Browsing by Author "Muhammad Arslan"

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Barium and Strontium doped Ceria Based Ceramic Electrolytes for Solid Oxide Fuel Cell
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Arslan; CIIT/Fa23-RPH-047/LHR; Prof. Dr. Rizwan Raza; LHR TP 10003
Renewable energy sources are becoming more and more important in today's society. They will be crucial shortly if climate change is to be prevented. This industry has seen a surge in interest in fuel cells due to its high conversion efficiency in comparison to conventional techniques for energy conversion. Fuel flexibility, minimal pollution and hazardous emissions, and affordable components make SOFCs a valuable source of energy conversion technology. There are still certain problems that need to be fixed, like the fact that at low- temperature conductivity and efficiency decreased. Electrolyte metals (SmNO3, BaNO3, Sr(NO3)3, CeNO3) doped SDC materials with enhanced properties, like chemical stability, durability, thermal coefficient compatibility, and conduction mechanism enhancement, are developed in this study using the co- precipitation method. These properties have a direct impact on the material's conductivity and electrochemical performance. Using Raman spectroscopy, a range of investigations, including rotational and vibration modes and structural features, are performed on the synthesized materials. Utilizing UV-visible analysis, the band gap energy (Eg) is determined. Electrochemical Impedance Spectroscopy (EIS) is utilized to analyze ohmic and polarization losses at varying temperatures. The fuel (hydrogen) is used to test the electrochemical performance. The Raman data indicate a diminishing gap between the conduction and valance bands as the size of the material particle rises owing to blue shifting. The band gap values of the materials dropped as the metal changed, according to UV-visible analysis. The Bandgap values is (Ba0.5 Sr0.5 Ce0.8 Sm0.2., Ba0.5 Sr0.5 Ce0.7 Sm0.3 , Ca0.5 Sr0.5 Ce0.8 Sm0.2, Ca0.5 Sr0.5 Ce0.7 Sm0.3 ) have been determined to be (Eg=2.65eV,3.01eV,3.02eV,3.04) respectively. The good electrochemical qualities of the produced samples were confirmed by the fact that the materials' conductivity increases as the sintering temperature rises. Since the transition metal-doped SDC electrolyte materials have improved electrical conductivity for SOFC applications, they have demonstrated good performance.
Investigating Cyclic Translation for Urdu Text Reuse Detection using Deep and Transfer Learning Methods
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Arslan; SP20-RCS-025; LHR TP 8466; Dr. Muhammad Sharjeel
This research study addresses the complex problem of generating text reuse corpora using Machine Translation (MT), effectively detecting text reuse in paraphrased text through a cyclic translation approach and investigating the behavior of newly proposed deep learning and transfer learning methods on various Urdu text reuse corpora. Prior research endeavors addressing the challenge of text reuse paraphrase detection through the application of deep learning methodologies have predominantly focused on the English language. However, a comprehensive investigation into the problem of text reuse detection in the Urdu language, specifically utilizing a cyclic translation approach in the processing of data for training and testing purposes, alongside the utilization of deep learning methods, remains relatively limited. To tackle these challenges, a translated dataset is constructed by employing the Python Google Translate API on the Counter Dataset. Two variations of deep neural networks, namely Siamese Bidirectional LSTM (BiLSTM) and Manhattan LSTM (MaLSTM), are employed to achieve optimal accuracy in the multi-classification task. The training process incorporates two well-known optimizers, RMSProp (Root Mean Square Propagation) and Adam (Adaptive Moment Estimation). The experimental results substantiate the effectiveness of the Bidirectional LSTM, which achieves an accuracy of 77.22%, and the MALSTM, which attains an accuracy of 76.20%. These findings provide valuable insights for researchers and practitioners, paving the way for further advancements in the study of text reuse and its implications in diverse linguistic contexts
Robot Task Planning using Semantic maps
(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Muhammad Arslan; CIIT/SP16-BCS-136/LHR; Asmara Safdar; LHR TP 6317
In this final year project, we are going to work on robot’s task planning. In task planning, robots plan a sequence of high-level actions to perform a given task autonomously. To perform a basic robot (e.g. such as a service robot) operation autonomously, spatial information and general knowledge (domain knowledge and meaning of the objects) about the objects is needed. Spatial information includes position and geometry of objects, a structure of space, connectivity among places and areas, and the appearance of the objects. In our work, for knowledge of the objects, we focus on the use of semantic knowledge which is integrated with the spatial information by semantic maps. Use of semantic knowledge can improve efficiency to perform certain tasks, we are using specific environment for our robot, in which the task planning will be done by the robot.
Vehicle Accident Alert and Rescue Solution
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Muhammad Arslan; SP20-BSE-031; Sana Maqbool
Traffic accidents remain a major public safety problem worldwide, causing loss of life and property. To address this critical issue, the Vehicle Accident Alert and Rescue Solution (VAARS) has been introduced as a comprehensive solution aimed at improving accident detection, alerting authorities, and accelerating rescue operations. VAARS leverages the latest technologies, including sensors, wireless communications, and data analytics, to create a robust and efficient system. When an accident occurs, VAARS uses on-board sensors to detect impact forces, vehicle orientation, and other critical parameters. This data is then processed in real-time and, if an accident is confirmed, an alert is immediately sent to emergency services and nearby vehicles, informing them of the location and severity of the incident. Important information is provided. The system's advanced functions include automatic communication with the vehicle's occupants, allowing real-time assessment of their status and needs. VAARS uses advanced artificial intelligence algorithms to analyse accident data, providing emergency services with information about potential hazards and the resources necessary for successful rescue operations. VAARS is designed to work seamlessly with existing emergency response infrastructure, ensuring a rapid and coordinated response by paramedics, police, and firefighters. Additionally, it incorporates GPS tracking and mapping capabilities, helping rescue teams reach the accident site accurately and efficiently. This report summarizes the key components of vehicle crash warning and rescue systems, highlighting their potential to reduce response times, improve crash outcomes, and contribute to safer roads. VAARS stands as an important solution in the field of road safety, offering a promising way to reduce the impact of accidents on our society.