http://hdl.handle.net/123456789/10340 2026-04-04T09:18:58Z http://hdl.handle.net/123456789/19033 2-D Seismic Data Interpretation of Qadirpur Area, Pakistan Ammad Ali Tariq Aim of the study is to interpret 2D-Seismic Reflection time section of the Qadirpur Area (Sindh Province) Pakistan. This seismic section is a Pre-stacked time migrated section and was provided by the Department of Earth Sciences, Bahria University Islamabad and this line bears the title 985-QPR-03 It is about 40 Kms in length and is oriented in SW-NE direction. OGDC acquired data in October 1998 and processed it in January 1999. The velocity information is in the form of RMS. DIX interval and DIX average at different times is given, and is provided at selected S.P. RMS velocity varies from 1500 m/s to 5000 m/s Interpreted part of this line from S.P. # 460 to S.P # 720, with CDPs from 920 to 1440 Length of this part of seismic section is 13 Kms. For interpretation of this part of Seismic section, four reflectors and 2 faults are marked on the basis of prominent reflections from subsurface horizons due to changes in lithology and diffractions. Using the RMS velocity given in the velocity panels on seismic section for selected shot points, calculate the time on constant velocity interval of 100m/sec Then using these calculated time and velocity values prepare the Iso-velocity graph and Iso-time graph (for mean line method) by taking constant velocity and time respectively, In Mean line Method of velocity estimation, a velocity vs time graph is prepared. From this graph, a mean average velocity is determined. From Seismic Section, arrival times (two ways) of each marked reflector are determined, Using these arrival times, Time Section is prepared. Also using these arrival times, calculate the average velocity for these times on mean line graph and then the depth of each reflector has been calculated using s(vt)/2 and is represented in Depth Section. Depth Section provides a reliable picture of reflectors and structures present in the subsurface of the area. Well correlation is also done, which satisfy the calculated depths, so horizons have been marked Interpretation of the Project Area shows that, extensional regime and calm environment prevails in the area. Reflectors are almost flat-lying, whereas Horst and Graben structures have been found. Supervised by Mr. Rashid Jamil 2907-01-01T00:00:00Z http://hdl.handle.net/123456789/20902 Communication Link Modelling of Vehicle-To-Everything (V2x) Networks Muhammad Sohail Sajid, 01-244232-006 A crucial enabling technology for intelligent transportation systems is vehicle-toeverything (V2X) communication, which provides dependable wireless connectivity to support applications related to traffc effciency and safety. Because of the high vehicle mobility, heavy traffc, and stringent latency and reliability requirements, accurate performance evaluation of V2X networks is critical. While existing analytical models frequently rely on simplifed assumptions like fxed communication ranges and ideal channel conditions, which limit their practical accuracy, simulation-based approaches offer detailed insights but are computationally costly. The analytical modeling of IEEE 802.11p-based V2X communication links with realistic wireless propagation and interference effects is the main focus of this thesis. The suggested framework takes into consideration changes in signal power, interference from nearby vehicles, hidden terminal effects, and packet reception failures due to channel impairments, in contrast to traditional models that assume error free communication within a predetermined range. Packet Delivery Ratio (PDR) and the likelihood of transmission failure due to propagation errors, packet collisions, receiver busy states, and sensing errors are among the important performance metrics that the model assesses. Extensive simulations with different vehicle densities, transmission ranges, and data rates are used to validate the analytical results. The accuracy of the suggested model is confrmed by the comparison, which shows strong agreement between analytical predictions and simulation results. The fndings also demonstrate how mobility and interference signifcantly affect the reliability of V2X communication, especially in situations with heavy traffc. All things considered, this work offers an analytical framework for assessing V2X communication performance that is more practical and scalable. In addition to supporting the creation and improvement of dependable V2X systems for upcoming intelligent transportation applications, the suggested model can help researchers and system designers better understand network behavior. Supervised by Dr. Junaid Imtiaz 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/20903 Ai-Driven Satellite-Assisted Route Optimization for Enhanced Mother-Daughter Drone Coordination in Efficient Parcel Delivery Faiq Afzal, 09-244241-001 The fast-paced development in artifcial intelligence (AI), satellite communication, and autonomous aerial vehicles are transforming contemporary logistics and delivery systems. Conventional drone-based delivery systems which include single drone per trip have a number of challenges, such as restricted range, high energy consumption, and limited payload capacity. To cater these challenges, this study investigates an AI-based satellite-assisted mother daughter drone coordination system for the optimize of lastmile delivery operations. In this envisioned system, a giant mother drone serves as a carrier, sending out several smaller daughter drones to effectively deliver light packages to different locations and mother drone for heavy packages. The system uses AI based hybrid algorithms involving Mixed-Integer Linear Programming (MILP) and Genetic Algorithm (GA) AI, for route optimization real-time weather data to adaptively modify flight routes to decrease travel distance, reduce delivery time, reduce energy expenditure, and enhance delivery effciency. This study utilizes simulation-based approach using Python. The important metrics include delivery time, total distance, energy consumption, scalability, and feasibility. The possible outcome of this study is the creation of an AI-based Mother daughter drone delivery system that is much more effcient, adaptable, and sustainable than traditional single-drone delivery systems. The results of this study have broad implications for e-commerce logistics, medical supply chain distribution, emergency response, and smart city infrastructure, and thus represent a pioneering contribution to the future of autonomous aerial transport Supervised by Dr. Junaid Imtiaz 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/20459 Resource Management of UAVs in 6G Network Anisa Zafar, 01-244202-027 Ground-based communication infrastructure is often damaged by natural disasters, disrupting the network connectivity, making it diffcult for effcient rescue and relief operations. In such scenarios, unmanned aerial vehicles (UAVs) can serve as aerial base stations to provide emergency network services. In this study, resource management of UAVs in disaster scenarios using 6G Network is investigated by providing a keen insight into effcient channel and bandwidth allocation to maximize the data rates. The three assignment algorithms, i.e., the Hungarian, Greedy, and Random, along with an artificial intelligence-based intra-band carrier aggregation (IBCA) approach, were analyzed. The results depicted that Hungarian provides globally optimal results when it comes to channel assignment and maximum data rates over fxed bandwidth, outperforming the other two algorithms. To address dynamic and heterogeneous bandwidth demands, IBCA was employed. The suggested AI-based model estimates the additional bandwidth required by the user and dynamically allocates available contiguous bandwidth chunks to satisfy heterogeneous users’ data rate requirements. The proposed AI-based IBCA model outperforms traditional scheduling algorithms in terms of data rates, bandwidth, and channel allocation. It also emphasizes the potential of UAVs and emerging 6G technologies in enabling resilient communication systems. Supervised by Dr. Saleem Aslam 2026-01-01T00:00:00Z