A multisource energy harvesting utilizing highly efficient ferroelectric PMN-PT single crystal

This paper demonstrates a multi-source energy harvester that is able to utilize simultaneously both piezoelectric and pyroelectric effects in lead magnesium niobate-lead titanate (PMN-PT) single crystal. The paper presents a study of PMN-PT single crystal with a (67:33) composition grown in our laboratory via a vertical gradient freeze method without any flux. The performance of the piezoelectric and pyroelectric energy harvester using unimorph device structure was evaluated via modeling and experiment. The theoretical study was implemented based on a distributed parameter electromechanical model and the modelling procedure was approximated using finite element analysis to predict the electromechanical behavior of the harvester. The maximum power density at a resonance frequency of 50 Hz and optimum resistance of 2 MΩ was 16.7 nW/(g² cm³) under a 1 g acceleration of vibration. The measured values of electrical output parameters were in good agreement with theoretical and modelling results using MATLAB and COMSOL Multiphysics, respectively. By using the pyroelectric effect along with the piezoelectric effect, the output voltage of the energy harvester was found to be enhanced at the optimum resistance and specific frequency values. It was noticed that the output voltage was increased monotonically with temperature-difference (ΔT) and reaches up to 180 % of its original value under temperature difference of 1.7 °C at a frequency value of 49 Hz.     

Generalized linear complementarity in a problem ofn-person games

In this paper, we introduce a generalization of the polymatrix game (a nonzero sum noncooperativen-person game) considered by Howson and relate the problem of computing an equilibrium set of strategies for such a game to the generalized linear complementarity problem of Cottle and Dantzig. For an even more general version of the game we prove the existence of an-equilibrium set of strategies. We also present a result on the stability of the equilibria based on degree theory.     

Preparation of anchored ceramic coatings on metal substrates: a modified sol-gel technique using colloidal silica sol

A single-step dip-coating technique was developed to produce stable films or washcoats of controlled thickness, surface area and pore-size distribution on alumina whisker-covered metal substrates (both flat plates and monoliths). Dip-coating slurries were prepared by dispersing fine porous powder in a colloidal silica sol. The method provided control over coating thickness and coating properties, such as pore-size distribution and surface area. The coating thickness could be varied between approximately 2 and 40 m by selecting slurry composition and withdrawal speed in the dipping procedure. The pore-size distribution and surface area could be varied by changing type and amount of porous filler material in the dipping slurry. Uniform and bimodal pore-size distributions were obtained using silica and ZSM-5 molecular sieves, respectively, yielding coatings with surface areas between 60 and 400 m²g^–1.     

Implementing enterprise resource planning systems: organizational performance and the duration of the implementation

This paper examines the impact of the duration of ERP implementation on firm performance both during and after implementation. Organizations choose either an accelerated implementation approach or a traditional (longer) implementation approach. The former approach gives the organization the advantage of speed, but the disadvantage of fitting its processes to that of a packaged (thus, undifferentiated from competitors) ERP. The latter approach allows the organization to redesign strategy and processes, and thus, search for ways to be unique from its competition. The study uses a regression model to capture the changes in various performance measures during and after implementation between firms that implemented the ERP, using the performance measure of a matched group of firms that did not implement an ERP as a benchmark/control sample, on the basis of the duration of the implementation. Financial data from Compustat, and data on start date and end date of ERP implementation between 1990 and 2005 for firms in the Oil and Gas industry was collected from an ERP vendor. Results show that measures such as return on sales improved after implementation. However, measures such as inventory turnover, which reflect operational benefits, improve during implementation. We find that accelerated implementation confers both operational and strategic benefits. This study highlights the strategic consequences of the different choices of implementation.     

Multimedia data mining: state of the art and challenges

Advances in multimedia data acquisition and storage technology have led to the growth of very large multimedia databases. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem. This challenge has opened the opportunity for research in Multimedia Data Mining (MDM). Multimedia data mining can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. The motivation for doing MDM is to use the discovered patterns to improve decision making. MDM has therefore attracted significant research efforts in developing methods and tools to organize, manage, search and perform domain specific tasks for data from domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. This paper presents a survey on the problems and solutions in Multimedia Data Mining, approached from the following angles: feature extraction, transformation and representation techniques, data mining techniques, and current multimedia data mining systems in various application domains. We discuss main aspects of feature extraction, transformation and representation techniques. These aspects are: level of feature extraction, feature fusion, features synchronization, feature correlation discovery and accurate representation of multimedia data. Comparison of MDM techniques with state of the art video processing, audio processing and image processing techniques is also provided. Similarly, we compare MDM techniques with the state of the art data mining techniques involving clustering, classification, sequence pattern mining, association rule mining and visualization. We review current multimedia data mining systems in detail, grouping them according to problem formulations and approaches. The review includes supervised and unsupervised discovery of events and actions from one or more continuous sequences. We also do a detailed analysis to understand what has been achieved and what are the remaining gaps where future research efforts could be focussed. We then conclude this survey with a look at open research directions.     

Multimodal fusion for multimedia analysis: a survey

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.     

On burst rescheduling in OBS networks with partial wavelength conversion capability

The core nodes in an optical burst switching (OBS) network are normally equipped with wavelength converters (WCs) to reduce the burst loss probability. Since WCs are expensive and still immature technologically, it is desirable to reduce the number of WCs in the network and to have partial wavelength conversion capability at the core nodes. Nevertheless, a majority of algorithms in the literature are proposed under the full wavelength conversion assumption. As a result, they do not consider the burst loss caused by insufficient WCs, i.e., bursts dropped due to the unavailability of free WCs to convert them to unused wavelengths. In this paper, we demonstrate how to use burst rescheduling to decrease the burst loss due to insufficient WCs and hence cut down on the overall burst loss probability in OBS networks. Two burst rescheduling algorithms are proposed. Their effectiveness in reducing the overall burst loss probability is verified through simulation experiments.     

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

Distributed evolutionary multi-objective mesh-partitioning algorithm for parallel finite element computations

Majority of the mesh-partitioning algorithms attempt to optimise the interprocessor communications, while balancing the computational load among the processors. However, it is desirable to simultaneously optimise the submesh aspect ratios in order to significantly improve the convergence characteristics of the domain decomposition based Preconditioned-conjugate-gradient algorithms, being used extensively in the state-of-the-art parallel finite element codes. Keeping this in view, a new distributed multi-objective mesh-partitioning algorithm using evolutionary computing techniques is proposed in this paper. Effectiveness of the proposed distributed mesh-partitioning algorithm is demonstrated by solving several unstructured meshes of practical-engineering problems and also benchmark problems.     

Generating human-like soccer primitives from human data

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.This paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. The approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. In a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently.The simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. Comparison is made with methods that do not include observation of human gait. The gait has been successfully used to control Robo-Erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.