Modern computational techniques for engineering applications

Modern computational techniques for engineering applications

"This book presents recent computational techniques used in the advancements of modern grids with the integration of non-conventional energy sources like wind and solar energy. It covers data analytics tools for smart cities, smart towns, and smart computing for sustainable developments"--

Detalles Bibliográficos
Otros Autores: Arora, Krishan (Electrical Engineering), editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2024]
Edición:First edition
Materias:
Sustainable engineering > Data processing.
Biomedical engineering > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009809021506719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • About the Editors
  • Contributors
  • Chapter 1: Paralysis support system using IoT
  • 1.1 Introduction
  • 1.2 Literature review
  • 1.3 Proposed healthcare system
  • 1.4 System architecture
  • 1.4.1 ESP32-NODE MCU
  • 1.4.2 Gyro and accelerometer sensor
  • 1.4.3 RF module
  • 1.4.4 Encoder and decoder
  • 1.4.5 LCD display
  • 1.4.6 Arduino IDE
  • 1.5 Results and discussion
  • 1.6 Conclusion and future scope
  • References
  • Chapter 2: Blockchain and its applications: A review
  • 2.1 Introduction
  • 2.2 Blockchain architecture
  • 2.3 Working of blockchain
  • 2.4 Application
  • 2.4.1 Healthcare
  • 2.4.2 Education
  • 2.4.3 Insurance
  • 2.4.4 E-Commerce
  • 2.4.5 Transportation
  • 2.4.6 Industry of strength
  • 2.5 Conclusion
  • References
  • Chapter 3: Data analytics tools for smart cities and smart towns
  • 3.1 Introduction
  • 3.2 The concept of big data analytics
  • 3.3 The concept of smart cities
  • 3.3.1 Data storage in smart cities
  • 3.4 Need for data analytics in smart cities
  • 3.4.1 Reliability
  • 3.4.2 Transport
  • 3.4.3 Planning
  • 3.4.4 Future proofing
  • 3.4.5 Effective spending
  • 3.4.6 Sustainability
  • 3.5 Data analytics tools
  • 3.6 Data analytics tools for smart cities and towns
  • 3.6.1 Hadoop
  • 3.6.2 Map Reduce
  • 3.6.3 Apache storm
  • 3.6.4 Apache spark
  • 3.6.5 Apache Flink
  • 3.6.6 Flume
  • 3.7 Conclusion
  • References
  • Chapter 4: Industrial Internet of Things and its applications in Industry 4.0 through sensor integration for a process parameter monitor and control
  • 4.1 Introduction
  • 4.2 Problem statement
  • 4.3 Objective
  • 4.4 Related works
  • 4.4.1 Inferences through the survey
  • 4.5 Existing methodology
  • 4.5.1 Level process station
  • 4.5.2 Monitoring and control of level process
  • 4.5.3 SCADA.
  • 4.5.4 Level process monitoring and control through SCADA replacement
  • 4.6 Proposed system
  • 4.6.1 Introduction to the proposed system
  • 4.6.2 Flowchart of the system
  • 4.6.3 Eclipse
  • 4.7 Cloud description
  • 4.8 Trail results
  • 4.8.1 TRAIL 1
  • 4.8.2 TRAIL 2
  • 4.8.3 TRAIL 3
  • 4.8.4 TRAIL 4
  • 4.9 Project setup
  • 4.10 Inference
  • 4.11 Conclusion
  • 4.12 Limitations of the proposed methodology
  • Annexure-1
  • References
  • Chapter 5: Automated computer-aided diagnosis of COVID-19 and pneumonia based on chest X-ray images using deep learning: Classification and segmentation
  • 5.1 Introduction
  • 5.2 Literature review
  • 5.2.1 Medical image segmentation
  • 5.2.2 Computer-aided detection and diagnosis
  • 5.2.3 Annotation-efficient
  • 5.3 Materials and methods
  • 5.3.1 Dataset description
  • 5.3.2 Pre-processing
  • 5.3.3 Convolutional neuronal network
  • 5.4 Experimental results
  • 5.4.1 Experimental setup
  • 5.4.2 Data augmentation
  • 5.4.3 Evaluation of performance metrics
  • 5.4.4 Graphical user interface
  • 5.5 Conclusion and future work
  • References
  • Chapter 6: Fuzzy logic and applications
  • 6.1 Introduction
  • 6.2 Fuzzy logic concepts
  • 6.3 Reasons for using fuzzy logic system
  • 6.3.1 Main usage of fuzzy logic systems is prevalent in the following areas
  • 6.3.2 Fuzzy logic in washing machines
  • 6.4 Image processing
  • 6.5 Anesthesia depth control using fuzzy logic
  • 6.6 Fuzzy logic in monitoring drug dosage
  • 6.7 Fuzzy logic-based water treatment system optimization
  • 6.8 Applications of fuzzy logic in industrial automation
  • 6.9 Software engineering
  • 6.10 Power system applications
  • 6.11 Fuzzy controllers' potential for use in RACs in the future
  • 6.12 The benefits of fuzzy logic
  • 6.13 Disadvantages of fuzzy logic
  • 6.14 Conclusion
  • References.
  • Chapter 7: IoT-based smart monitoring topologies for energy-efficient smart buildings
  • 7.1 Introduction
  • 7.2 IoT-based smart building
  • 7.2.1 Basic components of IoT
  • 7.2.2 Sensors
  • 7.2.3 Gateways
  • 7.2.4 Building monitoring systems
  • 7.2.5 IoT with integrating AI in smart buildings
  • 7.2.6 Building automation systems (BAS)
  • 7.2.7 IoT-based indoor localization
  • 7.2.8 Lighting system
  • 7.2.9 Shielding schedulers
  • 7.2.10 Fire management system
  • 7.2.11 Heating, ventilation and air conditioning
  • 7.2.12 Energy efficiency toward smart buildings
  • 7.2.13 Monitoring
  • 7.2.14 Information management
  • 7.2.15 Automation system
  • 7.2.16 Feedback
  • 7.2.17 User participation
  • 7.3 Smart building architecture
  • 7.3.1 Building Energy Management System (BEMS)
  • 7.3.2 Protective lock system
  • 7.3.3 Fire safety system
  • 7.3.4 HVAC
  • 7.3.5 Movement monitoring system
  • 7.3.6 Cloud infrastructure
  • 7.4 Monitoring techniques for smart buildings
  • 7.4.1 Occupancy detection
  • 7.4.2 Occupancy counting
  • 7.4.2.1 Counting by whole
  • 7.4.2.2 Counting at a specific place or zone
  • 7.4.3 Occupancy tracking
  • 7.4.4 Occupancy event recognition
  • 7.4.5 Sensor network-based occupancy monitoring technique
  • 7.4.6 CO 2 sensors
  • 7.4.6.1 Nondispersive infrared sensors
  • 7.4.6.2 Electrochemical sensors
  • 7.4.6.3 Metal oxide semiconductor sensors
  • 7.4.7 Humidity sensors
  • 7.4.8 Temperature sensor
  • 7.4.9 Microphone
  • 7.4.10 Data fusion for occupancy tracking
  • 7.4.11 Camera-based monitoring
  • 7.5 Opportunities and challenges
  • 7.5.1 Connectivity
  • 7.5.2 Security
  • 7.5.3 Poor testing
  • 7.5.4 Weak password
  • 7.5.5 Lack of visibility
  • 7.6 Conclusion
  • References
  • Chapter 8: Soft computing techniques for renewable energy systems
  • 8.1 Introduction
  • 8.2 The soft computing - development history
  • 8.3 Fuzzy Logic (FL).
  • 8.3.1 Fuzzy Logic in AI
  • 8.3.2 Fuzzy set applications in power system
  • 8.3.3 Fuzzy Logic applications in other fields
  • 8.4 Artificial Neural Network (ANN)
  • 8.4.1 Electrical engineering and machine learning applications for neural networks
  • 8.4.2 Engineering applications of neural network
  • 8.5 Adaptive Neuro Fuzzy Inference System (ANFIS)
  • 8.5.1 Blocks of FIS
  • 8.5.2 Steps of fuzzy reasoning
  • 8.5.3 Types of fuzzy reasoning
  • 8.5.4 Application areas of ANFIS
  • 8.6 Conclusion
  • References
  • Chapter 9: Maize diseases diagnosis based on computer intelligence: A systematic review
  • 9.1 Introduction
  • 9.2 Classification of maize diseases
  • 9.3 Identification of maize diseases using computer intelligence
  • 9.3.1 Image acquisition
  • 9.3.2 Image preprocessing
  • 9.3.3 Image segmentation
  • 9.3.4 Feature extraction and selection
  • 9.3.5 ML/DL classification models
  • 9.3.6 Performance evaluation
  • 9.4 Recent research works about the identification and classification of maize diseases
  • 9.5 Related work on disease identification and classification in different plants other than maize
  • 9.6 Conclusion
  • References
  • Chapter 10: Low-power architectural design and implementation of reconfigurable data converters for biomedical application
  • 10.1 Introduction
  • 10.2 System architecture
  • 10.3 Analog-to-digital converter
  • 10.4 Auto adaption unit
  • 10.4.1 Sampling rate control signal
  • 10.4.2 Resolution control signal
  • 10.5 Digital-to-analogue converter
  • 10.6 Performance parameters
  • 10.6.1 Static performance
  • 10.7 Interconnection system (ICS)
  • 10.8 DTMOS logic
  • 10.9 Results and discussion
  • 10.10 Performance
  • 10.11 Conclusion
  • References
  • Chapter 11: Sign language and hand gesture recognition using machine learning techniques: A comprehensive review
  • 11.1 Introduction.
  • 11.2 Challenges in recognition of hand gestures
  • 11.3 Approaches' types
  • 11.4 Literature review on vision-based gesture recognition
  • 11.5 Data acquisition
  • 11.6 Pre-processing of acquired data
  • 11.7 Segmentation
  • 11.7.1 Segmentation of skin color
  • 11.7.2 Other methods for segmentation
  • 11.7.3 Tracking
  • 11.8 Feature extraction
  • 11.8.1 Principal component analysis (PCA)
  • 11.8.2 Linear discriminant analysis (LDA)
  • 11.8.3 Shift-invariant feature transform (SIFT)
  • 11.9 Classification
  • 11.9.1 Artificial neural network (ANN)
  • 11.9.2 Support vector machine (SVM)
  • 11.9.3 Euclidean distance classifier
  • 11.9.4 K-nearest neighbor classifier
  • 11.10 Literature review on sensor-based gesture recognition
  • 11.10.1 Data glove
  • 11.10.2 Electromyography (EMG)
  • 11.10.3 Radar and WiFi
  • 11.11 Discussion
  • 11.11.1 Critical review on the previous survey
  • 11.11.2 Reviews on considered algorithms and methodologies
  • 11.12 Conclusion and future work
  • References
  • Index.

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