Authentication in wireless networks: state of the art and integration with smart card

Wirelesslan have spread very quickly over the past few years. Demand for wireless access tolans has raised due to new mobile computing devices, such as laptops and personal digital assistants, and a desire for seamless and permanent connections to networks. Nevertheless a lot of security issues remain and stop its deployment in corporations. One of the most important issues is the authentication of a terminal to an Access Point. We propose an interface to integrate the Extensible Authentication Protocol into smart cards and will show that smart cards could constitute the de-facto device for authentication in Wirelesslan as they are forgsm and will be forumts (Universal Mobile Telecommunication System).     

Chronic measurement of the stimulation selectivity of the flat interface nerve electrode

The flat interface nerve electrode (FINE) is an attempt to improve the stimulation selectivity of extraneural electrodes. By reshaping peripheral nerves into elliptical cylinders, central fibers are moved closer to the nerve-electrode interface, and additional surface area is created for contact placement. The goals of this study were to test the hypothesis that greater nerve reshaping leads to improved selectivity and to examine the chronic recruitment properties of the FINE. Three FINEs were developed to reshape peripheral nerves to different degrees. Four electrodes of each type were implanted on the sciatic nerves of 12 cats and tested for selectivity over at least three months. There was physiologic evidence of nerve injury in two cats with the tightest cuffs, but the other animals behaved normally. All cuff types were capable of selectively activating branches of the sciatic nerve, as well as groups of fibers within branches. The electrodes that moderately reshaped the nerves demonstrated the most selectivity. Both the selectivity measurements and the recruitment curve characteristics were stable throughout the implant period. From an electrophysiological standpoint, the FINE is a viable alternative for neuroprosthetic devices. A histological analysis of the nerves is under way to evaluate the safety of the FINE.     

Transcutaneous Blood Flow Measurements Using Pseudorandom Noise Doppler System

In order to obviate the limitations of the conventional coherent ultrasonic Doppler flowmeters we have developed a new device. In this apparatus the continuous wave is replaced by a pseudorandom signal. We show, both theoretically (Waag et al. [12]) and experimentally, that the pseudorandom system offers advantages. The most important result concerns the improvement of the signal-to-noise ratio which can be as large as 20 dB. Furthermore, it is possible to decrease the frequency and the distance ambiguity by a factor of two. Compared to the "random noise flowmeter" (Newhouse et al. [7], Jethwa et al. [5]) our system measures flow near the wall of vessels or in ultrasonic field in which there are important fixed echoes. To illustrate the feasibility of this new device, we show some results obtained by the transcutaneous method.     

Mesure du Débit d’Absorption Spécifique (das) des téléphones mobiles — contribution du projetcomobio à la normalisation internationale

Human exposure to electromagnetic fields emitted by mobile phones is characterised by the Specific Absorption Rate (sar). This value corresponds to therf power absorbed by unitary mass of the equivalent tissue. It represents the basic restriction used to define limit of human exposure torf electromagnetic fields. Academic and industrial labs have contributed through thecomobio project to define international standards for mobile phones certification.     

Microstructured air-silica fibres: Recent developments in modelling, manufacturing and experiment

The main modelling methods devoted to microstructured air-silica optical fibres (MOFS) are presented and discussed. Then, the specific propagation properties ofMOFS are studied in detail. Characteristics measured on fibres manufactured in our laboratory or reported in the literature are analysed. A large number of potential and demonstrated applications are presented and the obtained performances are discussed. A particular attention is given to hollow-core photonic bandgap fibres and their applications.     

Polarization independence of a one-dimensional grating in conical mounting

We demonstrate theoretically a polarization-independent guided-mode resonant filter with only a one dimensional grating. A rigorous method, the modal method by Fourier expansion, is used to compute the diffracted efficiencies of the grating. Wave-vector analysis fails to correctly design a polarization-independent structure. We show that a rigorous analysis of the resonances must be employed to obtain such a device; using a pole approach, we study the effects of grating parameters on the resonances of both polarizations.     

Constraint based approach for the steady-state simulation of complex systems: Application to ship control

When the steady states are largely predominant with respect to transitional phases, steady-state simulation seems sufficient to predict the behavior of a complex system. Over the past 20 years, different modeling languages and dedicated tools have been developed to improve steady state simulation.In this paper, focus is made on steady-state simulation for system control and design. A model combining an emission sub-model with a ship propulsion sub-model was implemented in a constraint programming (CP) approach. It will help to determine the efficiency (i.e. the ability to model and solve the problem) and complexity of implementation (i.e. difficulties encountered during the implementation) of this approach.First, requirements for the steady-state simulation of complex systems are defined. Then, CP approach is shown to be able to answer these issues through experiments. This approach is then compared to one of the main simulation languages: Modelica.Although the two approaches (i.e Modelica and CP) are able to reverse models, the study shows that the use of Modelica principles for steady-state simulation involves some crippling limitations, such as the non-management of under/over-constrained systems, or inequalities.This study also shows that the constraint programming approach permits to meet some needs for steady-state simulation not yet covered by current approaches.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Adapting groupware systems to changes in the collaborator’s context of use

Owing to the dynamic nature of collaborative environments, the software intended to support collaborative work should adapt itself to the different situations that may occur. This requirement is related to the concept of “context of use”, which has been considered as an important aspect in the design of interactive systems. Nevertheless, two main problems about this concept have been identified by current research in context-aware computing: (1) most of the studies have mainly focused on the context of a single user, so the context of multiple users involved in a common endeavor remains little explored, and (2) adaptability in context-aware systems generally takes into account a reduced number of contextual variables (mainly the user’s location and platform). In this paper, we firstly re-conceptualize the notion of “context of use”, in order to consider the main characteristics of collaborative environments. Based on this new notion, we then design and implement a framework that allows application developers to specify the adaptability of groupware systems in terms of the state of activities, roles, collaborators’ location, available resources, and other typical variables of working groups. This framework has been generalized from scenarios that highlight dynamic situations presented in real collaborative settings. Finally, we validate our proposal by a set of applications that are able to adapt their user interface and functionality, when significant changes are produced in the environment, the working group, and/or the used devices.