Innovations in Computer Vision and Data Classification : From Pandemic Data Analysis to Environmental and Health Monitoring (Eai/springer Innovations in Communication and Computing) / Ghani, Arfan

Innovations in Computer Vision and Data Classification : From Pandemic Data Analysis to Environmental and Health Monitoring (Eai/springer Innovations in Communication and Computing) （2024）

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Innovations in Computer Vision and Data Classification : From Pandemic Data Analysis to Environmental and Health Monitoring (Eai/springer Innovations in Communication and Computing) （2024）

Ghani, Arfan

ウェブストア価格 ¥33,338（本体¥30,308）
Springer International Publishing AG（2024/08発売）
外貨定価 US$ 159.99
ポイント 303pt

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製本 Hardcover:ハードカバー版／ページ数 148 p.
言語 ENG
商品コード 9783031601392
DDC分類 006.37

Full Description

This book delves into the dynamic realm of data classification, focusing on its real-world applications. Through an insightful journey, readers are introduced to the practical applications of reconfigurable hardware, machine learning, computer vision, and neuromorphic circuit design across diverse domains. The author explores topics such as the role of Field-Programmable Gate Arrays (FPGAs) in expediting pandemic data analysis and the transformative impact of computer vision on healthcare. Additionally, the book delves into environmental data classification, energy-efficient solutions for deep neural network applications, and real-time performance analysis of energy conversion algorithms. With the author's guidance, readers are led through practical implementations, ensuring a comprehensive grasp of each subject matter. Whether a seasoned researcher, engineer, or student, this book equips readers with the tools to make data-driven decisions, optimize systems, and innovate solutions across various fields, from healthcare to environmental monitoring.

Introduction.- Accelerating the classification of pandemic data using reconfigurable hardware (FPGA) and machine learning.- Computer vision based automated diagnosis for skin cancer detection.- Design and development of an integrated analytics platform for environmental data classification.- Design and development of multimodal healthcare data sensing and classification using Deep Neural Networks (DNNs).- Low-power analogue design with Spiking Neural Networks (SNN).- Full custom design of a sustainable, low-power environmental monitoring node.- Real-time performance analysis of Maximum-Power-Point Tracking (MPPT) algorithm for energy conversion on hardware platform (FPGA).- Computer-vision based real data generation for object classification.- Conclusion.