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  • الدراسات العليا

        الدراسات العليا في قسم هندسة الحاسوب: الجدول الاسبوعي المنهج الدراسي الاطاريح والرسائل شؤون الدراسات العليا شؤون الطلبة المبتعثين المواد الدراسية الخاصة بالامتحان التنافسي المستمسكات المطلوبة للتقديم للدراسات العليا جدول توقيتات التقديم والقبول بالدراسات العليا نظام التقديم و القبول للدراسات العليا مواصفات كتابة الرسالة او الاطروحة تحميل قالب الاطروحة  

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  • الخطة الدراسية

         الخطة الدراسية للفروع العلمية: هندسة المعلومات هندسة شبكات الحاسوب

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  • الخريجون

    خريجو القسم للدراسات الاولية و الدراسات العليا

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  • المحاضرات

    اطلع على اخر المحاضرات المنشورة من قبل اساتذة القسم

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  • التعليم الالكتروني

    DEPARTMENT OF COMPUTER Engineering - UNIVERSITY OF TECHNOLOGY E-LEARNING USING GOOGLE CLASSROOM تم تفعيل التعليم الإلكتروني في قسم هندسة الحاسوب حسب توجيهات رئاسة الجامعة التكنولوجية وحسب الخطوات التالي: - تصميم وإطلاق إستمارة التسجيل للحصول على البريد الرسمي لطلبة الدراسات الاولية على الرابط (https://goo.gl/YVKxS6) و طلبــة الدراســـات الـــعليا علــى الــــرابط (https://goo.gl/uDuKkB).  - مخاطبة مركز تكنولوجيا المعلومات لإنشاء الحسابات الرسمية للطلبة المسجلين. - البدء بإنشاء الصفوف الإلكترونية للدراسات العليا وإضافة التدريسيين وطلابهم حسب المواد العلمية.   فيديو تعريفي عن كيفية إستعمال بيئة التعليم الإلكتروني Google Classroom الجدول الاسبوعي للدراسة الصباحية الجدول الاسبوعي للدراسة المسائية    

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  • مشاريع التخرج

    مشاريع تخرج الطلبة الخاصة بقسم هندسة الحاسوب

الدراسات العليا

رشا خضير رجه

Payment Systems play a vital role in the economy of a country. A payment system is used to settle financial transactions through the transfer of monetary value and consists of the various mechanisms that facilitate for the transfer of funds from one party to another.Payment systems are used to conduct financial transactions that are vulnerable to theft or attack. However, biometric payments, such as face recognition, may appear to be a viable solution. Face recognition is an approach of biometric techniques that is used to identify and recognize facial features in humans.. Face recognition consists of three phases: face detection, feature extraction, and the recognition phase (classification). Accuracy is not the only factor that determines the performance of face recognition. Time is also a crucial concern in real-time applications. Time is regarded as the most significant challenge in real-time environments. As a result, the proposed system focuses on proposing an improved face recognition payment algorithm by addressing the time issue associated with the performance of automatic face recognition processing. The proposed system suggests several algorithms to solve the time problem, as follows: The ViolaJones and skin color detection algorithms are used for the face detection phase. Frontal face images are processed using the Viola-Jones algorithm, while the right and left sides of the face are processed using skin color detection. For the feature extraction phase, the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) are used. The multiple stages of reduction increase the speed of the entire system while decreasing complexity, processing time, and memory usage. A Support Vector Machine is used for classification (recognition phase), which has the ability to make a decision whether the person is known or not. Finally, we connect the system to an account for the payment system in which the user must enter the username and password to be certain that they are authenticated. If the user is authenticated, enter the amount of money to complete the transaction, or else cancel the payment process. The Olivetti Research Laboratory (ORL) and Head and Pose Images (HPI) datasets are used in the proposed system to evaluate the results. The proposed system reduces the execution time of the whole system and enhances the recognition rate as compared with other algorithms, such as using Viola-Jones with Linear Discriminant Analysis (LDA).The total time is reduced by half when using parallel processing. The total time without parallel processing is (1.226 sec) but in parallel processing, it II is (0.697 sec). The proposed system also enhanced the recognition rate (accuracy). The accuracy without parallel processing is 94%, but with parallel processing, it becomes 96%.

بلقيس رعد عبداللطيف

Abstract

The AODV routing protocol is based on blind flooding for route request packets (RREQ) dissemination in the route discovery mechanism. which can result in the same packet being received multiple times by the same nodes. Unnecessary packet forwarding  “re-transmission” will lead to network congestion and packet collision which are collectively known as the "Broadcast Storm Problem". In this work, the suggested protocol named Enhanced Neighbor Protocol “ENP” is a distance-based protocol employed to collect the data for the 2-hop-neighbour of all nodes. This information is important for selecting any node be it “forwarder” or not. Another notable approach is dominant pruning (DP) which is a distributed dominant set algorithm developed to mitigate the impact of flooding in MANETs. The dominant pruning (DP) requires 2-hop neighbor information. This information can be collected with the help of the neighbor discovery protocol.

The threshold values are set depending on these metrics (data delivery, overhead, and link broke). These metrics are partially or totally conflicted with each other, i.e.  the best data delivery achieved with a specific set of thresholds can incur the worst overhead. Therefore, the selection of threshold must attain a compromise among the conflicting metrics. In this work, the Multi-Objective Particle Swarm Optimization (MOPSO) was investigated and applied as the selection tool for the optimum threshold distances for each considered network size. There is no single solution that concurrently optimize all of the objectives for MOPSO. The objective functions are conflicting in that situation. The resulting solutions represent the best solutions known as "Non-dominated solutions”.  The aim may be to discover a representing set of "Pareto optimal solutions", and estimate the tradeoffs among various objectives (data deliver, link broke, and overhead).

ENP(Enhanced Neighbor Protocol) has been employed in conjunction with DP and both have incorporated AODV routing protocol to illustrate the advantages "ENP" of the protocol for different network sizes and a specific pair of threshold is selected with the aid of MOPSO approach. The performance of  ENP/MOPSO was compared with the original periodically neighbor discovery protocol in conjunction with DP “AODV/DP” and traditional routing protocols “AODV and OLSR”. It was also compared with a previous work that depends the “multi-criteria decision making (MCDM)” approach as the selection tool.

مجموعات فرعية

الاعلانات والاحداث القادمة

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  نتائج الامتحان التنافسي (الماجستير) للعام
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29 آب/أغسطس 2022
   جدول الامتحانات النهائية للدور الثاني للعام

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