Self-Organized Monitoring in Ad-Hoc Networks

We propose in this paper a self-organized monitoring architecture for mobile ad-hoc networks based on a selective scheme where subsets of nodes are managed. These nodes are determined based on their network behavior in order to favor subsets of well-connected nodes. The key idea is to relax the requirements of the management plane, and to use these manageable subsets to monitor the performance of the overall network. We therefore propose a new performance metric to be monitored, in order to estimate the capability of ad-hoc nodes to communicate end-to-end in the network. Extensive simulations show how different parameters affect this metric.     

Synthesis and applications of fluoroalkyl end-capped cooligomers having fullerene and mixed fullerenes in the main chain

Fluoroalkanoyl peroxides reacted with FULLERENES [fullerene (C₆₀) and commercially available fullerenes (Nanom Mix^TR and Nanom Black^TR)] and radical polymerizable comonomers such as acrylic acid, N,N-dimethylacrylamide, N-(1,1-dimethyl-3-oxobutyl)acrylamide, and acryloylmorpholine to afford fluoroalkyl end-capped cooligomers having FULLERENES in the main chain under very mild conditions. Fluoroalkyl end-capped Nanom Mix and Nanom Black cooligomers thus obtained were found to exhibit a similar solubility to that of the corresponding fluoroalkyl end-capped cooligomers having fullerene in the main chain. These fluorinated FULLERENES cooligomers were found to form the nanometer size-controlled self-assembled cooligomeric aggregates in aqueous solutions. These fluoroalkyl end-capped FULLERENES cooligomers were more effective for solubilizing fullerene, Nanom Mix and Nanom Black into water, compared to those of the corresponding fluoroalkyl end-capped homooligomers having no FULLERENES in the main chain. Fluoroalkyl end-capped fullerene- and Nanom Mix-acrylic acid cooligomers were found to exhibit fluorescence spectra related to fullerene and Nanom Mix in cooligomers, respectively, in aqueous solutions. Additionally, these fluorinated fullerene- and Nanom Mix-acrylic acid cooligomers were able to increase chemiluminescence intensity related to luminol, effectively, compared to the corresponding fluorinated acrylic acid homooligomers.     

Digestion and absorption of free and esterified fish oil fatty acids in rats

This study was designed to test the hypotheses that digestibility and post-absorption metabolism of fish oil are influenced by impaired lipolysis and by the stereospecific composition of its triacylglycerols. Male Wistar rats were fed nonpurified diets containing one of the following fat sources: 9% native fish oil (NFO), 9% autorandomized fish oil (RFO), 8.1% fish oil-derived free fatty acids (FO-FFA) plus 0.9% glycerol, or 9% soybean oil (SO) as a reference fat. In a 24-day balance study, apparent digestibility of total dietary fat averaged 93.1% in the SO, NFO and RFO groups, and 90.9% in the FO-FFA group. Randomization of fish oil had no effect on apparent digestibility of individual fatty acids. In rats fed FO-FFA, apparent absorption of saturated and monounsaturated fatty acids was lower when compared to the NFO and RFO groups. Feeding the FO-FFA diet tended to increase plasma triglyceride content. The hypocholesterolemic effect of polyunsaturated n−3 fatty acids was not influenced by the dietary source. Similar effects on fatty acid profiles of plasma and liver phospholipids were caused by the NFO, RFO and the FO-FFA diets. We conclude that once polyunsaturated n−3 fatty acids are absorbed, their effect on lipid metabolism is not determined by the dietary source.     

Projected sequential Gaussian processes: A C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed.     

Neural network wind retrieval from ERS-1 scatterometer data

This paper presents a neural network methodology to retrieve wind vectors from ERS1 scatterometer data. First, a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated direction are given. At least 75% of the most probable wind directions are consistent with ECMWF winds (at ±20°). Then the remaining ambiguities are solved by an adapted PRESCAT method, which uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the accuracy of the method. Its good performance is mainly due to the use of a spatial context and to the probabilistic approach for estimating the direction. Comparisons with other methods are also presented. The good performance of the neural method suggests that self-consistent wind retrieval is possible.     

Use of neural networks for predictions using time series: Illustration with the El Niño Southern oscillation phenomenon

This paper introduces the use of sets of multiple networks (bundled networks) to manage the variability due to different initialization parameters. This method makes it statistically impossible for the networks to be trapped in the same local minimum, and therefore allows better control of the confidence of the prediction eventually given. The spread of the forecasts given by these different networks can be used for prediction reliability purposes. An illustration of this usage is given with the El Niño phenomenon.     

Passive elastic mechanism to mimic fish-muscle action in anguilliform swimming

Swimmers in nature use body undulations to generate propulsive and manoeuvring forces. The anguilliform kinematics is driven by muscular actions all along the body, involving a complex temporal and spatial coordination of all the local actuations. Such swimming kinematics can be reproduced artificially, in a simpler way, by using the elasticity of the body passively. Here, we present experiments on self-propelled elastic swimmers at a free surface in the inertial regime. By addressing the fluid–structure interaction problem of anguilliform swimming, we show that our artificial swimmers are well described by coupling a beam theory with the potential flow model of Lighthill. In particular, we show that the propagative nature of the elastic wave producing the propulsive force is strongly dependent on the dissipation of energy along the body of the swimmer.     

Critical Heat Flux During Flow Boiling in Microchannels: A Parametric Study

The application of flow boiling in microchannels in copper cooling elements for very high heat flux dissipation from microprocessor chips is one of the promising technologies to replace air cooling and water cooling of these units, particularly in mainframes and servers. Recently, the authors have proposed a new theoretical model to predict the critical heat flux (CHF) in microchannels, and it is used here to perform a parametric study to investigate the effects of fluid, saturation temperature, mass flux, inlet subcooling, microchannel diameter, and heated length on CHF for this application. The parametric study shows that CHF is increased by: (i) decreasing channel length, (ii) lowering saturation temperature, (iii) increasing mass flux, (iv) increasing inlet subcooling, and (v) increasing microchannel diameter. The best coolant is water, but water is not feasible for the present application because of its very low saturation pressure at 30–40°C. Of the other four fluids simulated, their order of merit from best to worst is as follows: R-245fa, R-134a, R-236fa, and FC-72. FC-72, however, has a low saturation pressure (in fact, it would operate under vacuum at the saturation temperatures of 30–40°C envisioned here) and is not a candidate fluid for the flow boiling coolant here. Furthermore, the authors have also recently proposed a diabatic flow map for microchannels based on their database for R-134a and R-245fa in 0.5- and 0.8-mm channels. The new CHF model has been incorporated into their map here to predict the transition from annular flow to dry-out, which is a critical design limitation for microprocessor coolers. Importantly, this map then provides the feasible operating range of such coolers with flow boiling as the cooling process, in terms of mass flux and maximum vapor quality at the outlet to avoid CHF.     

Free viewpoint action recognition using motion history volumes

Action recognition is an important and challenging topic in computer vision, with many important applications including video surveillance, automated cinematography and understanding of social interaction. Yet, most current work in gesture or action interpretation remains rooted in view-dependent representations. This paper introduces Motion History Volumes (MHV) as a free-viewpoint representation for human actions in the case of multiple calibrated, and background-subtracted, video cameras. We present algorithms for computing, aligning and comparing MHVs of different actions performed by different people in a variety of viewpoints. Alignment and comparisons are performed efficiently using Fourier transforms in cylindrical coordinates around the vertical axis. Results indicate that this representation can be used to learn and recognize basic human action classes, independently of gender, body size and viewpoint.     

System integration of high voltage electrostatic MEMS actuators

A system integration for High Voltage (HV) electrostatic MicroElectroMechanical Systems (MEMS) actuators is introduced on a micro-Printed Circuit Board. The system includes a programmable microcontroller, a programmable DC/DC converter, a multi output HV interface and electrostatic MEMS actuators. The system produces high output voltages (10–300 V) and can control a large variety of MEMS capacitive loads (1 to 50 pF) by combining diverse semiconductor technologies. This system proves that technologies, such as low voltage CMOS of different processes, high voltage DMOS and MEMS, can interact, communicate and even be integrated as a System In Package (SIP), providing significant size and cost reductions. The system was programmed to control electrostatic MEMS actuator. The DC/DC converter was made from components of different technologies and two addressable high voltage CMOS interfaces were fabricated with DALSA's 0.8 μm High Voltage process. A prototype of the global system has been built and tested.