Beacon-Less Geographic Routing in Real Wireless Sensor Networks

Geographic Routing(GR)algorithms require nodes to periodically transmit HELLO messages to allow neigh- bors to know their positions(beaconing mechanism).Beacon-less routing algorithms have recently been proposed to reduce the control overheads due to these messages.However,existing beacon-less algorithms have not considered realistic physical layers.Therefore,those algorithms cannot work properly in realistic scenarios.In this paper we present a new beacon- less routing protocol called BOSS.Its design is based on the conclusions of our open-field experiments using Tmote-sky sensors.BOSS is adapted to error-prone networks and incorporates a new mechanism to reduce collisions and duplicate messages produced during the selection of the next forwarder node.We compare BOSS with Beacon-Less Routing(BLR) and Contention-Based Forwarding(CBF)algorithms through extensive simulations.The results show that our scheme is able to achieve almost perfect packet delivery ratio(like BLR)while having a low bandwidth consumption(even lower than CBF).Additionally,we carried out an empirical evaluation in a real testbed that shows the correctness of our simulation results.     

Shaping stable periodic motions of inertia wheel pendulum: theory and experiment

We consider an underactuated two‐link robot called the inertia wheel pendulum. The system consists of a free planar rotational pendulum and a symmetric disk attached to its end, which is directly controlled by a DC‐motor. The goal is to create stable oscillations of the pendulum, which is not directly actuated. We exploit a recently proposed feedback‐control design strategy based on motion planning via virtual holonomic constraints. This strategy is shown to be useful for design of regulators for achieving orbitally exponentially stable oscillatory motions. The main contribution is a step‐by‐step procedure on how to achieve oscillations with pre‐specified amplitude from a given range and an arbitrary independently chosen period. The theoretical results are verified via experiments with a real hardware setup. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

QoS-Broker for transactional workloads

Typical request processing systems, such as web servers and database servers, try to accommodate all requests as fast as possible, which can be described as a Best-Effort approach. However, different application items may have different quality-of-service (QoS) requirements, and this can be viewed as an orthogonal concern to the basic system functionality. In this paper we propose the QoS-Broker, a middleware for delivering QoS over servers and applications. We show its architecture to support contracts over varied targets including queries, transactions, services or sessions, also allowing expressions on variables to be specified in those targets. We also discuss how the QoS-Broker implements basic strategies for QoS over workloads. Our experimental results illustrate the middleware by applying priority and weighted- fair-queuing based differentiation over clients and over transactions, and also admission control, using a benchmark as a case-study.     

Prediction of the percentages of cows'', goats'' and ewes'' milk in “Iberico” cheese by electrophoretic analysis of whey proteins

Stepwise multiple linear regression (SMLR) and principal components regression (PCR) have been used to predict the percentages of cows', goats' and ewes' milk in Iberico cheese, using the results obtained by electrophoretic analysis (PAGE and IEF) of whey proteins, using standard cheeses. Similar predictions of the percentages of milks from the three species were obtained when either SMLR or PCR were applied to the electrophoretic data, i.e. the optical intensity of the electrophoretic bands (PAGE or IEF) of the whey proteins. The root mean square error of prediction in cross-validation (RMSEPCV) was lower than 4% in all cases.     

Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease

Magnetic Resonance Materials in Physics, Biology and Medicine - Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small...     

GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure

Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art.

     

Experience report on the application of genetic algorithms to reduce costs of the software validation process in the automotive sector during an engine control unit project

Software Quality Journal - The number of electronic control units (ECU) installed in vehicles is increasingly high. Manufacturers must improve the software quality and reduce cost by proposing...     

Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

Edge Fracture Prediction Using Uncoupled Ductile Fracture Models for DP780 Sheet

This paper is a contribution to the prediction of edge fracture behavior using uncoupled ductile fracture models. A fully integrated simulation framework for the edge fracture prediction is proposed with the shear-induced pre-damage considered. User-defined material subroutines are coded with uncoupled ductile fracture models (Lou-Huh, Oh, Brozzo) incorporated, which are calibrated using the fracture strains of various loading paths. A series of 3D numerical simulations are performed and compared with the results of hole-expansion tests. The effects of pre-damage field and fracture models are analyzed and discussed.

     

Life cycle assessment and optimization of an iron making system with a combined cycle power plant: a case study from China

In the steel industry, the iron making system deals with large quantities of materials and energy and so it can play a critical role in reducing emissions and production costs. More specifically, excess by-product gases should be used for electricity generation; otherwise, they lead to pollution. A life cycle analysis is performed to compare the environmental impact of an iron making system with a combined cycle power plant (CCPP), to a system producing the same amount of electricity in a coal power plant. The results for a Chinese steel plant show a 33% reduction in the energy conservation and emission reduction potential for the CCPP system, which is thus more environmentally friendly. A mathematical programming formulation is then proposed for optimal scheduling. It incorporates key technological constraints and is sensitive to hourly changing electricity prices. The outcome is a 19% increase in revenue from electricity sales compared to a schedule that does not dynamically adjust to the price profile. The results also show that emissions from by-product gases can be avoided completely. The paper ends with a sensitivity analysis to evaluate the impact of changes in product demand, gas storage and CCPP capacity, and emission cost.