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دراسة تاثير الاهتزاز والحرارة على نظام الاتصالات للطائرة بدون طيار STUDY THE EFFECT OF VIBRATION AND HEAT ON THE COMMUNICATION SYSTEM FOR DRONE

اسم المؤلف: احمد حسين عباس
اسم المشرف: احمد طه عبد السادة حسنين غني حميد
السنة: 2024
الموضوع الدقيق: هندسة الاتصالات
الدرجة: ماجستير
اللغة: الانكليزية
مكان الجامعة: النجف
الكلمات الدلالية:
  • drone
  • heat
  • vibration
المستخلص: Drone technology has evolved to become crucial in various fields. Vibrations and heat from external sources, as well as mechanical defects in design, can significantly impact the effectiveness of a drone's communication systems, thereby reducing their performance. This research uses the convolution neural network (CNN) technique and experiments and in a drone laboratory and simulation of theoretical model to look at how temperature and vibration affect two frequencies: the 2.4 GHz radio frequency and the 5.8 GHz radio frequency. The first step simulation of theoretical model and experiments were conducted in the drone laboratory, generating data that later served as input for artificial intelligence. The second step utilized a convolutional neural network (CNN) to regression the symbol error rate (SER), signal-to-noise ratio (SNR), and received signal strength indicator (RSSI). After creating the dataset in the first step, it was reprocessed and split into 70% training and 30% testing. Then, a graphical user interface (GUI) was created using MATLAB App Designer for user-friendly operation. The results showed that the drone communication system's performance declined as the vibration frequency, frequency carriers, and modulation order for M-QAM increased, primarily due to the influence of a vibrating antenna. Furthermore, the drone communication system's effectiveness decreased as the temperature increased.This research provides a valuable method for evaluating the efficiency of communication systems on unmanned aerial vehicle (UAV), which is particularly important for drone wireless system planning.
الملخص:
المصادر:

انتاج المياه العذبة باستخدام الطاقة الشمسية الفعالة باستخدام مواد ماصة مختلفة Production of Fresh Water Using Effective Solar Still Using Different Absorbing Materials

اسم المؤلف: ميس علاء نوري
اسم المشرف: باسل نوري عبد منتظر عبودي محمد الموسوي
الموضوع العام: هندسة الميكانيك
السنة: 2024
الموضوع الدقيق: قوى حرارية
الدرجة: ماجستير
اللغة: الانكليزية
مكان الجامعة: النجف
الكلمات الدلالية:
  • ميكانيك القوى
الصفحات الاولى:

استخدام الوقود البديل لتقليل التلوث في محركات الاحتراق الداخلي USING ALTERNATIVE FUELS TO REDUCE POLLUTION IN INTERNAL COMBUSTION ENGINE

اسم المؤلف: علي عبد الكاظم عبدالزهرة الجابري
اسم المشرف: حيدر حسن العبدلي مظفر الزهيري
الموضوع العام: هندسة الميكانيك
السنة: 2024
الموضوع الدقيق: قوى حرارية
الدرجة: ماجستير
اللغة: الانكليزية
مكان الجامعة: النجف
الكلمات الدلالية:
  • المحركات الحراريات
الصفحات الاولى:

كشف الاهداف خارج الاحداثيات الشبكية في شبكات الاستشعار اللاسلكية باستخدام التعلم البايزي المتناثر (SBL) Off-Grid Target Detection in Wireless Sensor Networks via SBL

اسم المؤلف: مصطفى عبد الرحمن جبار
اسم المشرف: احمد محمد زكي الحلى
السنة: 2024
الموضوع الدقيق: هندسة الاتصالات
الدرجة: ماجستير
اللغة: الانكليزية
مكان الجامعة: النجف
الكلمات الدلالية:
  • Energy-efficient
  • Off-Grid Target Detection
  • Target detection
  • Compressive sensing
  • Wireless Sensor Networks
  • Sparse Bayesian Learning
  • Adaptive Sparse Bayesian Learning
الصفحات الاولى:
المستخلص: Energy consumption is a significant challenge in Wireless Sensor Networks (WSNs). Compressed Sensing (CS) effectively reduces energy consumption but faces limitations, such as detecting a limited number of targets and assuming targets are on-grid. This thesis proposed using Sparse Bayesian Learning (SBL) for target detection in WSNs. SBL provided a model for off-grid target detection and also enhanced target detection accuracy and achieved nearly double the target detection compared to other methods like Basis Pursuit (BP). The SBL method successfully detected up to 30 targets with very high detection performance. In comparison, the BP method detected only 15 targets under the same environmental conditions. Two variables are considered in off-grid scenarios: displacement along the x-axis and y-axis. The Off-grid detection results demonstrate that the proposed approach, which incorporates dependent assumptions, can effectively localize targets within the framework of theoretical assumptions. This is in contrast to an approach based on independent assumptions, which may not achieve the same level of accuracy. Further, this thesis proposed an adaptive Sparse Bayesian Learning (A-SBL) approach to increasing the sensors’ lifetime in wireless sensor networks (WSNs). By combining the Bayesian model with adaptive compressive sensing (A-CS), the methodology minimizes the number of sensors required for successful target detection. Initially, a few sensors are selected randomly, and the Cluster Head (CH) then calls sensors that provide maximal information, achieving the greatest error reduction. This approach enhances resource use and energy efficiency, improving overall network performance. Results confirm that this method significantly reduces energy consumption v compared to other approaches, especially with fewer targets, contributing to advancements in WSN technology. Where The results show that the energy consumption of the A-SBL approach was 53% of the energy consumption by the traditional method when detecting 5 targets. As the number of targets increased to 15 and 30, the energy consumption dropped to 30% and 17%, respectively, compared to the energy consumed by the traditional method. Additionally, when the initial sensors were increased to 15 instead of 5, the proposed method’s energy consumption was 48%, 28%, and 17% for 5, 15, and 30 targets, respectively
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