Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1

The profound economic growth in the Arabian Gulf states over the past few decades has had a great impact on the lifestyle of the Qatari population. There has been a rapid appearance of fast food restaurants and other hallmarks of western society. These influences have been accompanied by a higher energy intake and decreased levels of physical activity. The potential impact on the younger population is particularly alarming. Throughout the recent past, the prevalence of type 2 diabetes (T2DM) among children and adolescents has increased significantly due to the high prevalence of obesity. Obese children can have a higher risk of premature mortality due to consequent cardiometabolic morbidity. According to WHO, obesity-induced medical conditions have now led to excess mortality surpassing that associated with tobacco. However, data on the body weight status of Qatari children are lacking for the past ten years. This study estimates the magnitude of the increase in BMI among Qatari adolescents (aged 12-18 years), by comparing our data (obtained during 2009) with published results from 2003 [Bener et al, JHPN 2005;23(3):250-8]. The present data originate from a pilot study on lung function conducted in Qatar in 2009. The subjects were chosen by random sampling of Qatari students attending government schools (grade 7-12). For our study, only students aged 12-18 years are included, resulting in a total number of 705 participants (400 girls and 305 boys). Although a large variety of data was collected, our study focused only on height, weight and BMI. The results reveal a substantial increase in BMI during this 7 year period for both Qatari boys and girls. For boys aged 12 years, mean BMI increased by 2 Kg m-2 which became a 5 Kg m-2 increase at the age of 17 years, and possibly as much as 8 Kg m-2 by 18 years. By contrast, the increase in mean BMI for girls remained more or less constant between the age of 12 years and 17 years, fluctuating between 2 Kg m-2 and 4 Kg m-2, before reaching almost 7 Kg m-2 at the age of 18 years. Using International Obesity Task Force (IOTF) criteria, the overall prevalence of Qatari children who were overweight or obese was 26.5% (boys) and 23.1% (girls) in 2003 [Bener, Food Nutr Bull 2006;27(1):39-45], and 47.2% (boys) and 40.8% (girls) in 2009. For boys, this represents a 21% increase, with a corresponding increase of 18% for girls. These results show that during this 7-year period, the prevalence of overweight and obesity among both boys and girls has increased by more than 75%. Based on these figures, the prevalence of childhood obesity is alarmingly high and points to an acute need for intervention, and a need for local research into the most appropriate and effective actions. In addition, there is also a need to systematically collect regular and ongoing observational data regarding body weight status of adolescent Qataris in order to continue to monitor this situation.

An electronic library is a computer-managed set of collections with services tailored for its user communities. The project team—a collaboration of four universities (Qatar University - QU, Virginia Tech, Pennsylvania State University, Texas A & M University), the Qatar National Library - QNL, and consultants—focused on the two project aims for Qatar: building community and building infrastructure (i.e., collections and information services). Thus we fit with Qatar's Thematic Pillar of Research on Computing and Information Technology, and overlap with a number of Research Grand Challenges (e.g., Cyber-security; Managing the Transition to a Diversified, Knowledge-based Society, and Culture, Arts, Heritage, Media and Language within the Arabic Context). With regard to our aim of building an electronic library community in Qatar, we have: 1. Participated in the Special Library Association Gulf Chapter, hosted in Qatar, to create awareness about electronic libraries; 2. Launched a consulting center at QU Library—with more than 30 new reference works, online educational resources, and specialized databases—and are sharing knowledge with librarians and information professionals to support those interested in collections and services; 3. Established a collaboration with Gulf Studies at QU, so we can identify and host content on this topic, and assist QU researchers and students; and 4. Collected citation-based and non-citation-based metrics (altmetrics), for Qatar and 35 nations that are competing with Qatar's annual scholarly production. We published a new approach for comparing the metrics and evaluating country-level scholarly impact. 5. Studied the evolving scholarly activities and needs of researchers in Qatar, and compared them with our findings from USA, informing ELISQ about requirements and solutions appropriate for international electronic libraries. With regard to our aim of building electronic library infrastructure in Qatar, we have built collections and provided related services: 1. Penn State's SeerSuite software is running at QU, allowing users to search the metadata and full-text of collections of PDF files from scholarly articles, e.g., QScience papers. SeerSuite gathers scholarly documents and automatically extracts metadata (authors, venues etc.) from crawled WWW content, allowing QNL and other libraries to harvest that metadata using OAI-PMH.. SeerSuite is being improved for searching on the content of the figures and tables in scholarly documents. 2. An historical collection of old Arabic documents has been assembled, indexed, and made accessible as well as data/text mined. 3. Using our QU server running Heritrix, gathered our first Arabic collection (8GB from 2,200 PDF files), from Qatari newspapers (Al-Rayah, Al-Watan, Qatar News Agency, Al-Arab, and Al-Sharq). This news collection was indexed with Apache Solr and is available for searching. Building upon the IPTC system we created a categorization system (taxonomy) for news stories, and then applied it through machine learning to train classifiers to aid browsing. 4. Both QNL and QU are building Web archives of portions of the WWW in Qatar, adapting Heritrix and the Wayback Machine, thus preserving history, culture, and Arabic content (including news, sports, government information, and university webpages) for future use and scholarly study.

In the 130 years since the invention of the legacy electric power system concept, electrical generation has been adjusted to match electrical consumption (i.e., the "load") as it varies throughout the time of day and seasons. This "generation follows load" paradigm is a major roadblock to the large-scale incorporation of renewable energy into the national power grid since energy sources such as wind and solar provide inconsistent, variable power that cannot easily be controlled to follow consumption. As a result, today's centrally planned and controlled power system design is no longer adequate. This paper is to introduce a new control approach to enable a "load follows generation" paradigm where a flexible engineered system with distributed control at the users' sites will revolutionize the power industry. Customers will be able to generate power on-site, purchase power from a variety of sources (including each other), sell power back to the grid, select the level of the supply reliability they wish to purchase, and choose how to manage their electricity use. The resulting solution will make the electricity grid significantly more efficient and robust by facilitating extensive use of renewable energy sources and reducing end-use losses in the system. With renewable energy sources widely distributed (e.g., roof-top solar panels and wind farms), the proposed approach will allow exchange of power among utilities, market service providers, consumers, and aggregators (services representing many loads), and also allow power exchange within a customer site. By incorporating this flexibility into their operations, utilities will be able to adjust the overall load they must serve to match available power. This "load follows generation" approach is essential to allow unhindered inclusion of low-emission renewables in the electricity grid. At the same time it makes consumers active agents in the energy exchange. Earlier research addressed discrete aspects of the scientific challenges, but the new approach called Flexible Load Energy eXchange (FLEX) will provide the system-level, holistic approach to achieve its vision. The overarching goal is to develop the science, technology, and control system design required to enable energy exchange between the customer and other parties in the electricity energy exchange ecosystem to unlock the huge potential of renewable generation. A central impediment is the stochastic variability of renewable energy sources such as wind and solar, prompting some to question if dependence on renewable sources will ever be viable. Even when wind (which tends to produce at night) and solar (produced during the day) are combined, severe variations in generation require huge adjustments (termed "ramping" within the industry), and spinning (on-line) reserves using conventional generation. This paper explores the missing critical component of smart grid development: smart and flexible loads. In addition, power interruptions, which are often caused by generation/load imbalances or faults on end-user radial connections, will be greatly reduced in a FLEX-enabled power grid by on-site or alternate customer generation. FLEX will also enable customer participation to create new market arrangements, such as wholesale and retail options, and incentives to increase energy efficiency not previously available.

The aim of this research is to investigate the utilisation of Computational Intelligence (CI) methods for constructing a World Cybersecurity Indicator (WCI) to enable consistent and transparent assessments of the cybersecu- rity capabilities of nation's through the utilisation of Synthetic Composite Indicators (SCI's) concept for ranking their readiness and progress. SCI are assessment tools usually constructed to evaluate and contrast entities perfor- mance by aggregating intangible measures in many areas such as technology and innovation. SCI key value is inhibited in its capacity to aggregate com- plex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing a CI approach to develop a new WCI. The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form a macro level cybersecurity indicator. To illustrate the method of construction a fully worked application is pre- sented. The application employs real variables of possible threats to the In- formation and Communication Technology (ICT). The weighting and aggre- gation results obtained were compared against classical approaches namely Principal Component Analysis, Factor Analysis and the Geometric Mean to weight and aggregate SCI's. The proposed model has the capability of weighting and aggregating major cybersecurity indicators into a single value that ranks nations even with limited data points. The validity and robustness of the WCI is evaluated using Monte Carlo simulation. In order to show the value added by the new cybersecurity index, the WCI is applied to the Middle East and North Africa (MENA) region as a special case study and then generalised. In total seventy-three countries were included, that are representative of developed, developing and under- developed nations. The nal and overall ranking results obtained, suggest novel and unbiased way compared to traditional or statistical methods when building, the WCI.

Social computing has led to new approaches for Knowledge Translation (KT) by overcoming the temporal and geographical barriers experienced in face-to-face KT settings. Social computing based discussion forums allow the formulation of communities of practice whereby a group of professionals disseminate their knowledge and experiences through online discussions on specialized topics. In order to successfully build an online community of practice, it is important to improve the connectivity between like-minded community members and between like-topic discussions. In this paper we present a Medical Online Discussion Analysis and Linkages (MODAL) method to identify affinities between members of an online social community by applying: (a) social network analysis to understand their social communication patterns during KT; and (b) semantic content analysis to establish affinities between different discussions and professionals based on their communicated content. Our approach is to establish linkages between users and discussions at the semantic and contextual levels—i.e. we do not just link discussions that share exact medical terms, rather we link practitioners and discussions that share semantically and contextually similar medical terms, thus accounting for vocabulary variations, concept hierarchies and specialized clinical scenarios. MODAL incorporates two novel semantic similarity methods to analyze online discussions using: (i) the Generalized Vector Space Model (GVSM) that leverages semantic and contextual similarity to find similarities between discussion threads and between practitioners; and (ii) an extension of the Balanced Genealogy Model (BGM) so that we are able to address non-leaf mappings, issues of homonymity noted in medical terminologies, and further contextualization of the similarity measures using information content measures. We have implemented a similarity metric that captures the concept of "interest" between users or threads, i.e., a numeric measure of how interested user A is in user B, or how much of the information contained in thread A is related to thread B. MODAL measures the "interest" one professional has in another professional within the online community, and then uses this metric to identify those professionals that are sought by other professionals for expert advice—the content experts. Furthermore, by incorporating the interest measures with SNA, MODAL is able to identify the content experts within the community, and analyze the content of their conversations to determine their areas of expertise. Given the short and unstructured nature of online communications, we use the MeSH medical lexicons and the medical text analysis tools, i.e. Metamap, to map the unstructured narrative of online discussions to formal medical keywords based on the MeSH lexicon. MODAL is tested on two online professional communities of healthcare practitioners: (a) Pediatric Pain Mailing List is a community of 460 clinicians from around the world--over a four year period 2505 messages were shared on 783 different discussion threads; (b) SURGINET is a community of 865 clinicians from around the world that use the forum to discuss general surgical issues-it contains over 17000 messages on 2111 threads by 231 users. MODAL is able to identify content experts and link like-minded practitioners based on the content of their conversations rather than on direct ties between them.

One of the main issues accompanied with the high penetration of PV distributed generation (DG) systems in low voltage (LV) networks is the overvoltage challenge. The amount of injected power to the grid is directly related to the voltage at the point of common coupling (PCC), which necessitates limiting the amount of injected power to the grid to conservative values compared to the available capacity from the PV panels particularly at light loading. In order to mitigate the tradeoff between injecting the maximum amount of electrical power and voltage rise phenomena, many control schemes were suggested in order to optimize the operation of PV DG energy sources as well as maintaining safe voltage levels. Unlike these conventional methods, this paper proposes a combined PV inverter-based distributed generation and flywheel energy storage system to ensure improved voltage regulation as well as making use of the maximum available power from the PV source at any instant, decoupling its relation with the terminal voltage. The concluded assumptions were simulated through Matlab/Simulink and verified experimentally.

Online social networks and online real time communication tools gained large popularity in the last years. In the Arab spring they emerged as one of the main tools to exchange ideas and to organize activities. Many users were unaware that their communication and their interaction patterns are traced. After the Arab spring and after the information demystified by Edward Snowden, users are becoming security aware and look for alternative technologies to communicate securely. Our contribution in this presentation is twofold, first we present the current technological developments in computer science and networking technologies that envision to provide tools to the users, so that their communication is secured and impossible to track. Based on peer-to-peer-technologies, known from file-sharing applications, several solutions for secure, reliable and untraceable communication already exist. In addition, opportunistic and delay-tolerant networks, provide asynchronous "online" communication which do not even use the Internet infrastructure, thus making it even harder to identify and to trace. As a second contribution, we present a technological framework based on peer-to-peer networking, which allows to build platforms for online social network. In depth, we show how this framework is capable to provide common functionality of online social networks, but at the same time is privacy-aware and impossible to trace or to shut of. The framework uses fully decentralized data storage and sophisticated cryptography for the user management, secure communication and access control to the data of the users. The quality of the presented framework, in terms of performance and communication costs, has been evaluated in simulations with up to 10,000 network nodes as well as in test with up to 30 participants. Our findings are, that up to now the usability, economical pressure and the security of (centralized) communication tools were incompatible. This observation changes with decentralized solutions which do not come with economic pressure. Both secure and usable solutions are about to emerge, fully distributed and secure, which will be impossible to shut down or to trace. For the future it will be interesting, whether the presented decentralized technology will be ignored or whether it will disrupt the economics and the market of social networks, like it was the case with Napster and the music industry, a case in which decentralized peer-to-peer networking unfolded it potential.

Cyber-Physical Systems (CPS) rely on advancements in fields such as robotics, mobile computing, sensor networks, controls, and communications, to advance complex real-world applications including aerospace, transportation, factory automation, and intelligent systems. The multidisciplinary nature of effective CPS research, diverts specialized researchers' efforts towards building expensive and complex testbeds for realistic experimentation, thus, delaying or taking the focus away from the core potential contributions to be made. We present Up and Away (UnA), a cheap and generic testbed composed of multiple autonomously controlled Unmanned Ariel Vehicle (UAV) quadcopters. Our choice of using UAVs is due to the their deployment flexibility and maneuverability enabling a wide range of CPS research evaluations in areas such as 3D localization, camera sensor networks, target surveillance, and traffic monitoring. Furthermore, we provide a vision-based localization solution that uses color tags to identify different objects in varying light intensity environments and use that system to control the UAVs within a specific area of interest. However, UnA's architecture is modular so that the localization system can be replaced by any other system (e.g. GPS) as deployment conditions change. UnA enables interaction with real world objects, treating them as CPS input, and uses the UAVs to carry out CPS specific tasks while providing sensory information from the UAV's array of sensors as output. We also provide an API that allows the integration of simulation code that will obtain input from the physical world (e.g. targets to track) then provide control parameters (i.e. number of quadcopters and destinations coordinates) to the UAVs. The UnA architecture is depicted in Figure 1a. To demonstrate the promise of UnA, we use it to evaluate another research contribution we make in the area of smart surveillance, particularly that of target coverage using mobile cameras. Optimal camera placement to maximize coverage has been shown to be NP-complete for both area and target coverage. Motivated by the need for practical computationally efficient algorithms to autonomously control mobile visual sensors, we propose efficient near-optimal algorithms for finding the minimum number of cameras to cover a high ratio of targets. First, we develop a basic method, called cover-set coverage to find the location/direction of a single camera for a group of targets. This method is based on finding candidate points for each possible camera direction and spanning the direction space via discretizing camera pans. We then propose two algorithms, (1) Smart Start K-Camera Clustering (SSKCAM) and (2) Fuzzy Coverage (FC), which divide targets into multiple clusters and then use the cover-set coverage method to find the camera location/direction for each cluster. Overall, we were able to integrate the implementation of these algorithms with the UnA testbed to witness real-time assessment of our algorithms as shown in Figure 1b.

In today's world, the need for more energy seems to be ever increasing. The high cost and limited sources of fossil fuels, in addition to the need to reduce greenhouse gasses, have made renewable energy sources (RES) attractive in today's world economy. However, the fluctuating and intermittent nature of these RES causes variations of power flow that can significantly affect the stability and operation of the electrical grid. In addition, the power output of these RES is not as easy to adjust to changing demand cycles as the output from the traditional power sources. To overcome these problems, energy from these RES must be stored when excess is produced and then released, when production levels are less than the required demand. Therefore, in order for RES to become completely reliable as primary sources of energy, energy storage systems (ESS) is a crucial factor. The impact of the ESS in future grid is receiving more attention than ever from system designers, grid operations and regulators. Energy storage technologies have the potential to support our energy system's evolution, they can be used for multiple applications, such as: energy management, backup power, load leveling, frequency regulation, voltage support, and grid stabilization. In this work, an overview of the current and future energy storage technologies used for electric power applications is carried out. Furthermore, an assessment of the dynamic performance of energy storage technologies for the stabilization and control of the power flow of emerging smart grid will be presented. The EES can enhance the operation security and ensure the continuity of energy supply in future smart grids.

Power system stability is one of the main factors in performance of electrical system. A control system must retain frequency and voltage size under any distortion such as sudden increase in load, to leave a generator from circuit or cut off a transmission line in a constant level. In this paper, Honey-Bee Mating Optimization (HBMO) algorithm has been used to design power system stabilizer. It is based on the mating between queen and bees. Meta-heuristics honey-bee mating optimization algorithm is considered to be as intelligent algorithm. Simulation results show that HBMO algorithm is simple to solve optimization issues. It works based on the changes in the system to adapt with reality and increase flexible parameters in comparison with other methods. Furthermore, considering performance level, it has suitable standard deviation and convergence of approximation.

The purpose of this paper is mainly aimed to provide an energy security strategy for the petroleum production and processing in the grand challenges. Fault detection and diagnosis is the central component of abnormal event management (AEM) [1]. According to the International Federation of Automatic Control (IFAC), a fault is defined as an unpermitted deviation of at least one characteristic property or parameter of the system from the acceptable/usual/standard condition [2-4]. Generally, there are three parts in a fault diagnosis system, detection, isolation, and identification [5, 6, 7]. Depending on the performance of fault diagnosis systems, they are called FD (for fault detection) or FDI (for fault detection and isolation) or FDIA (for fault detection, isolation and analysis) systems [5]. As the increasing needs for energy grows rapidly, energy security becomes an important issue especially in petroleum production and processing. The importance can be mainly considered in following perspectives: higher system performance, product quality, human safety, and cost efficiency [5, 8]. With this in mind, the purpose of this research is to develop a Fault Detection and Isolation (FDI) system which is capable to diagnosis multiple sensor faults in nonlinear cases. In order to lead this study closer to real world applications in oil industries, the system parameters of the applied system are assumed to be unknown. In the first step of the proposed method, phase space reconstruction techniques are used to reconstruct the phase space of the applied system. This step is aimed to infer the system property by the collected sensor measurements. The second step is to use the reconstructed phase space to predict future sensor measurements, and residual signals are generated by comparing the actually measured measurements to the predicted measurements. Since, in practice, residual signals will not perfectly equal to zero in the fault-free situation, Multiple Hypothesis Shiryayev Sequential Probability Test (MHSSPT) is introduced to further process those residual signals, and the diagnostic results are presented in probability. In addition, the proposed method is extended to a non-stationary case by using the conservation/dissipation property in phase space. The proposed method is examined by both of simulated data and real process data. The three tank model is modeled according to a nonlinear laboratory setup DTS200 and introduced for generating simulated data. On the other hand, the real process data collected from a sugar factory actuator system are also used to examine the proposed method. According to our results obtained from simulations and experiments, the proposed method is capable to indicate both of healthy and faulty situations. In the end, we have to emphasize that the proposed approach is not limited in the applications of petroleum production and processing. For example, our approach can also apply to enhance the quality of water and avoid the discharges, such as leakage, in the process of water resource management. Therefore, the proposed approach not only benefits the issue of energy security but also other issues in the grand challenges.

This paper presents a novel real-time scale adaptive visual tracking framework and its use in smart traffic monitoring where the framework robustly detects and tracks vehicles from a stationary camera. Existing visual tracking methods often employ a semi-supervised appearance modeling where a set of samples are continuously extracted around the vehicle to train a discriminant classifier between the vehicles and the background. While proving their advantage, many issues are still to be addressed. One is a tradeoff between high adaptability (prone to drift) and preserving original vehicle appearance (susceptible to tracking loss with significant appearance variations). Another issue is vehicle scale changes due to perspective camera effect which increases the potential for an inaccurate update and subsequently visual drifting. Still, scale adaptability has received little attention in vision-based discriminant trackers. In this paper we propose a three-step Scale Adaptive Object Tracking (SAOT) framework that adapts to scale and appearance changes. The framework is divided into three phases: (1) vehicle localization using a diverse ensemble, (2) scale estimation, and (3) data association where detected and tracked vehicles are correlated. The first step computes vehicle position by using an ensemble based on a compressed low-dimensional feature subsets projected from high-dimension feature space by random projections. This provides the diversity needed to accommodate for individual classifiers errors and different adaptability rates. The scale estimation step, applied after vehicle localization, is computed based on matched points between a pre-stored template and the localized vehicle. This doesn't only estimate the new scale of the vehicle but also serves as a correction step to prevent drifting by estimating the displacements between correspondences. The data association step is subsequently applied to link detected vehicle of current frame with the tracked vehicles. Data association must consider factors like the absence of detected target, false detections and ambiguity. Figure 1 illustrates the framework in operation. While the vehicle detection phase is executed per frame, the continuous tracking procedure ensures that all vehicles in the scene, no matter how complex it is, are correctly accounted for. The performance of the proposed Scale Adaptive Object Tracking (SAOT) algorithm is further evaluated with a different set of sequences with scale and appearance changes, blurring, moving camera and illumination. SAOT was compared to three established trackers in the literature: Compressive Tracking , Incremental Learning for Visual Tracking and Random Project with Diverse Ensemble Tracker using standard visual tracking evaluation datasets [4]. The initial target position for all sequences was initialized using manual ground truth. Centre Location Error (CLE) and Recall are calculated to evaluate the methods. Table 1 presents the CLE errors and recall in parentheses measured on a set of 2 sequences with different challenges. It clearly demonstrates that SAOT performs better than the other trackers.

The work of our group falls within the area of Cyber Security, which is one of Qatar's Research Grand Challenges. We are working on designing a new public key cryptosystem that can improve the security of communication networks. The most widely used cryptosystem at present (like RSA) are based on the difficulty of factorization of numbers that are constructed as product of two large primes. The security of such systems was put in doubt since these systems can be attacked with a help of quantum computers. We are working on a new cryptosystem that is based on different (noncommutative) structures, like algebraic groups and supergroups. Our system is based on the difficulty of computing invariants of actions of such groups. We have designed a system that uses invariants of (super)tori of general linear (super)groups. Effectively, we are building a "trapdoor function" enabling us to find a suitable invariant of high degree and do the encoding of the message quickly and efficiently but which provides an attacker with computationally very expensive and difficult task to find an invariant of that high degree. As with every cryptosystem, the possibility of its break have to be scrutinized very carefully and the system has to be investigated independently by other researchers. We have established theoretical results about minimal degrees of invariants of a torus that are informing possible selection of parameters of our system. We continue getting more general theoretical results and we are working towards an implementation and testing of this new cryptosystem. A second part of our work is an extension from the classical case of algebraic groups to the case of algebraic supergroups. We are concentrating especially on general linear supergroups. We have described the center of the distribution superalgebras of general linear groups GL(m|n) using the concept of an integral in the sense of Haboush and computed explicitly all generators of invariants of the adjoint action of the group GL(1|1) on its distribution algebra. The center of the distribution algebra is related via the Harish-Chandra map to infinitesimal characters. Understanding of these characters and blocks would lead us to the description of the linkage principle, that is of composition factors of induced modules. Finding and proving linkage principle for supergroups over the field of positive characteristics is one of our main interests. This extends classical results from the representation theory that are giving scientists, mathematicians and physicists, a tool to find a theoretical model where the fundamental rules of symmetries of the space-continuum are realized. Better theoretical background could lead to better understanding of the experimental data and predictions confirming or contradicting our current understanding of the universe. As happened many times in the past, finding the right point of view and developing new language can often lead to different level of understanding. Therefore we value the theoretical part of our work the same way as the practical work related to the cryptosystems.

Background & Objective: Blood vessel segmentation has various applications such as proper diagnosis, surgical planning, and simulation. However, the common challenges faced in blood vessel segmentation are mainly vessels of varying width and contrast changes. In this paper, we propose a segmentation algorithm, where: (1) a histogram-based approach is proposed to determine the initial patch (seeds) and (2) on this patch, a Gauss- Hermite quadrature filter is applied across different scales to handle vessels of varying width with high precision. Subsequently, a level set method is used to perform segmentation on the filter output. Methods: In spatial domain, a Gauss-Hermite quadrature filter is basically a complex filter pair, where the real component is a line filter that can detect linear structures, and the imaginary component is an edge filter that can detect edge structures; the filter pair is used for combined line-edge detection. The local phase is the argument of the complex filter response that determines the type of structure (line/edge), and the magnitude of the response determines the strength of the structure. Since the filter is applied in different directions, all filter responses are then combined to produce an orientation invariant phase map by summing filter responses for all directions. We use 6 filters with center frequency pi/2. To handle vessels of varying width, a multi-scale integration approach is implemented. Vessels of different width appear both as lines and edges across different scales. These scales are combined to produce a global phase map that is used for segmentation. The resulting global phase map contains detailed information about line and edge structures. For blood vessel segmentation, a local phase of 90 degree indicates edge structures. Therefore, it is necessary to consider only the real part of the quadrature filter response. Edges will be found at zero crossing whereas positive and negative values will be obtained for inside and outside of line structures. Therefore, level set (LS) approach is utilized that uses the real part of the phase map as a speed function to drive the deforming contour towards the vessel boundary. In this way, the blood vessel boundary gets extracted. An initial patch on the desired image object is a requirement in this algorithm to start calculating the local phase map. It is obtained by first selecting a few possible partitions using peaks (local maxima) in the intensity histogram. Then, optimal number of seeds is obtained by an iterative clustering of these peaks using their histogram heights and grey scale difference. The seeds around the object form the patch. Results & Conclusion: The proposed method has been tested on 6 subjects of Head MRT Angiography having resolution 416×512×112. We use 6 filters of size 7×7×7 and 4 scales in this experiment. The average time required by MATLAB R14 to perform segmentation is 3 m for one subject by a 2 GB RAM and core2duo processor (without optimization). The resulted segmentation is promising and robust in terms of boundary leakage as can be observed from the Figure.

Sensors are the eyes and ears in the service of people - especially in inaccessible areas where regular maintenance or battery replacement is extremely difficult. By using thermoelectric generators, which are capable of directly converting heat flux into electrical energy, self-powered sensor systems can be established wherever temperature differences of a few Kelvin exist. After installation of the sensors, they collect and transmit their data without any need for further maintenance like battery replacement. Intelligent building automation for instance is the key for significant energy reduction in buildings. Through precise control of sun blinds and set temperature for thermostats and air conditioning, radio signal sensors help to increase a building's efficiency massively. Thermoelectric self-powered sensors have the additional potential to introduce more flexibility in the area of building technology as complex wiring is avoided. Buildings can easier be adapted to altered utilization. Structural health monitoring is another field where energy autarkic sensors could be of vital use. In-situ measurements of e.g. temperature, humidity, strain and cracks of buildings are essential in order to determine the condition of construction materials. Respective sensors are hardly accessible, wiring or battery replacement is costly or even impossible. Sensors that are driven by thermoelectric generators are maintenance-free and can help enhancing the longevity of buildings as well as reducing the maintenance costs. Furthermore, leakages in water transport systems can be reduced by in-situ monitoring by self-powered sensors and thus, reduce unnecessary water losses. The large progress in the development of low-power sensors, power management and radio communication combined with the availability of high efficiency thermoelectric generators opens the possibility to run a self-powered sensor node with temperature gradients as low as 0.8K. This potential will be presented with respect to selected fields of application.

Digital devices and computer software have the prospect to help children with intellectual challenges (IC) in learning capabilities, profession growth, and self-consciousness living. However, most tools and existing software applications that these children utilize are prepared without observance of their particular deficiency. We conduct an Arabic ontology-based learning system that presents automatically illustrations to characterize the content of stories for children with IC in the state of Qatar. We utilize different mechanisms in order to produce these illustrations which comprise: Arabic natural language processing, animal domain-based ontology, word-to-word based relationship extraction, automatic online search-engine querying. The substantial purpose of our proposed system is to ameliorate children with IC the educational, comprehension, perception, and reasoning through the generated illustrations.

In this paper we introduce and study special types of magic squares of order six. We list some enumerations of these squares. We present a parallelizable code. This code is based on the principles of genetic algorithms. A magic square is a square matrix, where the sum of all entries in each row or column and both main diagonals yields the same number. This number is called the magic constant. A natural magic square of order n is a matrix of size n×n such that its entries consist of all integers from one to square of n. We define a new class of magic squares and present some listing of the counting carried out over two years.

INTRODUCTION: Minimally-invasive robotic surgery benefits the surgeon with increased dexterity and precision, more comfortable seating, and depth perception. Indeed, the stereo-endoscopic camera of the daVinci robot provides the surgeon with a high-resolution 3D view of the surgical scene inside the patient body. To leverage this depth information using advanced computational tools (such as augmented reality or collision detection), we need a fast and accurate stereo matching algorithm, which computes the disparity (pixel shift) map between left and right images. To improve this trade-off between speed and accuracy, we propose an efficient multi-scale approach that overcomes standard multi-scale limitations due to interpolation artifacts when upsampling intermediate disparity results from coarser to finer scale. METHODS: Standard stereo matching algorithms perform an exhaustive search of the most similar patch between the reference and target images (along the same horizontal line when images are rectified). This requires a wide search range in the target image to ensure finding the corresponding pixel in the reference image (Figure 1). To optimize this search, we propose a multi-scale approach that uses the disparity map resulting from previous iteration at lower resolution. Instead of directly using the pixel position in the reference image to place the search region in the target image, we shift it by the corresponding disparity value from previous iteration and reduce the width of the search region as it is expected to be closer to the optimal solution. We also add two additional search regions shifted by disparity values at left and right adjacent pixel positions (Figure 2) to avoid errors typically related to interpolation artifacts when resizing disparity map. To avoid important overlaps between different search regions, we only add them where the disparity map has strong gradients. MATERIAL: We used stereo images from the Middlebury dataset (http://vision.middlebury.edu/stereo/data/) and stereo-endoscopic images captured at full HD 1080i resolution using a daVinci S/Si HD Surgical System. Experiments were performed with a GPU implementation on a workstation with 128GB RAM, an Intel Xeon Processor E5-2690, and an NVIDIA Tesla C2075. RESULTS: We compared the accuracy and speed between standard and proposed methods using ten images from the Middlebury dataset that has the advantage to provide ground truth disparity maps. We used the sum of square difference (SSD) as a similarity metric between patches of size 3x3 in left and right rectified images, resized to half their original size (665x555). For the standard method, we set the search range offset and width to respectively -25 and 64 pixels. For the proposed method, we initialize the disparity to 0 followed by five iterations with a search range width of 16. Results in Table 1 show that we managed to improve the average accuracy by 27% without affecting the average computation time of 120ms. CONCLUSION: We proposed an efficient multi-scale stereo matching algorithm that significantly improves accuracy without compromising speed. In future work, we will investigate the benefits of a similar approach using temporal consistency between successive frames and its use in more advanced computational tools for image-guided surgery.

The magnetizing characteristic of a Self Excited Induction Generator (SEIG) defines relationship between its magnetizing reactance and air-gap voltage. The characteristic is essential for steady state, dynamic or transient analysis of SEIGs as the magnetizing inductance is the main factor responsible for voltage build-up and its stabilization in these machines. In order to obtain essential data to get this characteristic the induction machine is subjected to synchronous speed test. The data yielded by this test can be utilised to extract complete magnetizing behaviour of the test machine. In this paper a detailed study is carried out on a single phase induction machine to obtain its magnetizing characteristic. The procedure of performing synchronous speed test to record necessary data has been explained in detail along with relevant expressions for the calculation of different parameters. The magnetizing characteristic for the investigated machine is reported in the paper.

GRAPE-X processor is an experimental processor chip designed to achieve extremely high performance per watt. It was made using TSMC's 28 nm technology, and has achieved 30 Gflops/W. This number is three times higher than the performance of best GPGPU cards announced so far, using the same 28 nm technology. The power consumption has been the main factor which limits the performance improvement of HPC systems. This is because of the break of the so-called CMOS scaling law. Until early 2000's, or when the design rule of the silicon device was larger than 130nm, shrinking the transistor size by a factor of two results in: for times more transistors, two times higher clock frequency, half the supply voltage, and the same power consumption. Thus, one could achieve 8x performance improvement. However, with transistors smaller than 130nm design rules, it has become difficult to reduce the supply voltage, resulting in only a factor-of-two performance improvement for the same power consumption. As a result, reduction in the power consumption of the processor, when it is fully in operation, has become the most important issue. In addition, it has also become important to achieve high parallel efficiency on relatively small-sized problems. With large parallel machines, high peak performance is realized, but that peak performance is in many cases not so useful, since it is achieved only for unrealistically large problems. For the problems of practical interest, the efficiencies of large scale parallel machines are sometimes surprisingly low. In order to achieve high performance-per-watt and high parallel efficiency on small problems, we developed a highly streamlined processor architecture. In order to reduce the communication overhead and improve parallel efficiency, we adopted an SIMD architecture. To reduce the power consumption, we adopted the distributed-memory-on-chip architecture, in which each of SIMD processor core has its own main memory. Based on the GRAPE-X architecture, an exa-flops (10^18 flops) system with the power consumption less than 50 MW will be possible in 2018-2019 time-frame. For many real applications including those in the cyber security area, which requires 10TB or less memory, a parallel system based on our GRAPE-X architecture will provide the highest parallel efficiency and the shortest time to the solution at the same time. Oral presentation is preferred

The next generation sequencing technology creates a huge number of sequences (reads), which constitute the input for genome assemblers. After prefiltering the sequences, it is required to detect exact overlaps between the reads to prepare the necessary ingredients to assemble the genome. The standard method is to the find the maximum exact suffix-prefix match between each pair of reads after executing an error-detection technique. This is applied in most assemblers, however, a few studies worked on finding the approximate overlap. This direction can be useful when error detection and prefiltering techniques are very time consuming and not very reliable. However, there is a huge difference in term of complexity between finding exact and approximate matching techniques. Therefore, any improvement in time could be valuable when approximate overlap is the target. The naive technique to find approximate overlaps applies a modified version of dynamic programming (DP) on every pair of reads, which consumes O(n2) time where n is the total size of all reads. In this work, we take advantage of the fact that many reads share prefixes. Accordingly, it is obvious that some work is continuously repeated. For example, consider the sequences in Figure 1. If dynamic programming is applied on S1 and S2, assuming S2 and S3 share a prefix of length 4, then it is easy to notice that calculation of a portion of DP table of size |S1| X 5 can be avoided when applying the algorithm on S1 and S3 (the shaded area in Figure 1). Figure 1. DP table for S1,S2 alignment. We assume the following: gap = 1, match =0 and mismatch=1. no calculation for the shaded area is required when calculating S1,S3 table since S2,S3 share the prefix AGCC. The modification is based on the above observation: first, the reads are sorted in lexicographical order and the largest common prefix (LCP) between every two consecutive reads is found. Let group G denote the reads after sorting. For every string S, we find the DP table for S and every other string in G. Since the reads are sorted, a portion of DP table can be skipped for every string, depending on the size of LCP, which has already been calculated in the previous step. We implemented the traditional technique to find approximate overlap with and without the proposed modification. The results show that there is an improvement of 10-61% in time. The interpretation for this wide range is that the gain in performance depends on the number of strings. The larger the number of strings is, the better the gain in performance since the sizes of LCPs are typically larger.

Due to its inherent short-term dynamic, the power grid is a critical component of the energy system. When a dangerous event occurs in a section of the grid (i.e. a power line or a plant fails), the overall system is subject to the risk of a blackout. The time available to counteract the risk is very short (only a few milliseconds) and there are no tools to ensure the power to a number of selected critical facilities. A way to tackle the blackout risk and to implement a smart management of the remaining part of the grid is a distributed control system with preemptive commands. It's based on the idea, that in case of dangerous events, there will be definetly no time to inform the control center, to make a decision and to send the commands to the active components of the power grid where, finally, they will be executed. The idea consist in the implementation of an intelligent distributed control system that continuously controls the critical components of the power grid. It monitors the operational conditions and evaluates the ability of single components to work well and their probability of an outage. In parallel, the control system continuously imparts preemptive commands to eventually counteract the outages expected on a probabilistic base. The preemptive commands can be defined taking into account the sensitivity to specific outages by different network elements and of course, on the base of a priority rule that preserve the power for the strategic sites. In case of a dangerous event, the monitoring device directly sends messages to all the actuator devices, where the action will be performed only if a preemptive command was previously delivered. This means that the latency of the traditional control chain will be reduced to the latency of communications between monitoring and actuator devices. The first consequence of this policy is that an event, which is potentially the cause for a complete blackout will affect only a limited portion of the grid. The second consequence is , that the control system will choose the network elements that will be involved in the emergency procedure, preserving the strategic plants. The third consequence is that with this kind of control, the power grid goes from a N-1 stable status to another N-1 stable status. The system loses contributions of generation and load, but it keeps its stability and its standard operations.

Wireless networking plays a vital role in our daily life style and has tremendous applications in almost all fields of the economy. The wireless medium is a shared medium and, hence, user-scheduling is needed to allow multiple users access the channel jointly. Furthermore, the wireless channel is characterized by its time-based and location-based variations due to physical phenomena such as multi-path propagation and fading, etc. Therefore, unlike the traditional persistent round-robin scheduling schemes, the current standards of telecommunication systems support channel-aware opportunistic scheduling in order to exploit the varying channels of the users when they are at their peak conditions. The advantages of these schemes in enhancing the prospected throughput of the networks are evident and demonstrated. However, these schemes are basically based on selecting a single user to access a certain frequency sub-channel at a given time in order to avoid creating interference if more than one user access the same channel. Nevertheless, allowing multiple users to access the same channel can be feasible by using special coding techniques such as superposition coding with successive interference cancellation at the receivers. The main advantage of this is to improve the spectral efficiency of the precious wireless spectrum and to enhance the overall throughput of the network while maintaining the quality-of-service requirements of all users. Despite their advantages, multiple-users scheduling schemes require the use of proper resource allocation algorithms to process the channel conditions measurements in order to decide which users should be served in a given time slot and frequency sub-channel and the allocated data rate and power of each link in order to maximize the transmission efficiency. Failure to use a suitable resource allocation and scheduling scheme can degrade the performance significantly. We design and analyze the performance of efficient multiple-users scheduling schemes for wireless networks. One scheme is proven theoretically to be the most efficient one. However, the algorithm computation load is significant. The other scheme is a sub-optimal scheme that has low computation load to run the algorithm and it achieves very good performance which is comparable to the optimal scheme. Furthermore, we evaluate the performance gains of multiple-user scheduling over the conventional single-user scheduling under different constraints such as hard fairness and proportional fairness among the users and for fixed merit weights of the users based on their service class. In all of these cases, our proposed schemes can achieve a gain that may exceed 10% in terms of the data rate (bits/sec). This gain is significant taken into consideration that we use the same air-link and power resources of the conventional single-user scheduling schemes.

With the growth of social media and online blogs, people express their opinion and sentiment freely by providing product reviews, as well as comments about celebrities, and political and global events. These texts reflecting opinions are of great interest to companies and individuals who base their decisions and actions upon them. Hence, opinion mining on mobiles is capturing the interest of users and researchers across the world with the growth of available online data. Many techniques and applications have been developed for English while many other languages are still trying to catch up. In particular, there is an increased interest in easy access to Arabic opinion from mobiles. In fact, Arabic presents challenges similar to English for opinion mining, but also presents additional challenges due to its morphological complexity. Mobiles on the other hand present their own challenges due to limited energy, limited storage, and low computational capability. Since some of the state-of-the-art methods for opinion mining in English require the extraction of large numbers of features, and extensive computations, these methods are not feasible for real-time processing on mobile devices. In this work, we provide a solution to address the limitation of the mobile, and the required Arabic resources to derive opinion mining on mobiles. The method is based on matching stemmed tweets to our own developed Arabic sentiment lexicon (ArSenL). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies including limited size, unclear usability plan given Arabic's rich morphology, or non-availability publicly. ArSenL is the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) developed using a combination of English SentiWordnet (ESWN), Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). A public interface to browsing ArSenL is available at http://me-applications.com/test. The scores from the matched stems are then aggregated and processed through a decision tree for determining the polarity. The method was tested on a published set of Arabic tweets, and an average accuracy of 67% was achieved versus a 50% baseline. A mobile application was also developed to demonstrate the usability of the method. The application takes as input a topic of interest and retrieves the latest Arabic tweets related to this topic. It then displays the tweets superimposed with colors representing sentiment labels as positive, negative or neutral. The application also provides visual summaries of searched topics and a history showing how the sentiments for a certain topic has been evolving.

In recent years, we have seen many advances in distributed systems, in the form of cloud computing and distributed embedded mobile devices, drawing more research interest into better ways to harness and coordinate the combined power of distributed computation. While this has made distributed computing resources more readily accessible to main-stream audiences, the fact remains that implementing distributed software and applications that can exploit such resources via traditional distributed programming methodologies is an extremely difficult task. As such, finding effective means of programming distributed systems is more than ever an active and fruitful research and development endeavor. Our work here centres on the development of a programming language known as MSR3e, designed for implementing highly orchestrated communication behaviors of an ensemble of computing nodes. Computing nodes are either traditional main-stream computer architectures or mobile computing devices. This programming language is based on logic programming, and is declarative and concurrent. It is declarative in that it allows the programmer to express the logic of synchronization between computing nodes without describing any form of control flow. It is concurrent in that its operational semantics is based on a concurrent programming model known as multiset rewriting. The result is a highly expressive distributed programming language that provides a programmer with a high-level abstraction to implement highly complex communication behavior between computing nodes. This allows the programmer to focus on specifying what processes need to synchronize between the computing nodes, rather than how to implement the synchronization routines. MSR3e is based on a traditional multiset rewriting model with two important extensions: (1) Explicit localization of predicates, allowing the programmer to explicitly reference the locations of predicates as a first-class construct of the language (2) Comprehension patterns, providing the programmer a concise mean of writing synchronization patterns that matches dynamically sized sets of data. This method of programming often result to more concise codes (relative to main-stream programming methodologies) that are more human readable and easier to debug. Its close foundation to logic programming also suggests the possibilities of effective automated verification of MSR3e programs. We have currently implemented a prototype of MSR3e. This prototype is a trans-compiler that compiles a MSR3e program into two possible outputs: (1) a C++ program that utilizes the MPI libraries, intended for execution on traditional main-stream computer architectures (e.g., ×86, etc..) or (2) a Java program that utilizes WiFi direct libraries of the android SDK, intended for execution on android mobile devices. We have conducted preliminary experimentations on a small set of examples, to show that MSR3e works in practice. In future, we intend to refine our implementation of MSR3e, scaling up the experiment suites, as well as developing more non-trivial applications in MSR3e, as further proof of concept.