Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

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Subject: search.filters.subject.Optimization

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Now showing 1 - 10 of 21
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    Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing
    (IEEE, 2024-04) Chamola, Vinay
    Generative Artificial Intelligence(GAI) models such as ChatGPT , DALL-E , and the recently introduced Gemini have attracted considerable interest in both business and academia because of their capacity to produce material in response to human inputs. Cognitive computing is a broader field of machine learning that encompasses GAI, which particularly emphasizes systems capable of creating content, such as images, text, or sound, while semantic computing acts as a fundamental element of GAI, furnishing the comprehension of context and significance essential for GAI systems to generate content akin to human-like standards. GAI is becoming a game-changing technology for consumer electronics industry with a variety of applications that improve user experiences and product development. GAI can revolutionise architectural visualisation by facilitating quick prototyping and the investigation of cutting-edge design ideas. By creating unique compositions and graphics for a variety of applications, it also empowers media production and music composition. Our research identifies several applications of GAI in the consumer electronics industry. We analyze how GAI is utilized in augmented reality (AR) applications, optimizing user interactions and immersive experiences. Moreover, we explore the integration of GAI in voice assistants and virtual avatars, enhancing images, natural language understanding and delivering more personalized interactions. We present a novel case study on a Generative Artificial Intelligence-based Framework for answering consumer electronics queries. We have developed and presented the system using various GAI-based tools and integrations. The paper also discusses the challenges in implementing GAI in consumer electronics, such as ethical considerations, data privacy, compatibility with existing systems, and the need for continuous updates and improvements.
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    On-Device Generative AI: The Need, Architectures, and Challenges
    (IEEE, 2024-12) Chamola, Vinay
    The area of Generative Artificial Intelligence (GenAI) is rapidly expanding, as seen by the regular release of new models and applications every few months. While these GenAI models have impressive capabilities, their computational intensity has presented issues, especially in applications demanding low latency. Hence, substantial research is being conducted to develop ways to scale down these models so that they may be used for on-device computing on edge devices. Examining successful examples of GenAI models implemented on mobile devices with minimum latency becomes critical in understanding the practical consequences of these breakthroughs. Notable instances, such as the deployment of Diffusion-based GenAI models on flagship smartphones like Samsung S23 Ultra and iPhone 14, demonstrate the possibility and promise of bringing GenAI applications to consumers' fingertips. We further analyze and find out the approaches and strategies that make these on-device deployments successful.
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    Performance optimization of 60 nm channel length vertical MOSFETs using channel engineering
    (IEEE, 2001) Rao, V. Ramgopal
    A comprehensive study has been performed to optimize the electrical characteristics of delta doped channel MOSFETs (D2FETs) having channel length of 60 nm. Extensive 2D device simulations have been employed to show that D2FETs exhibit higher drain current drive and reduced short channel and hot carrier effects compared to MOSFETs having uniform channel doping. The improvement has been found significant when the delta peak is shifted near the source end of the channel. Device simulations show acceptable short channel effects for 60 nm D/sup 2/FETs when the gate oxide thickness is reduced to the 2.5-3 nm regime.
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    Device-circuit co-design for high performance level shifter by limiting quasi-saturation effects in advanced DeMOS transistors
    (IEEE, 2016) Rao, V. Ramgopal
    This paper presents a device-circuit co-design methodology for a DeMOS 5V GHz-speed high voltage level shifter. The limiting quasi-saturation effect is addressed by a codesign methodology. The co-design methodology is applied to the STI-DeMOS in a calibrated setup using experimental data. As a result, a 15% improvement in the speed is achieved for a high-performance level shifter circuit.
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    EFTA: An Energy-efficient, Fault-Tolerant, and Area-optimized UAV Placement Scheme for Search Operations
    (IEEE, 2022) Chalapathi, G.S.S.
    Unmanned aerial vehicle (UAV) networks have widespread applications, ranging from surveillance and disaster management in the military domain to transportation and delivery of goods in the civilian domain. Regardless of the application, the placement of routing UAV nodes (especially in networks spanning long distances) is crucial in determining network performance parameters such as network lifetime and data transmission delay. In this paper, an Energy-efficient, Fault-Tolerant, and Area-optimized UAV placement scheme (EFTA) is proposed for search operations. A cluster-based UAV network is considered, in which the Cluster Members (CMs) are mobile and scan the geographic area of interest. The Cluster Heads (CHs) are quasi-static and route information from the CMs to the Ground Control Station (GCS). A multi-objective Cuckoo Search Algorithm is used to determine the placement of the CHs while minimizing energy consumption, maximizing area coverage, and maximizing tolerance to node failures. Further, a comprehensive analysis was performed against a state-of-theart UAV placement algorithm. The analysis showed that EFTA gives a significant performance improvement when compared to the competing placement scheme in fault tolerance, power consumption, network lifetime, end-to-end delay, and packet delivery ratio.
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    Development of Ant Lion Optimizer toolkit in LabVIEW™
    (IEEE, 2016) Mishra, Puneet
    Metaheuristic or nature inspired optimization have evolved over time towards development of a general purpose optimizer. In this paper, a novel nature inspired algorithm, based on hunting mechanism of antlions, Ant Lion Optimizer toolkit is developed in LabVIEWTM environment. This paper provides an orderly procedure for development of toolkit including random walk of ants, building of traps, catching of prey and finally rebuilding of traps. LabVIEWTM is a highly productive development environment used largely in measurement and control applications. The only standard optimizer toolkit available in LabVIEW™ is Differential Evolution (DE) and therefore a need was felt to have an efficient optimizer. The toolkit so evolved in this paper when tested on different benchmark functions has performed adequately well over the DE toolkit. The results so obtained clearly prove that the submitted toolkit is able to provide superior results in terms of improved exploration, local optima avoidance, exploitation, and convergence.
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    Development of a Flower Pollination Algorithm toolkit in LabVIEW™
    (IEEE, 2016) Mishra, Puneet
    Flower Pollination Algorithm (FPA) is one of the latest evolutionary algorithms (EAs) inspired by the natural process of pollination of flowers. This Paper addresses the development of FPA toolkit in LabVIEW™, a versatile platform provided by National Instruments for test, measurement and control applications. It may be noted that LabVIEW™ has provided only one EA based optimization technique which is Differential Evolution (DE) algorithm named as Global Optimization.vi in its standard package. Since, several new and efficient techniques are available for optimization there is always a need to implement the best optimization technique in LabVIEW™ environment for the benefit of measurement and control engineers. The developed FPA toolkit has been tested on several benchmark test functions and its comparison is also carried out with the standard DE toolkit. Based on the investigations, it has been inferred that FPA toolkit results are better than DE toolkit in terms of convergence rate particularly for higher dimensional optimization problems.
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    Development of Backtracking Search Optimization Algorithm Toolkit in LabVIEW™
    (Elsevier, 2015) Mishra, Puneet
    In this paper a Backtracking Search Optimization Algorithm (BSA) toolkit has been developed in the LabVIEW™ environment. LabVIEW™ provides a graphical programming environment to design measurement and control applications. The development of BSA toolkit was motivated by the fact that only Differential Evolution (DE) toolkit was provided in LabVIEW™. Thus to design BSA toolkit, several modular virtual instruments have been developed for each BSA process. Developed BSA toolkit has been tested on several benchmark test functions and a comparative study with inbuilt DE toolkit has been performed, which shows results obtained from BSA toolkit are found superior to DE toolkit.
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    Development of a Grey Wolf Optimizer Toolkit in LabVIEW™
    (IEEE, 2015) Mishra, Puneet
    In this paper a Grey Wolf Optimizer (GWO) Toolkit developed in LabVIEW ™ environment, has been presented. Grey Wolf Optimizer (GWO) is inspired by Grey wolves (Canis lupus). The GWO algorithm, in nature, mimics the leadership hierarchy, and the hunting mechanism of grey wolves. LabVIEW ™ is a versatile tool for measurement and control applications and it is very popular among the industries. As Differential Evolution (DE) is the only optimization technique available in the LabVIEW ™ environment. The developed toolkit is examined on nine benchmark functions and results are compared with DE. A step by step systematic procedure for the toolkit development and a comparative study with the existing DE toolkit results has been presented in this paper.
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    Development of Bat Algorithm toolkit in LabVIEW™
    (IEEE, 2015-05) Mishra, Puneet
    Meta-heuristic optimization algorithms are very useful tool for obtaining an optimal solution of engineering optimization problems. Bat Algorithm (BA) is one of the recent meta-heuristic algorithms. It has been claimed to be superior to its counter parts. On the other hand LabVIEW ™ is a versatile software tool being utilized for measurement and control applications in various engineering domains worldwide. The standard LabVIEW ™ package is only supplied with the Differential Evolution (DE) Toolkit for optimization. At times need was felt to develop a better optimization support in the LabVIEW ™ package. In this work a genuine effort has been made for this task and a BA Toolkit has been developed in LabVIEW ™ . The detailed development strategy, component descriptions and their interfaces and a comparison of the obtained results with the DE optimization toolkit has been presented in this paper for a set of classical benchmark functions.