Artificial grain boundary Josephson junctions : properties and applications

Engineered grain boundary Josephson junctions in YBaCuO were formed on bicrystal Y-ZrO2 substrates. Laser deposited films were patterned into micron size microbridges. The authors obsd. a pronounced correlation between superconducting transport properties of grain boundary junctions and the misorientation angle θ between the two halves of the bicrystal. The crit. Josephson current Ic decreased about four orders of magnitude as θ was increased from 0 to 45 degrees. Clear microwave and magnetic field responses were obsd. at 77 K. At this temp., crit. current times normal resistance products, IcRn, of up to 1 mV were measured for low angle grain boundaries, and Shapiro steps were obsd. up to that voltage. DC SQUIDs were fabricated, and best performance at 77 K was obtained for θ = 32° with a 4-μm strip width. To utilize the higher IcRn value of a lower θ, submicron junctions have to be developed. [on SciFinder(R)]

Selection et controle de modes de deplacement pour un robot mobile autonome en environnements naturels

Autonomous navigation and locomotion of a mobile robot in natural environments remain a rather open issue. Several functionalities are required to complete the usual perception/decision/action cycle. They can be divided in two main categories : navigation (perception and decision about the movement) and locomotion (movement execution). In order to be able to face the large range of possible situations in natural environments, it is essential to make use of various kinds of complementary functionalities, defining various navigation and locomotion modes. Indeed, a number of navigation and locomotion approaches have been proposed in the literature for the last years, but none can pretend being able to achieve autonomous navigation and locomotion in every situation. Thus, it seems relevant to endow an outdoor mobile robot with several complementary navigation and locomotion modes. Accordingly, the robot must also have means to select the most appropriate mode to apply. This thesis proposes the development of such a navigation/locomotion mode selection system, based on two types of data: an observation of the context to determine in what kind of situation the robot has to achieve its movement and an evaluation of the behavior of the current mode, made by monitors which influence the transitions towards other modes when the behavior of the current one is considered as non satisfying. Hence, this document introduces a probabilistic framework for the estimation of the mode to be applied, some navigation and locomotion modes used, a qualitative terrain representation method (based on the evaluation of a difficulty computed from the placement of the robot's structure on a digital elevation map), and monitors that check the behavior of the modes used (evaluation of rolling locomotion efficiency, robot's attitude and configuration watching. . .). Some experimental results obtained with those elements integrated on board two different outdoor robots are presented and discussed.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.