البحث — المستودع الرقمي العراقي

المستخلص: In this thesis, a proposed control strategy is presented to improve the performance of the PWM boost type rectifier when operating under different supply voltage conditions (balanced, unbalanced, and distorted three - phase supply voltages). The proposed control strategy is classified into two parts, the first part is voltage controller and the second part is current controller. In the voltage controller, Repetitive Controller (RC) is used to reduce the even order harmonics in the regulated output dc voltage (5.62V (P.P.)) so small output capacitor (480µF) is used instead of large capacitor (920µF) that is used with the conventional controller (PI controller connected in series with a LPF). RC also reduces the even order harmonics (0.014A (P.P)) which appears in the reflected dc current (IMAX), this leads to reduce the odd order harmonics (0.01A) which appears in the input currents. While in the current controller, enhanced Phase Locked Loop (EPLL) technique is used to obtain sinusoidal and balanced three phases, to construct the reference currents, which are in phase with the fundamental supply voltages Therefore, the supply - side power factor is kept close to unity (0.9036 for extremely distorting case). As a result the calculations of the reference currents in the proposed control strategy does not require Clarke or Park transformations, the same for the calculations of the positive or negative sequence components. A proportional controller is used to give excellent tracking between the line and the reference currents. Using this approach makes the control strategy easy to understand and also it saves calculation time of the control algorithms if implements in real time. The complete system with the proposed control strategy is simulated using Matlab/Simulink (Version 2012). The obtained results for the complete system using repetitive voltage controller are compared to the results of the system with using the conventional voltage controller. The results with the repetitive controller show better responses and stable operation in the steady state under different input voltage conditions, as well as in the transient response under different disturbances.

تحقيق دائرة المعدل الثابت للانذار الكاذب باستعمال منظومة البوابات المنطقية المبرمجة موقعيا == Modified GO - CFAR Implementaion Using FPGA

اسم المؤلف: مصطفى صبحي كمال

اسم المشرف: رفعت طالب حسين | حكمت نجم عبد الله

الموضوع العام: هندسة الكهرباء والالكترون والاتصالات

السنة: 2006

الموضوع الدقيق: هندسة الكهرباء

الدرجة: ماجستير

الجامعة: الجامعة المستنصرية - كلية الهندسة - قسم الهندسة الكهربائية

اللغة: الانكليزية

مكان الجامعة: بغداد

الصفحات الاولى:

المستخلص: هذه الاطروحة تتعلق بتحليل ومعالجة تاثير الضوضاء المحيطية (Clutter) لمنظومة الرادار النبضي، يتطلب رادار الكشف الالي للهدف استعمال عتبة الية (Adaptive threshold) لتحقيق معدل ثابت للانذار الكاذب وذلك لغرض السيطرة على الانذار الكاذب الذي تسببه تغيرات في الضوض | This thesis deals with the analysis and processing of clutter for pulsed radar system. Automatic target detection radar requires adaptive thresholding achieved by Constant False Alarm Rate (CFAR) circuit in order to control the false alarm caused by variations in clutter background.This work focus on worst radar environment that happens when abrupt variation in clutter background merged with multi - interfering target, to detect target in such environments it need robust CFAR algorithm that excise the target spikes and clutter edges from CFAR window in order to give best possible estimation to the noise background. two important algorithms studied which are CA - CFAR and OS - CFAR algorithms in additional to the modified CA - CFAR algorithm. All these algorithms were simulated with mat lab v6.1 and applied them to three different clutter models that represent different environment cases the CA - CFAR family failed to handle model two and three also OS - CFAR family except OSGO - CFAR that handle all models successfully. For modified CA - CFAR family only modified GO - CFAR handle all models successfully and comparing with OSGO - CFAR the modified GO - CFAR need less hardware and processing time because it did not need sorting process that is essential for OSGO - CFAR. Therefore, the modified GO - CFAR is chosen to implement by using FPGA and another important feature in modified GO - CFAR algorithm that is parallel processing since the spike selection process is done at the same time with summing of samples process that make this algorithm much less in processing time from any other algorithm that work in the same environment. The FPGA chip that used to implement modified GO - CFAR algorithm need only three signals from the radar receiver to mach with the receiver circuit correctly which are time base clock signal period reset trigger signal and the pulse duration time. Therefore, the FPGA chip can work effectively with almost any radar receiver system

تحليل الاشارات المشفرة ثنائيا باستخدام الترشيح الموائم وغير الموائم لتقليل الفصوص الجانبية == Analysis Of Binary - Coded Signals Using Matched And Mismatched Filtering For Sidelobe Reduction

اسم المؤلف: صلاح حسن عباس

اسم المشرف: اسماعيل محمد جابر

الموضوع العام: هندسة الكهرباء والالكترون والاتصالات

السنة: 2009

الموضوع الدقيق: هندسة الكهرباء

الدرجة: ماجستير

الجامعة: الجامعة المستنصرية - كلية الهندسة - قسم الهندسة الكهربائية

اللغة: الانكليزية

مكان الجامعة: بغداد

الصفحات الاولى:

نظام الاخفاء الصوري المعتمد على تحويل المويجة والاشارات الفوضوية

اسم المؤلف: سرى فهمي يوسف

اسم المشرف: حكمت نجم عبد الله

الموضوع العام: هندسة الكهرباء والالكترون والاتصالات

السنة: 2014

الموضوع الدقيق: هندسة الكهرباء

الدرجة: ماجستير

الجامعة: الجامعة المستنصرية - كلية الهندسة - قسم الهندسة الكهربائية

اللغة: الانكليزية

مكان الجامعة: بغداد

الصفحات الاولى:

تطبيق الشبكة العصبية في تكوين الشعاع المتكيف لنظام الهوائي الذكي == Neural Network Application In Adaptive Beamforming For Smart Antenna System

اسم المؤلف: سليمان احمد غازي

اسم المشرف: عضيد حسن سلومي

الموضوع العام: هندسة الكهرباء والالكترون والاتصالات

السنة: 2015

الموضوع الدقيق: هندسة الكهرباء

الدرجة: ماجستير

الجامعة: الجامعة المستنصرية - كلية الهندسة - قسم الهندسة الكهربائية

اللغة: الانكليزية

مكان الجامعة: بغداد

الصفحات الاولى:

المستخلص: انظمة الهوائيات الذكية تعمل على تحسين الاداء وزيادة في السعة لانظمة الاتصالات المتنقلة. ويمكن لانظمة الهوائيات الذكية حل مشكلة نظام الاتصالات المتنقلة مثل التدخل في نفس القناة، والتاخير الحاصل نتيجة الانتشار والمسارات المتعددة التي تاخذها الاشارة عن طريق | The smart antenna systems are promise to provide performance improvement and capacity increasing for the base station of mobile communication systems. Smart antenna systems can solve the problem of mobile communication system such as co - channel interference , delay spread and multipath by an advance signal processing technology called beamforming. In this work artificial neural networks (Feed Forward Neural Network (FFNN) and feedback Elman Recurrent Neural Network (ERNN) ) are used for smart antenna adaptive beamforming for one and multiple targets.Neural network is used to calculate the optimum weights of antenna array to adapt the radiation pattern of the antenna array by directing multiple narrow beams toward the desired users and nulling interference or unwanted users. FFNN and ERNN are trained by supervised backpropagation learning algorithms, FFNN was trained by using Levenberg - Marquardt (lm),Resilient Back - Propagation (Rprop), Gradient descent with momentum and adaptive learning rate (gdx), Gradient descent and adaptive learning rate (gda) and biasing regulation(br), while lm and Rprop are used to train ERNN..The simulation results show that the best performance of smart antenna adaptive beamforming for one and multiple targets is obtained when the NN (FFNN and ERNN) trained by lm algorithm as compared with other algorithms, since it consider the fastest supervised training algorithm but with more memory requirement. Where the performance of FFNN training phase based (lm) algorithm for single target for five element uniform linear array antenna is [2.746641e - 14] at epoch 27 with best validation performance and best test performance approximately equal to the best training performance. While The performance of ERNN training phase based lm algorithm for single target is [1.121938e - 14] at epoch 38 with best validation performance equal to [1.682442e - 14] and the best test performance is [3.363946e - 14].The neural network model in adaptive beamforming are compared with smart antenna adaptive beamforming based on Least Mean Square (LMS) algorithm, and gave better performance than LMS.