1.7 AI-enabled WBAN architecture.Figure 1.8 IoT architecture for healthcare.Figure 1.9 AI-based remote healthcare management.2 Chapter 2Figure 2.1 Pneumatic position servo system.Figure 2.2 Phases in genetic algorithm.Figure 2.3 GA initialization process.Figure 2.4 Crossover procedure.Figure 2.5 Mutation.Figure 2.6 Fitness with crossover probabilities.Figure 2.7 Fitness with mutation probabilities.Figure 2.8 Flowchart of genetic algorithm.Figure 2.9 Genetic algorithm tool box.Figure 2.10 Control error.Figure 2.11 Control action.Figure 2.12 System output.Figure 2.13 (a) Control error for reference value 500 (error).Figure 2.13 (b) Control action for reference value 500 (error).Figure 2.13 (c) System output for reference value 500 (error).Figure 2.14 (a) Control error for reference value 500.Figure 2.14 (b) Control action for reference value 500.Figure 2.14 (c) System output for reference value 500 (error).Figure 2.15 (a) Control action for reference value 1,500.Figure 2.15 (b) Control action for reference value 1,500.Figure 2.15 (c) System output for reference value 1,500.Figure 2.16 Hardware setup of position servo system.Figure 2.17 CRO output waveform.Figure 2.18 CRO output waveform of 500 reference (Error).Figure 2.19 (a) CRO output waveform of 500 reference.Figure 2.19 (b) CRO output waveform of 1,500 reference.
3 Chapter 3Figure 3.1 Illustration of IWDH-HP-RDD scheme.Figure 3.2 Percentage increase in localization accuracy of the IWDH-HP-RDD schem...Figure 3.3 Percentage increase in warning data dissemination of the IWDH-HP-RDD ...Figure 3.4 Percentage decrease in localization error of the IWDH-HP-RDD scheme f...Figure 3.5 Percentage decrease in latency of the IWDH-HP-RDD scheme for varying ...Figure 3.6 Percentage increase in localization accuracy of the IWDH-HP-RDD schem...Figure 3.7 Percentage increase in warning data dissemination of the IWDH-HP-RDD ...Figure 3.8 Percentage decrease in localization error of the IWDH-HP-RDD scheme f...Figure 3.9 Percentage decrease in latency of the IWDH-HP-RDD scheme for varying ...Figure 3.10 Percentage increase in localization accuracy of the IWDH-HP-RDD sche...Figure 3.11 Percentage increase in warning data dissemination of the IWDH-HP-RDD...Figure 3.12 Percentage decrease in localization error of the IWDH-HP-RDD scheme ...Figure 3.13 Percentage decrease in latency of the IWDH-HP-RDD scheme for varying...
4 Chapter 4Figure 4.1 Fault prognosis process flow diagram.Figure 4.2 Frequency response curves of different analog filter circuits.Figure 4.3 Sallen-Key low-pass filter circuit.Figure 4.4 RUL prediction model curve of passive component.Figure 4.5 Curve fitting model of Sallen-Key low-pass filter without fault: (a) ...Figure 4.6 RUL prediction results: Sallen-Key low-pass filter: (a) R1 below the ...
5 Chapter 5Figure 5.1 Artificial intelligence in healthcare.Figure 5.2 Steps in developing a drug.Figure 5.3 Training and operation of an ANN for the drug Serotonin assembled int...Figure 5.4 A Cobot assisting the surgery.Figure 5.5 Picture of a moving robot used in suturing.Figure 5.6 Image processing analysis of a surgical image used to predict inimica...Figure 5.7 Collective process of surgical decision by AI using multimodal data u...Figure 5.8 Image of a CT scan [17].Figure 5.9 Medical imaging using AI in cardiovascular findings [19].Figure 5.10 Medical imaging using AI identifying hip fracture and wrist fracture...Figure 5.11 Medical imaging technique to spot pneumonia [18].
6 Chapter 6Figure 6.1 Seven decades of artificial intelligence.Figure 6.2 Phenomenon of AI.Figure 6.3 Tesla’s autopilot [3].Figure 6.4 Boxever [4].Figure 6.5 The embedding method sensors in a finger and hand gestures process.Figure 6.6 AI robot [6].Figure 6.7 Vinci [7].Figure 6.8 Affectiva [9].Figure 6.9 AlphaGo beats [10].Figure 6.10 Cogito [11].Figure 6.11 Siri [12].Figure 6.12 Alexa [13].Figure 6.13 Pandora [14].Figure 6.14 Design practice in the context of traditional human-intense operatin...Figure 6.15 Design practice in the context of AI Factories.
7 Chapter 7Figure 7.1 Machine learning workflow.Figure 7.2 Healthcare IoT layers.
8 Chapter 8Figure 8.1 A sample smart home.Figure 8.2 Architecture of smart homes.Figure 8.3 Bionetwork of smart cities.Figure 8.4 Architecture of smart cities.Figure 8.5 Components of smart cities.Figure 8.6 Smart infrastructure.Figure 8.7 Intelligent transportation system [11].Figure 8.8 Smart energy system [12].Figure 8.9 Illustration of smart healthcare [13].Figure 8.10 Architecture of telemedicine.Figure 8.11 Characteristics of smart cities.Figure 8.12 Challenges of smart cities.
9 Chapter 9Figure 9.1 Image of AI-assisted radiology and pathology.Figure 9.2 Biomedical image of human body.Figure 9.3 Surgical robots.Figure 9.4 Feature extraction using SVM model. (https://www.kindpng.com/imgv/how...Figure 9.5 Structure of neural network.Figure 9.6 Structure of Decision Tree.Figure 9.7 Structure of CNN.Figure 9.8 Architecture of diagram of proposed LR + RF model.Figure 9.9 Proposed system model overview.Figure 9.10 Performance analysis of classifiers based on the classifiers.
10 Chapter 10Figure 10.1 Factors influencing EV range prediction.Figure 10.2 Factors influencing EVs battery management system.Figure 10.3 Equivalent circuit of battery.Figure 10.4 Block diagram of the system.Figure 10.5 Flowchart for SoC prediction.Figure 10.6 Flowchart to estimate SoH.Figure 10.7 Measured voltage.Figure 10.8 Normalized profile for an automotive application.Figure 10.9 Reference SoC.Figure 10.10 SoC estimation for 5°C.Figure 10.11 SoC estimation for 25°C.Figure 10.12 Error bounds for 5°C.Figure 10.13 Error bounds for 25°C.Figure 10.14 Capacity estimation using AWTLS method.
11 Chapter 11Figure 11.1 Feature extraction in excel format.Figure 11.2 Convolutional neural network with its layers.Figure 11.3 Validation parameters.Figure 11.4 Test parameters.Figure 11.5 Validation parameters.Figure 11.6 Test parameters.
12 Chapter 12Figure 12.1 Petrol hydrometer.Figure 12.2 Water density at different temperatures.Figure 12.3 Effect of water pressure on air column inside a cylindrical pipe.Figure 12.4 Block diagram for petrol density measurement.Figure 12.5 Honeywell SSCSDRN010MD2A3 pressure sensor.Figure 12.6 Initial setup.Figure 12.7 Improved instrument.Figure 12.8 Final apparatus setup.Figure 12.9 MPX2010DP interfaced with INA114 in Eagle.Figure 12.10 Pull-up resistor PCB board for SSCSDRN010MD2A3 in Eagle.Figure 12.11 I2C bits readout.Figure 12.12 The sensor output value and ASTM value obtained are plotted.Figure 12.13 Sensor output observed on laptop.Figure 12.14 Sensor
