Smart embedded systems and applications /
| Corporate Author: | |
|---|---|
| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
Gistrup, Denmark :
River Publishers,
[2022]
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| Edition: | 1st ed. |
| Series: | Electronic Materials, Circuits and Devices' Ser.
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| 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.