of multiprocessors interconnected with very fast networks. The system aims to provide a programming style close to the shared memory programming model. This is achieved by a software layer between the programmer and the operating system that supports the communication between individual computational agents and that dynamically balances the work-load in the system. This layer effectively hides much of the complexity of programming distributed architectures from the programmer while being competitive in performance. The low-level communication in the di_pSystem is implemented using MPI as a backbone. Initial results indicate that the system is close in performance to MPI, a fact that we attribute to its ability to dynamically balance the work-load in the computations. © Springer-Verlag Berlin Heidelberg 1999.

Abstract
Using problem-specific background knowledge, computer programs developed within the framework of Inductive Logic Programming (ILP) have been used to construct restricted first-order logic solutions to scientific problems. However, their approach to the analysis of data with substantial numerical content has been largely limited to constructing clauses that: (a) provide qualitative descriptions ("high", "low" etc.) of the values of response variables; and (b) contain simple inequalities restricting the ranges of predictor variables. This has precluded the application of such techniques to scientific and engineering problems requiring a more sophisticated approach. A number of specialised methods have been suggested to remedy this. In contrast, we have chosen to take advantage of the fact that the existing theoretical framework for ILP places very few restrictions of the nature of the background knowledge. We describe two issues of implementation that make it possible to use background predicates that implement well-established statistical and numerical analysis procedures. Any improvements in analytical sophistication that result are evaluated empirically using artificial and real-life data. Experiments utilising artificial data are concerned with extracting constraints for response variables in the text-book problem of balancing a pole on a cart. They illustrate the use of clausal definitions of arithmetic and trigonometric functions, inequalities, multiple linear regression, and numerical derivatives. A non-trivial problem concerning the prediction of mutagenic activity of nitroaromatic molecules is also examined. In this case, expert chemists have been unable to devise a model for explaining the data. The result demonstrates the combined use by an ILP program of logical and numerical capabilities to achieve an analysis that includes linear modelling, clustering and classification. In all experiments, the predictions obtained compare favourably against benchmarks set by more traditional methods of quantitative methods, namely, regression and neural-network.