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  1. Information and Computer Science DepartmentResearch Profile Dr. Wasfi Al-Khatib Information and Computer Science Department King Fahd University of Petroleum & Minerals

  2. Information and Computer Science Faculty • 25 Professorial Rank faculty members • 1 Full Professor • 5 Associate Professors • 19 Assistant Professors • 2 PhD. Holders • 1 Instructor • 1 Lecturer

  3. ICS Research Areas • Computer Vision, Image Audio and Video Processing and Arabization. • Artificial Intelligence: Theorem Proving, Software and hardware Verification, machine learning, pattern recognition, Uncertainty and knowledge Reasoning • Computer Networks: Network design, Performance and Optimization, Mobile and Distributed Computing Systems, High-Speed Networks, Sensor Networks, Active Networks. • Operating Systems: OS for Mobile devices, Distributed Systems, Multi-Agent Systems, Multimedia Systems, Computer Security. • Software Engineering: Object-oriented Software Engineering, Software Design, Software Measurements • Computer Science Education and eLearning. • Computer Algorithms: Parallel Computing, Computational Geometry, Randomized Algorithms, Grid Computing, Web-mining, data mining. • Database Systems: Database Design, Query Optimization, XML Databases, Multimedia Databases

  4. ICS Research Projects: Computer Vision, Image, Audio, and Video Processing • Towards the Further Study of Designing with NURBS & ANURBS: The CAD/CAM/CAE Tools, KFUPM/SABIC, 2002-2004. • Automatic Text Recognition: A Need in Arabization, KFUPM, 2001-2005 • Automatic Font Generation: A step ahead in Arabization, KFUPM, 2000-2002 • Automatic Classification of music and speech in digitized audio.

  5. Recognition of License Plates • Objectives • Identification of car number plates in complex background. • Plate extraction from poor images with low contrast, glare affected intensity profiles and motion blur. • Character Segmentation from plates at high tilt or image skew. • Recognition under a rule base pertinent to Saudi Arabian number plate licensing standards. • Establishment of a standard number plate database that doesn’t exist for Saudi Arabia (Arabic plates) at the moment. • Development of novel and promoted techniques in the domain of Image Processing, Computer Vision and Machine Learning.

  6. Proposed and Implemented Approach • Approach • Mainly involves three phases: Extraction, Segmentation and Recognition • Achievements • Contrast Adjustment using Histogram Stretching • Local feature extraction based on prevalent image edge profiles and break lights. • Plate extraction using modified Fuzzy Vector/Euclidean edge detection based techniques. • Character segmentation using a bi-cluster Fuzzy C-means algorithms • Recognition of segmented character bitmaps using PCA.

  7. Development possibilities with Intel • A practical License Plate Recognition (LPR) System requires high quality Image Grabbing Devices for operations that are, otherwise, very time consuming under software simulators. • An LPR system in practice is a part of an Intelligent Transport System. • Supports numerous PC clusters for real-time data link. • Complex image processing tasks are performed in parallel using multiple computers. • A number of such operations that are built over hardware in real time systems are • Frame Averaging • Image Differencing • Color level conversions • Edge and Intensity Adjustment operations. • Segmentation and Labeling • Significant work has been done in the recognition of US, EU, Japanese, Korean and Chinese License plates. A practical License Plate Recognition (LPR) System is needed NOT just for the local need of the Kingdom but also at a global level which can include various other Arab countries in the neighbor. • The project will be useful for various applications and can be used to enforce speed limits on expressways/roads, monitor traffic flows at traffic signals, record parking statistics at lots, car theft monitoring, Border Crossing, etc.

  8. Arabization Projects • Arabic Text-to-Speech (ATTS) Two types of speech units were used independently: The first consists of 375 diphones of Arabic sounds, and the other has 178 allophones which cover Arabic and English sounds. The project developed extensive Arabic linguistic tools including: Arabic pronunciation rules, and tables of irregularly pronounced Arabic words, and allophone/diphone selection rules. A parametric model was also built to synthesize the speech and to give the user control over the pitch rate, stress, and speech tempo.

  9. Arabization Projects • Automatic Generation of the Arabic Diacritical Marks We formulated the problem of generating Arabic diacritized text from unvoweled text using Hidden Markov Models (HMM) approach. The word sequence of unvoweled Arabic text is considered an observation sequence from an HMM, where the hidden states are the possible diacritized expressions of the words. The optimal sequence of diacritized words (or states) are then obtained efficiently using a Viterbi like Algorithm. The first phase of this project has already achieved 94.5% letter accuracy.

  10. Arabization Projects • Arabic Speech Recognition System • The project aims at building sufficient know how and a state-of-the-art research base for the development of the next-generation speech recognition techniques for the Arabic language. • This project uses Carnegie Mellon University Sphinx-II, Sphinx-III and Sphinx-IV ASR engines and tools as a base for building a state-of-the-art large-vocabulary speaker- independent continuous Arabic ASR systems. • The project involves building a large Arabic speech corpus, an Arabic phonetic dictionary, training Arabic triphone parametric models, and development of extensive tools for modeling Arabic natural language. • The project is executed jointly with the Center of Speech and Phonological Science at King Abdulaziz City of Science and Technology. • Target application: Automatic TV/Radio news transcription.

  11. Arabization Projects • Neural Network based Speech recognition. The proposed project aims at investigating various structures for ANN/HMM models for phoneme recognition or next generation Arabic Speech recognition. Carnegie Mellon Sphinx-4 will be used as our testing platform.

  12. Automatic Classification of Speech and Music • Music reduction/removal from documentaries • Speech Segments Extraction • Automatic speech recognition • Indexing and retrieval • Speaker recognition • Improving audio coding/compression Feature Selection Classifier Features Features Speech Music Feature Extraction Process Classification Process

  13. Automatic Classification of Speech and Music: Methodology • Newly Proposed Features • RMS of Lowpass Signal • Mean of Discrete Wavelet Transform (DWT) • Variance of Discrete Wavelet Transform (DWT) • Range of Zero Crossings • Variance of Mel Frequency Cepstral Coefficients (MFCC) • Previously Used Features • Spectral Flux • Percentage of Low Energy Frames • Linear Predictive Coefficients (LPC) • Contribution of extracted features studied using Fuzzy C-Means Clustering • Classification Frameworks • Neural Networks • Multilayer Perceptron (MLP) • Radial Basis Function (RBF) • Statistical Models • Hidden Markov Model

  14. Automatic Classification of Speech and Music: Prototype System

  15. ICS Research Projects: Artificial Intelligence • Learning Prolog programs: theory and applications in data mining. • Critical Assessment of Key Analytical Methods for Sanding Prediction. 2005-2006. • Develop Fuzzy Logic Models to Generate Permeability Traces in Non-Cored Wells. 2005-2006. • Development of Artificial Intelligence System for Prediction and Quality Control of PVT Properties. 2005-2006. • Multi-Agent Based Ubiquitous Approach for Personalized Information Systems.

  16. 6 Decision Decision Region 1 Region 2 5 4 3 2 x 2 1 Decision 0 Boundary -1 2 3 4 5 6 7 8 9 10 x1 FUNCTIONAL NETWORKS AS A NEW FRAMEWORK FOR PATTERN RECOGNITION • Computer Science Approaches: • 1. Support Vector Machines • 2. Probabilistic Neural Network (NN) • 3. Radial Basis Functions Network • 4. Multilayer Perceptron NN Statistical Approaches: • 5. Discriminant Analyses • 6. Logistic Regression • 7. K-Nearest Neighbor Functional networksare a generalization of neural networks. They are capeable of capturing& representing complex input/output relationships.

  17. Functional Networks Classifier Learning Algorithm We assume that the probability can be written as: where are unknown, but unrestricted functions to be learned from the data, and p(.) must satisfy the two probability conditions, and is unknowns. For example, p(.) can be a Probit or Sigmoidal or CDF or Mulinomial logistic functions. In functional networks, we learn functions (not parameters) by approximate them by linearly independent family: The parameters can be learned using optimization methods. The response is: We useConstrained Least Squares, or Iterative Least Squares, or Maximum Likelihood

  18. Simulation and Real-World Applications of Functional Networks: A Comparative Study The real Databases under study are taken from: Machine learning repository database at UC Irvine: ftp://ftp.ics.uci.edu/pub/machine-learning-databases Thalassemias Data: p=4, c=3

  19. Functional Networks: Internal and External Validation Using p=4 and c=3

  20. Develop Fuzzy Logic Models to Generate Permeability Traces in Non-Cored Wells • Carbonate rocks pose an extreme challenge for mapping rock properties, especially porosity and permeability, due to their variable and complex pore structure • Fuzzy Logic asserts that the reservoir consists of several litho-types, each having characteristic distributions for permeability and electrical log values. Fuzzy Logic attempts to uncover the relationship between these distributions. • Objective:Develop expertise in fuzzy logic permeability modeling that uses conventional open-hole logs

  21. The Fuzzy Mathematics of Litho-Facies Prediction The normal distribution is given by: (1) P(x) is the probability density that an observation x is measured in the data-set described by a mean m and standard deviation s. In statistics the area under the curve described by the normal distribution represents the probability of a variable x falling into a range, say between x1 and x2. The curve itself represents the relative probability of variable x occurring in the distribution. That is to say, the mean value is more likely to occur than values 1 or 2 standard deviations from it. This curve is used to estimate the relative probability or “fuzzy possibility” that a data value belongs to a particular data set. If a litho-facies type has a porosity distribution with a mean m and standard deviation s the fuzzy possibility that a well log porosity value x is measured in this litho-facies type can be estimated using Equation 1. The mean and standard deviation are simply derived from the calibrating or conditioning data set, usually core data.

  22. Permeability Prediction in the Ula Field

  23. Permeability Prediction From NMR Data • Permeability prediction by fuzzy logic allows better choice of perforating intervals and can be applied to model building to map permeability, although it is still reliant on a good core permeability database. • The RGPZ and GAFL models work exceptionally well as a permeability predictor on core and log data, performing better overall than the SDR and Coates models.

  24. ICS Research Projects: Computer Networks • Analytical, Simulation, and Experimental Investigation of the Performance of Popular Interrupt Handling Schemes for Gigabit-Network Hosts, KFUPM, 2005-2007. • Deploying voice and videoconferencing over IP Networks, KFUPM, 2005-2006. • Fuzzy logic based trust modeling. • Trust modeling for Peer-to-Peer systems: Issues and approaches. • Applications of Genetic Algorithms to MPLS-Based Network Design. KFUPM July 2005-August 2005. • Performance Evaluation and Enhancement of TCP over Wireless. • Implementation of Multihoming and Multistreaming features to Fast TCP. • Performance analysis of SCTP over wireless networks.

  25. Trust Modeling and Its Applications for Peer-to-Peer Computing • What is peer-to-peer computing? • What is trust? • Why modeling trust? • Objectives: • Increase the overall work done by the resources • Decrease the risk associated with resource sharing • Enable resource accountability

  26. The Overall Trust Model

  27. Utility of The Trust Model • Integrating trust into resource management systems (RMSs) • The idea is to make trust cognizant resource allocations • Integrating trust into computing utility environments • Introducing the notion of trusted regions

  28. Friendly Active Network System • Objective: To introduce active networks in different areas • Improper website blocking • Access controls • Congestion Control • Active Network system with capabilities to handle both capsule-based and out-of-band architecture based on applications.

  29. Real-time and Simulation • Access control using expert system, artificial neural networks and parallel rules have been tested on both active and non-active platforms. • Real-time platform • Language used: Java. • Clustering of PCs using PVM. • Heterogeneous platforms used. • Processing on the fly was tested by linking the C code of PVM to handle MATLAB applications. • A 16-node Active Network system with both ergodic and non-ergodic capabilities have been tested on NS Simulator.

  30. Planned Future Work • In general, the access lists are fixed for a network and so its easy to parallelize them and then apply it using active networks approach. • Future work requires parallelize the rules on-the-fly and allocated job to the respective routers using active networks. • To induce routing decisions using active networks. One scenario is to make Link-state protocols stabilize faster.

  31. ICS Research Projects: Operating Systems • Natural Language Voice Interface for Controlling Audio-Video equipment • Multi-agent based Electronic Commerce as an integration technology for the next generation Web

  32. Portal Agents represent distributed Web portals that provide different services and information. Service Mining Agents represent the information or services presented by the URLs of the Web portal. Individual PCs CIA SMA 1 SMA 2 • System Kernel • Routing • Creating PAs communities • Customers Modeling • Sharing the customers Models • Advertising and recommending newly subscribed services or added information • Security tasks PA 1 CIA SMA n SMA 1 PA 2 CIA SMA 2 SMA n CIA SMA1 PA n SMA 2 SMA n Agentfying the E-Commerce Web Portals

  33. Experimental environment Natural Language InterfaceThe AgenTV MAS Commandinterpretation&responsegeneration User-interface Device control Device Speech Speech-to-text

  34. Speech input Text input Input Actuator 100 Program 1 Menu Action 8 Remove Add 11 Program Television Power list 7 3 End time VCR Window DVD Lights Start time Adjustment 5 2 5 3 10 6 Picture 5 Time/Date 9 Contrast Color Brightness Sound Channel 3 3 3 3 2 (TV=4) Contrast Brightness More Less Add Play Lights Remove On Sound Color Forward Stop Pause Record Off Channel Rewind Jump Jump Jump Jump Jump Jump

  35. ICS Research Projects: Software Engineering • Investigating Design Quality Characteristics for Refactoring and Refactoring To Patterns Using Software Metrics • Measuring Architectural Stability in Object Oriented Systems

  36. Software Engineering research project • Project: Investigating Design Quality Characteristics for Refactoring and Refactoring To Patterns Using Software Metrics • Objective: to confirm or invalidate the claims that cost and time put into refactoring are worthwhile. • In this research we will investigate: • An approach to detect the need to refactor early in the software process. • Two refactoring approaches: refactoring to produce design patterns, and refactoring that produces code without design patterns. • Using software metrics, we will quantitatively investigate whether those approaches really improve software quality or not

  37. External attribute Internal attribute Number of procedure parameters Maintainability Cyclomatic Complexity Reliability Program size in Lines of Code Portability Number of Error Messages Usability Length of User Manual An example of a quality model

  38. ICS Research Projects: Computer Science Education and eLearning • Building Computer-Adaptive Testing Using Reinforcement Learning. KFUPM, 2005-2006. • Critical thinking skills in computer science curriculum. • Technology-Based Education in KFUPM

  39. ICS Research Projects: Database Systems • Integrating XML documents: KFUPM 2005-2006. • Query optimization in XML databases.

  40. ICS Research Projects: Computer Algorithms • Two-way linear probing with reassignments. • Limit laws for sums of functions of subgraphs of random graphs.

  41. Information and Computer Science Faculty Research Profile

  42. Dr. Muhammad Sarfraz, Professor • Research Interests • Computer Graphics, Pattern Recognition, Geometric Modeling. • Recent Projects • Towards the Further Study of Designing with NURBS & ANURBS: The CAD/CAM/CAE Tools, KFUPM/SABIC, 2002-2004. • Automatic Text Recognition: A Need in Arabization, KFUPM, 2001-2005 • Automatic Font Generation: A step ahead in Arabization, KFUPM, 2000-2002 • Recent Publications • Sarfraz, M, (2005), Computer Aided Intelligent Recognition Techniques and Applications, ISBN: 0-470-09414-1, John Wiley and Sons. • Sarfraz, M, (2004), Geometric Modeling: Techniques, Applications, Systems and Tools, Kluwer Academic Publishers, ISBN: 1-4020-1817-7. • Habib, Z., Sarfraz, M., and Sakai, M. (2005), Rational Cubic Spline Interpolation with Shape Control, International Journal of Computers & Graphics, Elsevier Science, Vol. 29(4), 594-605.

  43. Dr. M.R.K. Krishna Rao, Associate Professor • Research Interests • Theorem proving, software and hardware verification, learning theory, data mining. • Recent Projects • Learning Prolog programs: theory and applications in data mining. • Critical thinking skills in computer science curriculum. • Recent Publications • M.R.K. Krishna Rao, S. Junaidu, T. Maghrabi, M. Shafique, M. Ahmad and K. Faisal (2005), Principles of curriculum design and revision: a case study in implementing computing curricula CC2001, Proc. of the ACM SIGCSE conf ITiCSE’2005. • M.R.K. Krishna Rao (2005), Input-termination of logic programs, Proc. of LOPSTR'2005, Lecture Notes in Artificial Intelligence, Springer-Verlag. • M.R.K. Krishna Rao (2004), Inductive Inference of Term Rewriting Systems from Positive Data, Proc. of Algorithmic Learning Theory, ALT'2004, Lecture Notes in Artificial Intelligence 3244, pp. 69-82, Springer-Verlag. • M.R.K. Krishna Rao (2004), Learnability of Simply-Moded Logic Programs from Entailment, Proc. of Asian Computing Science Conference, ASIAN'2004, Lecture Notes in Computer Science 3321, pp.128-141, Springer-Verlag.

  44. Dr. Mohammad Al-Suwaiyel, Associate Professor • Research Interests • Algorithms, Parallel Computing, Computational Geometry, Interconnection Networks. • Recent Publications • M. H. Alsuwaiyel, Algorithms: Design Techniques and Analysis, World Scientific Publishers, English Edition (1999), Chinese Edition (2003). • M. Gavrilova and M. H. Alsuwaiyel, “Computing the Euclidean Distance Transform”, Journal of Supercomputing, 25(2), June 2003, 177-185. • M. Gavrilova and M. H. Alsuwaiyel, “Two Algorithms For Computing The Euclidean Distance Transform,'' International Journal of Image and Graphics, Vol. 1, No. 4 (2002) 635-645. • M. H. Alsuwaiyel, “An Improved Parallel Algorithm for a Geometric Matching Problem with Applications,'' Journal of Parallel Algorithms and Applications, Vol 27(6), 2001, 861--865. • M. H. Alsuwaiyel, “An optimal parallel algorithm for the multi-selection problem,'' Parallel Computing, Vol 27(6), 2001, pp 861-865.

  45. Dr. Ebrahim Malalla, Assistant Professor • Research Interests • Probabilistic analysis of algorithms, randomized algorithms, and random data structures such as trees, graphs, and hash tables. • Research Projects • Two-way linear probing with reassignments. • Limit laws for sums of functions of subgraphs of random graphs. • Recent Publications • K. Dalal, L. Devroye, E. Malalla and E. McLeish, "Two-way Chaining with Reassignment," SIAM Journal of Computing, 2005.

  46. Dr. El-Sayed El-Alfy, Assistant Professor • Research Interests • Network design, performance and optimization, Mobile and distributed computing systems, and applications of soft computing in network-related problems • Recent Projects • Building Computer-Adaptive Testing Using Reinforcement Learning. KFUPM, 2005-2006. • Applications of Genetic Algorithms to MPLS-Based Network Design. KFUPM July 2005-Augest 2005. • Performance Evaluation and Enhancement of TCP over Wireless. • Recent Publications • E. El-Alfy, “A General Look at Building Applications for Mobile Devices,” IEEE Distributed Systems Online, Sep. 2005. • E. El-Alfy, Y. Yao and H. Heffes, “Adaptive resource allocation with prioritized handoff in cellular mobile networks under QoS provisioning,” IEEE VTS 54th Vehicular Technology Conference, VTC 2001, vol. 4, pp. 2113 – 2117, Oct. 2001. • E. El-Alfy, Y. Yao and H. Heffes, “A model-based Q-learning scheme for wireless channel allocation with prioritized handoff,” IEEE Global Telecommunications Conference, GLOBECOM '01, vol. 6, pp. 3668 – 3672, Nov. 2001.

  47. Dr. Emad El-Sebakhy, Assistant Professor • Research Interests • Artificial Intelligence, Machine Learning, Functional Networks, • Bioinformatics, Pattern Recognition, Uncertainty and knowledge Reasoning, Simulation, Non-Linear Optimization, and Statistics. • Soft Computing and Intelligence Systems and its applications in Petroleum Engineering, Web-Mining, Security, and E-Commerce. • Industrial Experience • Two years experience as a system analyst programmer for Micro Array Technology, Science and Technology ,Corning Incorporated, NY, USA. • Two years experience as a software engineering and research Scientist at AUTODESK INC., California, USA. • Recent Projects • “Critical Assessment of Key Analytical Methods for Sanding Prediction”. 2005-2006. • “Develop Fuzzy Logic Models to Generate Permeability Traces in Non-Cored Wells”. 2005-2006. • “Development of Artificial Intelligence System for Prediction and Quality Control of PVT Properties”. 2005-2006. • Publications • Emad A. El-Sebakhy, “Functional networks training algorithm for statistical pattern recognition”; IEEE Computers and Communications, 2004, Volume 1,  28:92 - 97. • Emad A. El-Sebakhy, “A Fast and Efficient Algorithm for Multi-class Support Vector Machines Classifier”, ICICS2004: 28-30 November. IEEE Computer Society: 397-412. • Emad A. El-Sebakhy, “The Maximum Likelihood Functional Networks as a Novel Approach for Pattern Classification Problems”, Journal of Neurocomputing, 2005, (In press).

  48. Dr. Farag Azzedin, Assistant Professor • Research Interests • Trust Modeling for Peer-to-Peer based computing systems. • Grid computing and next generation middleware architectures. • Resource management algorithms, dissemination, and discovery. • Computer security in the context of Grid computing. • Recent Projects • Fuzzy logic based trust modeling. • Trust modeling for Peer-to-Peer systems: Issues and approaches . • Recent Publications • F. Azzedin, M. Maheswaran, and N. Arnason ``A Synchronous Co-allocation Mechanism for Grid Computing Systems,'' Cluster Computing, The Journal of Networks, Software Tools and Applications, Vol. 7, No. 1, Jan. 2004, pp. 39-49. • F. Azzedin and M. Maheswaran, ``Trust Brokering System and Its Application to Resource Management in Public-Resource Grids,'' 2004 International Parallel and Distributed Processing Symposium (IPDPS 2004), April 2004. • F. Azzedin and M. Maheswaran, ``Integrating Trust into Grid Resource Management Systems,'' 2002 International Conference on Parallel Processing (ICPP 2002), Aug. 2002, pp. 47-54.

  49. Dr. Ishtiaq Chaudhry, Assistant Professor • Research Interests • Performance analysis of network protocols, Operating system for mobile devices. • Recent Projects • Implementation of Multihoming and Multistreaming features to Fast TCP. • Performance analysis of SCTP over wireless networks. • Recent Publications • A. Ishtiaq, Y. Okabe and M. Kanazawa “Management of Parallel UBR Flows over TCP in Congested ATM Networks,” Elsevier Science Journal on Computer Communications 27 (2004) pp. 801-808. • A. Ishtiaq, Y. Okabe and M. Kanazawa “Improving Performance of SCTP over Broadband High Latency Networks,” Proceedings of IEEE Conference on Local Computer Networks, Bonn, Germany, October 20-24, 2003.

  50. Dr. Junaidu Sahalu, Assistant Professor • Research Interests • Parallel Computing, Programming Languages,Computer Science Education, Electronic Learning. • Recent Projects • Technology-Based Education in KFUPM (Completed May 2004). • Development of a Strategic Plan for KFUPM (Completed June 2005). Co Investigator. • Recent Publications • M.R.K. Krishna Rao, S. Junaidu, T. Maghrabi, M. Shafique, M. Ahmad and K. Faisal (2005), Principles of curriculum design and revision: a case study in implementing computing curricula CC2001, Proc. of the ACM Special Interest Group on Computer Science Education Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE'2005). • S. Junaidu and J. Al-Ghamdi, Comparative Analysis of F2F and Online Course Offerings: KFUPM Experience. International Journal of Instructional Technology and Distance Learning (IJITDL), April 2004. • S. Junaidu and PW Trinder, Parallelising Large Irregular Programs, Journal of Information Scicences, 140 (2002) 229-240.