Читать книгу Human Communication Technology - Группа авторов - Страница 4

1 Chapter 1Figure 1.1 Description of the IoRT innovations.Figure 1.2 Conceptual model of IoRT Architecture.Figure 1.3 Protocol stacks of IoT.Figure 1.4 Platform of IoRT.Figure 1.5 Service oriented test platform.Figure 1.6 Illustrates the connection establishment.Figure 1.7 Data analysis.

2 Chapter 2Figure 2.1 Foremost branches of IoRT.Figure 2.2 Process flow of electromyography signal acquisition.Figure 2.3 Neurosky electroencephalograph headset available in Market.Figure 2.4 Electrode positioning of brain for extracting the electroencephalogra...Figure 2.5 Categories of feature extraction techniques.Figure 2.6 The flow of data speed, client connection and data transfer of mobile...Figure 2.7 The logical flow of brain–computer interface system software.Figure 2.8 GUI of brain–computer interface based software.

3 Chapter 3Figure 3.1 Sequence of states in IoRT systems.Figure 3.2 Model checking approach.Figure 3.3 PRISM model checking overview.Figure 3.4 UPPAAL basic structure.Figure 3.5 SPIN overview.Figure 3.6 Automated theorem proving and logic.Figure 3.7 Higher level view of alt-ergo.Figure 3.8 CodeSonar overview.Figure 3.9 IoRT Validation key concerns.Figure 3.10 Approach to IoRT testing framework.Figure 3.11 Automated test frameworks types.Figure 3.12 Modular driven framework.Figure 3.13 Library architecture.Figure 3.14 Data driven framework.Figure 3.15 Keyboard driven framework.Figure 3.16 Hybrid automation framework.Figure 3.17 BDD automation framework.Figure 3.18 IoRT testing.Figure 3.19 Priority of testing mode.Figure 3.20 Automated validation process.Figure 3.21 Key strategies for IoRT security.

4 Chapter 4Figure 4.1 Working principle of Li-Fi.Figure 4.2 M2M communication system.Figure 4.3 Basic building blocks of Li-Fi & its applications.Figure 4.4 Arduino Uno development board.Figure 4.5 Li-Fi transmitter circuit.Figure 4.6 Li-Fi receiver circuit.Figure 4.7 Li-Fi navigation system for blind people.Figure 4.8 Vehicle to vehicle communication using Li-Fi.Figure 4.9 Li-Fi at ICU room.

5 Chapter 5Figure 5.1 Cross-over representation.Figure 5.2 Representation of mutation process.Figure 5.3 Robot interaction for disease prediction—belief network.Figure 5.4 Data collection.Figure 5.5 HRV measures prediction.Figure 5.6 Attribute selection.Figure 5.7 Graphical representation of Bayesian belief network model.Figure 5.8 Final model tree for the combination of attributes.Figure 5.9 Final output of in terms of performance measures with classified resu...

6 Chapter 6Figure 6.1 Human-centric computing.Figure 6.2 Proposed methodology.Figure 6.3 Data partition in the network.Figure 6.4 The output is predicted after attaching weights to the subnetworks.Figure 6.5 Multimodal data performance.Figure 6.6 Performance metrics.Figure 6.7 PPV and NPV performance.Figure 6.8 Proposed methodology overall performance.

7 Chapter 8Figure 8.1 Communication architecture of IoRT.Figure 8.2 Robot as a node in IoT vs robot as a client of IoT.

8 Chapter 9Figure 9.1 Proposed system architecture.Figure 9.2 Flow diagram of the proposed system.Figure 9.3 MQ2 gas sensor module.Figure 9.4 MQ3 gas sensor module.Figure 9.5 MQ6 gas sensor.Figure 9.6 MQ135 gas sensor.Figure 9.7 NI-WSN-3202.Figure 9.8 Block diagram of NI WSN 3202.Figure 9.9 Analog input circuitry.Figure 9.10 Simplified circuit diagram of one DIO channel.Figure 9.11 NI WSN-3202 pin out.Figure 9.12 NI WSN gateway (NI9795).Figure 9.13 Mesh configuration example.Figure 9.14 Compact Rio (NI 9039).Figure 9.15 Architecture of compact Rio (NI9082).Figure 9.16 Sensor array in glass chamber for data set creation.Figure 9.17 Front panel of data acquisition system.Figure 9.18 Dataset of ethanol gas.Figure 9.19 Dataset of petroleum gas.Figure 9.20 Dataset of ammonia gas.Figure 9.21 Sensors voltage level at 1,500 ppm.Figure 9.22 Sensors voltage level at 1,600 ppm.Figure 9.23 Sensors voltage level at 1,200 ppm.Figure 9.24 Architecture of machine learning model.Figure 9.25 Simple neural network for gas classification.Figure 9.26 Simple neural network for gas concentration.Figure 9.27 Detailed classifier neural network.Figure 9.28 ReLU activation function.Figure 9.29 EPOCH vs LOSS.Figure 9.30 Distribution at output layer.Figure 9.31 The Rough programming concept used in building the system.Figure 9.32 The overall proposed system.Figure 9.33 Front panel when ammonia gas is detected.Figure 9.34 The front panel when no gas is detected.Figure 9.35 Data of sensor node 1 in cloud.

9 Chapter 10Figure 10.1 Block diagram of the hierarchical elitism genetic gravitational sear...Figure 10.2 Flow diagram of hierarchical elitism gene gravitational search.Figure 10.3 Echocardiogram video images from Cardiac Motion and Imaging Planes (...Figure 10.4 Graphical representation of computational time.Figure 10.5 Graphical representation of computational complexity.Figure 10.6 Graphical representation of pattern recognition accuracy.Algorithm 1 Additive kuan filter pre-processing.Algorithm 2 Hierarchical elitism genetic gravitational search.

10 Chapter 11Figure 11.1 Classification of ML algorithm.Figure 11.2 Neural network.Figure 11.3 IoT in routing and other network application domain.Figure 11.4 LarKC routing.Figure 11.5 IoT in living and everywhere.Figure 11.6 IoT in all types of industries.

11 Chapter 12Figure 12.1 Schematic of the hearing system showing forward and feedback path of...Figure 12.2 Presence of feedback in acoustic signals under frequency 1,800 Hz.Figure 12.3 Representation of the frequency response under feedback condition.Figure 12.4 Plot showing feedbacks with high-frequency cuts in an acoustic envir...Figure 12.5 When signals are (a) in-phase (b) out-of-phase and (c) with an arbit...Figure 12.6 Representation of the phase response under feedback condition.Figure 12.7 Frequency response of notch filter different orders at ω_c = 0.5 Hz.Figure 12.8 Impulse response of an acoustic system.Figure 12.9 Identification of an unknown system.Figure 12.10 Adaptive filter placed after the identification of unknown system.Figure 12.11 (a) Audio signal without adaptive filtering and (b) signal with ada...Figure 12.12 Mean square error provided by the LMS system.Figure 12.13 Mean square error under constant step-size in F-LMS.Figure 12.14 Mean square error under variable step-size in V-LMS at µ = 0.0076 a...Figure 12.15 Convergence rate under variable step-size in V-LMS at µ = 0.0076 an...

12 Chapter 13Figure 13.1 IoT cloud architecture.Figure 13.2 Schematic of user-cloud platform.Figure 13.3 Schematic of cloud platform—IoT device.Figure 13.4 Temperature sensor.Figure 13.5 Water quality sensor.Figure 13.6 Humidity sensor.Figure 13.7 Light depended resistor.Figure 13.8 Arduino Uno R3 embedded system.Figure 13.9 Basic embedded system structure.Figure 13.10 DHT-11 temperature & humidity sensor [11].Figure 13.11 Arduino soil and moisture level sensor [11].Figure 13.12 Raspberry Pi 4 model B [10].Figure 13.13 Analog to digital converter (ADS1115) [11].Figure 13.14 Interfacing ADC (ADS1115) to raspberry Pi 4 model B.Figure 13.15 Interfacing moisture sensor to the raspberry Pi 4 model B.Figure 13.16 Interfacing raspberry Pi 4 model B with DHT-11 temperature & humidi...Figure 13.17 Temperature & humidity management system circuit diagram.Figure 13.18 Light intensity management system circuit diagram.

13 Chapter 14Figure 14.1 Recycling process [64].Figure 14.2 Gender.Figure 14.3 Illustrates out of 141 student participants most were diploma holder...Figure 14.4 Effectiveness of Green Computing policy at the Institute of Health S...Figure 14.5 Green computing practices among users.Figure 14.6 Likelihood of green computing threats.Figure 14.7 The role of green computing training towards green computing awarene...Figure 14.8 Green computing awareness framework.

14 Chapter 15Figure 15.1 Architecture of a BSN.Figure 15.2 Main research areas in BSNs.Figure 15.3 Evolution of IoT.Figure 15.4 Structure flow of IoT.Figure 15.5 System overview of telemedicine system.Figure 15.6 Telemedicine—architecture.Figure 15.7 Application fields of BSNs.Figure 15.8 Example of patient monitoring using a wireless body area network (WB...Figure 15.9 Melee networking for monitoring the performance of a group of athlet...

15 Chapter 16Figure 16.1 Technology characteristics of the IoT.Figure 16.2 Middleware-based vs. Direct data collection approach through source ...Figure 16.3 Context-aware system.Figure 16.4 Software components relocation adaptation.Figure 16.5 Middleware work.Figure 16.6 Device registration.Figure 16.7 Sharing the data.Figure 16.8 Levels of sensor reading.Figure 16.9 Levels of the sensor reading.Figure 16.10 Levels of the sensor reading.