Energy Efficient Cloud Service Provisioning: Keeping Data Center Granularity in Perspective

The cost of power and its associated delivery are becoming significant factors in the total expenditure of large-scale data centers. Numerous techniques have been proposed to address the energy efficiency issue in cloud systems. Recently, some efforts have been made to decentralize the cloud via distributing data centers in diverse geographical positions, at different scales. In this paper, we elaborate on the energy effectiveness of service provisioning on different cloud architectures, from a mega-data center to a nano data center, which provides the extreme decentralization in terms of cloud architecture, as well as P2P-clouds or community network clouds. We study the energy consumption through an analytical and simulation framework for video streaming and MapReduce applications.     

Selective refinement queries for volume visualization of unstructured tetrahedral meshes

We address the problem of the efficient visualization of large irregular volume data sets by exploiting a multiresolution model based on tetrahedral meshes. Multiresolution models, also called Level-Of-Detail (LOD) models, allow encoding the whole data set at a virtually continuous range of different resolutions. We have identified a set of queries for extracting meshes at variable resolution from a multiresolution model, based on field values, domain location, or opacity of the transfer function. Such queries allow trading off between resolution and speed in visualization. We define a new compact data structure for encoding a multiresolution tetrahedral mesh built through edge collapses to support selective refinement efficiently and show that such a structure has a storage cost from 3 to 5.5 times lower than standard data structures used for tetrahedral meshes. The data structures and variable resolution queries have been implemented together with state-of-the art visualization techniques in a system for the interactive visualization of three-dimensional scalar fields defined on tetrahedral meshes. Experimental results show that selective refinement queries can support interactive visualization of large data sets.     

Variable Structure Control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects.     

Discrimination of vegetation types in alpine sites with ALOS PALSAR-, RADARSAT-2-, and lidar-derived information

Natural vegetation monitoring in the alpine mountain range is a priority in the European Union in view of climate change effects. Many potential monitoring tools, based on advanced remote sensing sensors, are still not fully integrated in operational activities, such as those exploiting very high-resolution synthetic aperture radar (SAR) or light detection and ranging (lidar) data. Their testing is important for possible incorporation in routine monitoring and to increase the quantity and quality of environmental information. In this study the potential of ALOS PALSAR and RADARSAT-2 SAR scenes' synergic use for discrimination of different vegetation types was tested in an alpine heterogeneous and fragmented landscape. The integration of a lidar-based canopy height model (CHM) with SAR data was also tested. A SPOT image was used as a benchmark to evaluate the results obtained with different input data. Discrimination of vegetation types was performed with maximum likelihood classification and neural networks. Six tested data combinations obtained more than 85% overall accuracy, and the most complex input which integrates the two SARs with lidar CHM outperformed the result based on SPOT. Neural network algorithms provided the best results. This study highlights the advantages of integrating SAR sensors with lidar CHM for vegetation monitoring in a changing environment.     

A new methodology to improve interpretability in neuro-fuzzy TSK models

The present paper puts forward a methodology which allows increasing interpretability of TSK models identified by means of neuro-fuzzy techniques, although it shall also be applicable to models identified through other hybrid or different techniques. With this purpose, this paper puts forward a method which allows oriented adjustment of the rules’ precedent and consequent parameters in TSK models. The methodology extends the adaptive phase with an adjustment phase (or fine tuning phase) based on overlap ratio and overlap area, where the gradient descendent algorithm is used to adjust precisely the adapted parameters in the fuzzy model. The adjustment based on the overlap ratio is applied to the parameters defining the rules’ precedent and consequent parts. The overlap area becomes a more precise tuning of parameters of precedent part of rules. After the adaptation of the neuro-fuzzy model by means of the developed methodology, the model acquires a clear physical meaning enabling its immediate linguistic interpretation. Finally, some examples are given to prove the validity of the developed methodology.     

Automatic detection of depression symptoms in twitter using multimodal analysis

The Journal of Supercomputing - Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data...     

The application of internet of things in healthcare: a systematic literature review and classification

Universal Access in the Information Society - The Internet of Things (IoT) is an ecosystem that integrates physical objects, software and hardware to interact with each other. Aging of population,...     

SNMPv3在网络安全管理系统中的应用

本文介绍了网络安全管理系统和简单网络管理协议SNMP的概念,并对系统的框架结构进行了深入研究,最后重点阐述了安全管理系统中SNMPv3的设计实现。     

Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm

Cell formation problem is the main issue in designing cellular manufacturing systems. The most important objective in the cell formation problem is to minimize the number of exceptional elements which helps to reduce the number of intercellular movements. Another important but rarely used objective function is to minimize the number of voids inside of the machine cells. This objective function is considered in order to increase the utilization of the machines. We present a bi-objective mathematical model to simultaneously minimize the number of exceptional elements and the number of voids in the part machine incidence matrix. An ε-constraint method is then applied to solve the model and to generate the efficient solutions. Because of the NP-hardness of the model, the optimal algorithms can not be used in large-scale problems and therefore, we have also developed a bi-objective genetic algorithm. Some numerical examples are considered to illustrate the performance of the model and the effectiveness of the solution algorithms. The results demonstrate that in comparison with the ε-constraint method, the proposed genetic algorithm can obtain efficient solution in a reasonable run time.     

Related Fritz Carlson type inequalities for Sugeno integrals

In this paper, two general related inequalities to Carlson type inequality for the Sugeno integrals on an abstract fuzzy measure space $(X, \mathcal{F})$ are studied. Several examples are given to illustrate the validity of these inequalities.