Smart Embedded Systems Advances and Applications
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press
[2024]
|
| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009809018306719 |
Tabla de Contenidos:
- Cover
- Half Title
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Editors
- Contributors
- Chapter 1: A reconfigurable FPGA-based epileptic seizures detection system with 144 μs detection time
- 1.1 Introduction
- 1.2 Software-based implementation
- 1.2.1 EEG data
- 1.2.2 Segmentation
- 1.2.3 Feature extraction
- 1.2.4 RF algorithm
- 1.3 Hardware-based implementation
- 1.3.1 Feature extraction
- 1.3.2 RF training module
- 1.3.3 The RF inference module
- 1.3.4 Display label
- 1.4 Experimental results
- 1.4.1 Software implementation results
- 1.4.2 FPGA implementation results
- 1.5 Conclusion
- Acknowledgment
- References
- Chapter 2: Hardware architecture for denoising of EOG signal using a differential evolution algorithm
- 2.1 Introduction
- 2.2 FIR filter architecture
- 2.3 Filter design for denoising of EOG signal using DE algorithm
- 2.4 DE algorithm with minimized coefficients
- 2.5 Functional verification of DE with minimized coefficient (DEWMC)-based denoised FIR filter
- 2.6 Synthesis results
- 2.7 Conclusion
- References
- Chapter 3: Implementation considerations for an intelligent embedded E-health system and experimental results for EEG-based activity recognition
- 3.1 Introduction
- 3.2 Embedded acquisition system for E-health
- 3.2.1 Hardware considerations, choices, and implementations
- 3.2.2 Software architecture and implementation
- 3.2.3 Related works
- 3.3 EEG-based classification of motor imagery activities
- 3.3.1 Segmentation
- 3.3.2 Neural network efficiency investigation
- 3.4 Conclusion
- References
- Chapter 4: Embedded and computational intelligence for diabetic healthcare: An overview
- 4.1 Introduction
- 4.2 Embedded intelligence glucose monitoring
- 4.2.1 BioSensors
- 4.2.2 Implanted micro systems
- 4.2.3 Wearable sensors.
- 4.2.3.1 Wearable interstitial fluid (ISF) CGM
- 4.2.3.2 Wearable sweat CGM
- 4.2.3.3 Wearable tear CGM
- 4.2.3.4 Wearable saliva CGM
- 4.3 Computational intelligence in glucose monitoring
- 4.3.1 Machine learning
- 4.3.2 Recommender systems
- 4.3.3 Mobile and WebApp
- 4.3.4 TeleMedicine
- 4.3.5 Auto-administer therapy
- 4.4 Conclusion and future scope
- References
- Chapter 5: A semi-definite programming-based design of a robust depth control for a submersible autonomous robot through state feedback control
- 5.1 Introduction
- 5.2 Modeling of SAR in depth plane
- 5.2.1 Problem statement
- 5.3 Design of robust optimal control
- 5.3.1 Robust optimal state feedback for polytopic SAR system
- 5.3.1.1 Preliminaries: LMI-based LQR controller
- 5.3.1.2 Robust LMI-based optimal controller
- 5.4 Results and discussion
- 5.5 Conclusion
- Acknowledgments
- References
- Chapter 6: Embedded system with in-memory compute neuromorphic accelerator for multiple applications
- 6.1 Introduction
- 6.2 Accelerator for in-memory compute applications
- 6.2.1 Background
- 6.2.2 Design and implementation of standard 32×40×10 in-memory compute architecture
- 6.2.2.1 Working principle
- 6.2.3 Potential benefits of the accelerator in various applications
- 6.3 In-memory compute accelerator: An embedded system perspective for multiple applications
- 6.3.1 Climate technology
- 6.3.2 Social sciences
- 6.3.3 Medical sciences
- 6.3.4 Finance technology
- 6.3.5 Gaming technology (GT)
- 6.4 Results and discussion
- 6.4.1 Parametric analysis of ideal resistive memory and swish activation function
- 6.4.2 Inference analysis for the in-memory compute accelerator
- 6.4.3 Training analysis for the in-memory compute accelerator
- 6.5 Conclusion
- References.
- Chapter 7: Artificial intelligence-driven radio channel capacity in 5G and 6G wireless communication systems in the presence of vegetation: Prospect and challenges
- 7.1 Introduction
- 7.2 Radio wave attenuation due to vegetation
- 7.3 Embedded architecture: AI-DR vegetation attenuation prediction system
- 7.4 SCC prediction
- 7.5 Conclusions
- Acknowledgments
- References
- Chapter 8: Smart cabin for office using embedded systems and sensors
- 8.1 Introduction
- 8.1.1 Smart office
- 8.1.2 Motivation
- 8.1.3 Objectives
- 8.2 Cabin security
- 8.2.1 Fingerprint verification
- 8.2.2 Face verification
- 8.2.3 Speech verification
- 8.3 Ambiance monitor
- 8.3.1 Temperature and relative humidity
- 8.3.2 Carbon dioxide
- 8.3.3 Total volatile organic compound (TVOC)
- 8.3.4 Air pressure
- 8.3.5 Dust level
- 8.4 Well-being monitor
- 8.4.1 Physical health
- 8.4.2 Mental health
- 8.4.2.1 Fatigue state
- 8.4.2.2 Emotional state
- 8.5 Data transmission and display
- 8.6 Results
- 8.7 Conclusion
- Acknowledgments
- References
- Chapter 9: Wireless protocols for swarm of sensors: Sigfox, Lorawan, and Nb-IoT
- 9.1 Introduction
- 9.1.1 Basic communication infrastructure
- 9.2 Sigfox
- 9.2.1 Overview
- 9.2.2 Regions of operation
- 9.2.3 Technical characteristics
- 9.2.4 Sigfox diversity
- 9.2.5 Network architecture
- 9.2.6 Message system
- 9.2.7 Summary of characteristics
- 9.3 Lorawan
- 9.3.1 Overview
- 9.3.2 Characteristics
- 9.3.3 Uplink and downlink communication
- 9.3.4 Classes of operation
- 9.3.5 Network architecture
- 9.3.6 Message system
- 9.3.7 Characteristics summary
- 9.4 Nb-IoT
- 9.4.1 Overview
- 9.4.2 Characteristics
- 9.4.3 Message system
- 9.5 Conclusion
- References
- Chapter 10: Design and test of thermal energy harvester for self-powered autonomous electronic load
- 10.1 Introduction.
- 10.1.1 Calculation of the maximum power conversion efficiency
- 10.2 Low-voltage starter
- 10.2.1 Enhanced swing ring oscillator
- 10.2.2 Dickson charge pump
- 10.2.3 AC model of ESRO
- 10.2.4 Important formulas for the LVS block
- 10.3 Power equations for convertor operating in DCM
- 10.3.1 Boost convertor
- 10.3.2 Maximum extraction of power from a low-voltage source V TEG
- 10.3.3 Sizing auxiliary and main stage inductors
- 10.4 Peripheral circuits
- 10.4.1 Current starved ring oscillator
- 10.4.2 Reference generator and node sensing network
- 10.4.3 Zero current switching network
- 10.4.4 Control logic
- 10.5 Test and measurement
- 10.5.1 Inductors for ESRO coupled with DCP
- 10.6 Conclusion
- Acknowledgments
- References
- Chapter 11: Managing concept drift in IoT health data streams: A dynamic adaptive weighted ensemble approach
- 11.1 Introduction
- 11.1.1 Concept of drift
- 11.1.2 Significance of adaptive learning in IoT with machine learning for health data
- 11.2 Literature survey
- 11.2.1 Drift phenomenon
- 11.2.2 Adaptive learning models towards IoT
- 11.3 Methodology
- 11.3.1 Dataset description
- 11.3.2 Data preprocessing
- 11.3.3 Implementation of DAWE
- 11.3.3.1 Base learners
- 11.3.3.2 Proposed ensemble strategy for DAWE
- 11.4 Experimental results
- 11.5 Discussion and future scope
- 11.6 Conclusions
- Acknowledgments
- References
- Chapter 12: GraLSTM: A distributed learning model for efficient IoT resource allocation in healthcare
- 12.1 Introduction
- 12.1.1 How is IoT useful for the healthcare sector?
- 12.1.2 Open discussion and challenges in IoT- AI with EHR
- 12.1.3 Recent trends in IoT health with AI
- 12.2 Literature survey
- 12.2.1 IoT towards decision-making
- 12.2.2 Paradigm shift in anomaly detection
- 12.2.3 Resource allocation with parallelization and distributed learning.
- 12.3 Methodology
- 12.3.1 Dataset description
- 12.3.2 Structured graph for anomaly detection
- 12.3.3 GraLSTM model
- 12.3.4 GraLSTM-IoT resource allocation
- 12.4 Experimental results
- 12.5 Conclusion
- Acknowledgments
- References
- Index.
