Teaching robust programming

Robust programming aims to prevent abnormal termination or unexpected actions and requires code to handle bad (invalid or absurd) inputs in a way that is consistent with the developer's intent. For example, if an internal error occurs, the program might terminate gracefully rather than simply failing, providing enough information for the programmer to debug the program and avoiding giving the user additional access or information. This article focuses on teaching these principles.     

Pseudorandom active UWB reflectors for accurate ranging

In this letter, the concept of pseudorandom active reflector, based on the ultra-wideband (UWB) technology, is introduced. It consists of a simple device that repeats a slightly delayed version of the received UWB signal only in certain time intervals according to a suitable pseudorandom time-hopping sequence. An example of application of this device for accurate ranging in precise location systems is given. The advantages of this solution are in the hardware simplicity (only the analog section is present), in the low power consumption of the reflector and in the low timing constraint regarding the relative transmitter and reflector clock rates.     

Low Temperature Physics at the Laboratoire National des Champs Magnétiques Pulsés in Toulouse

An overview over past and present activities and future developments at the Toulouse pulsed magnetic field facility is given, both as far as technical developments of the infrastructure, as well as low temperature physics performed at the LNCMP are concerned.     

Volumetric Change and Surface Properties of Temperature-Sensitive Polyvinylalcohol (PVA) Hydrogel

Polyvinylalcohol (PVA) polymer gel is a temperature sensitive polymeric gel, with a critical transition temperature (with H₂O) of 310 K. At higher than 310 K, this temperature sensitive polymer gel shrinks because of discharging water, whereas at lower temperatures, the gel swelled because of absorbing water. The reversibility of the gel's volume change was confirmed by temperature swing. The adsorption behavior of an organic compound onto the PVA polymer gel in water was tested at various temperatures. The amount of adsorbed organic compound increased remarkably at temperatures higher than about 310 K. Then, it was confirmed that the organic compound in PVA polymer gel could be reversibly adsorbed and desorbed by a temperature change between 293 and 323 K. The mechanism of adsorption of the organic compound onto the PVA polymer gel could be explained by hydration and dehydration of polymer gel.     

Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.     

Modification of ASM3 for the determination of biomass adsorption/storage capacity in bulking sludge control.

The selector activated sludge (SAS) systems are known to prevent excessive growth of filamentous microorganisms responsible for bulking sludge, but these systems were hardly ever modelled. This study aimed to develop a model capable of predicting rapid substrate removal in the SAS systems. For this purpose, the Activated Sludge Model No. 3 (ASM3) was extended with three processes (adsorption, direct growth on the adsorbed substrate under aerobic or anoxic conditions). The modified ASM3 was tested against the results of batch experiments with the biomass originating from two full-scale SAS systems in Germany. The endogenous biomass was mixed with various readily biodegradable substrates (acetate, peptone, glucose and wastewater) and the utilisation of substrate (expresses as COD) and oxygen uptake rates (OURs) were measured during the experiments. In general, model predictions fitted to the experimental data, but a considerable number of kinetic (5) and stoichiometric (2) parameters needed to be adjusted during model calibration. The simulation results revealed that storage was generally a dominating process compared to direct growth in terms of the adsorbed substrate utilisation. The contribution of storage ranged from 65-71% (Plant A) and 69-92% (Plant B).