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Hands-On Deep Learning for IoT

You're reading from  Hands-On Deep Learning for IoT

Product type Book
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Pages 308 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Profile icon Dr. Mohammad Abdur Razzaque
LinkedIn
Dr. Mohammad Abdur Razzaque is a senior lecturer in the School of Computing and Digital Technologies, Teesside University, UK. He has more than 14 years of research and development and teaching experience on distributed systems (Internet of Things, P2P networking, and cloud computing) as well as experience in cybersecurity. He is an expert in end-to-end (sensors-to-cloud) IoT solutions. He offers consultancy in the areas of IoT solutions and the use of machine learning techniques in businesses. He has successfully published more than 65 research papers in these areas. He holds a PhD in distributed systems (P2P wireless sensor networks, mobile ad hoc networks) from the School of Computer Science and Informatics, UCD, Dublin (2008).
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Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
LinkedIn
Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.
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Table of Contents (15) Chapters close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

An overview of indoor localization

With the rapid development of mobile internet, Location-Based Services (LBS) in large public indoor places is becoming increasingly popular. In such an indoor location, the Received Signal Strength Indicator (RSSI) is often used as an estimated measure of the power level that an IoT device is receiving from Wireless Access Points (WAPs). However, when the distance from the source is increased, the signal gets weaker and the wireless data rates get slower, leading to a lower overall data throughput.

Techniques for indoor localization

Several indoor localization technologies have been proposed to date based on measuring techniques such as ultrasound, infrared, image, light, magnetic field...

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Authors (2)

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Profile icon Dr. Mohammad Abdur Razzaque
LinkedIn
Dr. Mohammad Abdur Razzaque is a senior lecturer in the School of Computing and Digital Technologies, Teesside University, UK. He has more than 14 years of research and development and teaching experience on distributed systems (Internet of Things, P2P networking, and cloud computing) as well as experience in cybersecurity. He is an expert in end-to-end (sensors-to-cloud) IoT solutions. He offers consultancy in the areas of IoT solutions and the use of machine learning techniques in businesses. He has successfully published more than 65 research papers in these areas. He holds a PhD in distributed systems (P2P wireless sensor networks, mobile ad hoc networks) from the School of Computer Science and Informatics, UCD, Dublin (2008).
Read more
See other products by Dr. Mohammad Abdur Razzaque
Profile icon Md. Rezaul Karim
LinkedIn
Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.
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See other products by Md. Rezaul Karim
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