Buch, Englisch, Band 765, 209 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 540 g
Select Proceedings of EGTET 2020
Buch, Englisch, Band 765, 209 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 540 g
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-981-16-1549-8
Verlag: Springer Nature Singapore
This book comprises the select proceedings of the International Conference on Emerging Global Trends in Engineering and Technology (EGTET 2020), held in Guwahati, India. The chapters in this book focus on the latest cleaner, greener, and efficient technologies being developed for the implementation of smart cities across the world. The broader topical sections include Smart Buildings, Infrastructures and Disaster Management; Smart Governance; Technologies for Smart Cities, and Wireless Connectivity for Smart Cities. This book will cater to students, researchers, industry professionals, and policy making bodies interested and involved in the planning and implementation of smart city projects.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
- Technische Wissenschaften Umwelttechnik | Umwelttechnologie Abfallwirtschaft, Abfallentsorgung
- Geisteswissenschaften Architektur Ökologische Aspekte in der Architektur
- Technische Wissenschaften Energietechnik | Elektrotechnik Energieumwandlung, Energiespeicherung
- Geowissenschaften Geographie | Raumplanung Regional- & Raumplanung Stadtplanung, Kommunale Planung
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
Weitere Infos & Material
Integration of Internet of Things and Blockchain Technology for Smart Cities.- An IoT and Machine Learning Based Crop Prediction System for Precision Agriculture.- A Smart Feature Reduction Approach to Detect Botnet Attack in IoT.- An approach to handle Heterogeneous Healthcare IoT data using Deep Convolutional Neural Network.- Designing of NimbleArm – A Low-Cost and Interactive Semi-Autonomous Robotic Arm.- Cascade-based Pedestrian Detector using Edge and Pattern features.