CSE Masters and Senior Projects

2017

Spring

  • searchAn IOT Architecture for Ubiquitous Context-Aware Assessments.
    Student:

    Salsabeel Shapsough


    Advisor:

    Dr. Imran Zualkernan

    Abstract to be filled

  • searchPredicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques.
    Student:

    Mahitab Hassan


    Advisor:

    Dr. Tamer Shanableh

    Abstract to be filled

  • searchParallel Implementations for Eliminating Finite State Machine Mutants.
    Student:

    Emad Badawi


    Advisor:

    Dr. Khaled El Fakih & Dr. Gerassimos Barlas

    Abstract to be filled

  • searchFault-Tolerant Network Topologies for Datacenters.
    Student:

    Heba Attia


    Advisor:

    Dr.Rana Ahmed

    Abstract to be filled

  • searchAssessment of Computational Intelligence for Web-Based Applications' Interfaces.
    Student:

    Ali Mohamed


    Advisor:

    Dr.Tarik Ozkul

    Abstract to be filled

  • schoolHand movements and gestures recognition
    Students:

    Mustafa AlJamali , Eyad Shaklab & Abdulrahman Abdul-Dayem
    Advisors:

    Dr. Michel Pasquier & Dr. Tarik Ozkul

    Abstract to be filled

  • schoolSmartphone Job Application App
    Students:

    Abdallah Al Qallaf, Ahmed Al Mualla & Noora Al Qassimi
    Advisors:

    Dr. Gerassimos Barlas

    Abstract to be filled

  • schoolRobotic Rehabilitation System
    Students:

    Mohammed Elnawawy, Abid Farhan & Ahmed Mohamed
    Advisors:

    Dr. Assim Sagahyroon & Dr. Lutfi Albasha

    Abstract to be filled

  • schoolEducative Augmented Reality App
    Students:

    Haidar Mohammed, Mohammed Al-labadi & Ahmad Thabit
    Advisors:

    Dr. Tarik Ozkul & Dr. Raafat Aburukba

    Abstract to be filled

  • schoolMoney Saver App
    Students:

    Salma Abul Ella
    Advisors:

    Dr. Imran Zualkernan

    Abstract to be filled

  • schoolIOT:based smart utility meter
    Students:

    Mais Haj Hassan, Mohammad Abdelsalam, Mustapha Ezzeddine & Mohannad Baseet
    Advisors:

    Dr. Abdul-Rahman Al-Ali

    Abstract to be filled

  • schoolUsing AI techniques and a Body Sensor Networks
    Students:

    Yomna Omar, Abdullah Tasleem & Rowan Ibrahim
    Advisors:

    Dr. Assim Sagahyroon & Dr. Michel Pasquier

    Abstract to be filled

  • schoolibUMP++
    Students:

    Fayiz Basheer Parambath, Gurdit Singh Khera & Shruthi Srinivasan
    Advisors:

    Dr. Fadi Aloul & Dr. Imran Zualkernan

    Abstract to be filled

  • schoolSmart Learning Dice
    Students:

    Bana Sakhnini, Shouq Darwish & Amel Darwish
    Advisors:

    Dr. Imran Zualkernan

    Abstract to be filled

  • schoolAutism Learning App
    Students:

    Kamil Abid Kamili, Suad Ajmal & Anam Mahmood
    Advisors:

    Dr. Fadi Aloul & Dr. Raafat Aburukba

    Abstract to be filled

  • schoolEnvironment monitoring system
    Students:

    Razan Adi, Hussam Eddin Rahhal & Sharmeen Mir
    Advisors:

    Dr. Rana Ahmed & Dr. Ghassan Qadah

    Abstract to be filled

  • schoolePortfolio
    Students:

    Karim Mohsin, Hashim Noor & Fai
    Advisors:

    Dr. Raafat Aburukba & Dr. Taha Landolsi

    Abstract to be filled

  • schoolMobile App for Visually Impaired
    Students:

    Wafaa Ahmed, Nour Hawarneh & Gouy Sadek
    Advisors:

    Dr. Tamer Shanableh

    Abstract to be filled

  • schoolAUS Humonoid
    Students:

    Adnan Moahmmed Awad, Ahmad Mohamed Hassan, Mohamed Fadl Ali & Ahmad Ali
    Advisors:

    Dr. Tarik Ozkul

    Abstract to be filled

2016

Fall

  • schoolAn Intelligent Matress for Diagnosis of Sleep Apnea
    Students:

    German Shein & Sankar Sathyanarayanan
    Advisors:

    Dr. Michel Pasquier & Dr. Assim Sagahyroon

    Abstract to be filled

  • schoolSign Translator Android App
    Students:

    Karim Amer, Ahmed Mahfouz & Abdulrahman Osoble
    Advisors:

    Dr. Gerassimos Barlas

    Abstract to be filled

  • schoolMobile based Sign Language Translation using Sensor-Based and Image-Processing Approches
    Students:

    Abdalla Eqab & Hakam Abdelqader
    Advisors:

    Dr. Tamer Shanableh

    Abstract to be filled

  • schoolDistributed Wireless Irrigation System using NI-myRIO
    Students:

    Mohammad Shihab, Sheehan Fernandes & Khalil Ailabouni
    Advisors:

    Dr. Abulrahman Al- Ali & Dr. Shayok Mukhopadhyay

    Abstract to be filled

Summer

  • searchEfficient Algorithms for Distinguishing Experiments for Nondeterministic Finite State Machines.
    Student:

    Abdul Rahim Haddad


    Advisor:

    Dr. Khaled El Fakih & Dr. Gerassimos Barlas

    Derivation of input sequences for distinguishing states of a finite state machine (FSM) specification is well studied in the context of FSM-based functional testing. We present three heuristics for the derivation of distinguishing sequences for nondeterministic FSM specifications. The first is based on a cost function that guides the derivation process, and the second is a genetic algorithm that evolves a population of individuals of possible solutions (or input sequences) using a fitness function and a crossover operator specifically tailored for the considered problem. The third heuristic is a mutation based algorithm that considers a candidate distinguishing sequence, and if the candidate is not a distinguishing sequence, then the algorithm tries to find a solution by appropriately mutating the candidate. Experiments are conducted to assess the performance of the proposed heuristics in addition to an existing algorithm, called exact algorithm, that derives distinguishing sequences of optimal length. Performance is assessed with respect to execution time, virtual memory consumption, and quality (length) of obtained sequences. Experiments are conducted using randomly generated machines with various numbers of states, inputs, outputs, and degrees of nondeterminism. Further, we assess the impact of varying the number of states, inputs, outputs, and degree of nondeterminism. Finally, in addition to the three proposed heuristics, we present a parallel multithreaded implementation of the exact algorithm using Open Multi-Processing. Experiments are conducted to assess the performance of the parallel implementation as compared to the sequential using both execution time speedup and efficiency.
    Click here to download this thesis

  • searchA Mobile Based Platform for Monitoring Respiratory Diseases.
    Student:

    Fatma K. Zubaydi


    Advisor:

    Dr. Assim Sagahyroon, Dr. Fadi Ahmed Aloul & Dr. Hasan Saeed Mir

    Chronic respiratory diseases are diseases of the airways and other structures of the lung, usually resulting in difficulty in breathing and other symptoms. Chronic obstructive pulmonary disease (COPD) and Asthma are considered to be the most common of respiratory diseases. By taking into consideration the possibility of disease worsening over time and the negative impact on patient’s daily activities, the continuous monitoring and managing of these diseases has become a necessity. Currently, spirometry remains the recommended test for monitoring and diagnosing both, COPD and Asthma. A patient suffering from COPD or Asthma should be able to monitor his disease in order to avoid a worsening condition over time or exacerbation of the disease in severe cases. Proper monitoring requires regular visits to medical centers for spirometry checks, or else the purchase and use of a portable spirometer; both options are costly in terms of money and time. In this work and due to the pervasiveness and advancement of smartphones, we attempt to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile-based spirometer capable of diagnosing and managing COPD and Asthma in a reliable and cost effective manner. We developed a model that allows the computation of two critical lung parameters: FVC and FEV1 by establishing a relationship between the frequency response of human exhalation recorded by mobile microphone, and the actual flow rate. These two parameters and the FEV1/FVC ratio are critical in assessing the progress and status of the diseases. We designed a Pretest Activity that together with these computed lung parameters is used in the diagnosis phase. Sample data used to test the system is collected from patients at both Oriana, and Al Zahra hospitals in Sharjah, United Arab Emirates (UAE), under the supervision of consultant pulmonologists. Results and the medical diagnosis of the implemented system proved to be in very close proximity with those produced by clinical spirometers. Our work is an attempt among many to confirm the notion that mobile Health (m-Health) can and will play an important role within the healthcare industry in the near future.
    Click here to download this thesis

Spring

  • searchFuzzy Logic Bases Real Time Global Electricity Tariff Forecasting.
    Student:

    Moamin J.M. Albayed


    Advisor:

    Dr. Abdulrahman Khalaf Al-Ali

    Abstract to be filled

  • searchClassification of Cognitive Workload Levels under Vague Visual Stimulation.
    Student:

    Rwan Adil Osman Mahmoud


    Advisor:

    Dr. Tamer Jamal Shanableh & Dr. Hasan Awad Moh'd Al Nashash

    Abstract to be filled

  • schoolWearable Sleep Apnea Detection System
    Students:

    Fayez Barakji,Ahmad Jihad Samra & Yosr Islam
    Advisors:

    Dr. Fadi Aloul & Dr. Assim Sagahyroon

    Abstract to be filled

  • schoolOne-stop Parkinson mHealth (ParkNosis)
    Students:

    Karim Chehab, Osama Al Madani & Abdulwahab Sahyoun
    Advisors:

    Dr. Fadi Aloul & Dr. Assim Sagahyroon

    Abstract to be filled

  • schoolAndroid Application to Control Smart home Appliances
    Students:

    Mohammad Al-Hussein, Ala Al-Salami & Pooja Gandhi
    Advisors:

    Dr. Abdulrahman Al- Ali & Dr. Raafat Aburukhba

    Abstract to be filled

  • schoolRobotics Simulator for AI Course
    Students:

    Omar Al-Nabulsi, Issa Haddad & Firas Sardast
    Advisors:

    Dr. Michel Pasquier

    Abstract to be filled

  • schoolInteractive Educational Game for Children
    Students:

    Amr Amar, Mina Ghaly & Mohamed Nour
    Advisors:

    Dr. Imran Zualkernan

    Abstract to be filled

  • schoolHealthcare Provision and Administration Application
    Students:

    Jude Amin, Irfane Molou, Sifeddine Ghezala & Amal Amine
    Advisors:

    Dr. Gerassimos Barlas

    Abstract to be filled

2015

Fall

  • searchHigh-Level Design Decisions on an Unmanned Aerial Vehicle.
    Student:

    Sarah Yousef AlAmeeri


    Advisor:

    Dr. Imran Ahmed Zualkernan

    Abstract to be filled

  • searchA User Interface for Solving.
    Student:

    Hassan Bassam Al Najjar


    Advisor:

    Dr. Khaled El Fakih

    Abstract to be filled

  • searchAn Analysis of Incident Management Process in Information Technology Services for Smart Government.
    Student:

    Rayah Abdullah Al-Dmour


    Advisor:

    Dr. Imran Ahmed Zualkernan

    Abstract to be filled

  • school3D Physiotherapy Application using Leap Motion Controller
    Students:

    Nasser, Mohammed Amin & Maya
    Advisors:

    Dr. Michel Pasquire

    Abstract to be filled

  • schoolRead2Me: A Reading Aid for the Visual Impaired
    Students:

    Heba S., Anza Shaikh & Ragini Gupta
    Advisors:

    Dr. Assim Sagahyroon

    Abstract to be filled

  • schoolSmartphone Monitoring Application
    Students:

    Abdelaziz, Salama & Nihal
    Advisors:

    Dr. Tamer Shanableh

    Abstract to be filled

  • schoolAndroid Application names "AUS Essentials"
    Students:

    Osman
    Advisors:

    Dr. Geressimos Barlas

    Abstract to be filled

  • schoolGame Interactive Fitness Tracker
    Students:

    Mina, Jawad & Andrew
    Advisors:

    Dr. Tarik Ozkul

    Abstract to be filled

  • schoolRemote Monitoring and Control System for Industrial Motors
    Students:

    Lama, Haya & Mohamed
    Advisors:

    Dr. Abdulrahman Al-Ali & Dr. Ahmed

    Abstract to be filled

Summer

  • searchOn Studying the Effectiveness of Extended Finite State Machine Based Test Selection Criteria.
    Student:

    Noshad Khan Jadoon


    Advisor:

    Dr. Khaled El Fakih

    Automatic test derivation from formal specifications offers a rigorous discipline to functional conformance testing. In various application domains, such as communication protocols and other reactive systems, the specification can be represented in the form of an Extended Finite State Machine (EFSM). A number of methods can be used for deriving test suites from an EFSM specification. In practice, developing and applying these test suites to an implementation under test is time consuming and costly. Thus, it is desirable to determine high quality test suites in order to reduce the cost of testing. This research aims at determining and comparing the quality of various test suites. Using six realistic application examples, various known types of EFSM based test suites are derived and experiments are conducted to assess the fault coverage of these test suites. The assessment is carried out using EFSM mutants of these specifications, namely, EFSM mutants with single and double transfer faults, single assignment faults and single output parameter faults. The various types of considered test suites include single transfer fault, double transfer fault, all uses, single assignment fault, transition tour, state identifier, edge pair, prime path, prime path with side trip, and random test suites Ranking of the test suites, in terms of fault coverage and in terms of both coverage and test suite length, is established for each considered type of faults.
    Click here to download this thesis

  • searchPerformance Evaluation of LTE Uplink Scheduling Algorithms.
    Student:

    Hanin Mohamed Almuhallabi


    Advisor:

    Dr. Rana Ejaz Ahmed

    Abstract to be filled

Spring

  • searchDragon 12-plus Emulator.
    Student:

    Osama Tawfiq Al Aqel


    Advisor:

    Dr. Tarik Ozkul

    Abstract to be filled

  • searchParallel Algorithms for Distinguishing Nondeterministic Finite State Machines.
    Student:

    Mustafa Ali


    Advisor:

    Dr. Gerassimos Barlas & Dr. Khaled El Fakih

    Many methods are used for the development of experiments and conformance tests based on the specification given in the form Finite State Machines (FSMs). In FSM-based testing, we have an FSM or a black-box Implementation Under Test (IUT) about which we lack some information, and we want to deduce this information by conducting experiments on the IUT. An experiment consists of applying input sequences, observing corresponding output responses, and drawing conclusions about the IUT. An experiment is adaptive if at each step of the experiment the next input is selected which is based on the previously observed outputs. A distinguishing experiment determines the initial state of the FSM. In this thesis, we consider two implementations of an existing sequential algorithm for deriving the minimal length of an adaptive distinguishing experiment for a nondeterministic FSM. We show that the execution time for both of these implementations grows exponentially as the size or the number of transitions of the FSM increases. Accordingly, in order to obtain a solution in a reasonable time, we develop four parallel implementations of the considered sequential algorithms, namely, a multi-core implementation on Central Processing Unit, two Graphical Processing Unit (GPU) implementations based on the platforms like CUDA and Thrust, respectively, and an implementation on a Network of Workstations (NoWs). Comprehensive experiments are conducted to assess and compare the performance and the speedup of the developed implementations. Based on the results obtained from these experiments, the parallel implementation on a NoW provides the best performance and speedup, followed by the CUDA, then the Thrust, followed by the multi-core CPU implementation.
    Click here to download this thesis

  • schoolenPUT
    Students:

    Anas Ilaban, Hadi Al-Assaf & Mahitab Hassan
    Advisors:

    Dr. Gerassimos Barlas

    Abstract to be filled

  • schoolApplication of Graph Coloring Algorithms
    Students:

    Aysha Godil, Khadija AlBassam & Zainab Aqlan
    Advisors:

    Dr. Gerassimos Barlas

    Abstract to be filled

  • schoolAUS Parking System
    Students:

    Lionel Lobo, Tareq Najib & Aditya Apparaju
    Advisors:

    Dr. Tarik Ozkul

    Abstract to be filled

  • schoolEmotion Sensor
    Students:

    Ahmed Hesham Awad, Shams Edden Shabsough & Youssef Ahmed ElKhorazaty
    Advisors:

    Dr. Fadi Aloul & Dr. Imran Zualkernan

    Abstract to be filled

  • schoolMobile Education Platform
    Students:

    Ahmed Mohamed Nosseir, Ayesha Hafeez &Krishika Haresh Khemani
    Advisors:

    Dr. Imran Zualkernan

    Abstract to be filled

  • schoolVehicle Identification using Google Glass
    Students:

    Benna Iqbal, Maryam Hassan & Samina Abdul Rahman
    Advisors:

    Dr. Rana Ahmed

    Abstract to be filled

  • schoolWireless Home Automation
    Students:

    Nourhan Kandeel & Diala Hany
    Advisors:

    Dr. Abdulrahman Al-Ali

    Abstract to be filled

  • schoolEarthquake Warning App
    Students:

    Rahaf Halloul, Tariq Shahrouri & Mah'd M. Abu-Eideh
    Advisors:

    Dr. Tarik Ozkul, Dr. Magdi El-Emam & Mr. Aqeel Ahmed

    Abstract to be filled

  • schoolSmart LED Street Lighting System
    Students:

    Mohammed Rashid, Rizwan Hassan, Silpa Baburajan & Faisal AlZaooni
    Advisors:

    Dr. Abdulrahman Al-Ali & Dr. Ahmed Osman

    Abstract to be filled

2014

Fall

  • searchFuzzy Logic Algorithm for Wireless Bridge Monitoring.
    Student:

    Amro Abdel Kareem Al-Radaideh


    Advisor:

    Dr. Abdulrahman Khalaf Al-Ali & Dr. Salwa Mamoun Beheiry

    Abstract to be filled

  • searchEvaluation of TCP Performance for LTE Downlink MAC Schedulers.
    Student:

    Ismael Ibrahim Al-Shiab


    Advisor:

    Dr. Rana Ejaz Ahmed"

    Abstract to be filled

  • searchFault Coverage and Diagnosis of Protocols and Systems Modeled as Extended Finite State Machines.
    Student:

    Mark Habib Hassoun


    Advisor:

    Dr. Khaled El Fakih

    Automatic test derivation from formal specifications offers a rigorous discipline to functional conformance testing. In various application domains, such as communication protocols and other reactive systems, the specification can be represented in the form of an Extended Finite State Machine (EFSM). Many methods can be used for deriving test suites from an EFSM specification. In practice, developing and applying these test suites to an Implementation Under Test (IUT) is time consuming and costly. Thus, it is desirable to determine high quality test suites in order to reduce the cost of testing. To this end, in the first part of this thesis, using six realistic application examples, we conduct experiments, assess, determine the fault coverage, and accordingly rank various known types of EFSM-based test suites. While the purpose of conformance testing is to check if an IUT is different from its specification, an interesting, complementary, yet more complex step, is called fault diagnosis or diagnostic testing. The objective of fault diagnosis is to determine the faulty implementation, and thus find the differences between the specification and its implementation. In the second part of this thesis, we present a diagnostic method, conduct experiments, and assess the fault localization capabilities of the EFSM-based test suites considered in the first part of the thesis. The fault localization capability of a test suite is determined for many types of diagnostic candidates, representing possibly faulty EFSM implementations, such as candidates with single or double transfer faults, candidates with single assignment faults, and many other types of candidates. In addition, for each considered test suite, the method determines the diagnostic tests required, in addition to the considered test suite, for locating a faulty EFSM IUT.
    Click here to download this thesis

  • schoolMedical Assistance Using Mobile Phone Application
    Students:

    Thuraya Ezz Eldin & Mahbod Azadian
    Advisors:

    Dr. Ghassan Qadah

    Abstract to be filled

  • schoolA modular Toll Gate Research Platform
    Students:

    Yomna Abdallah, Sara Al-Qaisi & Sami Zein-ElAbdin
    Advisors:

    Dr. Michel Pasquier

    Abstract to be filled

  • schoolSun Protection Using Mobile Phones
    Students:

    Noura Alfayez, Manoj Sagar & Fatemeh Yarahmadi
    Advisors:

    Dr. Gerassimos Barlas

    Abstract to be filled

  • schoolAn Android-Based Smart Power Outlet
    Students:

    Tareq Nabil Hallawa, Wasim Nasr Ekila & Mohammad A.M Elassar
    Advisors:

    Dr. Abdulrahman Al-Ali

    Abstract to be filled

Spring

  • searchAn Implementation of a Dual-Processor System on FGPA.
    Student:

    Mohammed Eqbal Eshaq


    Advisor:

    Dr. Assim Sagahyroon & Dr. Fadi Ahmed Aloul

    Abstract to be filled

  • searchPredicting Hypoglycemia in Diabetic Patients Using Machine Learning Techniques.
    Student:

    Khouloud Safi El Jil


    Advisor:

    Dr. Ghassan Zaki Qadah & Dr. Michel Bernard Pasquier

    Diabetes is a chronic disease that needs continuous blood glucose monitoring and self-management. The improper control of blood glucose levels in diabetic patients can lead to serious complications such as kidney and heart diseases, strokes, and blindness. The proper treatment of diabetes, on the other hand, can help a person live a long and normal life. On the other hand, tighter glycemic controls increase the risk of developing hypoglycemia, a sudden drop in a patients’ blood glucose levels that can lead to coma and possibly death if proper action is not taken immediately. Continuous Glucose Monitoring (CGM) sensors placed on a patient body measure glucose levels every few minutes. They are also capable of detecting hypoglycemia. Yet detecting hypoglycemia sometimes is too late for a patient to take proper action, so a better approach is predicting the hypoglycemia event before it occurs. Recent research efforts have been made in predicting subcutaneous glucose levels at specific points in the future. Moreover, the models developed used are ill suited for predicting out-of-range glucose values, namely, hypoglycemia and hyperglycemia. Hence, in this research, we use machine learning techniques suitable for predicting hypoglycemia within a prediction horizon of thirty minutes. This period should be long enough to enable the diabetes patients to avoid hypoglycemia by taking proper action. In specific, we use and compare two approaches to perform the hypoglycemia prediction, namely, a time sensitive artificial neural networks (TS-ANN) and tree based temporal classification (TBTC) by applying feature extraction from the patient glucose signal. While the TS-ANN performed reasonably well (with average sensitivity= 80.19%, average specificity= 98.2%, and average accuracy= 97.6%), nevertheless, the TBTC approach outperformed the TS-ANN one with the ability to predict hypoglycemia events accurately (with average sensitivity= 93.9%, average specificity= 98.8, average accuracy= 98.16%) using three aggregate global features; mean, minimum, and difference, and two parameterized event primitives (PEPs), namely the negative slope and local minimum of the glucose signal.
    Click here to download this thesis

  • searchFPGA-Based Parallel Hardware Architecture for Real-Time Object Classification.
    Student:

    Murad (Mohammad Taisir) Qasaimeh


    Advisor:

    Dr. Tamer Jamal Shanableh & Dr. Assim Sagahyroon

    Object detection is one of the most important tasks in computer vision. It has multiple applications in many different fields such as face detection, video surveillance and traffic sign recognition. Most of these applications are associated with real-time performance constraints. However, the current implementations of object detection algorithms are computationally intensive and far from real-time performance. The problem is further aggravated in an embedded systems environment where most of these applications are deployed. The high computational complexity makes implementing an embedded object detection system with real-time performance a challenging task. Consequently, there is a strong need for dedicated hardware architectures capable of delivering high detection accuracy within an acceptable processing time given the available hardware resources. The presented work investigates the feasibility of implementing an object detection system on a Field Programmable Gate Array (FPGA) platform as a candidate solution for achieving real-time performance in embedded applications. A parallel hardware architecture that accelerates the execution of three algorithms is proposed. The algorithms are: Scale Invariant Feature Transform (SIFT) feature extraction, Bag of Features (BoF) and Support Vector Machine (SVM). The proposed architecture exploits different forms of parallelism inherent in the aforementioned algorithms to reach real-time constraints. A prototype of the proposed architecture is implemented on an FPGA platform and evaluated using two benchmark datasets. On average, the speedup achieved was ×55.06 times when compared with the feature extraction algorithm implemented in pure software. The speedup achieved in the classification algorithm was ×6.64 times. The difference in classification accuracy between our architecture and the software implementation was less than 3%. In comparison to existing hardware solutions, our proposed hardware architecture can detect an additional 380 SFIT features in real-time. Additionally, the hardware resources utilized by our architecture are less than those required by existing solutions.
    Click here to download this thesis

  • searchSensor-Based Continuous Arabic Sign Language Recognition.
    Student:

    Noor Ali Tubaiz


    Advisor:

    Dr. Tamer Jamal Shanableh & Dr. Khaled T Assaleh

    Arabic sign language is the most common way of communication between the deaf and the hearing individuals in the Arab world. Due to the lack of knowledge of Arabic sign language among the hearing society, deaf people tend to be isolated. Most of the research in this area is focused on the level of isolated gesture recognition using vision-based or sensor-based approaches. While few recognition systems were proposed for continuous Arabic sign language using vision-based methods, such systems require complex image processing and feature extraction techniques. Therefore, an automatic sensor-based continuous Arabic sign language recognition system is proposed in this thesis in an attempt to facilitate this kind of communication. In order to build this system, we created a dataset of 40 sentences using an 80-word lexicon. It is intended to make this dataset publicly available to the research community. In the dataset, hand movements and gestures are captured using two DG5-VHand data gloves. Next, as part of data labeling in supervised learning, a camera setup was used to synchronize hand gestures with their corresponding words. Having compiled the dataset, low-complexity preprocessing and feature extraction techniques are applied to eliminate the natural temporal dependency of the data. Subsequently, the system model was built using a low-complexity modified k-Nearest Neighbor (KNN) approach. The proposed technique achieved a sentence recognition rate of 98%. Finally, the results were compared in terms of complexity and recognition accuracy against sequential data systems that use common complex methods such as Nonlinear AutoRegressive eXogenous models (NARX) and Hidden Markov Models (HMMs).
    Click here to download this thesis

  • schoolFree Space Optical Communication System
    Students:

    Reem Al Askari, Zahraa Al-Sahlanee & Fatema Al Bloushi
    Advisors:

    Dr. Taha Landolsi & Dr. Aly Elrefaie

    Abstract to be filled

  • schooliPad Educational Learning to Teach Young Children About the Ghaf Tree
    Students:

    Hala Sarhan, Mohamed T. Amer & Sherif A. Elabd
    Advisors:

    Dr. Imran Zualkernan & Dr. Abdulrahman Al-Ali

    Abstract to be filled

  • schoolInquiry-based Ubiquitous Learning in Architecture: Software, Hardware and Architecture
    Students:

    Somaia Amin, Huda Ahmed & Salsabeel Shapsough
    Advisors:

    Dr. Imran Zualkernan

    Abstract to be filled

  • schoolReal Time Control of a 4-DOF Teleoperation Manipulator
    Students:

    Dua'a Beni Jaber, and Omar Al Muhairi & Ayesha Mujahid
    Advisors:

    Dr. Mamoun Abdel-Hafez, Dr. Mohammed Jaradat & Dr. Gerassimos Barlas

    Abstract to be filled

  • schoolPortable Face Recognition
    Students:

    Dina Allahham, Abdelrahman Elghassnawi & Khalid El Dhmashawy
    Advisors:

    Dr. Tamer Shanableh

    Abstract to be filled

  • schoolAndriod Based System for the Diagnosis and Monitoring of COPD
    Students:

    Basel A. Safieh & Haya Hassan
    Advisors:

    Dr. Fadi Aloul & Dr. Assim Sagahyroon

    Abstract to be filled

  • schoolContenet Aware Refrigerator
    Students:

    Roman Victor Chaves, Hanna Mattar & M. Saeed Safrini
    Advisors:

    Dr. Michel Pasquier

    Abstract to be filled

2013

Fall

  • searchAssessment Metrics for Intelligence of Human-Computer Interface.
    Student:

    Ahmed Tawfik Ahmed El Zarka


    Advisor:

    Dr. Tarik Ozkul

    The quality of human-computer interfaces is becoming increasingly important as smart devices are becoming an essential part of our lives. Often what makes or breaks the market success of a device is not the hardware, but the quality and ease-of-use of the user interface of the smart device. Just as it is possible to discuss the intelligence level of machines in terms of their “machine intelligence quotient,” it is becoming increasingly appropriate to discuss the “intelligence level” of a user interface. This new index would provide a quantitative assessment of user interface quality, and would be an indicator for rating the ease-of-use of the human-computer interface. In this study, a framework has been developed for the assessment of “user interface intelligence quotient” and is used to determine the quality of different smartphone interfaces. After conducting 200+ different human-smartphone experiments with popular smartphones and compiling the results using the methodologies developed, the results are compared to the actual opinion of the users. Results indicated that actual user opinions are in line with the calculated “intelligence” value of the smartphones. This study shows that there is a way to develop a “yardstick” to measure user satisfaction by using purely objective parameters. Search Terms: Machine Intelligence Quotient (MIQ), User Intelligence Quotient (UIQ), Mobile, User Interface, Smartphones, Usability, Fuzzy Logic, Sugeno, Mamdani, FIS.
    Click here to download this thesis

  • searchWiMAX Network Models for the Smart Grid.
    Student:

    Ban Abdul Elah Al-Omar


    Advisor:

    Dr. Abdulrahman Khalaf Al-Ali & Dr. Taha Landolsi

    The smart grid is the integration of the 21st century information and communications technologies with the 20th century traditional power grid. Such integration empowers the electricity utilities and their consumers to play an interactive role to better manage and operate their power consumptions and integrates their renewable energy resources to the grid. As wireless communication is evolving, it is expected that WiMAX will play a major role in the data and commands exchange between generation, transmission, distribution and consumption control and dispatch centers. This thesis proposes the design of two WiMAX network topologies to serve as a wireless communication network for the smart grid. Based on the smart grid applications’ quality of service requirements, network parameters and scheduling, simulation models were developed. The traffic was classified into five priority classes. Three scheduling algorithms namely; class-based weighted fair, class-based deficit weighted round-robin and class-based strict priority scheduling were used to simulate and assess the performance of the proposed models. Simulation results showed that the class-based strict priority queuing is better for the highest priority classes and the class-based weighted fair queuing preserved the quality of service requirements for all classes.
    Click here to download this thesis

  • searchSentiment Mining of Arabic Twitter Data.
    Student:

    Soha Galalaldin Ahmed


    Advisor:

    Dr. Michel Bernard Pasquier & Dr. Ghassan Zaki Qadah

    Social networking services such as Facebook and Twitter and social media hosting websites such as Flickr and YouTube have become increasingly popular in recent years. One key factor to their attractiveness worldwide is that these sites and services allow people to express and share their opinions, likes, and dislikes, freely and openly. The opinions posted range from criticizing politicians to discussing football matches, citing top news, appraising movies, and recommending new products and services such as mobiles, restaurants, and software. This development has fueled a new field known as sentiment analysis and opinion mining with the goal of extracting people’s sentiment from text to assist customers in their purchase decisions and vendors in enhancing their reputation. This emerging field has attracted a large research interest, but most of the existing work focuses on English text. Hence, in this thesis, we studied sentiment analysis of Arabic text retrieved from a well-known social media site, namely Twitter. Specifically, we studied the topic of target-dependent sentiment analysis of Arabic Twitter text, which has not been addressed in Arabic language before. We developed a system that will acquire Arabic text from Twitter and extract users’ opinions towards different topics and products. Key phases of the system are as follows. In the Data Acquisition phase, we collected tweets from Twitter related to specific topics. In the Tweet-Filtering phase, we reduced the noise in the collected tweets data to facilitate the Annotation phase, in which we annotated the collected tweets depending on the specified topic. In the Data Preprocessing phase, we added tags, normalized the words used in tweets, and removed spam tweets. In the Feature identification phase, we extracted stylistic, syntactic, and semantic features, and selected those yielding better results using features selection algorithms. In the Classification phase, the decision to annotate the tweets as negative, positive, or neutral towards a specific topic was made using a trained machine-learning algorithm. Results from different feature sets, classifiers, and datasets are reported in terms of classification accuracy, Kappa statistic, and F-measure.
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  • schoolGait Recognition Using Kinect
    Students:

    Mohamed Gamal Eldin & Yusur Al-Hadithi
    Advisors:

    Dr. Tamer Shanableh

    Abstract to be filled

  • schoolMulti-Protocol Gateway for Smart Home Appliances
    Students:

    AbdulRahim Haddad, MoaminAlBayed & Munir Bahaderi
    Advisors:

    Dr. Abdulrahman Al-Ali & Dr. Taha Landolsi

    Abstract to be filled

  • schoolRFID University Parking Control System
    Students:

    Abd Al Kareem Akilan, Ghaith Kabbani & Mohammed Al Nabtiti
    Advisors:

    Dr. Tarik Ozkul

    Abstract to be filled

Summer

  • searchBiometric Identification Based on Eyes' Dynamics Using Task-Driven and Task-Independent Stimuli.
    Student:

    Ali Abdulrazak Alhaj Darwish


    Advisor:

    Dr. Michel Bernard Pasquier

    This work investigates the feasibility of using the dynamic features of the eyes for biometric identification. Identifying individuals using eye movements is typically limited by a low accuracy, thus preventing this technique from becoming commercially viable. In addition, the human eyes constitute a rich source of information, still only partially understood so far, hence more research is needed to understand exactly what kind of information they can provide, and what technique should be applied to analyze such information. It is also largely unknown what kind of feature will yield accurate data most useful to biometric identification, or which stimuli most influence most the dynamic features of the eyes and their usability as a biometrical trait. We show that, by combining eye movement features and iris constriction and dilation parameters, the dynamic features of the eye can yield a good level of accuracy for biometric systems. The approach consists of recording and categorizing eye movements as well as changes in pupil size into segments consisting of saccades and fixations, and computing for each the many velocity and acceleration features that are used to train the classifier to perform the biometric identification. We tested four types of stimuli to hypothesize which will provide a viable stimulating method for extracting eye features. The results suggest that simple stimuli such as images and graphs can appropriately excite the dynamic features of the eye for the purpose of biometric identification.
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  • searchMethodology for Selection of Agile Practices.
    Student:

    Majd Haitham Saleh


    Advisor:

    Dr. Armin Paul-Gerhard Eberlein & Dr. Michel Bernard Pasquier

    Agile methods have received significant attention in the last ten years and have successfully been applied to many small- to medium-sized projects. They have enjoyed significant popularity amongst developers. Most of the time, the selection of agile methods and practices is based on personal preference or past experience rather than the characteristics of the project at hand. Furthermore, there are no sufficient guidelines for developers to make an appropriate selection. So far, research in this area focuses mainly on specifying the weaknesses and strengths of each method with little analysis of these methods and their practices. It also offers little guidance on how to choose the best-suited practice for a certain project. We believe that finding a way to link project properties and characteristics with the abilities of agile practices is of great importance. In this thesis, we try to find and propose a methodology for developing customized agile approaches by selecting the best agile practices for a given project. We also implement this methodology into an operational model.
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  • searchA Framework for Screening and Classifying Obstructive Sleep Apnea Using Smartphones.
    Student:

    Mamoun Tawfiq Al-Mardini


    Advisor:

    Dr. Fadi Ahmed Aloul & Dr. Assim Sagahyroon

    Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the golden standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In the proposed research, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone’s built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory efforts, and (3) an accelerometer to detect the body’s movements. The collected signals are then analyzed on the phone to deduce if the patient is suffering from OSA. In the proposed system, we have developed an Android application that is able to record and extract the physiological signals from the patients and analyze them solely on the smartphone without the need for any external resources. The smartphone is able to analyze the oximeter and accelerometer reading. Most health applications use smartphones to collect physiological readings, and then off load them to an external server for analysis. However, in this work we developed an integrated environment that collects and processes data on the smartphone, including the signal processing functions that analyze the recorded respiratory efforts. Finally, we examine our system's ability to screen the disease when compared to the golden standard by testing it on 17 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the proposed system as compared to the golden standard and emphasize the important role of smartphones in healthcare.
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Spring

  • searchModeling Smartphone Power.
    Student:

    Sameer Allan Alawnah


    Advisor:

    Dr. Assim Sagahyroon

    Battery technology has not advanced rapidly enough to keep pace with the growing energy demands of today‟s portable electronics. Leading this critical need for energy are smartphone devices which are being deployed and adopted at an increasing rate. Developing sound energy management techniques for these devices requires a good understanding of where and how battery energy is being utilized. Power consumption modeling is therefore crucial for understanding the inner working of these devices and for developing energy-efficient software to run on them. In this work, we attempt to develop power models for Android-based smartphones. A logger application is developed to monitor users‟ activity and collect data related to this activity. We then utilize regression techniques and neural networks to develop power models that relate power consumption to usage behavior. We demonstrate the feasibility of applying the two approaches and provide a detailed comparison between them.
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  • searchTesting and Assessment of Protocols and Systems Modeled as Extended Finite State Machines.
    Student:

    Tariq Mohammad Salameh


    Advisor:

    Dr. Khaled El Fakih

    Developing and selecting an appropriate test suite is an important issue for testing implementations of protocols and other reactive software systems. Many methods are known for the derivation of test suites based on a specification given in the form of Extended Finite State Machine (EFSM). In practice, developing test suites and applying these test suites to an implementation under test is time consuming and costly. Thus, determining high quality test suites reduces the cost of software testing. To this end, in this thesis, we first assess and compare the coverage of test suites derived using known EFSM-Based test derivation criteria and test suites derived using the traditional Data-Flow and Control-Flow criteria. In addition, we assess and compare the coverage of these test suites with randomly generated test suites. Finally, we propose an EFSM-Based test derivation method that derives tests with the guaranteed coverage of transfer faults. Experiments comparing the fault detection capability of derived tests with those derived using the considered EFSM-Based, random, and the traditional Data-Flow and Control-Flow testing criteria are presented.
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  • schoolEducation Assessment Tool
    Students:

    Arwa Awan, Hiba Riaz, Marina Rofail & Riham Abdel‐Moniem
    Advisors:

    Dr. Imran Zualkernan

    Abstract to be filled

  • schoolDetect Car Accidents
    Students:

    Farah Adel Alhaddad, Ruba Ali Abu-Salma, Rana Kanbar & Sarah Adel Alotaibi
    Advisors:

    Dr. Fadi Aloul & Dr. Imran Zualkernan

    Abstract to be filled

  • schoolWearable Medical Devices
    Students:

    Sherif Abou El-ella, Bassell Ajlani & Mayosore Fagunwa
    Advisors:

    Dr. Assim Sagahyroon

    Abstract to be filled

  • schoolContactless measurment
    Students:

    Al Moaataz Hassan, Mohammad Oman Hokan, Bashar Alkateb & Moussa
    Advisors:

    Dr. Tarik Ozkul & Dr. Hasan Mir

    Abstract to be filled

  • schoolOptical Fiber
    Students:

    Muaath Ali, Karim Imad Dakhlallah & Sara Ibrahim
    Advisors:

    Dr. Taha Landolsi & Dr. Aly Elrefaie

    Abstract to be filled

2012

Fall

  • searchFuzzy Logic Based Patients' Monitoring System.
    Student:

    Jumanah Abdullah Al-Dmour


    Advisor:

    Dr. Abdulrahman Khalaf Al-Ali & Dr. Assim Sagahyroon

    The ever increasing health care costs are becoming a major concern to both, individuals and authorities. This has tempted researchers to seek alternative models to the traditional and costly hospital-based monitoring and caring approach. One such an approach is the utilization of mobile units that allow for the remote observation and diagnosis of patients in their homes. Advances in VLSI circuits, single-chip embedded-system computing platforms, mobile telecommunications, and web services have provided valuable opportunities to enhance the design and performance of mobile patient‟s health monitoring platforms. In particular, Radio Frequency Identification (RFID) technology has emerged as one of the possible valuable solutions that can be utilized in future healthcare systems. RFID tags integrated with built-in vital signs sensors such as Body Temperature (TEMP), Blood Pressure (BP), Heart Rate (HR), Blood Sugar Level (BS) and Oxygen Saturation in Blood (SPO2) are useful in identifying and recording the state of a patient. In this work, we proposes the design, implementation, and testing of a mobile RFID-based health care system. The system consists of a wireless mobile vital signs data acquisition unit and a fuzzy-logic–based-software algorithm to monitors and assess patients‟ conditions on 24/7 bases. A set of fuzzy rules are developed to diagnose the monitored patient‟s status based on the received vital signs namely; TEMP, BP, HR, BS, and SPO2. The fuzzy algorithm will output an early warning of any patient‟s abnormality status. The system was implemented and tested at Rashid Center for Diabetes and Research (RCDR) hosted in Khalifa Hospital, Ajman, UAE using a representative sample of 26 patients. System performance is compared with the medically accepted standard, namely, the Modified Early Warning System (MEWS) that is currently widely used in practice. The proposed system has proven that it outperforms the MEWS system in many cases, and hence an indication of the usefulness of this fuzzy-based approach.
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  • searchPerformance Degradation of 100Gb/s and 400Gb/s Optical Coherent Systems due to Dispersion.
    Student:

    Rami Yousef Hasan Al-Dalky


    Advisor:

    Dr. Taha Landolsi, Dr. Aly Fahim Elrefaie & Dr. Mohamed Said Abdou Hassan

    In the near future, data rates of 100 Gb/s and 400 Gb/s will be used to match the increase in bandwidth demand for capacity. Wavelength division multiplexing (WDM) systems transmit multiple wavelengths simultaneously at high data rates over long distances where the signal passes through multiple optical add drop multiplexers (OADMs) along the fiber link towards the destination. The transmitted signals su_er from dispersion induced from the fiber and OADMs, where these e_ects are an important limiting factor. The success of high-bit rate, long-haul, point-to-point optical transmission networks depends on the management of the fiber’s linear and non-linear e_ects. In this thesis, we propose to study the impact of cascaded filters as the signals pass through multiple OADMs to determine its e_ect on the next generation network’s data rates. We aim to study the impact of cascaded filters on single-carrier and dual-carrier 100 and 400 Gb/s optical transmission systems. The eye closure penalty (ECP) will be used as a performance evaluation metric. The results indicate that the filter cascade has a severe impact on the performance of dual-carrier systems relative to the case of single-carrier systems. Secondly, chromatic dispersion (CD) e_ect will be mitigated electronically for 100 and 400 Gb/s systems using fiber-dispersion finite impulse response (FD-FIR) filter. The compensating FIR filter’s coe_cient will be computed from the impulse response of the inverse of the fiber’s transfer function. Bit error rate (BER) versus optical signalto- noise ratio (OSNR) curve will be used to evaluate the compensating technique. The results indicate that for 100 Gb/s PM-QPSK systems, using 2 samples/symbol with maximum number of taps is the best approach to compensate for CD. While for 400 Gb/s PM-16QAM systems, using 4 samples/symbol with 50% of the maximum number of taps is the best approach to compensate for CD.
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  • schoolPosture Monitoring System
    Students:

    Malak Awarnai, Manar Noaman & Nervana Nasser
    Advisors:

    Dr. Assim Sagahyroon

    Abstract to be filled

  • schoolVehicle Tracking and anti-Theft System
    Students:

    Mohammed ElAshri & Mohammed Moaz
    Advisors:

    Dr. Tarik Ozkul

    Abstract to be filled

  • schoolNatural User Interface Multi-Touch Table
    Students:

    Kareem Habib, Roaa Nasrallah & Ahmed Elsayed
    Advisors:

    Dr. Michel Pasquier

    Abstract to be filled

  • schoolAutomous Vacuum Cleaner
    Students:

    Reem Abdulaziz Alsinan, Lena T. Bazari & Hajara Mohammed Abdulrahman
    Advisors:

    Dr. Tarik Ozkul and Dr. Michel Pasquier

    Abstract to be filled

  • schoolGIS-based Wireless Monitoring System for Distribution Transformers
    Students:

    Mariam Al Shamsi, Meera Al Shamsi, Omar Al Muhairi & Salim Al Suwaidi
    Advisors:

    Dr. Abdulrahman Al Ali and Dr. Ahmed Osman

    Abstract to be filled

  • schoolGIS Based Energy Smart Meter
    Students:

    Fazel Ahmad Bashiri & Mohammed El-Kurdi
    Advisors:

    Dr. Abdulrahman Al Ali

    Abstract to be filled

  • schoolIbump-Smartphone accident Detection System
    Students:

    Humaid Al Ali & May Al Merri
    Advisors:

    Dr. Fadi Aloul & Dr. Imran Zualkernan

    Abstract to be filled

  • schoolWireless Cricket Training Kit Software and Hardware Architecture
    Students:

    Mahmoud Haque, Huzeifa Pedhiwala & Siddarth Dabrai
    Advisors:

    Dr. Imran Zualkenan and Dr. khaled Assaleh

    Abstract to be filled

2011

Fall

  • searchMANET Cluster Optimization Using ILP/SAT Techniques.
    Student:

    Syed Zohaib Hussain Zahidi


    Advisor:

    Dr. Assim Sagahyroon & Dr. Fadi Ahmed Aloul

    In recent years, there have been several improvements in the performance of Integer Linear Programming (ILP) and Boolean Satisfiability (SAT) solvers. These improvements have encouraged the modeling of complex engineering problems as ILP problems. These engineering problems are diverse in nature and include genetics, optimization of power consumption, scheduling, cryptography, and more. One such problem is the ‗clustering problem‘ in Mobile Ad-Hoc Networks (MANETs). The clustering problem in MANETs consists of selecting the most suitable nodes of a given MANET topology as clusterheads and ensuring that regular nodes are connected to clusterheads in such a way that the network lifetime is maximized. This thesis focuses on assessing the performance of state-of-the art generic ILP and 0-1 SAT-based ILP solvers in solving ILP formulations of the clustering problem. The thesis consists of four parts. The first part of this thesis consists of improving the existing ILP formulations of the clustering problem. The second part involves enhancing the ILP formulation of the clustering problem through the addition of intra-cluster communication, coverage constraints and multihop links. The third part focuses on the development of an improved tool to enable conversion of user-created on-screen topologies to an ILP formulation. The fourth and final part of this thesis is the detailed performance comparison of a selected set of Generic ILP and 0-1 SAT-based ILP solvers in solving the improved ILP formulations of the clustering problem generated using the tool. The results obtained indicate that from our selected set of solvers, generic ILP solvers are able to handle relatively large scale MANET topologies, while 0-1 SAT-based ILP solvers are the fastest, for small scale networks. For small scale networks the proposed ILP formulations, such as the Star-Ring base model, together with the high performance solvers would be suitable for use in real-world environments. However for large scale networks, as the time to cluster the network grows exponentially, the solvers will be unable to cluster the network in accordance with the demands of a real-world environment.
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  • searchEnergy-Aware QoS Scheduling at MAC Level in WiMAX.
    Student:

    Sanabel H. M. Al-Nourani


    Advisor:

    Dr. Taha Landolsi & Dr. Rana Ejaz Ahmed

    In a mobile wireless network, energy saving of mobile devices is one of the most important features for the extension of devices’ life-time and the network. In mobile networks, the device is expected to have several connections, each with different QoS (Quality of Service) requirements. Meeting the QoS requirements on such devices along with better power saving is a challenging task. Moreover, in realtime scenarios, connections are expected to join and leave the network randomly. Before admitting a connection to the network, its QoS requirements must be checked to make sure that the network has adequate resources to accommodate it. Without a proper call admission control mechanism, the system cannot provide the promised QoS to the real-time applications. This research proposes a scheduling algorithm and a call admission control policy for IEEE 802.16e broadband wireless access standard. The proposed scheduling algorithm is designed towards minimizing power consumption at mobile stations, while maintaining different QoS requirements for real-time traffic. The proposed algorithm considers the dynamic nature of connection joining and termination. Connections will be allowed to join the network only if their QoS parameters can be met without violating those of existing connections. Simulation results show that when QoS delay requirements of the connections are not too restrictive, power savings of approximately 75% and 50% at the mobile station can be achieved for low- and moderate- rate Unsolicited Grant Service traffic types, respectively.
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  • searchOptimal Routing Protocol in Multimedia Wireless Sensor Networks.
    Student:

    Hiba Al-Zurba


    Advisor:

    Dr. Taha Landolsi, Dr. Fouad Ben Abdelaziz & Mr Mohamed Said Ahmed

    Wireless Sensor Networks (WSNs) have attracted research interest in recent years due to the significance of the field of applications and the advances in sensor technology. In areas where catastrophic events occur such as environmental disasters and battle fields, the network infrastructure is lost and there is an urgent need to build a network in order to monitor the area and to help in rescue operations or troops deployment. An easy and fast way is to scatter scalar and video sensor nodes in an adhoc manner in the area of interest in order to establish a multimedia wireless sensor network (MWSN). Video sensor nodes provide better coverage of the area and enhance the interpretation of the monitored phenomenon. Two main challenges faced in MWSNs are quality of service (QoS) constraints in addition to energy constraints. Many routing protocols with various routing metrics have been developed for WSNs. However, limited research has been done on MWSN routing protocols and there is room for improvement in this area. Moreover, these routing protocols assume structured network architecture where deployment of nodes is pre-planned. Limited research has been done on routing protocol for MWSN deployed in ad-hoc manner that meets QoS requirements and at the same time considers energy efficiency for the purpose of prolonging the lifetime of the network. In this thesis, an optimal routing protocol is developed for MWSN that is energy-aware and QoS-aware. This routing protocol uses ant colony optimization to find the optimal routing path that maximizes the end-to-end path quality and reliability as well as the network lifetime. End-to-end delay is a constraint set depending on the application used. Each metric used in the path cost can be attributed an importance that varies depending on the application requirements. A simulation model is developed to implement the proposed ant colony optimization algorithm in MWSN. A detailed analysis of various parameters used in ant colony optimization is performed. The proposed algorithm is analyzed and its performance is evaluated. The proposed routing protocol not only provides an optimal path in terms of the QoS and energy metrics but it also has the flexibility to be used in various applications by adjusting the weights for the optimal path metrics based on their importance to the application.
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