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.     

Cutting force and hole quality analysis in micro-drilling of CFRP

Micro-drilling in carbon fiber reinforced plastic (CFRP) composite material is challenging because this material machining is difficult due to anisotropic, abrasive and non-homogeneous properties and also downscaling of cutting process parameters affect the cutting forces and micro-drilled hole quality extensively. In this work, experimental results based statistical analysis is applied to investigate feed and cutting speed effect on cutting force components and hole quality. Analysis of variance based regression equation is used to predict cutting forces and hole quality and their trend are described by response surface methodology. Results show that roundness error and delamination factor have similar trends to those of radial forces and thrust force, respectively. Non-linear trends of cutting forces and hole quality errors are observed during downscaling of the micro-drill feed value. Optimization results show that cutting forces and hole quality errors are minimum at a feed value which is almost equal to the tool edge radius rather than at the lowest feed value. Therefore, the presented results clearly show the influences of size effects on cutting forces and hole quality parameters in micro-drilling of CFRP composite material.     

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.     

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.     

Partitioning large networks without breaking communities

The identification of cohesive communities is a key process in social network analysis. However, the algorithms that are effective for finding communities do not scale well to very large problems, as their time complexity is worse than linear in the number of edges in the graph. This is an important issue for those interested in applying social network analysis techniques to very large networks, such as networks of mobile phone subscribers. In this respect, the contributions of this paper are twofold. First, we demonstrate these scaling issues using a prominent community-finding algorithm as a case study. Then, we show that a two-stage process, whereby the network is first decomposed into manageable subnetworks using a multilevel graph partitioning procedure, is effective in finding communities in networks with more than 10⁶ nodes.     

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.     

Stacking graphic elements to avoid over-plotting

An ongoing challenge for information visualization is how to deal with over-plotting forced by ties or the relatively limited visual field of display devices. A popular solution is to represent local data density with area (bubble plots, treemaps), color (heatmaps), or aggregation (histograms, kernel densities, pixel displays). All of these methods have at least one of three deficiencies:1) magnitude judgments are biased because area and color have convex downward perceptual functions, 2) area, hue, and brightness have relatively restricted ranges of perceptual intensity compared to length representations, and/or 3) it is difficult to brush or link to individual cases when viewing aggregations. In this paper, we introduce a new technique for visualizing and interacting with datasets that preserves density information by stacking overlapping cases. The overlapping data can be points or lines or other geometric elements, depending on the type of plot. We show real-dataset applications of this stacking paradigm and compare them to other techniques that deal with over-plotting in high-dimensional displays.     

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.