A Framework of Fog Computing: Architecture, Challenges and Optimization

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Fog Computing (FC) is an emerging distributed computing platform aimed at bringing computation close to its data sources, which can reduce the latency and cost of delivering data to a remote cloud. This feature and related advantages are desirable for many Internet-of-Things applications, especially latency sensitive and mission intensive services. With comparisons to other computing technologies, the definition and architecture of FC are presented in this article. The framework of resource allocation for latency reduction combined with reliability, fault tolerance, privacy, and underlying optimization problems are also discussed. We then investigate an application scenario and conduct resource optimization by formulating the optimization problem and solving it via a Genetic Algorithm. The resulting analysis generates some important insights on the scalability of FC systems.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/P020224/1] and the EU FP7 QUICK project under Grant Agreement No. PIRSES-GA-2013-612652. Yang Liu was supported by the Chinese Research Council

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60

Two-phase Flow Visualization Employing Gauss-Newton Method in Microchannel

Flow behaviour monitoring on the micro-scale is very important in many industrial and biochemical process. Electrical resistance tomography (ERT), as an alternative technique to visualize multi-phase flows, has high temporal resolution for monitoring fast transient processes. However, the complexity of microchannel fabrication end up with the difficulty of electrical data sampling and the non-linearity and ill-posedness of the inverse problem often cause the poor spatial resolution of the reconstructed images. Inthis paper, Agilent based data acquisition hardware was set up for reliable data retrieval from a novel microchannel. Gauss-Newton method with adaptive threshold (GNAT) technique was presented to improve the spatial resolution of ERT images.Preliminary experimental results reveal that ERT system based on Agilent data acquisition unit and GNAT method can effectively visualize the solid-liquid two-phase flow in microchannel

Kerr-Newman Black Hole Thermodynamical State Space: Blockwise Coordinates

A coordinate system that blockwise-simplifies the Kerr-Newman black hole's thermodynamical state space Ruppeiner metric geometry is constructed, with discussion of the limiting cases corresponding to simpler black holes. It is deduced that one of the three conformal Killing vectors of the Reissner-Nordstrom and Kerr cases (whose thermodynamical state space metrics are 2 by 2 and conformally flat) survives generalization to the Kerr-Newman case's 3 by 3 thermodynamical state space metric.Comment: 4 pages incl 2 figs. Accepted by Gen. Rel. Grav. Replaced with Accepted version (minor corrections

Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration

We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound. The method was tested on a dataset of human embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound

Strong correlations and orbital texture in single-layer 1T-TaSe2

Strong electron correlation can induce Mott insulating behaviour and produce intriguing states of matter such as unconventional superconductivity and quantum spin liquids. Recent advances in van der Waals material synthesis enable the exploration of Mott systems in the two-dimensional limit. Here we report characterization of the local electronic properties of single- and few-layer 1T-TaSe2 via spatial- and momentum-resolved spectroscopy involving scanning tunnelling microscopy and angle-resolved photoemission. Our results indicate that electron correlation induces a robust Mott insulator state in single-layer 1T-TaSe2 that is accompanied by unusual orbital texture. Interlayer coupling weakens the insulating phase, as shown by reduction of the energy gap and quenching of the correlation-driven orbital texture in bilayer and trilayer 1T-TaSe2. This establishes single-layer 1T-TaSe2 as a useful platform for investigating strong correlation physics in two dimensions

A systematic review of the effect of footwear, foot orthoses and taping on lower limb muscle activity during walking and running

Background: External devices are used to manage musculoskeletal pathologies by altering loading of the foot, which could result in altered muscle activity that could have therapeutic benefits. Objectives: To establish if evidence exists that footwear, foot orthoses and taping alter lower limb muscle activity during walking and running. Study design: Systematic literature review. Methods: CINAHL, MEDLINE, ScienceDirect, SPORTDiscus and Web of Science databases were searched. Quality assessment was performed using guidelines for assessing healthcare interventions and electromyography methodology. Results: Thirty-one studies were included: 22 related to footwear, eight foot orthoses and one taping. In walking, (1) rocker footwear apparently decreases tibialis anterior activity and increases triceps surae activity, (2) orthoses could decrease activity of tibialis posterior and increase activity of peroneus longus and (3) other footwear and taping effects are unclear. Conclusion: Modifications in shoe or orthosis design in the sagittal or frontal plane can alter activation in walking of muscles acting primarily in these planes. Adequately powered research with kinematic and kinetic data is needed to explain the presence/absence of changes in muscle activation with external devices. Clinical relevance: This review provides some evidence that foot orthoses can reduce tibialis posterior activity, potentially benefitting specific musculoskeletal pathologies

Symmetrical arrangement of proteins under release-ready vesicles in presynaptic terminals

Controlled release of neurotransmitters stored in synaptic vesicles (SVs) is a fundamental process that is central to all information processing in the brain. This relies on tight coupling of the SV fusion to action potential-evoked presynaptic Ca2+ influx. This Ca2+-evoked release occurs from a readily releasable pool (RRP) of SVs docked to the plasma membrane (PM). The protein components involved in initial SV docking/tethering and the subsequent priming reactions which make the SV release ready are known. Yet, the supramolecular architecture and sequence of molecular events underlying SV release are unclear. Here, we use cryoelectron tomography analysis in cultured hippocampal neurons to delineate the arrangement of the exocytosis machinery under docked SVs. Under native conditions, we find that vesicles are initially "tethered" to the PM by a variable number of protein densities (∼10 to 20 nm long) with no discernible organization. In contrast, we observe exactly six protein masses, each likely consisting of a single SNAREpin with its bound Synaptotagmins and Complexin, arranged symmetrically connecting the "primed" vesicles to the PM. Our data indicate that the fusion machinery is likely organized into a highly cooperative framework during the priming process which enables rapid SV fusion and neurotransmitter release following Ca2+ influx