Qatar Foundation Annual Research Forum Volume 2010 Issue 1

Depending on user profiles expressed by associated expected events, and conditions for raising alerts, from manual meticulous news annotation of an adequately selected corpus, an ontology domain is created. A cross-language information retrieval approach is used for automatic translation of financial documents corresponding to the particular domain corresponding to the user requirement. By this way, users may receive alerts and news expressed in their own language even if they are initially expressed in a different language. Manual annotation is used for knowledge extraction composed of general rules useful for automatic annotation of financial news arriving instantly to the system from reliable providers of information. As a first step in the loop, news are filtered, split into different sub-documents each one corresponding to a particular event and categorized. News are then mapped automatically to formatted data as instantiations of a sequence of predefined entities defined an event. By using alerting conditions given by the user, data analysis of structured tables might raise or not alerts to the user, with an adequate explanation of the cause of the alert. Automatically selected alerts initiate a new process for information generation to the user by starting a new browsing sequence of news containing events which are related to the raised alerts. Related news are recursively processed through the same structuring process in order to offer more historical data related to the alert helping the user to make decision.

The study of particle-laden coaxial, turbulent jets has been of interest due to its importance in several applications such as industrial burners, combustors and mixing devices. The addition of the second phase to the continuous phase jet can change the already complicated flow pattern and turbulent characteristics of the jets. Vast research efforts have been devoted to understanding such phenomena, but detailed investigation of particle-laden flows remains an active area of research.

The advent of laser diagnostics has helped to quantify the myriad details of the turbulent jet flow fields in great detail. However, the diagnostic tools are very expensive to use as a research tool. As a result, computational fluid dynamics (CFD) with an acceptable level of accuracy can complement the experimental results by providing additional details that are difficult to measure.

Nevertheless, even with the advancement of computational resources, modeling the turbulent characteristics remains a challenge due to its complex nature. Although recently, computational techniques have been developed to “solve” the turbulent quantities, these techniques are computationally too expensive to use in real time applications.

Hence, in this work, standard Reynolds-averaged Navier-Stokes, numerical simulations are carried out to predict the flow and turbulent characteristics of coaxial jets with and without the dispersed phase. The results are compared with the experimental data measured using molecular tagging velocimetry diagnostic technique. The key objective of this work is to investigate the flow field details that are difficult, if not impossible, to measure.

Nanoparticle image velocimetry (nPIV) uses evanescent-wave illumination to measure two velocity components, U and V, tangential to a wall in a region with thickness of the order of hundreds of nanometers. In this region the illumination intensity decays exponentially with distance normal to the wall, z, and hence tracers closer to the wall have ‘brighter’ and ‘bigger’ images than those that are further away, i.e. at larger z. Moreover, fluid velocity varies in this region with z and hence tracers at different distance from the wall move at different speeds. Furthermore, presence of the wall has a significant effect on particle distribution, and particle displacement due to local fluid velocity and Brownian displacement of particle tracers in this region. The variation in the displacement of particle images in this region, with different brightness and velocities, can bias the near-wall velocities obtained using standard correlation-based PIV method.

Artificial nPIV images of nanoparticles in a flow field with linear out-of-plane velocity profile were used in this work to investigate the impact of these issues upon the accuracy of nPIV data. Uniform and Gaussian random distribution noise were added to the images to simulate electronic noise and shot noise, respectively. The artificial images were obtained and processed for various experimental parameters to incorporate different illumination profiles, shear rates and distribution profiles. The results demonstrate that non-uniform illumination, as well as particle distribution, affects the bias in the estimated tracer velocity for the shear flow. Non-uniform intensity also affects the bias due to Brownian diffusion; however, correction for Brownian diffusion canreduce this bias error.

Cooperative communications is a technique to create a virtual antenna array using several distributed single antenna nodes in the system. It helps in increasing the area of coverage without the need of increased transmission power. As the destination receives multiple copies of the source's signal, it also improves the diversity order. The performance of cooperative networks has been thoroughly investigated in the past for various system models, protocols, forwarding techniques and fading environments.

Location estimation is another crucial process in cooperative relay networks, as it is for the other types of wireless communications networks. For instance, the range and location information can be used for network authentication, localization or cluster forming in cooperative networks. However, to the best of our knowledge, there is no study in the literature that addresses the location estimation problem in cooperative relay networks.

In this study, we investigate the performance of a cooperative relay network performing location estimation through time of arrival (TOA). We derive Cramer-Rao lower bound (CRLB) for the location estimates made using the relay network. The analysis is extended to obtain average CRLB considering the signal fluctuations in both relay and direct links. The effects of the channel fading of both relay and direct links and amplification factor and location of the relay node on average CRLB are investigated. Simulation results show that the channel fading of both relay and direct links and amplification factor and location of relay node affect the accuracy of TOA-based location estimation.

One of the most widely used measurement instruments in the turbulence community is the hot-wire probe. Amongst them, the four-wire probe is lately gaining popularity because of the enhanced accuracy and the extended directional working range due to the presence of the fourth ‘redundant’ wire. However, the need for exhaustive calibration makes the hot-wire probe a less preferred instrument to be used in atmospheric research. In the present study, development and testing of an effective data reduction scheme for a four-wire probe is reported. The robustness of the data reduction scheme enables one to obtain the same order of accuracy in measurement with reduced calibration points, and in turn reduced calibration effort. The data reduction scheme works based on the calculation of the directional sensitivity function of each wire, and then, minimization of an artificially constructed error function. Since the directional sensitivity function is smooth and continuous in space, the same order of accuracy can be obtained with less effort. In the present work, a four-wire probe was considered, and calibrated in a constant velocity jet facility. Following calibration, the data reduction scheme was applied to test the probe numerically for a random noise applied on four probes simultaneously, and the angular error was computed. The assessment of the involved accuracy of the data reduction scheme with reduced points of calibration was established.

Entity type recognition is used as a pre-processing step in common applications like summarization of text, classifying documents or automatic answering of questions posed in natural language. Here, ‘entity’ refers to concrete and abstract objects identified by proper and common nouns. Entity recognition focuses on detecting instances of types like person, location, organization, and so on. For example, an entity recognizer would take as input:

George Washington was the first President of the United States of America. and output:

George Washington was the first President of the United States of America .

The task can be performed using machine learning techniques to train a system that recognizes entities with performance comparable to a human annotator. Challenges like the lack of a large annotated training data corpus, impossible nature of listing all entity types, and ambiguity in language make this problem hard. There are existing entity recognizers which perform this task but with fair performance. One of the ways adopted to improve the performance of an existing entity recognizer is feature engineering. We initially find out which of the existing features, used in the recognizer, affect the performance most strongly. We accomplish this by adding and removing one or more features at a time from the feature list. We then use the training data to train a model and test to find out which set of features are important. The evaluation metric involves finding the precision, recall and f-score (which is the harmonic mean of precision and recall). As a next step, we add new features like word clusters and bigram word features to find out any improvements. Word clusters help when the training data does not have some words, but words belonging to the same cluster are present in the training data. This helps tagging unseen words in the test set. We also experiment with varying the size of the training data to find out how it affects the performance. Additionally, we look into Wikipedia as a source of additional features for the training data. Wikipedia has an elaborate internal link structure that can provide vital information about the category of a word. This category can be linked to a broader-sensed entity type.

The Office of Research and the Materials Technology Unit (MTU) at Qatar University (QU) have undertaken many challenges and initiatives in line with QU's role in achieving objectives set by the university's stakeholders. This includes taking the first steps to become a distinguished research university.

As materials science is an interdisciplinary field, MTU has a variety of research activities that extend across all faculties and departments, often crossing traditional subject boundaries. The materials research at MTU is linked to virtually every field of science and engineering. Our mission is to improve the development of Qatar through the discovery and development of new materials with novel properties for applications within Qatar that meet the needs of the environment, industry and society. Researchers and students at MTU are empowered to think outside the box to expand the frontiers of materials science and engineering and encouraged to help address and contribute to societal needs in strategic areas including safety, energy, economy and sustainability.

Qatar University researchers combine their expertise in materials science, chemistry, physics, biology and engineering along with advanced computation to understand and control properties of matter. MTU provides the opportunity for faculty members and students to interact with the industry and actively participate in various projects. MTU attracts leading international scholars and aims to provide universal knowledge of the four essential elements of materials science and engineering, namely: processing/synthesis, structure, properties and applications. Qatar University leads the materials research development in Qatar in sustainable materials, corrosion and nondestructive studies. MTU is starting a variety of recycling projects for polymers, papers and concrete. Materials research contributes to economic progress by developing advanced materials for new technologies, and lowering the cost and enhancing the performance of more established technologies.

The Arabian Gulf is shallow, sub-tropical and semi-enclosed, all factors that promote large variations in the properties of its water. The extreme natural variations, in association with global changes such as the increasing pH of the oceans and climate change, make the ecosystem balance of the Gulf very fragile. A one-year-long study was designed to evaluate the effectiveness of PAM (pulse amplitude modulation) chlorophyll a fluorometry in monitoring the health of sensitive ecosystems, such as coral reefs, around Qatar. The immediate objectives were: (i) to evaluate the efficacy of the PAM chlorophyll fluorometry technique as a means of assessing sub-lethal stress in corals; (ii) to calibrate and validate this technique for future ecosystem monitoring applications in this local environment; and (iii) to collect environmental data to attempt to correlate detected changes in stress status to changes in the magnitude of environmental factors that are known to affect these organisms. The study consisted of four field surveys conducted at approximately quarterly time intervals, at two monitoring stations. PAM fluorometry measurements were complemented by detailed visual assessments of the health status of the ecosystems following a traditional belt transect method, continuous recording of seawater temperature, underwater light intensity measurement and water quality monitoring.

Results of both types of observations indicated different degrees of sub-lethal stress on most of the coral species at Fuwayrit and Halul. While the visual signs of stress were difficult to quantify, the PAM fluorescence data provided a clearer indication of the stress being experienced by the organisms at the time of the surveys.

For one particular species of coral at the Fuwayrit site, PAM measurements of photosynthetic efficiency proved to be a good predictor of imminent mortality.

Based on data from the Halul site, it can be inferred that the PAM fluorometry method did not give false indications of stress for healthy corals.

This study has demonstrated that PAM fluorometry can improve our ability to monitor the health of corals in the Qatar and Gulf environment by providing objective data on the photosynthetic performances and the state of stress of these organisms.

As the State of Qatar endeavors to become the ‘Gas Capital of the World’, it is imperative to develop cost-effective technologies for gas processing. In this context, gas-to-liquid (GTL) technology represents a major pathway for the production of ultra-clean liquid hydrocarbons and value-added chemicals. The Fischer-Tropsch synthesis (FTS) reaction is the principal route for the GTL process.

Professor Bukur is exploring effects of novel and non-traditional activation (pretreatment) procedures, and promoters on performance of supported cobalt catalysts during FTS. This research is conducted in collaboration with a team of experts in both fundamental and applied aspects of catalysis. It may ultimately lead to establishing scientific basis for design of the next generation of supported cobalt catalysts for the GTL conversion process. Another area of research is development of a comprehensive kinetic model for FTS in a slurry reactor. The kinetic model developed in this study, coupled with the appropriate conservation equations and transport properties for a given reactor configuration (fixed bed or slurry bubble column), would be a valuable tool for optimizing product yield, simulating the plant design, and evaluating the economic cost benefits.

Dr. Elbashir's research activities are focused on the design of an advanced reactor technology for the FTS to leverage certain advantages over the current commercial technologies while at the same time overcoming several of their major limitations. The pillars of this innovative research approach are based on fundamental studies leading to better understanding of the complex nature of FTS, coupled with applied research work targeting the development of novel catalysts and reactors. All phases of Dr Elbashir's research activity in this field are performed in collaboration with leading scientists of multidisciplinary backgrounds and with the involvement of both graduate and undergraduate students from Qatar. This novel research approach is designed to lead to alternative FTS technologies, enabling Qatar to be on the forefront of technology development in GTL.

Qatar is a worldwide leader in liquid natural gas (LNG) production and is poised to lead the world in gas-to-liquids (GTL) production with the commissioning of the Pearl GTL facility. Unfortunately, Qatar's gas fields contain non-negligible quantities of corrosive and toxic hydrogen sulfide (H2S), resulting in the ongoing need for expensive and labor intensive pipe inspection to detect and monitor areas of corrosion. Such inspection is critical to plant integrity, worker safety, and to ensure the economic productivity of the facility. Current industry practice relies on manual sensors operated by a worker located externally to the pipe. The complex pipe geometries and sheer number of pipes, result in a sparse inspection process that forces inspectors to extrapolate measurements to large areas of the pipe network that are unseen.

To overcome these limitations, we are pursuing a radically different approach that uses an articulated robot to navigate inside the pipe, combined with a vision-based perception system that can build a detailed, registered, high resolution 3D appearance map of the inside pipe surface. By using an articulated robot, we can significantly increase the direct measurement coverage of the pipe network. By using a vision-based perception system, we can build models for visualization of the inside pipe surface that can be directly evaluated for corrosion damage. Moreover, our approach lays the foundation for automating corrosion detection by enabling changes in co-registered multi-sensor fusion (e.g. using magnetic flux leakage) to be evaluated over time.

Our work to date has focused on developing monocular and stereo visual odometry systems, which are the core component to building high resolution 3D appearance maps of the pipe surface from a robot crawler located inside the pipe. We have developed algorithms that take imagery collected from a robot moving inside the pipe, and are able to estimate the motion of the vehicle and the resulting structure and appearance of the pipe surface. We have evaluated our algorithms on pipe segments and have generated accurate, high resolution stitched images of the internal pipe surface. We will describe the details of our algorithms, current results, and next steps in our work.