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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.Optimization

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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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    Dimensioning stand-alone cellular base station using series-of-worst-months meteorological data
    (IEEE, 2014) Chamola, Vinay
    This paper presents a methodology for dimensioning the photo-voltaic (PV) and battery requirements of stand-alone, solar-powered cellular base stations. In contrast to existing methodologies that use intuitive methods or are based on Typical Meteorological Year (TMY) data, this paper proposes the use of series-of-worst-months data for dimensioning the base station. The proposed approach has the advantages of higher accuracy as well as being computationally more efficient. The proposed methodology has been verified using real meteorological data for a number of geographical locations.
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    Optimal Spectral Resource Allocation and Pricing for 5G and Beyond: A Game Theoretic Approach
    (IEEE, 2021-09) Bitragunta, Sainath; Chamola, Vinay
    Optimal allocation of the available spectrum is a crucial requirement of 5G and Beyond (B5G) for achieving higher Quality of Service (QoS) and low-latency. However, in 5G and Beyond, this requirement presents a potential need for dimensioning and managing the spectral resource in the cellular services. In this article, we address the issues of spectral distribution using DAG and Vickrey Clarke Groves (VCG) mechanisms by evaluating with a derived parameter for sustainable revenue and social welfare of the entire network. In particular, we modelled a framework to optimize social welfare of the users and the revenue of the cellular operator by proposing an efficient spectral allocation and pricing mechanism.