Smart embedded systems and applications /

Bibliographic Details
Corporate Author: Taylor & Francis
Other Authors: Motahhir, Saad (Editor)
Format: eBook
Language:English
Published: Gistrup, Denmark : River Publishers, [2022]
Edition:1st ed.
Series:Electronic Materials, Circuits and Devices' Ser.
Subjects:
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Cover
  • Half-Title
  • Smart Embedded Systems andApplications
  • RIVER PUBLISHERS SERIES IN ELECTRONIC MATERIALS, CIRCUITS AND DEVICES
  • Title
  • Copyrights
  • Dedicate
  • Contents
  • Preface
  • Acknowledgments
  • List of Reviewers
  • List of Figures
  • List of Tables
  • List of Notations and Abbreviations
  • SECTION 1 Smart Embedded Systems for the Automotive Industry
  • 1Functional Safety Audit/Assessment for Automotive Engineering
  • Abstract
  • 1.1 Introduction and Objectives
  • 1.2 ISO 26262 and ASIL Overview
  • 1.3 Functional Safety Audit/Assessment Program
  • 1.3.1 Internal functional safety audit procedure
  • 1.3.1.1 Definition and safety compliance procedures phases
  • 1.3.1.2 Objective and scope of internal FS audit
  • 1.3.1.3 Internal functional safety audit procedure:
  • 1.3.1.4 Non-Conformance
  • 1.3.2 Internal functional safety assessment procedure
  • 1.3.2.1 Objective and scope of internal FS assessment
  • 1.3.2.2 Project FSA procedure:
  • 1.3.3 External or supplier functional safety audit and functional safety assessment
  • 1.4 Functional Safety Audit / Assessment Planning
  • 1.5 Functional Safety Audit / Assessment Preparation
  • 1.6 Functional Safety Audit / Assessment Performance
  • 1.7 Functional Safety Audit / Assessment Report and Follow-up
  • 1.8 Conclusion
  • References
  • 2Comparison between AUTOSAR Platforms with Functional Safety for Automotive Software Architectures
  • Abstract
  • 2.1 Introduction
  • 2.2 Overview of the Future E/E Architectures
  • 2.2.1 Combination of different software platforms
  • 2.2.2 Service oriented communication
  • 2.3 The AUTOSAR Adaptive Platform
  • 2.4 AUTOSAR Foundation
  • 2.5 Classic AUTOSAR Vs Adaptive AUTOSAR
  • 2.6 Communication Between AUTOSAR Platforms
  • 2.7 Safety Preliminaries for E/E Architectures
  • 2.7.1 Functional safety overview
  • 2.7.2 ASIL determination
  • 2.8 Conclusion.
  • 6.2.2.2 Heterogeneous architectures
  • 6.3 Embedded Systems Applications in Biomedical Engineering: Case of the Pre-treatment of ECG Signal
  • 6.3.1 The proposed techniques for embedded systems implementations
  • 6.3.1.1 ADTF technique
  • 6.3.1.2 DWT Technique
  • 6.3.1.3 Hybrid DWT-ADTF technique
  • 6.3.2 Implementation of the ADTF technique usingmulti-CPU architectures
  • 6.3.3 HLS implementation of ADTF techniqueusing FPGA architecture
  • 6.3.4 VHDL implementation of ADTF techniqueusing FPGA architecture
  • 6.3.5 VHDL implementation of hybrid DWT-ADTF technique using FPGA architecture
  • 6.4 Conclusions
  • References
  • 7Acquisition and Processing of SurfaceEMG Signal with an Embedded Compact RIO-based System
  • Abstract
  • 7.1 Introduction
  • 7.2 EMG Signal Conditioning Circuit
  • 7.2.1 Instrumentation amplifier
  • 7.2.2 Band pass filter
  • 7.2.3 Analog-to-digital converter
  • 7.3 EMG Signal Processing
  • 7.3.1 Flowchart description
  • 7.4 Implementation Results
  • 7.4.1 Implementation on compact RIO-9035 controller
  • 7.4.2 EMG instrumentation based on NI-ELVIS II+
  • 7.4.3 Real-time evaluation
  • 7.5 Conclusion
  • 7.6 Funding
  • 7.7 ORCID ID
  • References
  • SECTION 4 The Application of Embedded System in Image Processing
  • 8Quick and Efficient Hardware-Software Design Space Exploration UsingVivado-HLS: A Case Study of Adaptive Algorithm for Image Denoising
  • Abstract
  • 8.1 Introduction
  • 8.2 High-level Synthesis
  • 8.3 Adaptive Algorithm
  • 8.3.1 LMS algorithm
  • 8.3.2 NLMS algorithm
  • 8.4 Implementation and Results
  • 8.4.1 Phase I
  • 8.4.2 Phase II
  • 8.4.3 Phase III
  • 8.5 Conclusion and Future Scope
  • References
  • 9Fast FPGA Implementation of A Moving Object Detection System
  • Abstract
  • 9.1 Introduction
  • 9.2 Detect Moving Objects Algorithm
  • 9.3 Implementation and Experiment Results
  • 9.3.1 Software simulation and evaluation.
  • 9.3.2 Embedded objects detection system
  • 9.4 Conclusion
  • References
  • 10Face Recognition based on CNN, Hog and Haar Cascade Methods using RaspberryPi v4 Model B
  • Abstract
  • 10.1 Introduction
  • 10.2 Implementation Methods
  • 10.2.1 Method 1. Haar cascade
  • 10.2.2 Method 2. Histogram of Oriented Gradients (HOG)
  • 10.2.3 Method 3. Convolutional Neural Networks (CNN)
  • 10.2.3.1 Convolution layer
  • 10.2.3.2 Pooling layer
  • 10.2.3.3 Fully connected layer
  • 10.3 Deployment Environments and Results
  • 10.3.1 Hardware environment
  • 10.3.1.1 Raspberry Pi4
  • 10.3.1.2 Camera Pi V2
  • 10.3.2 Software environment
  • 10.3.2.1 Python
  • 10.3.3 Application process/steps
  • 10.3.3.1 Dataset creation
  • 10.3.3.2 Training part
  • 10.3.3.3 Recognition part
  • 10.3.4 Implementation results and comparison
  • 10.4 Conclusion
  • References
  • SECTION 5 Internet of Things BasedEmbedded System
  • 11Survey Review on Artificial Intelligence and Embedded Systems for Agriculture Safety: A proposed IoT Agro-meteorology System for Local Farmers in Morocco
  • Abstract
  • 11.1 Introduction
  • 11.2 AI-enabled Embedded Systems for Agriculture
  • 11.2.1 Precision in water management
  • 11.2.2 Integrated food safety
  • 11.2.3 Crop productivity and fertility
  • 11.2.4 Automation: Unmanned aerial vehicles (UAVs) and robots
  • 11.2.5 Weather predictive analysis
  • 11.3 Proposed Solution for Familial Agriculture andSmall Farmers
  • 11.3.1 Description of the study area
  • 11.3.2 Model architecture
  • 11.3.3 Wireless sensors networks for agricultural Forecasting
  • 11.3.4 Communication modules
  • 11.4 Discussions: Questions and Challenges Raised by the use of AI and IoT in Agriculture
  • 11.4.1 The question of trust
  • 11.4.2 The question of applying AI stochastic algorithms
  • 11.4.3 The question of data
  • 11.4.4 The question of interpretability.
  • 11.5 Conclusion and Future Works
  • Appendix A
  • Appendix B
  • Appendix C
  • Appendix D
  • References
  • 12IoT-Based Intelligent Handicraft System Using NFC Technology
  • Abstract
  • 12.1 Introduction
  • 12.2 Preliminary and Related Work
  • 12.3 System Design
  • 12.3.1 Data acquisition
  • 12.3.2 Data analysis
  • 12.4 System Implementation
  • 12.4.1 System workflow
  • 12.4.2 Database design
  • 12.4.3 Mobile application prototype
  • 12.5 Conclusion
  • 12.6 Acknowledgments
  • References
  • SECTION 6 System on Chip and Co-design
  • 13SoC Power Estimation: A Short Review
  • Abstract
  • 13.1 Introduction
  • 13.2 Background
  • 13.2.1 Levels of parallelism
  • 13.2.2 Advances in processor microarchitecture
  • 13.2.2.1 Single cycle processor
  • 13.2.2.2 Multi cycle processor
  • 13.2.2.3 Pipelining
  • 13.2.2.4 Superscalar processor
  • 13.2.2.5 Vector processor
  • 13.2.2.6 Multicore processors
  • 13.2.3 Abstraction levels classification
  • 13.2.3.1 Layout
  • 13.2.3.2 Gate level
  • 13.2.3.3 Register transfer level
  • 13.2.3.4 Cycle accurate level
  • 13.2.3.5 Transactional level modeling
  • 13.2.3.6 Algorithmic level
  • 13.3 Physical Power Models
  • 13.3.1 Leakage power
  • 13.3.2 Dynamic power
  • 13.3.3 Short-circuit power
  • 13.4 Power Estimation Techniques
  • 13.4.1 WATTCH
  • 13.4.2 AVALACHE
  • 13.4.3 PowerVIP
  • 13.4.4 Hybrid System Level power consumptionestimation (HSL)
  • 13.4.5 Early Design Power Estimation (EDPE)
  • 13.4.6 Recent power and temperature modelling method
  • 13.5 Discussion
  • 13.6 Proposed Technique
  • 13.7 Conclusion
  • References
  • 14Hardware/Software Partitioning Algorithms: A Literature Review and New Perspectives
  • Abstract
  • 14.1 Introduction
  • 14.2 Overview of Partitioning Problem
  • 14.3 Exact Algorithms
  • 14.3.1 Integer linear programming
  • 14.3.2 Dynamic programming
  • 14.3.3 Branch and bound.