Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1

In the context of web Islamic data analysis and authentication an important task is to be able to authenticate the holy book if published in the net. For that purpose, in order to detect texts contained in the holy book, it seems obvious to first characterize words which are specific to existing chapters (i.e. "Sourat") and words characterizing each sentence in any chapter (i.e. "Aya"). In this current research, we have first mapped the text of the Quran to a binary context R linking each chapter to all words contained in it, and by calculating the fringe relation F of R, we have been able to discover in a very short time all specific words in each chapter of the holy book. By applying the same approach we have found all specific words of each sentence (i.e. "Aya") in the same chapter whenever it is possible. We have found that almost all sentences in the same chapter have one or many specific words. Only sentences repeated in the same chapter or those sentences included in each other might not have specific words. Observation of words simultaneously specific to a chapter in the holy book and to the sentence in the same chapter gave us the idea for characterizing all specific sentences in each chapter with respect to the whole Quran. We found that for 42 chapters all specific words of a chapter are also specific of some sentence in the same chapter. Such specific words might be used to detect in a shorter time website containing some part of the Quran and therefore should help for checking their authenticity. As a matter of fact by goggling only two or three specific words of a chapter, we observed that search results are directly related to the corresponding chapter in the Quran. Al results have been obtained for Arabic texts with or without vowels. Utilization of adequate data structures and threads enabled us to have efficient software written in Java language. The present tool is directly useful for the recognition of different texts in any domain. In the context of our current project, we project to use the same methods to characterize Islamic books in general. ACKNOWLEDGMENT: This publication was made possible by a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 06-1220-1-233. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.

Traffic congestion is a major problem in many big cities around the world. According to a study performed by the world bank in Egypt in 2010 and concluded in 2012, the traffic congestion was estimated to 14 Billion EGP in the Cairo metropolitan area and to 50 Billion EGP (4\% of the GDP) in the entire Egypt. Few of the reasons of the high monetary cost of the traffic congestion are: (1) travel time delay, (2) travel time unreliability, and (3) excess fuel consumption. Smart traffic management addresses some of the causes and consequences of traffic congestion. It can predict congested routes, take preventive decisions to reduce congestion, disseminate information about accidents and work zones, and identify the alternate routes that can be taken. In this project, we develop a real-time and scalable data storage and analysis framework for traffic prediction and management. The input to this system is a stream of GPS and/or cellular data that has been cleaned and mapped to the road network. Our proposed framework allows us to (1) predict the roads that will suffer from traffic congestion in the near future, and traffic management decisions that can relieve this congestion; and (2) a what-if traffic system that is used to simulate what will happen if a traffic management or planning decision is taken. For example, it answers questions, such as: "What will happen if an additional ring road is built to surround Cairo?" or "What will happen if point of interest X is moved away from the downtown to the outskirts of the city. This framework has the following three characteristics. First, it predicts the flow of the vehicles in the road based on historical data. This is done by tracking vehicles every day trajectories and using them in a statistical model to predict the vehicles movement on the road. It then predicts the congested traffic zones based on the current vehicles in the road and their predicted paths. Second, historical traffic data are heavily exploited in the approach we use to predict traffic flow and traffic congestion. Therefore, we develop new techniques to efficiently store traffic data in the form of graphs for fast retrieval. Third, it is required to update the traffic flow of vehicles and predict congested areas in real-time, therefore we deploy our framework in the cloud and employ optimization technique to speedup the execution of our algorithms.

Some lecturers gave reports and showed instances of moving part or all of their electronic course support from the Virtual Learning Environment (VLE) to social networking systems like Youtube, MySpace and Facebook because of greater student engagement with these kinds of social networking tools. Recent student interviews in Aspire Academy in Qatar have revealed that students are not concerned with what they are taught (e.g. through lectures, seminars, distance learning sessions, or through a blended learning approach) so long as the instruction was good. The latter reason opens great opportunities for delivering courses through SC media over the VLE, but also raises the question: To what extent can VLE and Social Media be leveraged as a good practice in learning and teaching in different modalities? In this research, the new experience of enriching the VLE with SC tools in Aspire Academy is presented through developing a new system prototype as a more effective solution. The prototyping process included usability testing with Aspire student-athletes and lecturers, plus heuristic evaluation by Human Computer Interaction (HCI) experts. Implementing the prototype system in academic institutions is expected to develop better learning levels and consequently better educational outcomes.

Background & Objectives Semantic Web technologies are used extensively in the health domain to enable expressive, standards-based reasoning. Deploying Semantic Web reasoning processes directly on mobile devices has a number of advantages, including robustness to connectivity loss and more timely results. By leveraging local reasoning processes, Clinical Decision Support Systems (CDSS) can thus present timely alerts given dangerous health issues, even when connectivity is lacking. However, a number of challenges arise as well, related to mobile platform heterogeneity and limited computing resources. To tackle these challenges, developers should be empowered to benchmark mobile reasoning performance across different mobile platforms, with rule- and datasets of varying scale and complexity, and under typical CDSS reasoning process flows. To deal with the current heterogeneity of rule formats, a uniform interface on top of mobile reasoning engines also needs to be provided. System We present a mobile, cross-platform benchmark framework, comprising two main components: 1) a generic Semantic Web layer, supplying a uniform, standards-based rule- and dataset interface to mobile reasoning engines; and 2) a Benchmark Engine, to investigate mobile reasoning performance. This framework was implemented using the PhoneGap cross-platform development tool, allowing it to be deployed on a range of mobile platforms. During benchmark execution, the benchmark rule- and dataset (encoded using the SPARQL Inferencing Notation (SPIN) and Resource Description Framework (RDF)) are first passed to the generic Semantic Web layer. In this layer, the local Proxy component contacts an external Conversion Web Service, where converters perform conversion into the different rule engine formats. Developers may develop new converters to support other engines. The results are then communicated back to the Proxy and passed on to the local Benchmark Engine. In the Benchmark Engine, reasoning can be conducted using different process flows, to better align the benchmarks with real-world CDSS. To plugin new reasoning engines (JavaScript or native), developers need to implement a plugin realizing a uniform interface (e.g., load data, execute rules). New process flows can also be supplied. In the benchmarks, data and rule loading times, as well as reasoning times, are measured. From our work in clinical decision support, we identified two useful reasoning process flows: * Frequent Reasoning: To infer new facts, the reasoning engine is loaded with the entire datastore each time a certain timespan has elapsed, and the relevant ruleset is executed. * Incremental Reasoning: In this case, the datastore is kept in-memory, whereby reasoning is applied each time a new fact has been added. Currently, 4 reasoning engines (and their custom formats) are supported, including RDFQuery (https://code.google.com/p/rdfquery/wiki/RdfPlugin), RDFStore-JS (http://github.com/antoniogarrote/rdfstore-js), Nools (https://github.com/C2FO/nools) and AndroJena (http://code.google.com/p/androjena/). Conclusion In this paper, we introduced a mobile, cross-platform and extensible benchmark framework for comparing mobile Semantic Web reasoning performance. Future work consists of investigating techniques to optimize mobile reasoning processes.

Shannon's theory of information was developed to address the fundamental problems of communication, such as the reliable data transmission over a noisy channel and the optimal data compression. During the years, it has expanded to find wide range of applications in many areas ranging from cryptography and cyber security to economics and genetics. Recent technological advances designate information theory as a promising and elegant tool to analyze and model information structures within living organisms. The key characteristics of data transmission within organisms are that they consider sources and channels with memory and feedback, they handle their information in a fascinating Shannon-optimum way, while the transmission of the data is delayless. Despite the extensive literature on memoryless sources and channels, the literature regarding sources and channels with memory is limited. Moreover, the optimality of communication schemes for these general sources and channels is completely unexplored. Optimality is often addressed via Joint Source Channel Coding (JSCC) and it is achieved if there exists an encoder-decoder scheme such that the Rate Distortion Function (RDF) of the source is equal to the capacity of the channel. This work is motivated by neurobiological data transmission and aims to design and analyze optimal communication systems consisting of channels and sources with memory, within a delay-sensitive environment. To this aim, we calculate the capacity of the given channel with memory and match it to a Markovian source via an encoder-decoder scheme, utilizing concepts from information theory and stochastic control theory. The most striking result to emerge from this research is that optimal and delayless communication for sources and channels with memory is not only feasible, but also it is achieved with the minimum complexity and computational cost. Though the current research is stimulated by a neurobiological application, the proposed approach and methodology as well as the provided results deliver several noteworthy contributions to a plethora of applications. These, among others, include delay sensitive and real time communication systems, control-communication applications and sensor networks. It addresses issues such as causality, power efficiency, complexity and security, extends the current knowledge of channels and source with memory, while it contributes to the inconclusive debates of real time communication and uncoded data transmission.

Photovoltaic (PV) cells can absorb up to 80% of the incident solar radiation of the solar spectrum, however, only certain percentage of the absorbed incident energy is converted into electricity depending on the conversion efficiency of the PV cell technology used, while the remainder energy is dissipated as heat accumulating on the surface of the cells causing elevated temperatures. Temperature rise at the PV cell level is addressed as one of the most critical issues influencing the performance of the cells causing serious degradations and shortens the life-time of the PV cells, hence cooling of the PV module during operation is essential. Hybrid PV designs which are able to simultaneously generate electrical energy and utilize the waste heat have been proven to be the most promising solution. In this study, analytical investigation of a hybrid system comprising of a Heat Pipe-based Photovoltaic-Thermoelectric Generator (HP-PV/TEG) for further enhanced performance is presented. The system presented incorporates a PV panel for direct electricity generation, a heat pipe to absorb excessive heat from the PV cells and assist uniform temperature distribution on the surface of the panel, and a thermoelectric generator (TEG) to perform direct heat-to-electricity conversion. A mathematical model based on the heat transfer process within the system is developed to evaluate the cooling capability and predict the overall thermal and electrical performances of the hybrid system. Results are presented in terms electrical efficiencies of the system. It was observed that the integration of TEG modules with PV cells aid improving the performance of the PV cells through utilizing the waste-heat available, leading to higher output power. The system presented can be applied in regions with hot desert climates where electricity demand is higher than thermal energy.

Harvesting electric energy from renewable energy resources is seen as one of the solutions to secure the energy sustainability due to depleting resources of fossil fuel, the conventional resources of electric energy. Renewable energy is typically connected to conventional distribution network which were not designed to accomodate any sources of electricity reduce the security of the energy supply system. Moreover the variency of renewable resources create many operational challenges to the distribution network operator. Higher shares of distributed energy sources lead to unpredictable network flows, greater variations in voltage, and different network reactive power characteristics as already evidence in many distribution networks. Local network constraints occurs more frequently, adversely affecting the quality of supply. Yet distribution network operators are nevertheless expected to continue to operate their networks in a secure way and to provide high-quality service to their customers. Active management of distribution network may provide some answers to these problems. Indeed, distribution management will allow grids to integrate renewable energu resources efficiently by leveraging the inherent characteristics of this type of generation. The growth of renewable energy resources requires changes to how distribution networks are planned and operated. Bi-directional flows need to be taken into account: they must be monitored, simulated and managed. This paper will describe features of smart grid concept that can be employed in distribution network for active management to facilitate the integration of renewable energy resources. The concepts include coordinated voltage control, microgrid operation and intelligent reactive power management to name a few. The development of physical testbed to test these new strategies in managing distribution network will also be described. The heart of these strategies is intelligent controller which acting as energy management system. The development of this controller will also be described and its operationality will be eplained.

Transparent electrodes that conduct electrical current and allow light to pass through are widely used as the essential component in various opto-electric devices such as light emitting diodes, solar cells, photodectectors and touch screens. Currently, Indium Tin oxide (ITO) is the best, commercially available transparent conducting electrode (TCE). However, ITO is too expensive owing high cost on indium. Furthermore ITO thin films are too brittle to be used in flexible devices. To fulfill the demand of TCEs for wide range of applications, high performance ITO alternatives are required. Herein we demonstrate an approach for the successful, solution based synthesis of high aspect ratio copper nanowires, which were later combined with reduced graphene oxide (rGO), in order to produce smooth thin film TCEs on both glass and flexible substrate. Structure and component characterization for these electrodes was carried out through Four Probe, Spectrophotometer, Scanning electron Microscope (SEM), Transmission Electron Microscope (TEM) and Atomic Field Microscopy (AFM). In addition to the morphological and electrical characterization, these samples were also tested for their durability by carrying out experiments that involved exposure to various environmental conditions and electrode bending. Our fabricated transparent electrodes exhibited high performance with a transmittance of 91.6% and a sheet resistance of 9 O/sq. Furthermore, the electrodes showed no notable loss in performance during the durability testing experiments. Such results make them as replacement for indium tin oxide as a transparent electrode and presents a great opportunity to accelerate the mass development of devices like high efficiency hybrid silicon photovoltaics via simple and rapid soluble processes.

A process is a series of actions executed in a particular environment in order to achieve a goal. It is often the case that several concurrent processes coexist in an environment in order to achieve several goals simultaneously. However, executing multiple processes is not always a possibility in an environment due to the following reasons: (1) All processes might be needed to be executed by a single agent that is not capable of executing more than one process at a time; (2) Multiple processes may have interactions between them that hamper their concurrent executions by multiple agents. As an example, there might be conflicting actions between several processes that their concurrent execution will stop those processes from achieving their goals. The existing solution to address the abovementioned complications is to merge several processes into a unified conflict-free and improved process before execution. This unified merged process is then executed by a single agent in order to achieve goals of all processes. However, we believe this is not the optimal solution because (a) in some environments, it is unrealistic to assume execution of all processes merged into one single process can be delegated to a single agent; (b) since merging is performed before actual execution of the unified process, some of the assumptions made regarding execution flow in individual processes may not be true during actual execution which will render the merged process irrelevant. In this paper, we propose a semantic web based solution to merge multiple processes during their concurrent execution in several agents in order to address the above-mentioned limitations. Our semantic web Process Merging Framework features a Web Ontology Language (OWL) based ontology called Process Merging Ontology (PMO) capable of representing a wide range of workflow and institutional Process Merging Constraints, mutual exclusivity relations between those constraints and their conditions. Process Merging Constraints should be respected during concurrent execution of processes in several agents in order to achieve execution-time process merging. We use OWL axioms and Semantic Web Rule Language (SWRL) rules in the PMO to define the formal semantics of the merging constraints. A Process Merging Engine has also been developed to coordinate several agents, each executing a process pertaining to a goal, to perform process merging during execution. This engine runs the Process Merging Algorithm that utilizes Process Merging Execution Semantics and an OWL reasoner to infer the necessary modifications in actions of each of the processes so that Process Merging Constraints are respected. In order to evaluate our framework we have merged several clinical workflows each pertaining to a disease represented as processes so that they can be used for decision support for comorbid patients. Technical evaluations show efficiency of our framework and evaluations with the help of domain expert shows expressivity of PMO in representation of merging constraints and capability of Process Merging Engine in successful interpretation of the merging constraints. We plan to extend our work to solve problems in business process model merging and AI plan merging research areas.

ORYX GTL is an environmentally responsible company committed to creating an efficient, diversified energy business, developing its employees, and adding value to Qatar's natural resources. ORYX GTL considers the monitoring and reporting consistent environmental data and setting accurate targets is a critical component to increase the operational efficiency on sustainable manner. Monitoring key metrics such as air emissions, criteria pollutants, flaring and energy can provide an opportunity to reduce the environmental impacts and cost savings. ORYX GTL has adopted a state-of-art information technology (IT) solution to enhance the data handling process in support of the company's environmental performance reports such as the greenhouse gas (GHG) accounting and reporting (A&R) program required by Qatar Petroleum (QP). The automated system to report environmental data is proven to be more efficient and accurate which also increases consistency & requires fewer resources to report the data in a reliable manner. The system selected by ORYX GTL is the Data Integration and Automation (DIA) system designed developed by Trinity Consultants on the Microsoft® .net platform. The objective of this paper is to share the challenges and experience during the design, develop and implement this advanced DIA system for critical environmental reporting functions at ORYX GTL as a part of the company's commitment to improve environmental performance. The DIA application can be used as the central data storage/handling system for all environmental data reporting. The DIA software includes several functions built on a state-of-art IT platform to achieve near real-time environmental monitoring, performance tracking, and reporting. The key functions include: -Hourly data retrieval, aggregation, validation, and reconciliation from the plant process historian on a pre-defined schedule. The data retrieved from the process historian may include data such as hourly fuel usage and continuous emission monitoring data, and sampling data collected on routine basis. -Powerful calculation engine allows user to build complex emission calculation equations. Calculated results are stored in the database for use in reporting. In addition to user specified equations, the system also includes a complete calculation module to handle complex calculations for tank emissions. -Through the web interface of the DIA, users can manage system reporting entity hierarchy and user security, set up tags/sources, create manual data entries, create/modify equations, and execute emission reports. The DIA application sends email notifications of errors of tag data and calculation results at user specified intervals. Email recipients can provide timely response to the system with proper root causes and corrective actions. -Custom reports can be designed to generate regulatory reports in the format required by QP or the Qatar Ministry of Environment. The DIA system has significantly enhanced the quality of ORYX GTL's environmental reporting by reducing human interactions required for process data extraction, validation, reconciliation, calculations, and reporting. ORYX GTL's proactive approach to implement and integrate DIA system provided the opportunity to improve reporting functions and stakeholder & regulator satisfaction as well as it ensures the principles of environmental data reporting such as complete, consistent, transparent and accurate.

The deployment of highly intelligent and efficient machine vision systems accomplished to achieve new heights in multiple fields of human activity. A successful replacement of manual intervention with their automated systems assured safety, security and alertness in the transportation field. Automatic number plate recognition (ANPR) has become a common aspect of the intelligent transportation systems. In addition to the license plate information, identifying the exact make and model of the car is suitable to provide many additional cues in certain applications. Authentication systems may find it useful with an extra confirmation based on the model of the vehicle also. Different car models are characterized by the uniqueness in the overall car shape, position and structure of headlights etc. Majority of the research works rely on frontal/rear view of car for the recognition while some others are also there based on an arbitrary viewpoint. A template matching strategy is usually employed to find an exact match for the query image from a database of known car models. It is also possible to select and extract certain discriminative features from the region of interest (ROI) in the car image. And with the help of a suitable similarity measure such as euclidean distance it is able to demarcate between the various classes/models. The main objective of the paper is to understand the significance of certain detectors and descriptors in the field of car make and model recognition. The performance evaluation of SIFT, SURF, ORB feature descriptors for implementing a car recognition system was already available in literature. In this paper, we have studied the effectiveness of various combinations of feature detectors and descriptors on car model detection. The combination of the 6 detectors DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Harris, Multiscale Hessian with the 3 descriptors SIFT, liop and patch was tested on three car databases. Scale Invariant Feature Transform (SIFT), a popular object detection algorithm allows the user to match different images and spot the similarities between them. The algorithm based on keypoints selection and description offers feature independent of illumination, scale, noise and rotation variations. Matching between images has been executed using Euclidian distance between descriptors. For the given keypoints in the test image, the smallest Euclidian distance between corresponding descriptor and all the descriptors of the training image indicates the best match. Our experiments were carried out in MATLAB using the VLFeat ToolBox. It was found to achieve a maximum accuracy of 91.67% with DoG-SIFT approach in database 1 comprising cropped ROI of toy car images. For the database 2 consisting of cropped ROI of real car images, the Multiscale Hessian-SIFT yielded the maximum accuracy of 96.88%. The database 3 comprised of high resolution real car images with background. The testing was conducted on the cropped and resized ROI's of these images. A maximum accuracy of 93.78% was obtained when the Multiscale Harris-SIFT feature descriptor was employed. As a whole these feature detectors and descriptors succeeded in recognizing the car models with an overall accuracy above 90%.

Abstract: SCADA ( Supervisory control and data acquisition) is a system which allows a control of remote industrial equipments, over a communication channel. These legacy communication channels were designed before the cyber space era,and hence they lack any security measures in their network which makes them vulnerable to any cyber attack. RasGas a joint venture between QP and ExxonMobil, was one victim of such attack, it was hit with an unknown virus. The nuclear facility in Iran was hit with a virus called “Stuxnet”, it particulary targets Siemens industrial control systems. The goal of this project is to design a model of a SCADA system that is secured against network attacks. Lets consider for example a simple SCADA system which consist of a Water tank in a remote location and a local control room. the operator controls the water level and temperature using a control panel. The communication channels uses a TCP/IP , protocols through WIFI. The operator raises the temperature of the water by raising the power of the heater, then reads the real temperature of the heater and the water via installed sensors. We consider a man-In-The middle (Adversary) which has access to the network through WIFI. With basic skills s/he is able to redirects tcp/ip traffic to his machine (tapping) and alter data. He can for instance raise water level to reach overflow, or increase the temperature above the "danger zone", and sends back fake sensors data by modifying their response. We introduce an encryption device that encrypt the data such that without the right security credentials , the adversary wont be able to interpret the data and hence not able to modify it. The device is installed at both the control room and the remote tank, and we assume both places are physically secured. To demonstrate the Model. We design and setup a SCADA Model emulator server, that represents and serves as a Water tank, which consists of actuators and sensors. Which is connected to a work station through a network switch. We also setup an adversary workstation that taps and alters the communication between them. We Design Two hardware encryption/decryption devices using FPGA boards and connect them at the ports of both the server and control workstation which we assume to be in a secured zone. and then we analyze the flow of data stream through both secured and non secured state of the channel.

News media are usually biased toward some political views. Also, the coverage of news is limited to news reported by news agencies. Social media is currently a hub for users to report and discuss news. This includes news reported or missed by news media. Developing a system that can generate news reports from social media can give a global unbiased view on what is hot in a given region. In this talk, we present the research work performed in QCRI for two years, which tackle the problem of using social media to track and follow posts on ongoing news in different regions and for different topics. Initially, we show examples of the presence of bias in reporting news by different news media. We then explore the nature of social media platforms and list the research questions that motivated this work. The challenges for tracking topics related to news are discussed. An automatically adapting information filtering approach is presented that allows tracking broad and dynamic topics in social media. This technique enables automatically tracking posts on news in social media while coping with the high changes occurring in news stories. Our developed system, TweetMogaz, is the demoed, which is an Arabic news portal platform that generated news from Twitter. TweetMogaz reports in real-time what is happening in hot regions in the Middle East, such as Syria and Egypt, in the form of comprehensive reports that include top tweets, images, videos, and news article shared by users on Twitter. It also reports news on different topics such as sports. Moreover, Search is enabled to allow users to get news reports on any topic of interest. The demo would be showing www.tweetmogaz.com live, where emerging topics in news would appear live in front of the audience. By the end of the talk, we would show some of the interesting examples that were noticed on the website in the past year. In addition, a quick overview would be presented on one of the social studies, which was carried out based on the news trend changes on TweetMogaz. The study shows the changes of people behavior when reporting and discussing news during major political changes such as the one happened in Egypt in July 2013. This work is an outcome of two years of research in the Arabic Language Technology group in Qatar Computing Research Institute. The work is published in the form of six research and demo papers in tier 1 conferences such as SIGIR, CSCW, CIKM, and ICWSM. The TweetMogaz system is protected by two patent applications filed in 2012 and 2014. Currently the website serves around 10,000 users, and the number is expected to significantly increase when officially advertised. Please feel free to visit TweetMogaz website for checking the system live: www.tweetmogaz.com Note: A new release with a better design to the website is expected by the time of the conference

Abstract Smoking-related illnesses are costly to the NHS and a leading cause of morbidity and mortality. Pharmacological treatments including nicotine replacement, some antidepressants, and nicotine receptor partial agonists, as well as individual- and group-based behavioural approaches, can help stop people from smoking. Circa 40% of smokers attempt to quit smoking each year, yet most have rapid relapses. The development of new tools acceptable by a wide range of smokers should be of particular interest. Smartphone interventions such as text messaging have shown some promise in helping people stop smoking. However most of these studies were based on text-messaging interventions with no interactive functionality that can provide better feedback to the smoker. In addition there is increasing evidence that smart mobile phones act as a conduit to behavioural change in other forms of healthcare. A study of currently available iPhone apps for smoking cessation have shown a low level of adherence to key guidelines for smoking cessation; few, if any, recommended or linked the user to proven treatments such as pharmacotherapy, counselling or a “quit line” and smoking cessation program. Hence there is a need for clinical validation of the feasibility of app-based intervention in supporting smoking cessation programmes in community pharmacy settings. The goal of this study is to design and develop an m-health programme platform to support smoking cessation in a community setting. The primary objectives are ascertaining what users require from a mobile app-based smoking cessation system targeting and supporting smokers, and looking into the literature for similar solutions. The study also involves the design and development of an end-to-end smoking cessation management system based on these identified needs; this includes the Patients Hub, Cloud Hub, and Physician/Social Worker Hub, as well as the design and development of a decision support system based on data mining and an artificial intelligent algorithm. Finally, it will implement the system and evaluate it in a community setting.

* Background & Objectives All critical infrastructure can be modeled as networks, or systems of nodes and connections, and many systems such as the electric grid, water supply, or telecommunications exist explicitly as networks. Infrastructures are interdependent, for instance, telecommunications depend on electric power, and control of the electric grid depends increasingly upon telecommunications, creating the possibility for a negative feedback loop following a disturbance. The performance of these systems under disturbance are related to their inherent network characteristics, and network architecture plays a fundamental role in reliability. What characteristics of networks affect their robustness? Could, for instance, the vulnerability of the electric grid to cascading failure be reduced? * Methods We create a failure model of the network where each node and connection is initially in the operative state. At the first discrete time step a network element is changed to the failed state. At subsequent time steps a rule is applied which determines the state of random network elements based upon the state of their neighbors. Depending upon the rule and the distribution of the degree of connectedness of the network element, failures may be contained to a few nodes or connections, or may cascade until the entire network fails. * Results Quantitative measures from the model are the probability of network failure based upon the loss of a network element, and the expected size distribution of failure cascades. Additionally, there is a critical threshold below which infrastructure networks fail catastrophically. The electrical grid is especially vulnerable as it operates close to the stability limit, and there is a low critical threshold after which the network displays a sharp transition to a fragmented state. Failures in the electrical grid result not only in the loss of capacity in the network element itself, but load shifting to adjacent network elements, which contributes to further instability. While most failures are small, failure distributions are heavy tailed indicating occasional catastrophic failure. Many critical infrastructure networks are robust to random failure, but the existence of highly connected hubs give them a high clustering coefficient which makes the network vulnerable to targeted attacks. * Conclusions It is possible to design network architectures which are robust to two different conditions: random failure and targeted attack. It is also possible to alter architecture to increase the critical threshold at which failed network elements cause failure of the network as a whole. Surprisingly, adding more connections or capacity sometimes reduces robustness by creating more routes for failure to propagate. Qatar is in an ideal position to analyze and improve critical infrastructure from a systemic perspective. Modeling and simulation as detailed above are readily applicable to analyzing real infrastructure networks.

With data proliferation at an unprecedented rate, data accessibility while in motion has increased the demand of VANETs of late. VANETs use moving cars as nodes to create a mobile network. VANETs are designed with the goals of enhancing driver safety and providing passenger comfort. Providing security to VANETs is important in terms of providing user anonymity, authentication, integrity and privacy of data. Attacks in VANETs include the Sybil attack, DDoS attack, misbehaving and faulty nodes, sinkhole attack, spoofing, traffic analysis attack, position attack and illusion attack. Illusion attack is a recent threat in which the attacker generates fraud traffic messages to mislead the passengers thereby changing the passenger's driving behaviour. Thus, the attacker achieves his goal by moving the target vehicle along a traffic free route and creating traffic jams in areas where he wishes. Illusion attack is devised mainly by thieves and terrorists who require a clean get away path. The existing method used to prevent Illusion attack is the Plausibility Validation Network (PVN). In PVN the message validation is based on a set of rules depending on the message type. This is an overhead as the rule set for all possible message types have to be stored and updated in the database. Hence, an efficient mechanism is required to prevent illusion attacks. In this paper, our proposed system: TRUC verifies the message by a Message Content Validation (MCV) Algorithm thus ensuring the safety of the car drivers and passengers. The possibilities of attacker creating an Illusion attack is explored in all dimensions and the security goals are analyzed by our proposed design, TRUC

Background & Objectives Qatar's IT infrastructure is rapidly growing to encompass the evolution of businesses and economical growth the country is increasingly witnessing throughout its industries. It is now evident that the country's e-government requirements and associated data management systems are becoming large in number, highly dynamic in nature, and exceptionally attractive for cybercrime activities. Protecting the sensitive data e-government portals are relying on for daily activities is not a trivial task. The techniques used to perform cybercrimes are becoming sophisticated relatively with the firewalls protecting them. Reaching high-level of data protection, in both wired and wireless networks, in order to face recent cybercrime approaches is a challenge that is continuously proven hard to achieve. In a common IT infrastructure, the deployed network devices contain a number of event logs that reside locally within its memory. These logs are in large numbers, and therefore, analyzing them is a time consuming task for network administrators. In addition, a single network event often generates a redundancy of similar event logs that belong to the same class within short time intervals. This makes it difficult to manage them during forensics investigation. In most cybercrime cases, a single alert log does not contain sufficient information about malicious actions background and invisible network attackers. The information for a particular malicious action or attacker is often distributed among multiple alert logs and among multiple network devices. Forensic investigators mission is to reconstruct incident scenarios is now very complex considering the number as well as the quality of these event logs. Methods My research will focus on involving mathematics and algorithm science for each proposed sub models in the alerts correlation model. After collecting alert logs from network sensors; then it will be stored in the alert logs warehouse. The stored alert log contains redundancy data and irrelevant information. The alert correlation model used to filter out all redundancy data and irrelevant information from the alert logs. This model contains two stages; format standardization and redundancy management. The format standardization process aims unified different event logs format into one format, while the redundancy management process aims to reduce the duplication of the single event. Furthermore, this research will try to utilized criminology science to enhance security level of the proposed model and forensics experiments tools to validate the proposal approach. Results In response to attacks and potential of attacks against network infrastructure and assets, my research focuses on how to build an organized legislative e-government environment. The idea of this approach is to forensically utilize the current network security output by collect, analysis and present evidence of network attack in an efficient manner. After data mining process we can utilize our preprocessing results for e-government awareness purpose. Conclusions This research proposed Qatar e-government alerts correlation model. The proposed model used to process and normalize the captured network event logs. The main point of designing the model is to find a way to forensically visualize the evidence and attack scenario in e-government infrastructure.

Motivated by freeing the user from specialized devices and leveraging natural and contextually relevant human movements, gesture recognition systems are becoming popular as a fundamental approach for providing HCI alternatives. Indeed, there is a rising trend in the adoption of gesture recognition systems into various consumer electronics and mobile devices. These systems, along with research enhancing them by exploiting the wide range of sensors available on such devices, generally adopt various techniques for recognizing gestures including computer vision, inertial sensors, ultra-sonic, and infrared. While promising, these techniques experience various limitations such as being tailored for specific applications, sensitivity to lighting, high installation and instrumentation overhead, requiring holding the mobile device, and/or requiring additional sensors to be worn or installed. We present WiGest, a ubiquitous WiFi-based hand gesture recognition system for controlling applications running on off-the-shelf WiFi-equipped devices. WiGest does not require additional sensors, is resilient to changes within the environment, and can operate in non-line-of-sight scenarios. The basic idea is to leverage the effect of the in-air hand motion on the wireless signal strength received by the device from an access point to recognize the performed gesture. As shown in Figure 1, WiGest parses combinations of signal primitives along with other parameters, such as the speed and magnitude of each primitive, to detect various gestures, which can then be mapped to distinguishable application actions. There are several challenges we address WiGest including handling noisy RSSI values due to multipath interference and other electromagnetic noise in the wireless medium; handling gesture variations and their attributes for different humans or the same human at different times; handling interference due to the motion of other people within proximity of the user's device; and finally being energy-efficient to suit mobile devices. To address these challenges, WiGest leverages different signal processing techniques that can preserve signal details while filtering out the noise and variations in the signal. We implement WiGest on off-the-shelf laptops and evaluate frequencies on the RSSI, creating a signal composed of three primitives: rising edge, falling edge, and pause. We evaluate its performance with different users in an apartment and engineering building settings. Various realistic scenarios are tested covering more than 1000 primitive actions and gestures each in the presence of interfering users in the same room as well as other people moving in the same floor during their daily life. Our results show that WiGest can detect the basic primitives with an accuracy of 90% using a single AP for distances reaching 26 ft including through-the-wall non-line-of-sight scenarios. This increases to 96% using three overheard APs, a typical case for many WiFi deployments. When evaluating the system using a multimedia player application case study, we achieve a classification accuracy of 96%.

Cooperative communication is a promising technology that has attracted significant attention recently thanks to its ability to achieve spatial diversity in wireless networks with only single-antenna nodes. The different nodes of a cooperative system can share their resources so that a virtual Multiple Input Multiple Output (MIMO) system is created which leads to spatial diversity gains. To exploit this diversity, a variety of cooperative protocols have been proposed in the literature under different design criteria and channel information availability assumptions. Among these protocols, two of the most-widely used are the amplify-and-forward (AF) and decode-and-forward (DF) protocols. However, in large-scale relay networks, the relay selection process becomes highly complex. In fact, in many applications such as device-to-device (D2D) communication networks and wireless sensor networks, a large number of cooperating nodes are used, which leads to a dramatic increase in the complexity of the relay selection process. To solve this problem, the sparsity of the relay selection vector has been exploited to reduce the multiple relay selection complexity for large AF cooperative networks while also improving the bit error rate performance. In this work, we extend the study from AF to large-scale decode-and-forward (DF) relay networks. Based on exploiting the sparsity of the relay selection vector, we propose and compare two different techniques (referred to as T1 and T2) that aim to improve the performance of multiple relay selection in large-scale decode-and-forward relay networks. In fact, when only few relays are selected from a large number of relays, the relay selection vector becomes sparse. Hence, utilizing recent advances in sparse signal recovery theory, we propose to use different signal recovery algorithms such as the Orthogonal Matching Pursuit (OMP) to solve the relay selection problem. Our theoretical and simulated results demonstrate that our two proposed sparsity-aware relay selection techniques are able to improve the outage performance and reduce the computation complexity at the same time compared with conventional exhaustive search (ES) technique. In fact, compared to ES technique, T1 reduces the selection complexity by O(K^2 N) (where N is the number of relays and K is the number of selected relays) while outperforming it in terms of outage probability irrespective of the relays' positions. Technique T2 provides higher outage probability compared to T1 but reduces the complexity making a compromise between complexity and outage performance. The best selection threshold for T2 is also theoretically calculated and validated by simulations which enabled T2 to also improve the outage probability compared with ES techniques. Acknowledgment This publication was made possible by NPRP grant #6-070-2-024 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Presence of toxic gases and accidental explosions in gas industries have turned the researcher to innovate an electronic nose system which can indicate the nature and the parameters of the gas passing through different vessels. Therefore, in this research we propose a low power Radio Frequency Identification (RFID) based gas sensor tag which can monitor the parameters and indicate the type of gas. The research work is divided in to three main parts. The first two parts cover the design and analysis of low power multi-sensors and processing unit, while the last part focuses on a passive RFID module which can provide communication between the sensor and the processing unit, as shown in Fig. 1. In passive RFID applications, power consumption is one of the most prominent parameter because most of the power is harvested from the coming RF signal. Therefore a ring-oscillator based low power temperature sensor is designed to measure the gas thermodynamic conditions. The oscillator is designed using the Thyristor based delay element [7], in which the current source present for temperature compensation has been displaced to make the delay element as temperature dependent. The proposed temperature sensor consumes 47nW power at 27 °C, which increases linearly with temperature. Moreover, a 4x4 array of tin-oxide gas sensor based on convex Micro hotplates (MHP), is also utilized to identify the type of gas. The array is designed such that each sensor of an array provide different pattern for the same gas. The power consumption caused by the temperature and gas sensor is in the order of few µW's. The prime advantage of MHP can be visualized by the 950 °C annealed MHP, which exhibit the thermal efficiency of 13 °C /mW. Moreover it requires a driving voltage of only 2.8V to reach 300 °C in less than 5ms, which make it compatible with power supplies required by CMOS ICs. The gas sensor will provide 16 feature points at a time, which can results in hardware complexity and throughput degradation of the processing unit. Therefore, a principle component analysis (PCA) algorithm is implemented to reduce the number of feature points. Thereafter, a binary decision tree algorithm is adopted to classify the gases. We implemented both algorithms on heterogeneous Zynq platform. It is observed that the execution of PCA on Zynq programmable SoC is 1.41 times faster than the corresponding software execution, with a resource utilization of only 23% . Finally, a passive ultrahigh-frequency (UHF) RFID transponder is developed for communicating between the sensing block and processing unit. The designed module is responsible to harvest the power from the coming RF signal and accomplish the power requirement of both sensors. The designed transponder IC achieved minimum sensitivity of -17dBm with a minimum operational power of 2.6µW.

Whilst the state of security within the Cloud is still a contentious issue, some privacy and security issues are well known or deemed to be a likely threat. When considering the ongoing threat of malicious insiders the promised security expertise might be deemed untrusted. The focus of our research is determining the extent of issues related to the underlying technology, which support Cloud environments, mainly virtualization platforms. It is often argued that virtualization is secure over conventional shared resources due to the inherent isolation. However much literature may be seen which cites examples to the contrary and as such it should be considered that, as with all software, virtualization applications are susceptible to exploitation and subversion. In fact, it might even be argued that the complexity and heterogeneous nature of the environment may even facilitate further security vulnerabilities. To illustrate and investigate this point we consider the security threat of malware within the context of cloud environments. With this evolution of malware combined with the knowledge that Cloud software is susceptible to vulnerabilities, it is argued that complex malware might exist for the Cloud and if it were successful, would shed light on the security of these technologies. Whilst there are many examples of state of the art malware detection and protection for Cloud environments, this work tends to focus on examining virtual machines (VMs) from another layer. The primary flaw identified in all of the current approaches is failing to take into account malware, which is aware of the Cloud environment; thus be in a position to subvert this detection process. Traditional malware security applications tend to take a defensive approach by looking for existing malware through signature analysis or behavior monitoring. Whilst such approaches are acceptable for traditional environments they become less effective for distributed and dynamic ones. We argued that due to this dynamic nature of the Cloud as well as its uncertain security concerns, a malware type application may be a suitable security defense and thus operate as a proactive, self-protecting element. We present an architecture for Multi-Agent Cloud-Aware Self-Propagating Agents for Self-Protection. By adapting this architecture to include constraints (such as a kill switch) the application may be effectively controlled and thus any negative effects minimized. This application will then cross the multiple layers within the network, having high privilege. Its dynamic and distributed architecture will allow it survive removal from malware whilst hunting down malicious agents and patching systems as necessary. In order to survive in the hostile and dynamic cloud environment, the software incorporates a multi-component and multi-agent architecture which has shown success in the past with malware that propagate in heterogeneous environments. The components consist of passive and active sensors to learn about the environment, distributed storage to provide redundancy and controller/constructor agents for localized coordination. The proposed architecture has been implemented with success and desired results were achieved. The research outputs hold significant potentials, particularly for complex and highly dynamic infrastructures such as those aimed for DigitalQatar.

A frequency divider is one of the key building blocks in phased-lock loops (PLL) and frequency synthesizers, which are essential subsystems in wireless communication and sensing systems. A frequency divider divides the output frequency of a voltage-controlled oscillator (VCO) in order to compare it with a reference clock signal. Among the different types of frequency dividers, the injection-locked frequency divider (ILFD) is becoming more popular due to its low power and high frequency characteristics. However, the ILFD has an inherent problem of narrow locking range, which basically defines a range over which a frequency-division operation is supported. In order to increase the locking range, one of the most obvious ways is to inject higher power. In fully integrated PLLs, however, the injection signal is supplied by an internal VCO, which typically has limited fixed output power and hence enhancing the locking range with large injection signal power poses difficulty. In this work, we present the development of a fully integrated 10.5-GHz divide-by-3 (1/3) injection-locked frequency divider (ILFD) that can provide extra locking range with a small fixed injection power. The ILFD consists of a previously measured on-chip 10.5-GHz VCO functioning as an injection source, a 1/3 ILFD core, and an output inverter buffer. A phase tuner implemented using an asymmetric inductor is proposed to increase the locking range through even-harmonic (the 2rd harmonic for this design) phase tuning. With a fixed internal injection signal power of only -18 dBm (measured output power of the standalone VCO with a 50-? reference), a 25% enhancement in the locking range from 12 to 15 MHz is achieved with the proposed phase tuning technique without consuming an additional DC power. The frequency tuning range of the integrated 1/3 ILFD is from 3.3 GHz to 4.2 GHz. The proposed 1/3 ILFD is realized in a 0.18-µm BiCMOS process, occupies 0.6 mm × 0.7 mm, and consumes 10.6 mA while the ILFD alone consumes 6.15 mA from 1.8-V supply. The main objective of this work is proposing a new technique of phase-tuning of the even-harmonics that can "further" increase the locking range with an "extra" amount beyond what can be achieved by other techniques. Since the developed technique can enhance the locking range further at a fixed injection power, it can be used in conjunction with other techniques for further enhancing the locking range. For instance, the locking range can be increased by using different injection powers and then further enhanced by tuning the phase of the even harmonics at each power level. The "extra" locking range, not achievable without the even-harmonic phase tuning, amounts to 25%, which is very attractive for PLL applications. Furthermore, additional tuning mechanisms such as use of a capacitor bank can be employed to achieve even wider tuning range for applications such as PLL.

INTRODUCTION: The advent of da Vinci surgical robot (Intuitive Surgical, California, USA) has allowed complex surgical procedures in urology, gynecology, cardiothoracic, and pediatric to be performed with better clinical outcomes. The end effectors of these robots exhibits enhanced dexterity with improved range of motion leading to better access and precise control during the surgery. Understanding the design and kinematics of these end effectors (imitating surgical instruments' tooltips) would assist in replication of their complex motion in a computer-generated environment. This would further support the development of computer-aided robotic surgical applications. The aim of this work is to develop a software framework comprising of the geometric three-dimensional models of the surgical robot tool-tips along with their kinematic analysis. METHODS: The geometric models of the surgical tooltips were designed based on the EndoWristTM instruments of the da Vinci surgical robot. Shapes of the link and inter-link distances of the EndoWristTM instrument were measured in detail. A three-dimensional virtual model was then recreated using CAD software (Solidworks, Dassault Systems, Massachusetts, USA). The kinematic analysis was performed considering trocar as the base-frame for actuation. The actuation mechanism of the tool composed of a prismatic joint (T1) followed by four revolute joints (Ri ; i = 1 to 4) in tandem (Figure 1). The relationship between the consequent joints was expressed in form of transformation matrices using Denavit-Hartenberg (D-H) convention. Equations corresponding to the forward and the inverse kinematics were then computed using D-H parameters and applying geometrical approach. The kinematics equations of the designed tooltips were implemented through a modular cross-platform software framework developed using C/C++. In the software, the graphical rendering was performed using openGL and a multi-threaded environment was implemented using Boost libraries. RESULTS AND DISCUSSION: Five geometric models simulating the articulated motion of the EndoWristTM instruments were designed (Figure 2). These models were selected based on the five basic interactions of the surgical tooltip with the anatomical structures, which included: Cauterization of the tissue, Stitching using needles, Applying clips on vascular structures, Cutting using scissors, and Grasping of the tissues. The developed software framework, which includes kinematics computation and graphical rendering of the designed components, was evaluated for applicability in two scenarios (Figure 3). The first scenario demonstrates the integration of the software with a patient-specific simulator for pre-operative surgical rehearsal and planning (Figure 3a). The second scenario shows the applicability of the software in generation of virtual overlays of the tooltips superimposed with the stereoscopic video stream and rendered on the surgeon's console of the surgical robot (Figure 3b). This would further assist in development of vision-based guidance for the tooltips. CONCLUSION: The geometrical modeling and kinematic analysis allowed the generation of the motion of the tooltips in a virtual space that could be used for both pre-operatively and intra-operatively, before and during the surgery, respectively. The resulting framework can also be used to simulate and test new tooltip designs.

Although different spectrum bands are allocated to specific services, it has been identified that these bands are unoccupied or partially used most of the time. Indeed, recent studies show that 70% of the allocated spectrum is not utilized. As wireless communication systems evolve, an efficient spectrum management solution is required in order to satisfy the need of current spectrum-greedy applications. In this context, cognitive radio (CR) has been proposed as a promising solution to optimize the spectrum utilization. Under the umbrella of cognitive radio, spectrum-sharing systems allow different wireless communication systems to coexist and cooperate in order to increase their spectral efficiency. In these spectrum-sharing systems, primary (licensed) users and secondary (unlicensed) users are allowed to coexist in the same frequency spectrum and transmit simultaneously as long as the interference of the secondary user to the primary user stays below a predetermined threshold. Several techniques have been proposed in order to meet the required quality of service of the secondary user while respecting the primary user's constraints. While these techniques, including multiple-input multiple-output (MIMO) solutions, are optimized from a spectrally-efficiency perspective, they are generally not well designed to address the related complexity and power consumption issues. Thus, the achievement of high data rates with these techniques comes at the expense of high-energy consumption and increased system complexity. Due to these challenges, a trade-off between spectral and energy efficiencies has to be considered in the design of future transmission technologies. In this context, we have recently introduced adaptive spatial modulation (ASM), which comprises both adaptive modulation (AM) and spatial modulation (SM), with the aim of enhancing the average spectral efficiency (ASE) of multiple antenna systems. This technique was shown to offer high energy efficiency and low system complexity thanks to the use of SM while achieving high data rates thanks to the use of AM. Motivated by this technique and the need of such performance in a CR scenario, we study in this abstract the concept of ASM in spectrum sharing systems. In this work, we propose the ASM-CR scheme as an energy-efficient, spectrally-efficient, and low-complexity scheme for spectrum sharing systems. The performance of the proposed scheme is analyzed in terms of ASE and average bit error rate and confirmed with selected numerical results using Monte-Carlo simulations. These results confirm that the use of such techniques comes with an improvement in terms of spectral efficiency, energy efficiency, and overall system complexity.

It is very common to study the patient's data hospitalization to get useful information to improve the health care system. According to the American Hospital Association, in 2006, over $30 billion was spent on unnecessary hospital admissions. If patients that are likely to be hospitalized can be identified, the admission will be avoided as they will get the necessary treatments earlier. In this context, in 2013, the Heritage Provider Network (HPN) launched the $3 million Heritage Health Prize in order to develop a system that uses the available patient data (health records and claims)to predict and avoid unnecessary hospitalizations. In this work we take this competition data, and we try to predict the patient's hospitalization number. The data encompasses more than 2,000,000 of patient admission records over three years. The aim is to use the data of the ?rst and second year to predict the number of hospitalization of the third year. In this context, a set of operations mainly: data transformation, outlier detection, clustering, and regression algorithms are applied. Data transformation operations are mainly: (1) As the data is big enough to be processed, dividing the data into chunks is mandatory. (2) Missing values are either replaced or removed. (3) As the data is raw and cannot be labeled, different operations of aggregation are applied. After transforming the data, outlier detection, clustering, and regression algorithms are applied in order to predict the third year hospitalization number for each patient. Results show, by applying directly regression algorithms, the relative error is only 79%. However, by applying the DBSCAN clustering algorithm followed by the regression algorithm, the relative error decreased to be 67%. This is because the attribute that has been generated by the pre-processing clustering step helped the regression algorithm to predict more accurately the number of hospitalization; and this is why the relative error has dropped. The relative error can be decreased more if we apply the clustering pre-processing step twice. That means, the clusters generated in the first clustering step are re-clustered to generated sub-clusters. Then, the regression algorithm is applied to these sub-clusters. The relative error dropped significantly from 67% to 32%. Patients share common hospitalization history are grouped into clusters. This clustering information is used to enhance the regression results.