Vanshika Jain – författare

Academic Paper in the subject Engineering - Robotics, , language: English, abstract: This paper's goal is to give a concise overview of the various development streams that have led to the current status of the UGV area. Any piece of mechanized equipment that moves on the ground and is used to carry or transport something, but expressly does not carry a human body is considered a UGV in the broadest "e;dictionary"e; sense. Unmanned Vehicles are the common part of Military campaigns that reduce the load of soldiers. UVs (Unmanned vehicles) equipped with sensors, sonar, cameras and various algorithms provide real-time information which is helpful for the commanders to take quick decisions. Also, they provide access to the inaccessible areas in the enemy s territory. They are used in search operations as well as in the rescue operations. They provide day and night vision which is fed to their artificial intelligence pre-trained algorithms that predicts the output to give information. Multiple robots can be combined to increase their working efficiency in the adverse environments. Unmanned vehicles are ones that are in close proximity to the ground and run without the assistance of a human operator. The sensors on Unmanned Ground Vehicles comprise the operating system for research and rescue. The robot is a significant entity in this context because it can mimic team characteristics like collaboration and communication while acting independently and intelligently. UGVs are more efficient in combating terrorism and in remote locations. Unmanned Ground Vehicles help and enhance the front-line soldier positions. This robot's ability is mostly contingent on keeping the soldiers safe or, at the absolute least, reducing the amount of casualties sustained during combat.

Academic Paper from the year 2024 in the subject Computer Sciences - Artificial Intelligence, , language: English, abstract: Our book provides a succinct summary of Artificial Intelligence applications across multiple Very-Large-Scale Integration (VLSI) design flow and testing domains. One technique of fabrication, CMOS technology, is where the evolution of VLSI begins. Complementary Metal Oxide Semiconductor (CMOS) offers numerous advantages but to pack high number of Integrated Circuits (ICs) in compact geometries, VLSI enters into picture. We can design VLSI using Computer Aided Tools like Electronic Design Automation (EDA). VLSI design consists of two primary parts: the front end and the back end. Fabrication procedures such as Architectural Design, Gate Level Design, Simulation, Hardware Descriptive Language (HDL), Circuit Level Design, Verification, and Fabrication are included at the back end. Descriptive languages for hardware are the main emphasis of front end. HDL is essential to system design use because it enables the behavior of the core system to be verified and modelled prior to the design being converted by synthesis tools into physical hardware, such as gates and wires. Different chemical and physical processes are needed for the backend operations. It is possible to create Artificial Intelligence algorithms that aid in raising the yield of physical processes. Artificial Intelligence and Machine Learning (AI-ML) algorithms assist in predicting parametric yield at the device level. Furthermore AI-ML algorithms are implemented at the gate level to analyse the total latency of the gate connection. Also, AI-ML algorithms can be used at the circuit level to maximize the performance of circuits. A combination of AI-ML algorithms in EDA tools allows for the examination of propagation delay, power leakage, etc. During lithography, transfer learning can be used to produce data. Effective testing becomes a crucial task as electronic systems and components becoming more complex, striking a balance between the need for high quality and cost effectiveness. In spite of its labour-intensive nature, testing has become a critical technique for cutting total costs in the production of electronic systems, boards, and VLSI chips. It plays a critical role in ensuring that VLSI design satisfy quality requirement by identifying circuit faults. In order to handle the complexity of contemporary systems, recent developments have incorporated machine learning (ML) and artificial intelligence (AI) into established VLSI testing techniques.