“…Maryam covers the role of confounders in the hiring process, explores the complexity of job descriptions, and shares how she extracted signal from noise to extract the key conclusions she presents. …”

Tabla de Contenidos: “…7.5 Dynamics and Control of Wind Turbines -- 7.5.1 Pitch Control -- 7.5.2 Yaw Control -- 7.5.3 Passive and Active Stall Power Control -- 7.5.3.1 Passive Stall Control -- 7.5.3.2 Active Stall Control -- 7.6 Wind Turbine Condition Monitoring -- 7.7 Wind Energy Conversion Systems (WECS) -- 7.7.1 Based on Capacity of Power Generation -- 7.7.2 Systems without Power Electronics -- 7.8 Offshore Wind Energy -- 7.8.1 Offshore Wind Turbines -- 7.8.2 Foundation -- 7.8.3 Electrical Connection and Installation -- 7.8.4 Operation and Maintenance -- 7.9 Advantages of Offshore Wind Energy Systems -- 7.10 Environmental Impact of Wind Energy Systems -- 7.10.1 Impact of Noise -- 7.10.2 Electromagnetic Interference -- 7.11 Combining the Wind Power Generation System with Energy Storage -- 7.12 Summary -- References -- Chapter 8 Biomass Energy Systems -- 8.1 Biomass Energy -- 8.2 Biomass Production -- 8.2.1 Forest Industries -- 8.2.2 Forest Residues -- 8.2.2.1 Forest Thinnings -- 8.2.3 Agriculture Residues -- 8.2.4 Energy Crops -- 8.2.5 Food and Industrial Wastes -- 8.3 Biomass Conversion Process -- 8.4 Thermochemical Conversion -- 8.4.1 Combustion -- 8.4.2 Gasification -- 8.4.2.1 Applications -- 8.4.3 Pyrolysis -- 8.4.3.1 Torrefaction -- 8.4.4 Liquefaction -- 8.5 Biochemical/Biological Conversion -- 8.5.1 Fermentation -- 8.5.2 Anaerobic Digestion -- 8.5.3 Anaerobic Digestion Technologies Suitable for Dairy Manure -- 8.6 Classification of Biogas Plants -- 8.7 Mechanical Extraction (with Esterification) -- 8.8 Municipal Solid Waste to Energy Conversion -- 8.9 The Production of Electricity from Wood and Other Solid Biomass -- 8.10 Summary -- References -- Chapter 9 Geothermal Energy -- 9.1 The Origin of Geothermal Energy -- 9.2 Types of Geothermal Resources -- 9.3 Hydrothermal Resources -- 9.3.1 Vapour‐Dominated Systems -- 9.3.2 Water‐Dominated Systems…”

Tabla de Contenidos: “…Astigmatism of Vision -- 2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI -- 2.14 In Service of Society -- 2.15 Data Science in Service of Society: Knowledge and Performance from PEAS -- 2.16 Temporary Conclusion -- Acknowledgements -- References -- Chapter 3 Machine Learning Techniques for IoT Data Analytics -- 3.1 Introduction -- 3.2 Taxonomy of Machine Learning Techniques -- 3.2.1 Supervised ML Algorithm -- 3.2.1.1 Classification -- 3.2.1.2 Regression Analysis -- 3.2.1.3 Classification and Regression Tasks -- 3.2.2 Unsupervised Machine Learning Algorithms -- 3.2.2.1 Clustering -- 3.2.2.2 Feature Extraction -- 3.2.3 Conclusion -- References -- Chapter 4 IoT Data Analytics Using Cloud Computing -- 4.1 Introduction -- 4.2 IoT Data Analytics -- 4.2.1 Process of IoT Analytics -- 4.2.2 Types of Analytics -- 4.3 Cloud Computing for IoT -- 4.3.1 Deployment Models for Cloud…”

Tabla de Contenidos: “…-- 12.5 How to get notified of a failing task -- 12.5.1 Alerting within DAGs and operators -- 12.5.2 Defining service-level agreements -- 12.6 Scalability and performance -- 12.6.1 Controlling the maximum number of running tasks -- 12.6.2 System performance configurations -- 12.6.3 Running multiple schedulers -- Summary -- 13 Securing Airflow -- 13.1 Securing the Airflow web interface -- 13.1.1 Adding users to the RBAC interface -- 13.1.2 Configuring the RBAC interface -- 13.2 Encrypting data at rest -- 13.2.1 Creating a Fernet key -- 13.3 Connecting with an LDAP service -- 13.3.1 Understanding LDAP -- 13.3.2 Fetching users from an LDAP service -- 13.4 Encrypting traffic to the webserver -- 13.4.1 Understanding HTTPS -- 13.4.2 Configuring a certificate for HTTPS -- 13.5 Fetching credentials from secret management systems -- Summary -- 14 Project: Finding the fastest way to get around NYC -- 14.1 Understanding the data -- 14.1.1 Yellow Cab file share -- 14.1.2 Citi Bike REST API -- 14.1.3 Deciding on a plan of approach -- 14.2 Extracting the data -- 14.2.1 Downloading Citi Bike data -- 14.2.2 Downloading Yellow Cab data -- 14.3 Applying similar transformations to data -- 14.4 Structuring a data pipeline -- 14.5 Developing idempotent data pipelines -- Summary -- Part 4. …”

Tabla de Contenidos: “…Optique de la production : méthodes applicables par branche d'activité -- Agriculture -- Activités extractives -- Activités manufacturières -- Construction -- Commerce -- Restaurants et hôtels -- Transports et communications -- Services aux entreprises et services professionnels et techniques -- Éducation, santé et services personnels -- Services domestiques…”

Tabla de Contenidos: “…Taxes sur les produits chimiques et l'extraction de ressources -- Notes -- Chapitre 4. Résoudre le problème de la compétitivité -- 4.1. …”

Tabla de Contenidos: “…4.3.1.3 - Scope of Business Keys -- 4.3.1.4 - Difference between Business Keys and Surrogate Keys -- 4.3.2 - Hub Entity Structure -- 4.3.2.1 - Hash Key -- 4.3.2.2 - Business Key -- 4.3.2.3 - Load Date -- 4.3.2.4 - Record Source -- 4.3.2.5 - Last Seen Date -- 4.3.3 - Hub Examples -- 4.4 - Link Definition -- 4.4.1 - Reasons for Many-to-Many Relationships -- 4.4.2 - Flexibility of Links -- 4.4.3 - Granularity of Links -- 4.4.4 - Link Unit-of-Work -- 4.4.5 - Link Entity Structure -- 4.4.5.1 - Hash Key -- 4.4.5.2 - Dependent Child Key -- 4.4.6 - Link Examples -- 4.5 - Satellite Definition -- 4.5.1 - Importance of Keeping History -- 4.5.2 - Splitting Satellites -- 4.5.2.1 - Splitting by Source System -- 4.5.2.2 - Splitting by Rate of Change -- 4.5.3 - Satellite Entity Structure -- 4.5.3.1 - Parent Hash Key -- 4.5.3.2 - Load Date -- 4.5.3.3 - Load End Date -- 4.5.3.4 - Hash Difference -- 4.5.3.5 - Extract Date -- 4.5.4 - Satellite Examples -- 4.5.5 - Link Driving Key -- References -- Chapter 5 - Intermediate Data Vault Modeling -- 5.1 - Hub Applications -- 5.1.1 - Business Key Consolidation -- 5.2 - Link Applications -- 5.2.1 - Link-on-link -- 5.2.2 - Same-as Links -- 5.2.3 - Hierarchical Links -- 5.2.4 - Nonhistorized Links -- 5.2.5 - Nondescriptive Links -- 5.2.6 - Computed Aggregate Links -- 5.2.7 - Exploration Links -- 5.3 - Satellite Applications -- 5.3.1 - Overloaded Satellites -- 5.3.2 - Multi-Active Satellites -- 5.3.3 - Status Tracking Satellites -- 5.3.4 - Effectivity Satellites -- 5.3.5 - Record Tracking Satellites -- 5.3.6 - Computed Satellites -- References -- Chapter 6 - Advanced Data Vault Modeling -- 6.1 - Point-in-Time Tables -- 6.1.1 - Point-in-Time Table Structure -- 6.1.2 - Managed PIT Window -- 6.2 - Bridge Tables -- 6.2.1 - Bridge Table Structure -- 6.2.2 - Comparing PIT Tables with Bridge Tables -- 6.3 - Reference Tables…”

Tabla de Contenidos: “…Downloading files -- 4.1.3. Extracting files -- 4.2. Importing and exporting shapefiles -- 4.2.1. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions…”

Tabla de Contenidos: “…CYN1 z/OS subsystem -- 7.1 What the CYN1 subsystem does and how -- 7.2 Install CYN1 -- 7.2.1 Preallocate data sets on z/OS -- 7.2.2 Extract the TAR file with CYN data sets -- 7.2.3 Transfer files from the PC to z/OS -- 7.2.4 Receive the files on z/OS -- 7.2.5 Define the subsystem CYN1 -- 7.2.6 Provide RACF authorization -- 7.2.7 Update PROC00 -- 7.2.8 Authorize data sets and update LNKLST -- 7.2.9 Install the IEFU83 exit -- 7.2.10 Start CYN1PROC -- 7.2.11 Optional customization for large WebSphere environments -- 7.3 Verify the CYN1 installation -- 7.3.1 Display commands to verify your installation -- 7.3.2 SCYNAUTH listing -- 7.3.3 SCYNPROC listing -- Chapter 8. …”

Tabla de Contenidos: “…4.2.2 Catalog subsystem -- 4.2.3 Order subsystem -- 4.2.4 Inventory subsystem -- 4.2.5 Offering subsystem (prices) -- 4.2.6 Store subsystem -- 4.3 Content Manager and the Digital Media repository -- 4.3.1 Understanding the Content Manager datatype -- 4.3.2 Specifying data for the WebSphere Commerce catalog -- 4.3.3 Loading digital goods into the repository -- 4.3.4 Extracting digital media attribute values for a view -- Part 2 ITSO digital media working example -- Chapter 5. …”

Tabla de Contenidos: “…Application integration -- 10.1 Integration overview -- 10.2 Integration with portal applications using RSS -- 10.3 Introduction to RSS files -- 10.3.1 Creating a news feed -- 10.3.2 Integrating a context-sensitive RSS news feed portlet -- 10.3.3 Portlet messaging -- 10.3.4 Case study - Ascendant Technology RSS integration -- 10.3.5 Summary for integrating an RSS news feed -- 10.4 Integration with other Domino applications -- 10.4.1 Creating the menu component -- 10.4.2 Using LotusScript and LotusFormula -- 10.5 Integration with relational databases -- 10.6 Integration with Lotus Sametime -- 10.6.1 Prerequisites -- 10.6.2 Initialize some variables -- 10.6.3 Initialize Sametime -- 10.7 Integration with IBM Content Manager -- 10.7.1 Introduction to DB2 Content Manager product portfolio -- 10.7.2 Prerequisites -- 10.7.3 Configuration -- 10.7.4 Overview of extracting Content Object from Content Manager -- Chapter 11. …”

Tabla de Contenidos: “…Verification commands and methods -- 10.1 Key concepts -- 10.2 Introduction to Cluster 1600 system checking -- 10.3 Key commands -- 10.3.1 Verify installation of software -- 10.3.2 Verify system partitions -- 10.3.3 Verifying the authentication services -- 10.3.4 Checking subsystems -- 10.3.5 Monitoring hardware status -- 10.3.6 Monitoring node LEDs: spmon -L, spled -- 10.3.7 Extracting SDR contents -- 10.3.8 Checking IP connectivity: ping/telnet/rlogin -- 10.3.9 SMIT access to verification commands -- 10.4 Graphical user interface -- 10.5 Key daemons -- 10.5.1 Sdrd -- 10.5.2 Hardmon -- 10.5.3 Worm…”

Tabla de Contenidos: “…Implement the development environment -- 6.1 Development environment configuration options -- 6.1.1 Motivation for an end-to-end development environment -- 6.1.2 Solution overview for an end-to-end development environment -- 6.1.3 Development environment configurations -- 6.1.4 Hardware and software used in the development environment -- 6.2 Prerequisite software installation -- 6.2.1 DB2 Server installation -- 6.2.2 WebSphere Studio Application Developer installation -- 6.2.3 Jakarta Ant overview -- 6.3 WebSphere Commerce and WebSphere Studio Application Developer configuration -- 6.3.1 Prerequisite software -- 6.3.2 Commerce Enhancement Pack Toolkit installation -- 6.3.3 Create the WebSphere Commerce development instance -- 6.3.4 Publishing stores using Store Services (seed environment) -- 6.4 WebSphere Portal and WebSphere Studio Application Developer configuration -- 6.4.1 Prerequisite software -- 6.4.2 WebSphere Portal Toolkit V4.2.5 and WebSphere Portal V4.2.1 test environment installation -- 6.4.3 WebSphere Portal test environment server configuration -- 6.5 Integrating WebSphere Portal and WebSphere Commerce within WebSphere Studio Application Developer -- 6.5.1 Extract SSL certificate from WebSphere Commerce development environment -- 6.5.2 Enable SSL for WebSphere Portal and import the SSL certificate from WebSphere Commerce to WebSphere Portal -- 6.5.3 Commerce Enhancement Pack personalization installation on WebSphere Studio Application Developer 5 WebSphere Portal Test Environment (optional) -- 6.5.4 WebSphere Portal content publishing wizard installation (optional) -- 6.5.5 Deploy the ITSO CEP B2B sample -- Chapter 7. …”

Tabla de Contenidos: “…3.3.1 Entropy and Mutual Information 65 -- 3.3.2 Encoding of an Information Source 66 -- 3.3.3 Variable Length Coding 68 -- 3.3.4 Quantization 71 -- 3.4 Encoder Architectures 73 -- 3.4.1 DPCM 73 -- 3.4.2 Hybrid Transform-DPCM Architecture 77 -- 3.4.3 A Typical Hybrid Transform DPCM-based Video Codec 79 -- 3.4.4 Motion Compensation 82 -- 3.4.5 DCT and Quantization 83 -- 3.4.6 Procedures Performed at the Decoder 84 -- 3.5 Wavelet-Based Video Compression 86 -- 3.5.1 Motion-Compensated Temporal Wavelet Transform Using Lifting 90 -- References 94 -- 4 4G Wireless Communications and Networking 97 -- 4.1 IMT-Advanced and 4G 97 -- 4.2 LTE 99 -- 4.2.1 Introduction 101 -- 4.2.2 Protocol Architecture 102 -- 4.2.3 LTE Layer 2 107 -- 4.2.4 The Evolution of Architecture 110 -- 4.2.5 LTE Standardization 110 -- 4.3 WIMAX-IEEE 802.16m 112 -- 4.3.1 Network Architecture 113 -- 4.3.2 System Reference Model 114 -- 4.3.3 Protocol Structure 114 -- 4.3.4 Other Functions Supported by IEEE 802.16m for Further Study 125 -- 4.4 3GPP2 UMB 125 -- 4.4.1 Architecture Reference Model 126 -- 4.4.2 Layering Architecture and Protocols 127 -- Acknowledgements 133 -- References 133 -- 5 Advanced Video Coding (AVC)/H.264 Standard 135 -- 5.1 Digital Video Compression Standards 135 -- 5.2 AVC/H.264 Coding Algorithm 138 -- 5.2.1 Temporal Prediction 139 -- 5.2.2 Spatial Prediction 147 -- 5.2.3 The Transform 148 -- 5.2.4 Quantization and Scaling 151 -- 5.2.5 Scanning 151 -- 5.2.6 Variable Length Lossless Codecs 152 -- 5.2.7 Deblocking Filter 155 -- 5.2.8 Hierarchy in the Coded Video 156 -- 5.2.9 Buffers 158 -- 5.2.10 Encapsulation/Packetization 159 -- 5.2.11 Profiles 160 -- 5.2.12 Levels 163 -- 5.2.13 Parameter Sets 167 -- 5.2.14 Supplemental Enhancement Information (SEI) 167 -- 5.2.15 Subjective Tests 168 -- References 168 -- 6 Content Analysis for Communications 171 -- 6.1 Introduction 171 -- 6.2 Content Analysis 173 -- 6.2.1 Low-Level Feature Extraction 174 -- 6.2.2 Image Segmentation 179 -- 6.2.3 Video Object Segmentation 185.…”

Tabla de Contenidos: “…-- 9.4.3 Our data source: The LUNA Grand Challenge -- 9.4.4 Downloading the LUNA data -- 9.5 Conclusion -- 9.6 Summary -- 10 Combining data sources into a unified dataset -- 10.1 Raw CT data files -- 10.2 Parsing LUNA's annotation data -- 10.2.1 Training and validation sets -- 10.2.2 Unifying our annotation and candidate data -- 10.3 Loading individual CT scans -- 10.3.1 Hounsfield Units -- 10.4 Locating a nodule using the patient coordinate system -- 10.4.1 The patient coordinate system -- 10.4.2 CT scan shape and voxel sizes -- 10.4.3 Converting between millimeters and voxel addresses -- 10.4.4 Extracting a nodule from a CT scan -- 10.5 A straightforward dataset implementation -- 10.5.1 Caching candidate arrays with the getCtRawCandidate function -- 10.5.2 Constructing our dataset in LunaDataset.…”

Tabla de Contenidos: “…Automatic problem ticketing -- 15.1 Automatic problem ticketing -- 15.2 Defining the auto ticketing rule -- 15.3 Tivoli Information Management for z/OS integration overview -- 15.4 Installation and configuration -- 15.4.1 Enable problem management -- 15.4.2 Extract and customize the input-output processor -- 15.4.3 Setting up the task server and Tivoli NetView for z/OS -- 15.4.4 Application customization -- 15.5 Using the problem ticket interface -- 15.5.1 Manual problem ticket operation -- 15.5.2 Closing a problem ticket -- Chapter 16. …”

Tabla de Contenidos: “…Working with XML documents stored in XML columns -- 5.1 Retrieving, extracting, updating, and deleting XML columns -- 5.2 Retrieving XML documents stored in XML columns -- 5.2.1 Retrieving the entire XML document -- 5.2.2 Retrieving elements and attributes from XML documents -- 5.2.3 Retrieving fragments from data stored in an XML column -- 5.2.4 Filtering the XML documents you want to retrieve -- 5.3 Retrieving XML columns using an XML wrapper…”

Tabla de Contenidos: “…11.2.6.1 OM composition in seawater -- 11.2.6.2 Fouling behaviour of organic matter -- 11.2.6.3 Effectiveness of pretreatment methods -- 11.3 CONCLUSIONS -- 11.4 REFERENCES -- Chapter 12: Fluorescence Excitation Emission Matrix (EEM) Spectroscopy -- 12.1 INTRODUCTION -- 12.2 SAMPLING &amp -- STORAGE -- 12.3 BENCHTOP INSTRUMENTATION -- 12.4 QUALITY ASSURANCE -- 12.5 INTERFERENCES -- 12.6 DATA PROCESSING -- 12.7 DATA ANALYSIS -- 12.7.1 PARAFAC -- 12.8 APPLICATION IN MEMBRANE SYSTEMS -- 12.9 REFERENCES -- Chapter 13: Transparent Exopolymer Particles -- 13.1 INTRODUCTION -- 13.2 QUANTIFICATION METHODS -- 13.2.1 Alcian blue dye preparation -- 13.2.2 TEP0.4μm measurement -- 13.2.3 TEP10kDa measurement -- 13.2.4 Method calibration -- 13.2.4.1 Xanthan gum standard preparation -- 13.2.4.2 TEP0.4μm calibration -- 13.2.4.3 TEP0.4μm calibration -- 13.2.4.4 TEP10kDa calibration -- 13.2.5 Other considerations -- 13.2.5.1 Limit of detection -- 13.2.5.2 Impact of storage on TEP concentration -- 13.2.6 Application and interpretation -- 13.3 SUMMARY AND OUTLOOK -- 13.4 REFERENCES -- Part 5: Biological fouling -- Chapter 14: Genomics Tools to Study Membrane-Based Systems -- 14.1 INTRODUCTION -- 14.2 EXPERIMENTAL DESIGN AND SAMPLE PREPARATION -- 14.2.1 Experimental Design in a Metagenomics -- 14.2.2 Sample Collection and Preservation -- 14.2.3 DNA Extraction -- 14.2.4 Library Preparation -- 14.2.5 Sequencing platforms -- 14.3 BIOINFORMATICS ANALYSIS -- 14.3.1 Data Pre-treatment -- 14.3.2 Amplicon-based approach -- 14.3.3 Metagenomics, read-based approach -- 14.3.4 Metagenomics, assembly-based approach -- 14.3.5 Metagenome-assembled Genome (MAG) Binning -- 14.3.6 Supervised and unsupervised binning -- 14.3.7 Functional annotation -- 14.3.8 Genome-resolved Metatranscriptomics -- 14.4 DATA SHARING AND STORAGE -- 14.5 BIOINFORMATICS ANALYSIS WORKFLOW EXAMPLES…”

Tabla de Contenidos: “…7.1.2 Management of Conflict in Agile RM Framework -- 7.1.2.1 A Holistic View of Technology -- 7.1.3 Ratings and Context Information -- 7.1.4 Dynamic Traffic Steering -- 7.1.4.1 Reduced-Overhead Interference Mitigation -- 7.1.5 Reducing Overhead Interference Mitigation -- 7.1.5.1 RIM and 5G NR -- 7.1.5.2 Contributions and Differences -- 7.1.6 High-Level 5NR - RIM Framework -- 7.1.7 OS RIM-RS -- 7.1.7.1 OS RIM-RS -- 7.1.8 Radio-Access Network -- 7.1.9 Improve RAN Coverage -- 7.1.9.1 Improving RAN Coverage Can Take Many Forms -- 7.1.10 Related Work -- 7.1.11 Conclusion -- References -- Chapter 8 Interference Techniques Based on Deep Learning in Wireless Networks -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Suppression Techniques -- 8.3.1 Deep Learning Based Approach -- 8.3.2 Adjacent Channel Interference (ACI) -- 8.3.3 Co-Channel Interference -- 8.3.4 Self-Interference -- 8.3.5 Homogeneous Technology Interference -- 8.3.6 Heterogeneous Technology Interference -- 8.4 Classification of Interference Suppression Techniques -- 8.5 A Blind Approach of Interference Cancellation Using Neural Networks -- 8.6 Interference Estimation -- 8.6.1 Feature Extraction -- 8.6.2 Channel Usage Ratio -- 8.7 Machine Learning Model-Decision Tree -- 8.8 Lookup Table -- 8.9 Conclusion -- References -- Chapter 9 Implementing SDN Process-Based MRMC Wireless Networks -- 9.1 Multi-Radio Multi Channel Network in Wireless Network -- 9.1.1 The Concept of QoS inWN -- 9.2 QoS Challenges in MRMC Based Wireless Networks -- 9.2.1 Bandwidth Limitation -- 9.2.2 Removal of Redundancy -- 9.2.3 Energy and Delay Trade-Off -- 9.2.4 Buffer Size Limitation -- 9.3 Opportunistic Routing -- 9.4 The Need for Software-Defined Network in WSN for Enhancing QoS -- 9.4.1 QoS Management -- 9.5 ILP Problem Design -- 9.5.1 Network Model -- 9.5.2 Interference Model -- 9.5.3 VL Request Design…”