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The high impedance faults (HIF) in microgrids are characterized by low magnitude fault current with inherent non‐linearity and random behavior. The possible weather intermittency in PV integrated microgrids and the intrusion of harmonics due to power converters and non‐linear loading, further reduce the fault current during HIF. The insignificant fault current does not allow detection of HIF by conventional overcurrent relays. In this regard, a reliable and robust protection scheme based on the combined framework of probabilistic modelling, least square and Adaline algorithm, sine cosine algorithm (SCA) and support vector machine (SVM), has been proposed to perform the tasks of mode detection, fault detection/classification and section identification during HIF in microgrid. The algorithm involves online extraction of harmonic components and stochastic modelling of intermittency in solar irradiance. The performance of the proposed scheme has been analysed in terms of robustness against harmonics intrusion and weather intermittency during HIF. The performance comparison of the proposed scheme with the decision tree (DT) and classical SVM based techniques reflects its effectiveness with improved immunity against harmonic intrusion and weather intermittency. Further, the proposed scheme has been validated using OPAL‐RT digital simulator for evaluating its appropriateness in real‐time settings.

The intermittency in weather complicates the task of determining the optimal setting of protective relays in hybrid photovoltaic (PV)–wind-based microgrids. Classical overcurrent relays quite often maloperate during fluctuations in wind speed and solar irradiance. Motivated by the significance of reducing the sensitivity of microgrid protection against weather intermittency, this article proposes a protection scheme based on the combined framework of stochastic modeling of weather intermittency using probability distribution function and ensemble-based classifier. Considering the possible similar weather scenario to which both the distributed energy resources are subjected to, the covariability has been considered to develop the joint probabilistic model of variation in wind speed and solar irradiance. The model derived using the historical data allows incorporating the weather intermittency in the formulation of the protection algorithm. The data generated under intermittency are processed using a wavelet transform to derive discriminatory attributes. Using the derived attributes, a rotation forest-based classifier has been developed to perform fault detection/classification and faulty section identification. The scheme has been validated for a wide range of weather and fault scenarios. The comparison of the proposed scheme with other reported techniques reflects its significance in providing resilient and robust protection to the microgrid against weather intermittency.

Wide area monitoring and control of modern power network demand real-time estimation of state variables from sensor measurements. Maintaining a high degree of reliability and accuracy in the state estimation process is important in avoiding any disruption in the electricity market operation. The market operation in power networks aims at providing a win-win situation for both the utility and consumer. The exposure and vulnerability of cyber components in smart grids allow for manipulating the electricity market by falsifying the state variables. The attacker can cause intentional profit/loss to the utility/consumer by misdirecting the estimated states through the injection of false data into the sensor information. Hence, maintaining integrity in the market operation demands a mechanism for detecting false data injection attack (FDIA). This paper proposes a classification-based approach for detecting FDIAs aiming at electricity market disruption. For any variation in the predicted and real-time nodal electricity price, the proposed decision tree (DT) based ensemble classifier is executed using state information to identify the prevailing scenario as a contingency or FDIA. The effectiveness of the proposed scheme has been extensively validated for various contingency and FDIA scenarios in IEEE 14 bus, 39 bus, and 57 bus test power systems.

The present load demand and intermittency in the operation of photovoltaic (PV) sources has led to the wide usage of power electronic converters in PV integrated microgrids. A resilient microgrid operation requires early restoration to the healthy state post occurrence of faults in the converter or distribution line. The reported microgrid protection schemes aim at fast and accurate detection and classification of faults. Alongwith the intended protection task of fault detection and classification, the protection scheme designed for the distribution system should be selective to faults in the line only. However, imparting selectivity is quite often challenging because of the similar voltage-current profile during converter switching faults and symmetrical/unsymmetrical line faults. The similarity leads to nuisance tripping and unreliable operation of microgrid with classical protection schemes. In this regard, a Convolutional neural network (ConvNet) based protection scheme has been proposed for islanded microgrids to discriminate between inverter faults in the PV system and symmetrical/unsymmetrical faults in the distribution line, in addition to detecting/classifying the faults and identifying the faulty section. The voltage and current signals recorded at the relaying buses are converted into grayscale images to form the input dataset, which is utilized by ConvNet to derive the required relaying action. The performance of the proposed scheme has been examined and compared with Decision tree (DT) and Support vector machine (SVM) based microgrid protection schemes in terms of reliability indices i.e., dependability, security and accuracy for different scenarios. The proposed scheme has also been substantiated on OPAL-RT digital simulator to examine its feasibility for real-time field applications.

The ever increasing power demand and stress on reducing carbon footprint have paved the way for widespread use of photovoltaic (PV) integrated microgrid. However, the development of a reliable protection scheme for PV integrated microgrid is challenging because of the similar voltage-current profile of PV array faults and symmetrical line faults. Conventional protection schemes based on pre-defined threshold setting are not able to distinguish between PV array and symmetrical faults, and hence fail to provide separate controlling actions for the two cases. In this regard, a protection scheme based on sparse autoencoder (SAE) and deep neural network has been proposed to discriminate between array faults and symmetrical line faults in addition to perform mode detection, fault detection, classification and section identification. The voltage-current signals retrieved from relaying buses are converted into grey-scale images and further fed as input to the SAE to perform unsupervised feature learning. The performance of the proposed scheme has been evaluated through reliability analysis and compared with artificial neural network, support vector machine and decision tree based techniques under both islanding and grid-connected mode of the microgrid. The scheme has been also validated for field applications by performing real-time simulations on OPAL-RT digital simulator.

Operation of wind turbine-based microgrids is characterized by increased intermittency due to the uncertainty in wind speed, which significantly affects the voltage-current profile. Conventional overcurrent relays based on prespecified threshold setting quite often are not able to detect faults under sporadic behavior of wind generators. With the aim of developing a protection scheme which is immune to the stochastic variation in wind speed under both grid-connected and islanding mode, a technique based on Discrete wavelet transform (DWT) and Extreme learning machine (ELM) has been proposed for mode detection, fault detection/classification and section identification. Uncertainty in wind speed has been modeled using Weibull distribution function and further incorporated in the protection modules. The proposed scheme has been validated using different statistical indices and compared with reported techniques for varying fault scenarios. Further, the effectiveness of the proposed scheme has also been validated for practical field applications by performing real-time simulations.

We consider the Variational Data Assimilation (VarDA) problem in an operational framework, namely, as it results when it is employed for the analysis of temperature and salinity variations of data collected in closed and semi closed seas. We present a computing approach to solve the main computational kernel at the heart of the VarDA problem, which outperforms the technique nowadays employed by the oceanographic operative software. The new approach is obtained by means of Tikhonov regularization. We provide the sensitivity analysis of this approach and we also study its performance in terms of the accuracy gain on the computed solution. We provide validations on two realistic oceanographic data sets.

Of late, induction motors (IMs) have been established as one of the most important candidates for electric vehicle (EV) applications. Satisfactory performance of IM driven EV is immensely reliant on the faithful working of all of its major components of which sensors are the integral part. However, current sensors are mostly disposed to the failure. Sudden failure of current sensor degrades the performance of a drive system and may even be catastrophic in nature. Under these circumstances, the present paper proposes a flux-linkage observer-based current sensor fault-tolerant control scheme for indirect vector controlled IM driven EV. The performance of the proposed scheme is verified in MATLAB/Simulation. The FTC scheme is further validated experimentally through dSPACE-1103-based laboratory standard prototype.

This paper describes an experiment involving visual object fMRI brain data and the NeuCube [1] architecture. fMRI spatio- and spectro- temporal data (SSTD), apart from EEG, audio and video data, comprises both space and time information, that requires a specific and specialized architecture to process, interpret and visualize the data for better understanding and interpretation of the information it may carries. At the same time, any patterns can be better recognized and thus new knowledge that may be embedded within the pattern can be extracted. From the experiment with the case study of Haxby fMRI data, NeuCubeB has accomplished better accuracy in recognizing the brain patterns compared with the standard machine learning techniques (i.e. SVM and MLP). In addition, the NeuCube method assists deep learning of the SSTD and deeper analysis of the spatio-temporal characteristics and patterns in the fMRI SSTD.

The recent technological advancements and rising power demand at the distribution level have motivated the wider utilization of distributed energy resources (DER) in the distribution system. However, the use of DERs in the microgrid has led to several protection challenges due to dynamic operational behavior of DERs and associated power electronic interfaces. In this context, a protection algorithm based on the joint framework of discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS) for PV integrated microgrid with non-linear loading has been developed in this paper. ANFIS combines the adaptive ability of ANN and qualitative approach of fuzzy logic thus forming a hybrid model with improved accuracy. The proposed approach includes the derivation of discriminatory attributes (the standard deviation (SD) of the approximate co-efficient) from voltage-current signals through discrete wavelet transform (DWT), which is further fed as input to the train the ANFIS model separately for the islanded and grid-connected mode. The test result analysis and performance comparison of the proposed scheme with ANN clearly validate the reliability and effectiveness of the scheme in imparting possible protection to the microgrid.

A convoy usually involves many vehicles travelling together in a procession towards the same destination with a marshal whom is usually appointed for a safer, enjoyable and efficient driving from the start until the end of the journey. The trip may involve several stopping points, passing through several traffic lights, junctions, township and other obstacles that may affect the convoy’s overall journey. To ensure efficient communication between drivers in the convoy, walkie-talkie has been a preferred communication tool. However, walkie-talkie only transmits voices hence the group are not able to visualize the route while travelling. This study proposes an analysis of multi-platform mobile application for navigation of vehicles travelling together in a convoy. During the analysis phase, requirements engineering diagrams consisting of goal model, scenario, and use case diagram are produced. Next, the application prototype that capitalizes on Global Positioning Service (GPS) is developed.

The need for enhancing grid resilience has led to wider acceptance of photo voltaic (PV) integrated microgrid. The variation in fault current during grid connected and islanding operation makes the microgrid protection task challenging. A protection scheme based on ensemble of classifiers has been devised by exploiting the effectiveness of a classifier set coupled with voting strategy. Unlike the existing classifier-based approaches involving single classifier, the ensemble-based approach is insensitive to the biasness of individual classifier and dimension/size of dataset. The proposed scheme is formulated to simultaneously perform the tasks of mode detection, fault detection/classification, section identification and location. The scheme is able to discriminate between faults and power quality disturbances, which avoids unintended false tripping. Along with accuracy, the reliability assessment of proposed scheme has been carried out using two indices, i.e. dependability and security for different faults, operating mode and contingencies. For evaluating the reliability of the fault locator, a stochastic approach (Monte-Carlo simulation) has been adopted. The simulation results justify the applicability of the proposed ensemble classifier-based scheme for complete protection and reliable operation of microgrids with PV penetration. The scheme also has been validated for real-time settings using hardware in loop simulations.

In this work, we present an optimization strategy for implementing the BiCGStab iterative solver on graphic processing units (GPU) for computing incompressible viscous flows governed by the unsteady Navier–Stokes (N–S) equations on a CUDA platform. A recently developed $$\psi $$ –v formulation is used to discretize the biharmonic form of the N–S equation. Special emphasis was given on optimizing the matrix-vector multiplication and the dot product kernels in GPU as the bulk of the computational efforts was seen to be occupied by these two kernels. The parallel code was implemented on the benchmark problem of lid-driven square cavity flow and remarkable speed-up up to 20 times was achieved on finer grids. The GPU implementation enabled us to compute the flow on extremely fine grids and very small scales were resolved with remarkable accuracy.

The aim of this module is to learn the method of outcome-based project cycle for rights-based Integrated Child Protection Centres. It starts by discussing the conceptual framework of the outcomes-based project cycle, where the outcome is central to assessment, planning, implementation, monitoring and evaluation of a project. It then focuses on the first stage of planning this cycle through the stage of identifying rights-based outcomes and developing quantitative and qualitative indicators for child protection and selection of primary and secondary sources of data. The next stage is learning about outcome-based situational assessment necessary for creating the baseline data and identification of needs, for using them for planning of projects for child protection. The importance, planning, criteria and standards of monitoring and evaluation, which form the important core of the outcome-based project cycle, are then discussed, followed by the concept and types of monitoring and the concept and types of evaluation. Finally, the module facilitates learning the skills of outcomes-based report writing, drawing recommendations and action planning, and dissemination and utilisation of the report, for improved planning for child protection.

The manifested merits associated with solar energy including high sustainability, zero greenhouse gas emission and economic operation have encouraged wide penetration of photovoltaic (PV) systems in the microgrid, during the last few decades. However, the intermittency caused due to the fluctuating nature of solar irradiance demands an efficient maximum power point tracking (MPPT) algorithm for PV-integrated microgrids. The scenarios related to partial shading and faults in PV array impact the voltage–current behaviour resulting in the failure of conventional MPPT techniques in accurately estimating the operating point. The incorrect estimation by MPPT techniques quite often affects the operation of overcurrent protection modules. In this regard, this chapter presents an accurate sine cosine optimization (SCA)-based MPPT algorithm which will search the global operating point irrespective of the condition (i.e. in the event of partial shading or array faults), while avoiding undesired activation of the protection system. Besides this, a reliable protection scheme is proposed to detect and classify the faults in the distribution line under dual operating modes of microgrid (i.e. grid-connected and islanding). The instantaneous voltage–current signals recorded at the relaying bus are preprocessed through discrete wavelet transform (DWT) to obtain the discriminatory attributes, which are further utilized by the hybrid framework of artificial neural network (ANN) and SCA to perform the intended protection tasks under both modes of microgrid operation. The performance of the proposed MPPT technique and protection scheme has been analysed against a wide range of operating scenarios with real-time validation on OPAL-RT digital simulation platform.

It is a great pleasure to introduce this special issue of the International Journal of Parallel Programming on Hybrid Parallelism in new HPC Systems. The idea of this publication is born during the “organization” of the Third Workshop on Models, Algorithms and Methodologies for Hybrid Parallelism in New HPC Systems MAMHYP-15, held in Krakow (Poland) in September 2015, jointly with the 11th International Conference on Parallel Processing and Applied Mathematics (PPAM 2015). Since 2011

Data assimilation (DA) is a methodology for combining mathematical models simulating complex systems (the background knowledge) and measurements (the reality or observational data) in order to improve the estimate of the system state (the forecast). The DA is an inverse and ill posed problem usually used to handle a huge amount of data, so, it is a large and computationally expensive problem. Here we focus on scalable methods that makes DA applications feasible for a huge number of background data and observations. We present a scalable algorithm for solving variational DA which is highly parallel. We provide a mathematical formalization of this approach and we also study the performance of the resulted algorithm.

The present power scenario has witnessed an unprecedented rise in the penetration level of renewable distributed energy resources (DERs). The wider acceptance of photovoltaic (PV) and wind-based DERs in microgrids poses severe protection challenges due to the uncertain variation in solar irradiance level and wind speed. The sporadic variation often results in underreach and overreach of overcurrent relays. With the aim of imparting robustness to the protection scheme against weather intermittency, a technique based on meteorological data-dependent joint probabilistic modeling of the stochastic variation in solar irradiance and wind speed has been integrated with a discrete wavelet transform (DWT) based feature extraction module and an optimal random forest (ORF) based classifier to perform fault detection/classification and zone identification. The performance of the proposed protection scheme has been examined for diverse test cases and compared with other computational intelligence techniques using numerical and real-time simulations.

PV integrated microgrids are characterized by increased intermittency because of the uncertainty in solar irradiance level. In an islanded mode, the irradiance variation significantly affects the current profile both during the healthy and faulty scenario, thereby increasing the risk of maloperation of conventional relays based on threshold settings. A fault-resilient and reliable microgrid demands the development of a protection scheme, which is robust to varying irradiance levels. In this regard, this paper proposes a protection scheme based on Convolutional neural network (ConvNet) approach of deep learning for fault detection/classification, section identification and location during islanded mode. The use of ConvNet allows identifying discriminatory attributes from complex datasets with reduced computational cost. The time-domain voltage and current signals retrieved from the relaying bus post-fault are fed as input to the respective ConvNets trained to perform the intended tasks of classification and regression. The uncertainty in irradiance has been incorporated by modeling irradiance levels over a period using probability distribution function (PDF). The incorporation of spatio-temporal data-based distribution function within the framework of spatial data based ConvNet allows for improved mapping between the multi-domain data and state of the microgrid. The performance of the proposed technique under varying irradiance and fault scenarios has been analyzed through statistical indices and Monte-Carlo simulations. Further, the appropriateness of proposed technique has also been validated for real-time setting using the OPAL-RT digital simulator.

Augmented reality (AR) is a technology that overlay the computer generated image on real world object. AR has been implemented in various fields and one of the fields is edutainment-based game. Edutainment-based game is a game designed to make the learning process become more fun. Among the edutainment gameplay genre such as tile-matching, action puzzles, traditional puzzle games, and hidden object. However, there are still a lack of edutainment gameplay that utilizes AR technology for hidden object game genre. The hidden object game in the market today does not use 3D scene as their background. Therefore, the objective of this project is to develop an AR edutainment game in 3D scene view by using hidden object puzzle genre. This edutainment game can be used as an alternative edutainment teaching aids in kindergarten and primary school. Player’s enjoyment is measured based on the evaluation of user acceptance level towards the gameplay, functionality and performance level. Results of user testing shows that 67% of the total respondents strongly agreed that this game is enjoying to be played. In addition, there are 53% of the respondents agreed that the game is stable when being played in a mobile device. Next, there are 67% of the respondents agreed that the presentation of the information and instruction are clear.

The growing penetration of distributed energy resources (DERs) in modern power distribution networks operating as microgrid poses a great challenge for the conventional protection scheme due to significant variation in the fault current levels under the grid-connected and islanded mode of operation. In this regard, this paper has devised an efficient protection scheme based on discrete wavelet transform (DWT) and k-nearest neighbour (kNN) for fault detection/classification implemented for dual modes of microgrid operation considering the photovoltaic PV source and nonlinearity in the load. The proposed approach utilizes the three-phase voltage and current signals obtained during shunt faults in the distribution line under widely varying fault parameters. The pre-processing of signals through DWT determines the approximate coefficient. The standard deviation (SD) of the approximate coefficient so obtained is further fed as the input to the kNN-based classifier for fault detection/classification task separately for grid-connected and islanded mode. The test result analysis clearly reveals the effectiveness of the proposed approach and hence validates the performance.

With the advent of distributed generation in the power distribution network due to rising concern towards the green energy, the power generation scenario has witnessed a gradual transition from large sized power plants to the micro-sized distributed generating units involving the large penetration of distributed energy resources (DER). The associated protection problems due to distinct operating characteristics of DERs and loads with different operational dynamics associated with dual operating modes, demand for efficient protection technique. In this regard, this paper presents a wavelet and ensemble of SVM classifier based scheme to perform fault detection/classification considering non-linear load under dual mode of microgrid. The proposed scheme utilizes standard deviation of the approximate coefficients obtained during feature extraction process through DWT as the input feature to train the ensemble of SVMs. The test results of the proposed scheme against varying fault parameters and the performance comparison with single SVM classifier clearly reveal the effectiveness of the developed scheme for providing accurate and reliable protection to the microgrid.

This article proposes a new current sensor fault detection scheme for speed sensorless field oriented control (FOC) of induction motor drive (IMD). Detection of the faulty signal, isolation of the faulty sensor and reconfiguration by proper estimation are the three steps involved in the present fault tolerant control (FTC) scheme. Third-difference operator (TDO) is used here for the detection of the faulty current sensor while the concept of vector rotation is employed to estimate the stator current from the reference d-q current. A decision-making logic circuit does the task of isolating the faulty signal and reconfiguring the control scheme by estimated signal. Model reference adaptive system (MRAS) based on reactive power is applied for estimating the rotor speed to make the control scheme speed sensorless. The effectiveness of the proposed scheme is shown by MATLAB/Simulation study. The experimental results in dSPACE-1103 based IM drive further validate the performance of the proposed scheme.

OceanVar is a Data Assimilation (DA) software which is being used in Italy within the Mediterranean Forecasting System (MFS) to combine observational data (Sea level anomaly, sea-surface temperatures, etc.) with backgrounds produced by computational models of ocean currents for the Mediterranean Sea (namely, the NEMO framework). OceanVAR is based on a three-dimensional variational approach. We describe computational efforts aimed to design a fully parallel OceanVar software, based on Domain Decomposition (DD), which involves modification of the variational scheme on each sub domain. Our approach aims to face to the ever greater multi-level parallelism and scalability of the current and of the next generation of leadership computing facility systems, while fulfilling the specific requirements of OceanVar within the MFS.

We present a numerical algorithm for solving large scale Tikhonov Regularization problems. The approach we consider introduces a splitting of the regularization functional which uses a domain decomposition, a partitioning of the solution and modified regularization functionals on each sub domain. We perform a feasibility analysis in terms of the algorithm and software scalability, to this end we use the scale-up factor which measures the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Data Assimilation problem for oceanographic models).

Matrix multiplication (MM) is a computationally-intensive operation in many algorithms used in scientific computations. Not only one of the kernels in numerical linear algebra, the problem of matrix multiplication is also fundamental for almost all matrix problems such as least square and eigenvalues problem. The performance analysis of the MM needs to be re-evaluated to find out the best-practice algorithm on novel architectures. This motivated the analysis which is presented in this article and which is carried out by means of the new modelling framework that the authors have already introduced (L. D’Amore et al. On a Mathematical Approach for Analyzing Parallel Algorithms, 2015). The model exploits the knowledge of the algorithm and the multilevel parallelism of the target architecture and it could help the researchers for designing optimized MM implementations.

The development of a parallel software for simulating large-scale time-dependent 3D problems involving the flow of multimode viscoelastic fluids is presented. The computing environment relies on PETSc [27] (Portable, Extensible Toolkit for Scientific Computation) components integrated with a finite element solver. A DEVSS-G/SUPG formulation together a log-representation of the conformation tensor are used to stabilize the code. An operator-splitting time-integration scheme is implemented whereby the solution of the continuity and momentum balance equations are decoupled. The large linear system arising from the continuity and momentum discretization is solved by the multifrontal, massively parallel solver (MUMPS) [37]. The time-integration scheme applied to the constitutive equation gives rise to six, decoupled linear systems (one for each stress component) solved by MUMPS as well. Three test cases are selected to show the performances and the limitations of the parallel code. In the first example, the flow of a Giesekus fluid in a pressure-driven square-shaped channel is examined. In the second test, the unsteady drag on a sphere is computed and, in the third example, a solid particle in a sheared viscoelastic fluid is considered, thus involving a moving boundary. The parallel software gives high performances for problems such that the finite element mesh is independent of time. In this case, the coefficients of the Stokes-like system are constant in time and the matrix factorization is computed at the first time step only and kept during the whole simulation, drastically reducing the total computational time. On the other hand, whether the factorization needs to be computed at each time step, the solution time increases by more than one order of magnitude, suggesting to seek for alternative iterative solvers. Finally, for all the test cases, scalability of parallel software is evaluated using the speed-up normalized with respect to the software execution time on a number of processors greater than 1. This was necessary due to the high memory requirements. Experimental results validate the performance gain of the parallel code as both the problem size and the processor number increase. (C) 2011 Elsevier B.V. All rights reserved.

Large-scale problems are computationally expensive and their solution requires designing of scalable approaches. Many factors contribute to scalability, including the architecture of the parallel computer and the parallel implementation of the algorithm. However, one important issue is the scalability of the algorithm itself. We have developed a scalable algorithm for solving large scale Data Assimilation problems: starting from a decomposition of the mathematical problems, it uses a partitioning of the solution and a modified regularization functionals. Here we briefly discuss some results.

This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.

Deep learning machine that employs Spiking Neural Network (SNN) is currently one of the main techniques in computational intelligence to discover knowledge from various fields. It has been applied in many application areas include health, engineering, finances, environment, and others. This paper addresses a classification problem based on a functional Magnetic Resonance Image (fMRI) brain data experiment involving a subject who reads a sentence or looks at a picture. In the experiment, Signal to Noise Ratio (SNR) is used to select the most relevant features (voxels) before they were propagated in an SNN-based learning architecture. The spatiotemporal relationships between Spatio Temporal Brain Data (STBD) are learned and classified accordingly. All the brain regions are taken from data with label star plus-04847-v7.mat. The overall results of this experiment show that the SNR method helps to get the most relevant features from the data to produced higher accuracy for Reading a Sentence instead of Looking a Picture.

Modern Graphics Processing Units (GPUs) have been at the leading edge of increasing parallelism over the last ten years. This fact has encouraged the use of GPUs in a broader range of applications, where developers are required to lever age this technology with new programming models which ease the task of writing programs to run efficiently on GPUs. In this paper, we discuss the main guidelines to assist the developer when porting sequential scientific code on modern GPUs. These guidelines were carried out by porting the L-BFGS, the (Limited memory -) BFGS algorithm for large scale optimization, available as Harwell routine VA15. The specific interest in the L-BFGS algorithm arises from the fact that this is the computational module with the longest running time of a Oceanographic Data Assimilation application software, on which some of the authors are working.

A key issue in designing application codes which are able to effectively take advantage of the available high performance computing resources is to deal with the software management and development complexity. This is even more true for imaging applications. This work is the first dowel in a rather wide mosaic, aimed at construction of a PSE (Problem Solving Environment) oriented to imaging applications. We discuss computational efforts towards the development of a distributed software environment, enabling the transparent use of high performance computers and storage systems for 3-D Echocardiographic sequences denoising via nonlinear diffusion filtering. More precisely, we describe a component-based approach for the development of an integrated software environment relying on the Portable, Extensible Toolkit for Scientific Computation (PETSc). Our approach uses a distributed memory model where we hide, within the PETSc parallel objects, the details of internode communications while intranode communications have been handled at a higher level. We report some experiences we made on an in vivo acquired 3-D Echocardiographic sequence obtained by means of a rotational acquisition technique using Tomtec Imaging system. btained by means of a rotational acquisition technique using Tomtec Imaging system.

This paper addresses the scientific challenges related to high level implementation strategies which steer the NEMO (Nucleus for European Modelling of the Ocean) code toward the effective exploitation of the opportunities offered by exascale systems. We consider, as case studies, two components of the NEMO ocean model (OPA-Ocean PArallelization): the Sea Surface Height equation solver and the Variational Data Assimilation module. The advantages rising from the insertion of consolidated scientific libraries in the NEMO code are highlighted: such advantages concern both the “software quality” improvement (see the software quality parameters like robustness, portability, resilience, etc.) and the reduction of time spent for software development and maintenance. Finally, we consider the Shallow Water equations as a toy model for NEMO ocean model to show how the use of PETSc objects predisposes the application to gain a good level of scalability and efficiency when the most suitable level of abstraction is used.

Cloud computing is the use of computing assets or resources (hardware and software) that are consigned as a service over the internet. If cloud computing environment is deployed in a data centre than users can store their data in the cloud centers (i.e. Cloud storage) using storage as a service. When data centre provides storage as a service, the capacity of data centre is biggest challenge. The management required to maintain the capacity at the data centre result in increase of overhead and cost of service on cloud. Therefore to provide scalable and elastic cloud storage to the users or consumers virtualization of data centers is required. In this work, to manage the capacity of these virtual data centers efficiently, it is proposed to do the scheduling of resources over the data centers. The results of the implementation of the proposed methodology not only provides efficient capacity management but also reduces communication overheads and minimize computational cost of the cloud.

Real time image sequences analysis is a challenge. Using high performance computing technologies, a parallel algorithm for performing data sequence analysis is proposed. We call it pipelined algorithm PA. The idea underlying the design of PA comes from the Pipes and Filters design approach: to partition the sequence into ordered subsets and to overlap tasks execution via pipelining. Moreover, in order to improve the performance gain of the PA algorithm, tasks' execution is distributed among multicore processors. The approach chosen for introducing concurrency takes into account the hierarchical parallelism of system architecture of multicore multiprocessors. More precisely, three parallelization strategies of PA are considered: first strategy distributes the execution of each task among the same number of cores employing a fine-grained task parallelism we call it inter-task data parallelism, second strategy refers to the execution of each task to one core introducing concurrency at a coarser level we call it intra-task functional parallelism, and the last one combines the previous two approaches: it refers to the mapping of each task to a group of cores intra-task functional parallelism distributing task's execution within each group inter-task data parallelism. We prove, both theoretically and experimentally that the third strategy is more effective than the others in terms of speed up improvement as the data length increases. As testbed the segmentation of ultrasound sequences is considered. Experiments on real data are carried out using a multicore-based parallel computer system relying on PETSc Portable Extensible Toolkit for Scientific computation, a high level software computing environment.

Mobile games have become one of the most popular entertainment medium nowadays. Mobile games with wide variety of genres can be found and downloaded easily through digital marketplace such as Google Play and Apps Store. Elements of cultural festival has also been developed as mobile games. However, the amount of existing mobile games that contain Malaysia's local cultural content are limited. Malaysia is a unique country consists of multiple ethnics and cultures. Therefore, a game has been developed in this project to introduce the unique elements of Malaysia's culture to the user. The game is created based on the theme of Chinese New Year celebration in Malaysia. Game Development Life Cycle (GDLC) was used as a methodology. The game is implemented in mobile platform and in English language. The game uses a 2D side scrolling design approach where swiping method is used to control the game character. Technology Acceptance Model (TAM) is adapted to measure user acceptance level in terms of game design, functionality and overall game performance. Results of user acceptance test shows that 50 % of respondents agreed that the developed mobile game able to provide general idea of how Chinese New Year is celebrated in Malaysia.

In this article, we propose a new architecture-level parameterized dynamic thermal behavioral modeling algorithm for emerging thermal-related design and optimization problems for high-performance multicore microprocessor design. We propose a new approach, called ParThermPOF , to build the parameterized thermal performance models from the given accurate architecture thermal and power information. The new method can include a number of variable parameters such as the locations of thermal sensors in a heat sink, different components (heat sink, heat spreader, core, cache, etc.), thermal conductivity of heat sink materials, etc. The method consists of two steps: first, a response surface method based on low-order polynomials is applied to build the parameterized models at each time point for all the given sampling nodes in the parameter space. Second, an improved Generalized Pencil-Of-Function (GPOF) method is employed to build the transfer-function-based behavioral models for each time-varying coefficient of the polynomials generated in the first step. Experimental results on a practical quad-core microprocessor show that the generated parameterized thermal model matches the given data very well. The compact models by ParThermPOF offer two order of magnitudes speedup over the commercial thermal analysis tool FloTHERM on the given examples. ParThermPOF is very suitable for design space exploration and optimization where both time and system parameters need to be considered.

This work is carried out within a research collaboration with the Astronomical Observatory of Arcetri (Florence, Italy) aimed at the development of a parallel version of the adaptive optics simulation software named CAOS (Code for Adaptive Optics System). CAOS is currently used at the Arcetri Observatory to simulate the behaviour of an adaptive optic system, from the atmospheric turbulence corruption to the subsequent adaptive correction made on the wavefronts coming from the astronomical objects onto the pupils of the ground-based telescopes. We describe the computational efforts towards the integration of the CAOS package into a high-performance computing architecture, implementing three different parallelization approaches of a fast fourier transform (FFT)-based CAOS module and discuss the performance using standard performance metrics.

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.

In this work, we present an optimization strategy for implementing the BiCGStab iterative solver on graphic processing units (GPU) for computing incompressible viscous flows governed by the unsteady Navier-Stokes (N-S) equations on a CUDA platform. A recently developed ψ-v formulation is used to discretize the biharmonic form of the N-S equation and we obtain remarkable speed up of 40 times on finer grids for the lid-driven square cavity flow. The GPU implementation enabled us to compute the flow in extremely finer grids and very small scales were resolved with remarkable accuracy.

Wireless Sensor Networks are heavily applied in many domains. WSN consist of two or more sensing nodes which have capacity to sense compute and transmit-receive. WSN was initially developed for military and disaster rescue purposes. The salient features in Wireless Sensor Network make it special among other networks are; self-organization, low electricity usage, low memory usance, low bandwidth requirement for communication, large-scale nodes, wireless and infrastructure-less architecture. However each node in the sensor network is comprised of its own sensor, processor and radio transceiver, so each of the sensor-nodes have the ability to sense, process the data and the analysis of the data collected can be transited over the network to communicate the results with each other. There is a great range of domains and research areas where the WSNs have places, such as patient tracking, target tracking etc. In this survey various existing MAC protocols are discussed and compared with each-other.

In this work, a parallel double adaptive algorithm for the computation of a multidimensional integral on multicore based multicomputer systems is described. This new algorithm is the revision of a procedure developed by one of the present authors for multicomputer systems, with the aim to introduce features for an efficient implementation in multicore based hierarchical environments. Two different adaptive strategies have been combined together in the algorithm: a first procedure is responsible for load balancing among the system nodes and a second one is responsible for coordinating the cores within a single node. The performance is analyzed and experimental results on a Blade Server with 8 nodes and 2 quad-core CPUs per node have been achieved. © 2011 Springer Science+Business Media, LLC.

In this paper, we consider nonlinear partial differential equations (PDEs) of diffusion/advection type underlying most problems in image analysis. As case study, we address the segmentation of medical structures. We perform a comparative study of numerical algorithms arising from using the semi-implicit and the fully implicit discretization schemes. Comparison criteria take into account both the accuracy and the efficiency of the algorithms. As measure of accuracy, we consider the Hausdorff distance and the residuals of numerical solvers, while as measure of efficiency we consider convergence history, execution time, speedup, and parallel efficiency. This analysis is carried out in a multicore-based parallel computing environment.

Recent Washington State Ferries (WSF) origin–destination travel survey data collection and model development activities are described. WSF performed similar activities in 1993. The lessons learned from the initial project helped to develop a better survey design and sampling plan. The overall survey response rate was 37%, about 28% higher than the response rate for the 1993 travel survey. The improved survey design and sampling plan also provided a richer data platform from which to establish more representative observed baseline ferry travel patterns, by boarding method and access and egress mode combinations. Compared with 1993, the 1999 travel survey results reflect a significant increase in the systemwide use of transit for access to and egress from ferry terminals. This increase was primarily attributed to improved transit service, timed ferry-bus connections, and ferry-bus passes implemented by Kitsap Transit and other transit agencies. In addition to the 1999 travel survey, WSF conducted a stated preference survey. The combined data from these two surveys provided information on ferry users’ revealed and stated preferences, which allowed a more rigorous statistical mode and route choice estimation analysis to be performed. This analysis helped develop a more robust and internally consistent model, as reflected in model performance. For walk-on and automobile-boarding passengers, the updated WSF model has produced more accurate route-level validation results than the previous model. The results from the model estimation and validation analyses are presented. Specific improvements made in the updated model are highlighted.

We describe a parallel software library, named MEDITOMO, designed for processing MEDIcal images obtained by SPECT (single photon emission computed tomography) TOMOgraphic systems. MEDITOMO is the core library of the PSE (problem solving environment) MEDIGRID, oriented to medical imaging analysis, which the authors are currently developing. MEDIGRID is employed in a Grid-computing infrastructure involving clinical departments and research institutes. The algorithms of MEDITOMO are the standard ones that are usually applied in the SPECT analysis, i.e. the conjugate gradient and the expectation maximization. The main contribution of this work concerns the introduction of the total variation seminorm as the edge-preserving regularization in both algorithms and the development of the parallel software library. Experiments carried out on synthetic and clinical data are shown.

This paper presents a new method and a case study on fMRI spatio- and spectro-temporal data (SSTD) classification with the use of the recently proposed NeuCube architecture [1]. NeuCube is a three dimensional brain-like model of evolving spiking neurons that can be trained with SSTD such as fMRI, EEG and other brain data. This SSTD is mapped, analyzed, modeled and trained, and the result from these processes can be used to better understand the brain processes and to better recognize brain patterns, and thus to extract new knowledge that may reside within the SSTD. From the experimental results we can conclude that the NeuCube architecture is capable of producing significantly more accurate classification results when compared with standard machine learning methods such as SVM and MLP. Moreover, the NeuCube method facilitates deep learning of the SSTD and deeper analysis of the spatio-temporal characteristics and patterns in the fMRI SSTD.

We describe a parallel software for three-dimensional single photon emission computed tomography (SPECT) imaging. The software has been developed within a multi-disciplinary research activity aimed at improving both the mathematical models and the processing time in nuclear medicine. The numerical algorithm implements an edge-preserving regularization approach based on the total variation seminorm for solving the inverse and ill-posed problem describing the SPECT image reconstruction problem. The parallel software has been tested on a low-cost implementation of a parallel architecture made up of a Linux cluster (Beowulf). The proposed approach is compatible with diagnostic times in terms of availability of the results in a suitable turnaround time and of the overall cost of the system infrastructure.