Energy-efficient neighbor discovery protocol for mobile wireless sensor networks

Low energy consumption is a critical design requirement for most wireless sensor network (WSN) applications. Due to minimal transmission power levels, time-varying environmental factors and mobility of nodes, network neighborhood changes frequently. In these conditions, the most critical issue for energy is to minimize the transactions and time consumed for neighbor discovery operations. In this paper, we present an energy-efficient neighbor discovery protocol targeted at synchronized low duty-cycle medium access control (MAC) schemes such as IEEE 802.15.4 and S-MAC. The protocol effectively reduces the need for costly network scans by proactively distributing node schedule information in MAC protocol beacons and by using this information for establishing new communication links. Energy consumption is further reduced by optimizing the beacon transmission rate. The protocol is validated by performance analysis and experimental measurements with physical WSN prototypes. Experimental results show that the protocol can reduce node energy consumption up to 80% at 1–3 m/s node mobility.     

Automatic feature selection for context recognition in mobile devices

In mobile devices there exist several in-built sensor units and sources which provide data for context reasoning. More context sources can be attached via wireless network connections. Usually, the mobile devices and the context sources are battery powered and their computational and space resources are limited. This sets special requirements for the context recognition algorithms. In this paper, several classification and automatic feature selection algorithms are compared in the context recognition domain. The main goal of this study is to investigate how much advantage can be achieved by using sophisticated and complex classification methods compared with a simple method that can easily be implemented in mobile devices. The main result is that even a simple linear classification algorithm can achieve a reasonably good accuracy if the features calculated from raw data are selected in a suitable way. Usually context recognition algorithms are fitted to a particular problem instance in an off-line manner and modifying methods for on-line learning is difficult or impossible. An on-line version of the Minimum-distance classifier is presented in this paper and it is justified that it leads to considerably higher classification accuracies compared with the static off-line version of the algorithm. Moreover, we report superior performance for the Minimum-distance classifier compared to other classifiers from the view point of computational load and power consumption of a smart phone.     

An orthogonal family of quincunx wavelets with continuously adjustable order.

We present a new family of two-dimensional and three-dimensional orthogonal wavelets which uses quincunx sampling. The orthogonal refinement filters have a simple analytical expression in the Fourier domain as a function of the order lamda, which may be noninteger. We can also prove that they yield wavelet bases of L2(R2) for any lambda > 0. The wavelets are fractional in the sense that the approximation error at a given scale a decays like O(a(lamda)); they also essentially behave like fractional derivative operators. To make our construction practical, we propose a fast Fourier transform-based implementation that turns out to be surprisingly fast. In fact, our method is almost as efficient as the standard Mallat algorithm for separable wavelets.     

Solution of population balances with growth and nucleation by high order moment-conserving method of classes

The high order method of classes, developed in our earlier work [Alopaeus, V., Laakkonen, M., Aittamaa J., 2006a. Solution of population balances with breakage and agglomeration by high order moment-conserving method of classes. Chemical Engineering Science 61, 6732-6752] for solution of population balances (PBs), is extended to problems with growth and primary nucleation. The growth problem leads to a hyperbolic partial differential equation with fundamentally different numerical characteristics than the PB with breakage and agglomeration only. However, we show that the principle of moment conservation in the numerical solution can also be applied to this advection-type problem, leading to extremely accurate numerical solutions. The method is tested for two numerical cases. The first one is mass transfer induced particle growth, and the second one is primary nucleation with constant growth (similar to the Riemann advection problem). For mass transfer induced growth, we first analyze functional form of the growth rate from mass transfer correlation viewpoint, and derive a general analytical solution for the power-law growth. The numerical results from the moment conserving method are also compared to one well established high resolution numerical method for advection problems, namely the Lax-Wendroff method with van Leer flux limiter. It was shown that the present method is far superior by predicting the distribution moments with several order of magnitudes lower numerical error. For the Riemann problem with constant growth rate, the present method predicts the shock front location exactly without any numerical diffusion.     

Windage power loss reductions in high-speed gear pairs

Forschung im Ingenieurwesen - This paper deals with recent experimental and numerical research in the possible reduction of windage power losses (WPL). It is accepted that these load-independent...     

Visual measurement and tracking in laser hybrid welding

This paper presents a novel system for the automatic analysis of a hybrid welding process. High-speed imaging and laser illumination are used to measure the regularity of electric arc frequency and flight directions of filler metal droplets. A fuzzy c-means clustering method is used to detect arcs and segment the video sequences. The droplets are localized by combining principal component analysis and a support vector machine classifier. The flight of a droplet is tracked using Kalman filtering. Experiments indicate that the system is able to track the flights of droplets and to determine the regularity of the arc frequency with a high accuracy if the imaging conditions are stable.

High-Quality Volumetric Reconstruction on Optimal Lattices for Computed Tomography

Within the context of emission tomography, we study volumetric reconstruction methods based on the Expectation Maximization (EM) algorithm. We show, for the first time, the equivalence of the standard implementation of the EM-based reconstruction with an implementation based on hardware-accelerated volume rendering for nearest-neighbor (NN) interpolation. This equivalence suggests that higher-order kernels should be used with caution and do not necessarily lead to better performance. We also show that the EM algorithm can easily be adapted for different lattices, the body-centered cubic (BCC) one in particular. For validation purposes, we use the 3D version of the Shepp-Logan synthetic phantom, for which we derive closed-form analytical expressions of the projection data. The experimental results show the theoretically-predicted optimality of NN interpolation in combination with the EM algorithm, for both the noiseless and the noisy case. Moreover, reconstruction on the BCC lattice leads to superior accuracy, more compact data representation, and better noise reduction compared to the Cartesian one. Finally, we show the usefulness of the proposed method for optical projection tomography of a mouse embryo.     

A comprehensive taxonomy of algorithm animation languages

In this paper, we present a taxonomy of algorithm animation languages that augments Price's well-known taxonomy of software visualization. Whereas Price's taxonomy is directed to classifying features and characteristics of visualization systems, the new taxonomy focuses on evaluating current animation languages. The taxonomy can be used by algorithm visualization system designers as a tool to compare visualization system languages with each other as well as for designing and implementing new systems and language features. In addition, the taxonomy provides guidelines to the features that are needed for transferring animations between systems. This is an ongoing project that elaborates upon the work reported on in a briefer version of the taxonomy.     

Capillarity in microfluidic channels with hydrophilic and hydrophobic walls

The authors investigate the spontaneous filling of microchannels with mixed hydrophilic and hydrophobic walls. We show that in these channels, unlike that case when all the walls are either hydrophilic or hydrophobic, the local distribution of capillary drive can make partial filling of the channel the most favorable filling type. A strategy for finding the most favorable filling type is presented and tested against capillary filling experiments made on oxygen plasma treated SU-8 microchannels. Good agreement is found between the theory and the experiments.