Computer und IT
Designing Internet of Things Solutions with Microsoft Azure
Build a strong and efficient IoT solution at industrial and enterprise level by mastering industrial IoT using Microsoft Azure. This book focuses on the development of the industrial Internet of Things (IIoT) paradigm, discussing various architectures, as well as providing nine case studies employing IoT in common industrial domains including medical, supply chain, finance, and smart homes.The book starts by giving you an overview of the basic concepts of IoT, after which you will go through the various offerings of the Microsoft Azure IoT platform and its services. Next, you will get hands-on experience of IoT applications in various industries to give you a better picture of industrial solutions and how you should take your industry forward. As you progress through the chapters, you will learn real-time applications in IoT in agriculture, supply chain, financial services, retail, and transportation. Towards the end, you will gain knowledge to identify and analyze IoT security and privacy risks along with a detailed sample project.The book fills an important gap in the learning of IoT and its practical use case in your industry. Therefore, this is a practical guide that helps you discover the technologies and use cases for IIoT. By the end of this book, you will be able to build industrial IoT solution in Microsoft Azure with sensors, stream analytics, and serverless technologies.WHAT YOU WILL LEARN* Provision, configure, and connect devices with Microsoft Azure IoT hubStream analytics using structural data and non-structural data such as images * Use stream analytics, serverless technology, and IoT SaaS offerings* Work with common sensors and IoT devicesWHO THIS BOOK IS FORIoT architects, developers, and stakeholders working with the industrial Internet of Things.NIRNAY BANSAL is certified solution architect and working at Microsoft Corp, Redmond, WA since 2015. He graduated in computer science from BITS, Pilani and MBA from Louisiana State University (S). He has been working for over 15 years on large and complex IT projects. He is a technical specialist in providing architecture, development, and consultancy, using Microsoft technologies including Microsoft Azure. Among his past clients are Frontier communication, Fidelity, PricewaterHouseCoopers and Dell.Nirnay is one of the well-known experts when it comes to designing cloud-based solution and data scenarios. Additionally, he participates in public events as speaker for Code Camps. Along with various Microsoft certifications, he is a Microsoft Certified Trainer (MCT) and a certified Solution Architect from IASA. He is Co-founder and CTO of Mobile application development company www.TechValens.com, helping small to mid size client across the globe.He loves trekking and skating. He spends his spare time and holidays with wife Dharna, son Neev along with his father Rajkishore and mother Kusumlata in the India.You can contact Nirnay through his Twitter handle @nirnaybansal, on LinkedIn at www.linkedin.com/in/nirnaybansal or by sending him an e-mail at nirnaybansal@gmail.comCHAPTER 1: BASIC IOT CONCEPTS 15This chapter provides overview of exciting and relevant technical areas essential to professionals in the IoT industry. Chapter provides an introduction to Internet of Things (IoT) and covers the concepts, hardware, and platforms of an IoT solution available in the market.IntroductionBuilding blocksDesign PrinciplesIoT Devices and SensorsIoT PlatformsIndustrial IoTCHAPTER 2: MICROSOFT AZURE IOT PLATFORM 20This chapter introduces with various offerings of Microsoft Azure IoT platform and its service offerings. Reader will learn various solution architecture suiting with different business needs, like single and bi-directional communication Architecture.IntroductionIoT ServicesArchitectCHAPTER 3: STREAMING IOT DATA TO MICROSOFT AZURE 20This chapter provides hands-on experience registering and configuring device, setting up IoT environment and invoking messages. Students will learn to stream data (ingesting the telemetry) from simulated device to IoT hub.Manage IoT hubDevice registrationStream structural data and non-structural dataStoring dataLab - Using simulated deviceCHAPTER 4: IOT APPLICATIONS IN MANUFACTURING 25This chapter introduces possible real-time applications of IoT in Manufacturing business. Managers will learn How to run an IoT enabled Manufacturing business using Microsoft Azure. Developers will gain hands-on experience in Stream Analytics by analyzing stream of data in real-time using a SQL-like language. This makes it possible for monitor asset, detecting anomalies, checking conditions and displaying real-time data for preventive maintenance.IntroductionApplicationsLab - Asset Monitoring and Preventive maintenanceCHAPTER 5: IOT APPLICATIONS IN AGRICULTURE 25This chapter introduces possible real-time applications of IoT in Agriculture business. Managers will learn How to run an IoT enabled Agriculture business using Microsoft Azure. Developers will gain hands-on experience on getting information from various sensors. We will focus on the most common ones like the humidity, temperature and Location sensor.IntroductionApplicationsLab - Working with sensorsCHAPTER 6: IOT APPLICATIONS IN ENERGY 25This chapter introduces possible real-time applications of IoT in Energy business. Managers will learn How to run an IoT enabled Energy business using Microsoft Azure. Developers will gain hands-on experience of analyzing most common nonstructural data source like images from drone camera.IntroductionApplicationsLab - Camera-mounted dronesIoT enabled solar panelsCHAPTER 7: IOT APPLICATIONS IN HOME AUTOMATION AND SMART HOMES 25This chapter introduces possible real-time applications of IoT in smart home business. Managers will learn How to run an IoT enabled home automation business using Microsoft Azure. Developers will gain hands-on experience with the smart smoke detector, smart lock and like.IntroductionApplicationsLab - Working with Personal assistants like Amazon’s EchoCHAPTER 8: IOT APPLICATIONS IN SUPPLY CHAIN 25This chapter introduces possible real-time applications of IoT in supply chain business. Managers will learn How to run an IoT enabled home supply chain business using Microsoft Azure. Developers will gain hands-on experience of inventories tracking using structured data of RFID tag systems.SUB -TOPICSIntroductionApplicationsLab - Working with Radio-frequency identification (RFID)CHAPTER 9: IOT APPLICATIONS IN FINANCIAL SERVICES 25This chapter introduces possible real-time applications of IoT in financial services business. Managers will learn How to run an IoT enabled security system in financial services business using Microsoft Azure. Developers will gain hands-on experience of building real-time face recognition and weapon recognition security system.SUB -TOPICSIntroductionApplicationsLab - IoT as vehicle for greater securityCHAPTER 10: IOT APPLICATIONS IN HEALTHCARE 25This chapter introduces possible real-time applications of IoT in healthcare services business. Managers will learn How to run an IoT enabled patient care in healthcare business using Microsoft Azure. Developers will gain hands-on experience of working with wearable devices.SUB -TOPICSIntroductionApplicationsLab - audio devices and WearablesCHAPTER 11: IOT APPLICATIONS IN RETAIL 25This chapter introduces possible real-time applications of IoT in Retail business. Managers will learn How to run an IoT enabled Retail business using Microsoft Azure. Developers will gain hands-on experience querying and visualizing data and learn visual analytics using use case of Retail businesses.SUB -TOPICSIntroductionApplicationsLab - Visual analyticsCHAPTER 12: IOT APPLICATIONS IN TRANSPORTATION 25This chapter introduces possible real-time applications of IoT in transportation business. Managers will learn How to run an IoT enabled transportation business using Microsoft Azure. Developers will gain hands-on experience working with offline devices due to no signal zones and how to get data in batch.SUB -TOPICSIntroductionApplicationsLab - logistics monitoring and routingCHAPTER 13: RISK 10In this chapter, reader will gain knowledge to identify and analyze IoT security and privacy risks, and concept design secure hardware and software. Learn how to design a secure system.SUB -TOPICSPrivacySafetyIoT Standards & RegulationsSAMPLE PROJECT 2Sample project for you to engage fully in the process of designing an IoT solution, from initial analysis to planning out the product, design, and implement.CONCLUSION 1INDEX 3
MCA Modern Desktop Administrator Practice Tests
EXAM MD-100 AND MD-101* Provides 1,000 practice questions covering all exam objectives.* Compliments the MCA Modern Desktop Administrator Complete Study Guide: Exam MD-100 and Exam MD-101QUICK, FOCUSED REVIEW FOR MD-100 AND MD-101Microsoft's new Certified Associate Modern Desktop qualification verifies your skill as an administrator of Windows 10 technologies and modern desktop management. With a focus on the intricacies of Microsoft 365, this certification is in high demand. The 2 practice exams PLUS domain-by-domain questions in this book will help you target your study and sharpen your focus—1000 questions total! So now tackle the certification exam with confidence. Expertly crafted questions cover 100% of the objectives for both the MD-100 and MD-101 exams, enabling you to be fully prepared. COVERAGE OF 100% OF ALL EXAM OBJECTIVES IN THESE PRACTICE TESTS MEANS YOU'LL BE READY FOR:* Desktop and Device Deployment* Windows Management and Monitoring* OS Updates and Upgrades* Core Services Support* Data Access and Usage* Networking Security* Driver and Device Installation* Remote Access Configuration* System Backup and RestoreINTERACTIVE LEARNING ENVIRONMENTTake your exam prep to the next level with Sybex's superior interactive online study tools. To access our learning environment, simply visit WWW.WILEY.COM/GO/SYBEXTESTPREP, register to receive your unique PIN, and instantly gain one year of FREE access to the interactive test bank with two practice exams and domain-by-domain questions. 1000 questions total!* INTERACTIVE TEST BANK Use the interactive online version of the book's 2 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you're ready to take the certification exam. 100 questions total!ABOUT THE MCA PROGRAMThe MCA Microsoft 365 Certified: Modern Desktop Administrator Associate certification helps Modern Desktop Administrators deploy, configure, secure, manage, and monitor devices and client applications in an enterprise environment. Exam MD-100, Windows 10, measures your ability to accomplish the following technical tasks: deploy Windows; manage devices and data; configure connectivity; and maintain Windows. Exam MD-101, Managing Modern Desktops, measures your ability to accomplish the following technical tasks: deploy and update operating systems; manage policies and profiles; manage and protect devices; and manage apps and data. Visit www.microsoft.com/en-us/ learning/modern-desktop.aspx for more information. ABOUT THE AUTHORCRYSTAL PANEK, MCP, MCP+I, MCSA, MCSE, MCTS, MCDBA. For many years, Crystal trained as a contract instructor teaching at such locations as MicroC, Stellacon Corporation, and the University of New Hampshire. She then became the vice-president for a large IT training company and for 15 years she developed training materials and courseware to help 1000's of students get through their certification exams. She currently works on a contract basis creating courseware for several large IT training facilities. Introduction xviiPART I MODERN DESKTOP ADMIN, EXAM MD-100 1Chapter 1 Deploy Windows 3Chapter 2 Manage Devices and Data 25Chapter 3 Configure Connectivity 57Chapter 4 Maintain Windows 83PART II MODERN DESKTOP ADMIN, EXAM MD-101 115Chapter 5 Deploy and Update Operating Systems 117Chapter 6 Manage Policies and Profiles 151Chapter 7 Manage and Protect Devices 183Chapter 8 Manage Apps and Data 211Chapter 9 Practice Exam 1: MD-100 237Chapter 10 Practice Exam 2: MD-101 249APPENDIX ANSWERS AND EXPLANATIONS 261Chapter 1: Deploy Windows 262Chapter 2: Manage Devices and Data 280Chapter 3: Configure Connectivity 303Chapter 4: Maintain Windows 323Chapter 5: Deploy and Update Operating Systems 349Chapter 6: Manage Policies and Profiles 375Chapter 7: Manage and Protect Devices 402Chapter 8: Manage Apps and Data 432Chapter 9: Practice Exam 1: MD-100 460Chapter 10: Practice Exam 2: MD-101 468Index 479
Learn Windows Subsystem for Linux
Become productive with seamless interoperability between Windows and the Linux subsystem, and understand the problems that Windows Subsystem for Linux (WSL) solves. Microsoft has pushed the boundaries of open source research with WSL and you don't want to miss this ride.You will learn keywords, definitions, new features, setup, and use cases around WSL, starting from downloading to setup to interoperability between Windows and Linux subsystems. You will understand the architecture of WSL and all the new features in WSL 2. This book includes wonderful use cases, including a dedicated chapter to how to start programming and web development on WSL, and the ability to use containerization solutions like Docker and Kubernetes.WSL is a great solution to work natively in a Linux environment from your Windows 10 machines. Modern applications demand integration of cross-platform tools, services and technologies. WSL makes life for developers and system administrators easy because it allows Linux applications to run on Windows without worrying about installing a Linux distribution on a traditional Virtual Machine. It is remarkable product with powerful functionality – get started with it using this book today.WHAT YOU'LL LEARN* Review the workings and internals of WSL and WSL2 * Run Linux-based applications natively on Windows* Establish your development environment in WSL* Build mixed experiences (Windows-Linux)* Set up and manage WSL and supported distribution packages.WHO THIS BOOK IS FORProgrammers, web developers and system administrators working on Windows and Linux environments who want to bridge the gap between operating systems by running a Linux as a subsystem on Windows to boost their overall productivity, performance and delivery.PRATEEK SINGH is an IT Infrastructure and cloud developer, an avid PowerShell blogger, and an open source community contributor. In 2017 and 2018, FeedSpot and SQLHack recognized his blog RidCurious.com as among the “Top 50 PowerShell blogs in the world”. Prateek has written more than 250 articles on his blog and several other websites such as 4SysOps.com, IPSwitch.com, and TechTarget.com and also runs a YouTube channel on PowerShell Scripting and Azure.1. What is WSL?2. Downloading, Installation and Setup3. Building Mixed Experiences4. Managing WSL Distributions5. Exploring WSL26. File System7. Networking8. Linux Development on WSL9. Linux Desktop on WSL
Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles
ELECTRIC VEHICLES ARE CHANGING TRANSPORTATION DRAMATICALLY AND THIS UNIQUE BOOK MERGES THE MANY DISCIPLINES THAT CONTRIBUTE RESEARCH TO MAKE EV POSSIBLE, SO THE READER IS INFORMED ABOUT ALL THE UNDERLYING SCIENCE AND TECHNOLOGIES DRIVING THE CHANGE.An emission-free mobility system is the only way to save the world from the greenhouse effect and other ecological issues. This belief has led to a tremendous growth in the demand for electric vehicles (EV) and hybrid electric vehicles (HEV), which are predicted to have a promising future based on the goals fixed by the European Commission's Horizon 2020 program.This book brings together the research that has been carried out in the EV/HEV sector and the leading role of advanced optimization techniques with artificial intelligence (AI). This is achieved by compiling the findings of various studies in the electrical, electronics, computer, and mechanical domains for the EV/HEV system. In addition to acting as a hub for information on these research findings, the book also addresses the challenges in the EV/HEV sector and provides proven solutions that involve the most promising AI techniques. Since the commercialization of EVs/HEVs still remains a challenge in industries in terms of performance and cost, these are the two tradeoffs which need to be researched in order to arrive at an optimal solution. Therefore, this book focuses on the convergence of various technologies involved in EVs/HEVs. Since all countries will gradually shift from conventional internal combustion (IC) engine-based vehicles to EVs/HEVs in the near future, it also serves as a useful reliable resource for multidisciplinary researchers and industry teams.CHITRA A. received her PhD from Pondicherry University and is now an associate professor in the School of Electrical Engineering, at Vellore Institute of Technology, Vellore, India. She has published many papers in SCI journals and her research areas include PV-based systems, neural networks, induction motor drives, reliability analysis of multilevel inverters, and electrical vehicles. SANJEEVIKUMAR PADMANABAN obtained his PhD from the University of Bologna, Italy, in 2012, and since 2018, he has been a faculty member in the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has authored more than 300 scientific papers. JENS BO HOLM-NIELSEN currently works at the Department of Energy Technology, Aalborg University and is Head of the Esbjerg Energy Section. He has executed many large-scale European Union and United Nations projects in research aspects of bioenergy, biorefinery processes, the full chain of biogas and green engineering. He has authored more than 100 scientific papers. S. HIMAVATHI received her PhD degree in the area of fuzzy modelling from Anna University, Chennai, India in 2003. Currently, she is a professor in the Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pondicherry, India. Preface xiii1 IOT-BASED BATTERY MANAGEMENT SYSTEM FOR HYBRID ELECTRIC VEHICLE 1P. Sivaraman and C. Sharmeela1.1 Introduction 11.2 Battery Configurations 31.3 Types of Batteries for HEV and EV 51.4 Functional Blocks of BMS 61.4.1 Components of BMS System 71.5 IoT-Based Battery Monitoring System 11References 142 A NOBLE CONTROL APPROACH FOR BRUSHLESS DIRECT CURRENT MOTOR DRIVE USING ARTIFICIAL INTELLIGENCE FOR OPTIMUM OPERATION OF THE ELECTRIC VEHICLE 17Upama Das, Pabitra Kumar Biswas and Chiranjit Sain2.1 Introduction 182.2 Introduction of Electric Vehicle 192.2.1 Historical Background of Electric Vehicle 192.2.2 Advantages of Electric Vehicle 202.2.2.1 Environmental 202.2.2.2 Mechanical 202.2.2.3 Energy Efficiency 202.2.2.4 Cost of Charging Electric Vehicles 212.2.2.5 The Grid Stabilization 212.2.2.6 Range 212.2.2.7 Heating of EVs 222.2.3 Artificial Intelligence 222.2.4 Basics of Artificial Intelligence 232.2.5 Advantages of Artificial Intelligence in Electric Vehicle 242.3 Brushless DC Motor 242.4 Mathematical Representation Brushless DC Motor 252.5 Closed-Loop Model of BLDC Motor Drive 302.5.1 P-I Controller & I-P Controller 312.6 PID Controller 322.7 Fuzzy Control 332.8 Auto-Tuning Type Fuzzy PID Controller 342.9 Genetic Algorithm 352.10 Artificial Neural Network-Based Controller 362.11 BLDC Motor Speed Controller With ANN-Based PID Controller 372.11.1 PID Controller-Based on Neuro Action 382.11.2 ANN-Based on PID Controller 382.12 Analysis of Different Speed Controllers 392.13 Conclusion 41References 423 OPTIMIZATION TECHNIQUES USED IN ACTIVE MAGNETIC BEARING SYSTEM FOR ELECTRIC VEHICLES 49Suraj Gupta, Pabitra Kumar Biswas, Sukanta Debnath and Jonathan Laldingliana3.1 Introduction 503.2 Basic Components of an Active Magnetic Bearing (AMB) 543.2.1 Electromagnet Actuator 543.2.2 Rotor 543.2.3 Controller 553.2.3.1 Position Controller 563.2.3.2 Current Controller 563.2.4 Sensors 563.2.4.1 Position Sensor 563.2.4.2 Current Sensor 573.2.5 Power Amplifier 573.3 Active Magnetic Bearing in Electric Vehicles System 583.4 Control Strategies of Active Magnetic Bearing for Electric Vehicles System 593.4.1 Fuzzy Logic Controller (FLC) 593.4.1.1 Designing of Fuzzy Logic Controller (FLC) Using MATLAB 603.4.2 Artificial Neural Network (ANN) 633.4.2.1 Artificial Neural Network Using MATLAB 633.4.3 Particle Swarm Optimization (PSO) 673.4.4 Particle Swarm Optimization (PSO) Algorithm 683.4.4.1 Implementation of Particle Swarm Optimization for Electric Vehicles System 703.5 Conclusion 71References 724 SMALL-SIGNAL MODELLING ANALYSIS OF THREE-PHASE POWER CONVERTERS FOR EV APPLICATIONS 77Mohamed G. Hussien, Sanjeevikumar Padmanaban, Abd El-Wahab Hassan and Jens Bo Holm-Nielsen4.1 Introduction 774.2 Overall System Modelling 794.2.1 PMSM Dynamic Model 794.2.2 VSI-Fed SPMSM Mathematical Model 804.3 Mathematical Analysis and Derivation of the Small-Signal Model 864.3.1 The Small-Signal Model of the System 864.3.2 Small-Signal Model Transfer Functions 874.3.3 Bode Diagram Verification 964.4 Conclusion 100References 1005 ENERGY MANAGEMENT OF HYBRID ENERGY STORAGE SYSTEM IN PHEV WITH VARIOUS DRIVING MODE 103S. Arun Mozhi, S. Charles Raja, M. Saravanan and J. Jeslin Drusila Nesamalar5.1 Introduction 1045.1.1 Architecture of PHEV 1045.1.2 Energy Storage System 1055.2 Problem Description and Formulation 1065.2.1 Problem Description 1065.2.2 Objective 1065.2.3 Problem Formulation 1065.3 Modeling of HESS 1075.4 Results and Discussion 1085.4.1 Case 1: Gradual Acceleration of Vehicle 1085.4.2 Case 2: Gradual Deceleration of Vehicle 1095.4.3 Case 3: Unsystematic Acceleration and Deceleration of Vehicle 1105.5 Conclusion 111References 1126 RELIABILITY APPROACH FOR THE POWER SEMICONDUCTOR DEVICES IN EV APPLICATIONS 115Krishnachaitanya, D., Chitra, A. and Biswas, S.S.6.1 Introduction 1156.2 Conventional Methods for Prediction of Reliability for Power Converters 1166.3 Calculation Process of the Electronic Component 1186.4 Reliability Prediction for MOSFETs 1196.5 Example: Reliability Prediction for Power Semiconductor Device 1216.6 Example: Reliability Prediction for Resistor 1226.7 Conclusions 123References 1237 MODELING, SIMULATION AND ANALYSIS OF DRIVE CYCLES FOR PMSM-BASED HEV WITH OPTIMAL BATTERY TYPE 125Chitra, A., Srivastava, Shivam, Gupta, Anish, Sinha, Rishu, Biswas, S.S. and Vanishree, J.7.1 Introduction 1267.2 Modeling of Hybrid Electric Vehicle 1277.2.1 Architectures Available for HEV 1287.3 Series—Parallel Hybrid Architecture 1297.4 Analysis With Different Drive Cycles 1297.4.1 Acceleration Drive Cycle 1307.4.1.1 For 30% State of Charge 1307.4.1.2 For 60% State of Charge 1317.4.1.3 For 90% State of Charge 1317.5 Cruising Drive Cycle 1327.6 Deceleration Drive Cycle 1327.6.1 For 30% State of Charge 1347.6.2 For 60% State of Charge 1367.6.3 For 90% State of Charge 1377.7 Analysis of Battery Types 1397.8 Conclusion 140References 1418 MODIFIED FIREFLY-BASED MAXIMUM POWER POINT TRACKING ALGORITHM FOR PV SYSTEMS UNDER PARTIAL SHADING CONDITIONS 143Chitra, A., Yogitha, G., Karthik Sivaramakrishnan, Razia Sultana, W. and Sanjeevikumar, P.8.1 Introduction 1438.2 System Block Diagram Specifications 1468.3 Photovoltaic System Modeling 1488.4 Boost Converter Design 1508.5 Incremental Conductance Algorithm 1528.6 Under Partial Shading Conditions 1538.7 Firefly Algorithm 1548.8 Implementation Procedure 1568.9 Modified Firefly Logic 1578.10 Results and Discussions 1598.11 Conclusion 162References 1629 INDUCTION MOTOR CONTROL SCHEMES FOR HYBRID ELECTRIC VEHICLES/ELECTRIC VEHICLES 165Sarin, M.V., Chitra, A., Sanjeevikumar, P. and Venkadesan, A.9.1 Introduction 1669.2 Control Schemes of IM 1679.2.1 Scalar Control 1679.3 Vector Control 1689.4 Modeling of Induction Machine 1699.5 Controller Design 1749.6 Simulations and Results 1759.7 Conclusions 176References 17710 INTELLIGENT HYBRID BATTERY MANAGEMENT SYSTEM FOR ELECTRIC VEHICLE 179Rajalakshmi, M. and Razia Sultana, W.10.1 Introduction 17910.2 Energy Storage System (ESS) 18110.2.1 Lithium-Ion Batteries 18310.2.1.1 Lithium Battery Challenges 18310.2.2 Lithium–Ion Cell Modeling 18410.2.3 Nickel-Metal Hydride Batteries 18610.2.4 Lead-Acid Batteries 18710.2.5 Ultracapacitors (UC) 18710.2.5.1 Ultracapacitor Equivalent Circuit 18710.2.6 Other Battery Technologies 18910.3 Battery Management System 19010.3.1 Need for BMS 19110.3.2 BMS Components 19210.3.3 BMS Architecture/Topology 19310.3.4 SOC/SOH Determination 19310.3.5 Cell Balancing Algorithms 19710.3.6 Data Communication 19710.3.7 The Logic and Safety Control 19810.3.7.1 Power Up/Down Control 19810.3.7.2 Charging and Discharging Control 19910.4 Intelligent Battery Management System 19910.4.1 Rule-Based Control 20110.4.2 Optimization-Based Control 20110.4.3 AI-Based Control 20210.4.4 Traffic (Look Ahead Method)-Based Control 20310.5 Conclusion 203References 20311 A COMPREHENSIVE STUDY ON VARIOUS TOPOLOGIES OF PERMANENT MAGNET MOTOR DRIVES FOR ELECTRIC VEHICLES APPLICATION 207Chiranjit Sain, Atanu Banerjee and Pabitra Kumar Biswas11.1 Introduction 20811.2 Proposed Design Considerations of PMSM for Electric Vehicle 20911.3 Impact of Digital Controllers 21111.3.1 DSP-Based Digital Controller 21211.3.2 FPGA-Based Digital Controller 21211.4 Electric Vehicles Smart Infrastructure 21211.5 Conclusion 214References 21512 A NEW APPROACH FOR FLUX COMPUTATION USING INTELLIGENT TECHNIQUE FOR DIRECT FLUX ORIENTED CONTROL OF ASYNCHRONOUS MOTOR 219A. Venkadesan, K. Sedhuraman, S. Himavathi and A. Chitra12.1 Introduction 22012.2 Direct Field-Oriented Control of IM Drive 22112.3 Conventional Flux Estimator 22212.4 Rotor Flux Estimator Using CFBP-NN 22312.5 Comparison of Proposed CFBP-NN With Existing CFBP-NN for Flux Estimation 22412.6 Performance Study of Proposed CFBP-NN Using MATLAB/SIMULINK 22512.7 Practical Implementation Aspects of CFBP-NN-Based Flux Estimator 22912.8 Conclusion 231References 23113 A REVIEW ON ISOLATED DC–DC CONVERTERS USED IN RENEWABLE POWER GENERATION APPLICATIONS 233Ingilala Jagadeesh and V. Indragandhi13.1 Introduction 23313.2 Isolated DC–DC Converter for Electric Vehicle Applications 23413.3 Three-Phase DC–DC Converter 23813.4 Conclusion 238References 23914 BASICS OF VECTOR CONTROL OF ASYNCHRONOUS INDUCTION MOTOR AND INTRODUCTION TO FUZZY CONTROLLER 241S.S. Biswas14.1 Introduction 24114.2 Dynamics of Separately Excited DC Machine 24314.3 Clarke and Park Transforms 24414.4 Model Explanation 25114.5 Motor Parameters 25214.6 PI Regulators Tuning 25414.7 Future Scope to Include Fuzzy Control in Place of PI Controller 25614.8 Conclusion 257References 258Index 259
Weniger schlecht Projekte managen
Projektmanagement - die unorthodoxe Anleitung* Ratgeber für alle Aspekte, Fragen und Fallstricke rund um die Rolle und die Aufgaben eines Projektmanagers* Informative und unterhaltsame Lektüre für angehende, aber auch gestandene Projektmanager*innen, die ihr Methodik-Wissen auffrischen wollenAnne Schüßler studierte aus Interessensüberforderung erst brotlose Kunst, kriegte dann aber doch noch die Kurve und macht jetzt was mit Software. Sie bloggt über die Welt so im Allgemeinen oder twittert rum. Wenn ihr langweilig ist, guckt sie Bahnhöfe an. Sie ist ein bisschen zu oft in diesem Internet. Peter Schüßler ist zertifizierter Senior Projektmanager (IPMA Level B) und verantwortete in den letzten fünf Jahren als Leiter eines Projektmanagement-Office die konzeptionelle und operative Weiterentwicklung der Projektmanagementsystematik für Großprojekte im nationalen und internationalen Umfeld.
Basiswissen Automotive Softwaretest
Kompaktes Grundlagenwerk für den Certified Automotive Software Tester.Das Buch gibt einen fundierten Überblick über die Besonderheiten von Softwaretests im automobilen Umfeld und vermittelt das notwendige Praxiswissen für den Automotive Software Tester. Es erläutert ausführlich, wie bei der Auswahl von angemessenen Testverfahren die grundlegenden Anforderungen der relevanten Normen und Standards wie Automotive SPICE, ISO 26262 und AUTOSAR berücksichtigt werden.Auch auf das Testen in virtuellen Testumgebungen wird im Detail eingegangen. Zur Vertiefung finden sich im Anhang weiterführende Informationen zu ISO 26262 und Automotive SPICE.»Basiswissen Automotive Softwaretest« eignet sich mit vielen erläuternden Beispielen gleichermaßen für das Selbststudium, zur Vorbereitung auf die Zertifizierung sowie als kompaktes Basiswerk zum Thema in der Praxis und an Hochschulen.Über die Autoren:Ralf Bongard ist Geschäftsführer und Trainer der ISARTAL akademie und war über 15 Jahre in der Automobilindustrie als Entwickler und Projektleiter sowie als Consultant für Anforderungs- und Testmanagement tätig. Er ist Mitglied des GTB und stellvertretender Leiter der GTB- Arbeitsgruppe „Certified Automotive Software Tester“.Klaudia Dussa-Zieger ist leitende Beraterin bei der imbus AG und verfügt über 20 Jahre Berufserfahrung in den Bereichen Softwaretest, Testmanagement und Testprozessberatung. Seit 2018 ist sie die Vorsitzende des GTB.Prof. Dr. Ralf Reißing ist Informatiker und seit über 17 Jahren im Automobilbereich tätig - aktuell als Professor für Automobilinformatik an der Hochschule Coburg. Er ist Gründer und Leiter des Steinbeis-Transferzentrums Automotive Software Engineering sowie Mitglied des GTB.Alexander Schulz arbeitet bei der BMW Group in der Fahrzeugentwicklung im Bereich der Funktionssicherheit. Er ist seit 2012 schwerpunktmäßig im Bereich der Funktionalen Sicherheit nach IEC 61508 und ISO 26262 tätig.Alle Autoren dieses Buchs waren aktiv an der Entwicklung des Lehrplans zum „ISTQB Certified Automotive Software Tester“ beteiligt.
Implementing Effective Code Reviews
The ideal code review process differs from organization to organization, but the needs all boil down to the same foundational factors. A software development team cannot properly grow if its code reviews are not consistent, straightforward, and aspire to hit several company goals at once, such as security, performance, longevity, and more. Implementing Effective Code Reviews is the manual your team has been seeking.Author Giuliana Carullo uses her expert background to guide you through the basics of building and maintaining clean code, and she is known for distilling complex concepts into entertaining and easy-to-grasp lessons. Healthy code requires incremental improvements, and knowing how to execute this is essential for conducting effective reviews on your team. While complex and fancy code can be interesting to work with, it will not always achieve business goals or solve urgent problems. Good coding practices are at the heart of a high-performing team, and Carullo instills these core values in a simple, straight-forward way in Implementing Effective Code Reviews.Whether you are a passionate programmer looking to go the extra mile at the office, or an experienced software engineer seeking a guide to how to improve your leadership and code review process, this book covers it all. With each chapter wrapped up in a handy checklist of crucial takeaways, Carullo has created an essential handbook for coders everywhere. There are a lot of myths that dominate the programming landscape, and Implementing Effective Code Reviews grounds the process and gets to the heart of the matter.WHAT YOU WILL LEARN* Understand how to work with your team to implement effective code reviews* Master good programming practices that will build healthy code* Discover how you should tackle different complex areas during code review like, security and performance WHO THIS BOOK IS FORPassionate programmers willing to go the extra mile to be better at their jobs, new programmers looking to strengthen their programming skills, and experienced software engineers looking for a quick guide on how to review codeGiuliana Carullo, CCSK, PSM certified, is a Research Engineering Manager at Tenable. With over 15 years of engineering experience, she has grown her expertise mainly in the networking, security, cloud computing, telecommunications, and Internet of Things (IoT) industries. Through her career, she's has worn many hats, including researcher, engineer, project manager, and engineering manager. Giuliana has been doing research in a number of application fields for over 7 years, 5 of which in the InfoSec area. She dealt with research in a number of application fields, from academia to industrial research, within SMEs (small and mid-size enterprises) and corporations, including Intel and Ericsson. As the author of 15 research papers and several books, Giuliana loves to make even difficult concepts entertaining and easy to grasp.
Reinforcement Learning
In uralten Spielen wie Schach oder Go können sich die brillantesten Spieler verbessern, indem sie die von einer Maschine produzierten Strategien studieren. Robotische Systeme üben ihre Bewegungen selbst. In Arcade Games erreichen lernfähige Agenten innerhalb weniger Stunden übermenschliches Niveau. Wie funktionieren diese spektakulären Algorithmen des bestärkenden Lernens? Mit gut verständlichen Erklärungen und übersichtlichen Beispielen in Java und Greenfoot können Sie sich die Prinzipien des bestärkenden Lernens aneignen und in eigenen intelligenten Agenten anwenden. Greenfoot (M.Kölling, King’s College London) und das Hamster-Modell (D.Bohles, Universität Oldenburg) sind einfache aber auch mächtige didaktische Werkzeuge, die entwickelt wurden, um Grundkonzepte der Programmierung zu vermitteln. Wir werden Figuren wie den Java-Hamster zu lernfähigen Agenten machen, die eigenständig ihre Umgebung erkunden.Nach seinem Studium der Informatik und Philosophie mit Schwerpunkt künstliche Intelligenz und maschinelles Lernen an der Humboldt-Universität in Berlin und einigen Jahren als Projektingenieur ist Uwe Lorenz derzeit als Gymnasiallehrer für Informatik und Mathematik tätig. Seit seinem Erstkontakt mit Computern Ende der 80er Jahre hat ihn das Thema Künstliche Intelligenz nicht mehr losgelassen.Bestärkendes Lernen als Teilgebiet des Maschinellen Lernens.-Grundbegriffe des Bestärkenden Lernens.-Optimale Entscheidungen in einem bekannten Umweltsystem.-Dynamische Programmierung.- rekursive Tiefensuche.-Entscheiden und Lernen in einem unbekannten Umweltsystem.-Q- und Sarsa Learning, Eignungspfade, Dyna-Q.-Policy Gradient und Actor Critic.- Monte Carlo-Evaluationen und Monte Carlo-Baumsuche (MCTS).-Künstliche neuronalen Netze als Schätzer für Zustandsbewertungen und Handlungspreferenzen.-Werden digitale Agenten bald intelligenter als Menschen sein?.-Leitbilder in der K.I..
Machine Learning and Big Data
THIS BOOK IS INTENDED FOR ACADEMIC AND INDUSTRIAL DEVELOPERS, EXPLORING AND DEVELOPING APPLICATIONS IN THE AREA OF BIG DATA AND MACHINE LEARNING, INCLUDING THOSE THAT ARE SOLVING TECHNOLOGY REQUIREMENTS, EVALUATION OF METHODOLOGY ADVANCES AND ALGORITHM DEMONSTRATIONS.The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems.Subjects covered in detail include:* Mathematical foundations of machine learning with various examples.* An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview.* Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth.* Hands-on machine leaning open source tools viz. Apache Mahout, H2O.* Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning.* Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.UMA N. DULHARE is a Professor in the Department of Computer Science & Eng., MJCET affiliated to Osmania University, Hyderabad, India. She has more than 20 years teaching experience years with many publications in reputed international conferences, journals and online book chapter contributions. She received her PhD from Osmania University, Hyderabad. KHALEEL AHMAD is an Assistant Professor in the Department of Computer Science & Information Technology at Maulana Azad National Urdu University, Hyderabad, India. He holds a PhD in Computer Science & Engineering. He has published more than 25 papers in refereed journals and conferences as well as edited two books. KHAIROL AMALI BIN AHMAD obtained a BSc in Electrical Engineering in 1992 from the United States Military Academy, West Point, MSc in Military Electronic Systems Engineering in 1999 from Cranfield University, England, and PhD from ISAE-SUPAERO, France in 2015. Currently, he is the Dean of the Engineering Faculty at the National Defense University of Malaysia. Preface xixSECTION 1: THEORETICAL FUNDAMENTALS 11 MATHEMATICAL FOUNDATION 3Afroz and Basharat Hussain1.1 Concept of Linear Algebra 31.1.1 Introduction 31.1.2 Vector Spaces 51.1.3 Linear Combination 61.1.4 Linearly Dependent and Independent Vectors 71.1.5 Linear Span, Basis and Subspace 81.1.6 Linear Transformation (or Linear Map) 91.1.7 Matrix Representation of Linear Transformation 101.1.8 Range and Null Space of Linear Transformation 131.1.9 Invertible Linear Transformation 151.2 Eigenvalues, Eigenvectors, and Eigendecomposition of a Matrix 151.2.1 Characteristics Polynomial 161.2.1.1 Some Results on Eigenvalue 161.2.2 Eigendecomposition 181.3 Introduction to Calculus 201.3.1 Function 201.3.2 Limits of Functions 211.3.2.1 Some Properties of Limits 221.3.2.2 1nfinite Limits 251.3.2.3 Limits at Infinity 261.3.3 Continuous Functions and Discontinuous Functions 261.3.3.1 Discontinuous Functions 271.3.3.2 Properties of Continuous Function 271.3.4 Differentiation 28References 292 THEORY OF PROBABILITY 31Parvaze Ahmad Dar and Afroz2.1 Introduction 312.1.1 Definition 312.1.1.1 Statistical Definition of Probability 312.1.1.2 Mathematical Definition of Probability 322.1.2 Some Basic Terms of Probability 322.1.2.1 Trial and Event 322.1.2.2 Exhaustive Events (Exhaustive Cases) 332.1.2.3 Mutually Exclusive Events 332.1.2.4 Equally Likely Events 332.1.2.5 Certain Event or Sure Event 332.1.2.6 Impossible Event or Null Event (ϕ) 332.1.2.7 Sample Space 342.1.2.8 Permutation and Combination 342.1.2.9 Examples 352.2 Independence in Probability 382.2.1 Independent Events 382.2.2 Examples: Solve the Following Problems 382.3 Conditional Probability 412.3.1 Definition 412.3.2 Mutually Independent Events 422.3.3 Examples 422.4 Cumulative Distribution Function 432.4.1 Properties 442.4.2 Example 442.5 Baye’s Theorem 462.5.1 Theorem 462.5.1.1 Examples 472.6 Multivariate Gaussian Function 502.6.1 Definition 502.6.1.1 Univariate Gaussian (i.e., One Variable Gaussian) 502.6.1.2 Degenerate Univariate Gaussian 512.6.1.3 Multivariate Gaussian 51References 513 CORRELATION AND REGRESSION 53Mohd. Abdul Haleem Rizwan3.1 Introduction 533.2 Correlation 543.2.1 Positive Correlation and Negative Correlation 543.2.2 Simple Correlation and Multiple Correlation 543.2.3 Partial Correlation and Total Correlation 543.2.4 Correlation Coefficient 553.3 Regression 573.3.1 Linear Regression 643.3.2 Logistic Regression 643.3.3 Polynomial Regression 653.3.4 Stepwise Regression 663.3.5 Ridge Regression 673.3.6 Lasso Regression 673.3.7 Elastic Net Regression 683.4 Conclusion 68References 69SECTION 2: BIG DATA AND PATTERN RECOGNITION 714 DATA PREPROCESS 73Md. Sharif Hossen4.1 Introduction 734.1.1 Need of Data Preprocessing 744.1.2 Main Tasks in Data Preprocessing 754.2 Data Cleaning 774.2.1 Missing Data 774.2.2 Noisy Data 784.3 Data Integration 804.3.1 χ2 Correlation Test 824.3.2 Correlation Coefficient Test 824.3.3 Covariance Test 834.4 Data Transformation 834.4.1 Normalization 834.4.2 Attribute Selection 854.4.3 Discretization 864.4.4 Concept Hierarchy Generation 864.5 Data Reduction 884.5.1 Data Cube Aggregation 884.5.2 Attribute Subset Selection 904.5.3 Numerosity Reduction 914.5.4 Dimensionality Reduction 954.6 Conclusion 101Acknowledgements 101References 1015 BIG DATA 105R. Chinnaiyan5.1 Introduction 1055.2 Big Data Evaluation With Its Tools 1075.3 Architecture of Big Data 1075.3.1 Big Data Analytics Framework Workflow 1075.4 Issues and Challenges 1095.4.1 Volume 1095.4.2 Variety of Data 1105.4.3 Velocity 1105.5 Big Data Analytics Tools 1105.6 Big Data Use Cases 1145.6.1 Banking and Finance 1145.6.2 Fraud Detection 1145.6.3 Customer Division and Personalized Marketing 1145.6.4 Customer Support 1155.6.5 Risk Management 1165.6.6 Life Time Value Prediction 1165.6.7 Cyber Security Analytics 1175.6.8 Insurance Industry 1185.6.9 Health Care Sector 1185.6.9.1 Big Data Medical Decision Support 1205.6.9.2 Big Data–Based Disorder Management 1205.6.9.3 Big Data–Based Patient Monitoring and Control 1205.6.9.4 Big Data–Based Human Routine Analytics 1205.6.10 Internet of Things 1215.6.11 Weather Forecasting 1215.7 Where IoT Meets Big Data 1225.7.1 IoT Platform 1225.7.2 Sensors or Devices 1235.7.3 Device Aggregators 1235.7.4 IoT Gateway 1235.7.5 Big Data Platform and Tools 1245.8 Role of Machine Learning For Big Data and IoT 1245.8.1 Typical Machine Learning Use Cases 1255.9 Conclusion 126References 1276 PATTERN RECOGNITION CONCEPTS 131Ambeshwar Kumar, R. Manikandan and C. Thaventhiran6.1 Classifier 1326.1.1 Introduction 1326.1.2 Explanation-Based Learning 1336.1.3 Isomorphism and Clique Method 1356.1.4 Context-Dependent Classification 1386.1.5 Summary 1396.2 Feature Processing 1406.2.1 Introduction 1406.2.2 Detection and Extracting Edge With Boundary Line 1416.2.3 Analyzing the Texture 1426.2.4 Feature Mapping in Consecutive Moving Frame 1436.2.5 Summary 1456.3 Clustering 1456.3.1 Introduction 1456.3.2 Types of Clustering Algorithms 1466.3.2.1 Dynamic Clustering Method 1486.3.2.2 Model-Based Clustering 1486.3.3 Application 1496.3.4 Summary 1506.4 Conclusion 151References 151SECTION 3: MACHINE LEARNING: ALGORITHMS & APPLICATIONS 1537 MACHINE LEARNING 155Elham Ghanbari and Sara Najafzadeh7.1 History and Purpose of Machine Learning 1557.1.1 History of Machine Learning 1557.1.1.1 What is Machine Learning? 1567.1.1.2 When the Machine Learning is Needed? 1577.1.2 Goals and Achievements in Machine Learning 1587.1.3 Applications of Machine Learning 1587.1.3.1 Practical Machine Learning Examples 1597.1.4 Relation to Other Fields 1617.1.4.1 Data Mining 1617.1.4.2 Artificial Intelligence 1627.1.4.3 Computational Statistics 1627.1.4.4 Probability 1637.1.5 Limitations of Machine Learning 1637.2 Concept of Well-Defined Learning Problem 1647.2.1 Concept Learning 1647.2.1.1 Concept Representation 1667.2.1.2 Instance Representation 1677.2.1.3 The Inductive Learning Hypothesis 1677.2.2 Concept Learning as Search 1677.2.2.1 Concept Generality 1687.3 General-to-Specific Ordering Over Hypotheses 1697.3.1 Basic Concepts: Hypothesis, Generality 1697.3.2 Structure of the Hypothesis Space 1697.3.2.1 Hypothesis Notations 1697.3.2.2 Hypothesis Evaluations 1707.3.3 Ordering on Hypotheses: General to Specific 1707.3.3.1 Most Specific Generalized 1717.3.3.2 Most General Specialized 1737.3.3.3 Generalization and Specialization Operators 1737.3.4 Hypothesis Space Search by Find-S Algorithm 1747.3.4.1 Properties of the Find-S Algorithm 1767.3.4.2 Limitations of the Find-S Algorithm 1767.4 Version Spaces and Candidate Elimination Algorithm 1777.4.1 Representing Version Spaces 1777.4.1.1 General Boundary 1787.4.1.2 Specific Boundary 1787.4.2 Version Space as Search Strategy 1797.4.3 The List-Eliminate Method 1797.4.4 The Candidate-Elimination Method 1807.4.4.1 Example 1817.4.4.2 Convergence of Candidate-Elimination Method 1837.4.4.3 Inductive Bias for Candidate-Elimination 1847.5 Concepts of Machine Learning Algorithm 1857.5.1 Types of Learning Algorithms 1857.5.1.1 Incremental vs. Batch Learning Algorithms 1867.5.1.2 Offline vs. Online Learning Algorithms 1887.5.1.3 Inductive vs. Deductive Learning Algorithms 1897.5.2 A Framework for Machine Learning Algorithms 1897.5.2.1 Training Data 1907.5.2.2 Target Function 1907.5.2.3 Construction Model 1917.5.2.4 Evaluation 1917.5.3 Types of Machine Learning Algorithms 1947.5.3.1 Supervised Learning 1967.5.3.2 Unsupervised Learning 1987.5.3.3 Semi Supervised Learning 2007.5.3.4 Reinforcement Learning 2007.5.3.5 Deep Learning 2027.5.4 Types of Machine Learning Problems 2037.5.4.1 Classification 2047.5.4.2 Clustering 2047.5.4.3 Optimization 2057.5.4.4 Regression 205Conclusion 205References 2068 PERFORMANCE OF SUPERVISED LEARNING ALGORITHMS ON MULTI-VARIATE DATASETS 209Asif Iqbal Hajamydeen and Rabab Alayham Abbas Helmi8.1 Introduction 2098.2 Supervised Learning Algorithms 2108.2.1 Datasets and Experimental Setup 2118.2.2 Data Treatment/Preprocessing 2128.3 Classification 2128.3.1 Support Vector Machines (SVM) 2138.3.2 Naive Bayes (NB) Algorithm 2148.3.3 Bayesian Network (BN) 2148.3.4 Hidden Markov Model (HMM) 2158.3.5 K-Nearest Neighbour (KNN) 2168.3.6 Training Time 2168.4 Neural Network 2178.4.1 Artificial Neural Networks Architecture 2198.4.2 Application Areas 2228.4.3 Artificial Neural Networks and Time Series 2248.5 Comparisons and Discussions 2258.5.1 Comparison of Classification Accuracy 2258.5.2 Forecasting Efficiency Comparison 2268.5.3 Recurrent Neural Network (RNN) 2268.5.4 Backpropagation Neural Network (BPNN) 2288.5.5 General Regression Neural Network 2298.6 Summary and Conclusion 230References 2319 UNSUPERVISED LEARNING 233M. Kumara Swamy and Tejaswi Puligilla9.1 Introduction 2339.2 Related Work 2349.3 Unsupervised Learning Algorithms 2359.4 Classification of Unsupervised Learning Algorithms 2389.4.1 Hierarchical Methods 2389.4.2 Partitioning Methods 2399.4.3 Density-Based Methods 2429.4.4 Grid-Based Methods 2459.4.5 Constraint-Based Clustering 2459.5 Unsupervised Learning Algorithms in ML 2469.5.1 Parametric Algorithms 2469.5.2 Non-Parametric Algorithms 2469.5.3 Dirichlet Process Mixture Model 2479.5.4 X-Means 2489.6 Summary and Conclusions 248References 24810 SEMI-SUPERVISED LEARNING 251Manish Devgan, Gaurav Malik and Deepak Kumar Sharma10.1 Introduction 25210.1.1 Semi-Supervised Learning 25210.1.2 Comparison With Other Paradigms 25510.2 Training Models 25710.2.1 Self-Training 25710.2.2 Co-Training 25910.3 Generative Models—Introduction 26110.3.1 Image Classification 26410.3.2 Text Categorization 26610.3.3 Speech Recognition 26810.3.4 Baum-Welch Algorithm 26810.4 S3VMs 27010.5 Graph-Based Algorithms 27410.5.1 Mincut 27510.5.2 Harmonic 27610.5.3 Manifold Regularization 27710.6 Multiview Learning 27710.7 Conclusion 278References 27911 REINFORCEMENT LEARNING 281Amandeep Singh Bhatia, Mandeep Kaur Saggi, Amit Sundas and Jatinder Ashta11.1 Introduction: Reinforcement Learning 28111.1.1 Elements of Reinforcement Learning 28311.2 Model-Free RL 28411.2.1 Q-Learning 28511.2.2 R-Learning 28611.3 Model-Based RL 28711.3.1 SARSA Learning 28911.3.2 Dyna-Q Learning 29011.3.3 Temporal Difference 29111.3.3.1 TD(0) Algorithm 29211.3.3.2 TD(1) Algorithm 29311.3.3.3 TD(λ) Algorithm 29411.3.4 Monte Carlo Method 29411.3.4.1 Monte Carlo Reinforcement Learning 29611.3.4.2 Monte Carlo Policy Evaluation 29611.3.4.3 Monte Carlo Policy Improvement 29811.4 Conclusion 298References 29912 APPLICATION OF BIG DATA AND MACHINE LEARNING 305Neha Sharma, Sunil Kumar Gautam, Azriel A. Henry and Abhimanyu Kumar12.1 Introduction 30612.2 Motivation 30712.3 Related Work 30812.4 Application of Big Data and ML 30912.4.1 Healthcare 30912.4.2 Banking and Insurance 31212.4.3 Transportation 31412.4.4 Media and Entertainment 31612.4.5 Education 31712.4.6 Ecosystem Conservation 31912.4.7 Manufacturing 32112.4.8 Agriculture 32212.5 Issues and Challenges 32412.6 Conclusion 326References 326SECTION 4: MACHINE LEARNING’S NEXT FRONTIER 33513 TRANSFER LEARNING 337Riyanshi Gupta, Kartik Krishna Bhardwaj and Deepak Kumar Sharma13.1 Introduction 33813.1.1 Motivation, Definition, and Representation 33813.2 Traditional Learning vs. Transfer Learning 33813.3 Key Takeaways: Functionality 34013.4 Transfer Learning Methodologies 34113.5 Inductive Transfer Learning 34213.6 Unsupervised Transfer Learning 34413.7 Transductive Transfer Learning 34613.8 Categories in Transfer Learning 34713.9 Instance Transfer 34813.10 Feature Representation Transfer 34913.11 Parameter Transfer 34913.12 Relational Knowledge Transfer 35013.13 Relationship With Deep Learning 35113.13.1 Transfer Learning in Deep Learning 35113.13.2 Types of Deep Transfer Learning 35213.13.3 Adaptation of Domain 35213.13.4 Domain Confusion 35313.13.5 Multitask Learning 35413.13.6 One-Shot Learning 35413.13.7 Zero-Shot Learning 35513.14 Applications: Allied Classical Problems 35513.14.1 Transfer Learning for Natural Language Processing 35613.14.2 Transfer Learning for Computer Vision 35613.14.3 Transfer Learning for Audio and Speech 35713.15 Further Advancements and Conclusion 357References 358SECTION 5: HANDS-ON AND CASE STUDY 36114 HANDS ON MAHOUT—MACHINE LEARNING TOOLUma N. Dulhare and Sheikh Gouse14.1 Introduction to Mahout 36314.1.1 Features 36614.1.2 Advantages 36614.1.3 Disadvantages 36614.1.4 Application 36614.2 Installation Steps of Apache Mahout Using Cloudera 36714.2.1 Installation of VMware Workstation 36714.2.2 Installation of Cloudera 36814.2.3 Installation of Mahout 38314.2.4 Installation of Maven 38414.2.5 Testing Mahout 38614.3 Installation Steps of Apache Mahout Using Windows 10 38614.3.1 Installation of Java 38614.3.2 Installation of Hadoop 38714.3.3 Installation of Mahout 38714.3.4 Installation of Maven 38714.3.5 Path Setting 38814.3.6 Hadoop Configuration 39114.4 Installation Steps of Apache Mahout Using Eclipse 39514.4.1 Eclipse Installation 39514.4.2 Installation of Maven Through Eclipse 39614.4.3 Maven Setup for Mahout Configuration 39914.4.4 Building the Path- 40214.4.5 Modifying the pom.xml File 40514.4.6 Creating the Data File 40714.4.7 Adding External Jar Files 40814.4.8 Creating the New Package and Classes 41014.4.9 Result 41114.5 Mahout Algorithms 41214.5.1 Classification 41214.5.2 Clustering 41314.5.3 Recommendation 41514.6 Conclusion 418References 41815 HANDS-ON H2O MACHINE LEARNING TOOL 423Uma N. Dulhare, Azmath Mubeen and Khaleel Ahmed15.1 Introduction 42415.2 Installation 42515.2.1 The Process of Installation 42515.3 Interfaces 43115.4 Programming Fundamentals 43215.4.1 Data Manipulation 43215.4.1.1 Data Types 43215.4.1.2 Data Import 43515.4.2 Models 43615.4.2.1 Model Training 43615.4.3 Discovering Aspects 43715.4.3.1 Converting Data Frames 43715.4.4 H2O Cluster Actions 43815.4.4.1 H2O Key Value Retrieval 43815.4.4.2 H2O Cluster Connection 43815.4.5 Commands 43915.4.5.1 Cluster Information 43915.4.5.2 General Data Operations 44115.4.5.3 String Manipulation Commands 44215.5 Machine Learning in H2O 44215.5.1 Supervised Learning 44215.5.2 Unsupervised Learning 44315.6 Applications of H2O 44315.6.1 Deep Learning 44315.6.2 K-Fold Cross-Authentication or Validation 44815.6.3 Stacked Ensemble and Random Forest Estimator 45015.7 Conclusion 452References 45316 CASE STUDY: INTRUSION DETECTION SYSTEM USING MACHINE LEARNING 455Syeda Hajra Mahin, Fahmina Taranum and Reshma Nikhat16.1 Introduction 45616.1.1 Components Used to Design the Scenario Include 45616.1.1.1 Black Hole 45616.1.1.2 Intrusion Detection System 45716.1.1.3 Components Used From MATLAB Simulator 45816.2 System Design 46516.2.1 Three Sub-Network Architecture 46516.2.2 Using Classifiers of MATLAB 46516.3 Existing Proposals 46716.4 Approaches Used in Designing the Scenario 46916.4.1 Algorithm Used in QualNet 46916.4.2 Algorithm Applied in MATLAB 47116.5 Result Analysis 47116.5.1 Results From QualNet 47116.5.1.1 Deployment 47116.5.1.2 Detection 47216.5.1.3 Avoidance 47316.5.1.4 Validation of Conclusion 47316.5.2 Applying Results to MATLAB 47316.5.2.1 K-Nearest Neighbor 47516.5.2.2 SVM 47716.5.2.3 Decision Tree 47716.5.2.4 Naive Bayes 47916.5.2.5 Neural Network 47916.6 Conclusion 484References 48417 INCLUSION OF SECURITY FEATURES FOR IMPLICATIONS OF ELECTRONIC GOVERNANCE ACTIVITIES 487Prabal Pratap and Nripendra Dwivedi17.1 Introduction 48717.2 Objective of E-Governance 49117.3 Role of Identity in E-Governance 49317.3.1 Identity 49317.3.2 Identity Management and its Buoyancy Against Identity Theft in E-Governance 49417.4 Status of E-Governance in Other Countries 49617.4.1 E-Governance Services in Other Countries Like Australia and South Africa 49617.4.2 Adaptation of Processes and Methodology for Developing Countries 49617.4.3 Different Programs Related to E-Governance 49917.5 Pros and Cons of E-Governance 50117.6 Challenges of E-Governance in Machine Learning 50217.7 Conclusion 503References 503Index 505
Electronics for Beginners
Jump start your journey with electronics! If you’ve thought about getting into electronics, but don’t know where to start, this book gives you the information you need. Starting with the basics of electricity and circuits, you'll be introduced to digital electronics and microcontrollers, capacitors and inductors, and amplification circuits – all while gaining the basic tools and information you need to start working with low-power electronics.Electronics for Beginners walks the fine line of focusing on projects-based learning, while still keeping electronics front and center. You'll learn the mathematics of circuits in an uncomplicated fashion and see how schematics map on to actual breadboards. Written for the absolute beginner, this book steers clear of being too math heavy, giving readers the key information they need to get started on their electronics journey.WHAT YOU’LL LEARN* Review the basic “patterns” of resistor usage—pull up, pull down, voltage divider, and current limiter* Understand the requirements for circuits and how they are put together* Read and differentiate what various parts of the schematics do* Decide what considerations to take when choosing components* Use all battery-powered circuits, so projects are safeWHO THIS BOOK IS FORMakers, students, and beginners of any age interested in getting started with electronics. Jonathan Bartlett is a software developer, researcher, and writer. His first book, Programming from the Ground Up, has been required reading in computer science programs from DeVry to Princeton. He has been the sole or lead author for eight books on topics ranging from computer programming to calculus. He is a technical lead for ITX, where his specialty is getting stuck projects unstuck. Jonathan regularly writes for the blog MindMatters.ai. Chapter 1: IntroductionChapter 2. Before We BeginPart I – Basic ConceptsChapter 3. Dealing with UnitsChapter 4. What is Electricity?Chapter 5. Voltage and ResistanceChapter 6. Your First CircuitChapter 7. Constructing and Testing CircuitsChapter 8. Analyzing Series and Parallel CircuitsChapter 9. Diodes and How to Use ThemChapter 10. Basic Resistor Circuit PatternsChapter 11. Understanding PowerPart II – Digital Electronics and MicrocontrollersChapter 12. Integrated Circuits and Resistive SensorsChapter 13. Using Logic ICsChapter 14. Introduction to MicrocontrollersChapter 15. Building Projects with ArduinoChapter 16. Analog Input and Output on an ArduinoPart III – Capacitors and InductorsChapter 17. Capacitor IntroductionChapter 18. Capacitors as TimersChapter 19. Introduction to Oscillating CircuitsChapter 20. Producing Sound with OscillationChapter 21. InductorsChapter 22. Inductors and Capacitors in CircuitsChapter 23. Reactance and ImpedancePart IV – Amplification CircuitsChapter 24. DC MotorsChapter 25. Amplifying Power with TransistorsChapter 26. Transistor Voltage AmplifiersChapter 27. Examining Partial CircuitsChapter 28. Going FurtherAppendicesA. GlossaryB. Electronics SymbolsC. Integrated Circuit Naming ConventionsD. Electronics Equations and Where They Come FromE. Simplified Datasheets for Common Devices
Neue Möglichkeiten für die Motorsteuergeräte-Software durch Car-to-Cloud-Vernetzung
Lars Hagen zeigt Anwendungsszenarien auf, wie „Connected Car“ und insbesondere Vernetzung durch Car-to-Cloud in der Softwareentwicklung sowie im Serieneinsatz für die Motorsteuerung eingesetzt werden können. Dabei legt der Autor ein Augenmerk auf Themen, die über das reine Datensammeln hinausgehen und sowohl den Up- als auch Download von Daten am Fahrzeug miteinbeziehen. Die externe Rechenleistung auf einer Cloud findet ebenso Berücksichtigung wie die limitierte Datenrate des Fahrzeugbusses und des Mobilfunks. Der Autor: Lars Hagen promovierte am Institut für Fahrzeugtechnik Stuttgart (IFS)der Universität Stuttgart am Lehrstuhl für Fahrzeugantriebe. Außerdem arbeitet er als Software-Funktionsentwickler im Bereich der Motorsteuerung bei einem deutschen Automobilzulieferer. Anwendungsfelder für Car-to-Cloud im Bereich der Motorsteuerung.- Anwendungsbeispiele Applikation und Diagnose für Car-to-Cloud in der Motorsteuergeräte-Software.
CompTIA CySA+ Practice Tests
EFFICIENTLY PREPARE YOURSELF FOR THE DEMANDING COMPTIA CYSA+ EXAMCompTIA CySA+ Practice Tests: Exam CS0-002, 2nd Edition offers readers the fastest and best way to prepare for the CompTIA Cybersecurity Analyst exam. With five unique chapter tests and two additional practice exams for a total of 1000 practice questions, this book covers topics including:* Threat and Vulnerability Management* Software and Systems Security * Security Operations and Monitoring * Incident Response* Compliance and AssessmentThe new edition of CompTIA CySA+ Practice Tests is designed to equip the reader to tackle the qualification test for one of the most sought-after and in-demand certifications in the information technology field today.The authors are seasoned cybersecurity professionals and leaders who guide readers through the broad spectrum of security concepts and technologies they will be required to master before they can achieve success on the CompTIA CySA exam. The book also tests and develops the critical thinking skills and judgment the reader will need to demonstrate on the exam.MIKE CHAPPLE, PHD, CYSA+, CISSP, is Teaching Professor of IT, Analytics, and Operations at the University of Notre Dame. He's a cybersecurity professional and educator with over 20 years of experience. Mike provides cybersecurity certification resources at his website, CertMike.com. DAVID SEIDL, CYSA+, CISSP, PENTEST+, is Vice President for Information Technology and CIO at Miami University. David co-led Notre Dame's move to the cloud, and has written multiple cybersecurity certification books. Introduction xviiChapter 1 Domain 1.0: Threat and Vulnerability Management 1Chapter 2 Domain 2.0: Software and Systems Security 105Chapter 3 Domain 3.0: Security Operations and Monitoring 151Chapter 4 Domain 4.0: Incident Response 207Chapter 5 Domain 5.0: Compliance and Assessment 265Chapter 6 Practice Exam 1 289Chapter 7 Practice Exam 2 315APPENDIX ANSWERS TO REVIEW QUESTIONS 347Answers to Chapter 1: Domain 1.0: Threat and Vulnerability Management 348Answers to Chapter 2: Domain 2.0: Software and Systems Security 381Answers to Chapter 3: Domain 3.0: Security Operations and Monitoring 403Answers to Chapter 4: Domain 4.0: Incident Response 425Answers to Chapter 5: Domain 5.0: Compliance and Assessment 450Answers to Chapter 6: Practice Exam 1 461Answers to Chapter 7: Practice Exam 2 470Index 481
Android Apps Security
Gain the information you need to design secure, useful, high-performing apps that expose end-users to as little risk as possible. This book shows you how to best design and develop Android apps with security in mind: explore concepts that you can use to secure apps and how you can use and incorporate these security features into your apps.WHAT YOU WILL LEARN* Identify data that should be secured* Use the Android APIs to ensure confidentiality and integrity of data* Build secure apps for the enterprise* Implement Public Key Infrastructure and encryption APIs in apps* Master owners, access control lists, and permissions to allow user control over app properties* Manage authentication, transport layer encryption, and server-side securityWHO THIS BOOK IS FORExperienced Android app developers.Sheran Gunasekera is a security researcher and software developer with more than 13 years of information security experience. He is director of research and development for ZenConsult Pte. Ltd., where he oversees security research in both the personal computer and mobile device platforms. Sheran has been very active in BlackBerry and mobile Java security research and was the author of the whitepaper that revealed the inner workings of the first corporate-sanctioned malware application deployed to its subscribers by the UAE telecommunications operator Etisalat. He has spoken at many security conferences in the Middle East, Europe and Asia Pacific regions and also provides training on malware analysis for mobile devices and secure software development for both web and mobile devices. He also writes articles and publishes research on his security-related blog.1. Introduction.- 2. Recap of Secure Development Principles.- 3. Changes in Security Architecture.- 4. Security when Building Apps to Scale.- 5. Testing the Security of Your App (this covers pentesting and bug bounties).- 6. The Toolbag.- 7. Rooting an Android phone. 8. Looking at your App's Data through a Root shell.- Bypassing SSL Pinning (the holy grail of hacking apps).- 10. Reverse Engineering Android Apps.- 11. Incident Response.
Practical Bootstrap
Learn to use one of the most popular CSS frameworks and build mobile-friendly web pages. Used for numerous websites and applications, Bootstrap is a key tool for modern web development.You will explore the grid system and then be introduced to the power of Bootstrap in practical projects. You’ll make navigation bars, use themes and styling, create and manipulate cover pages, admin dashboards, forms, and modal dialogs. You’ll learn to use Scrollspy and create tooltips and popovers.Today's web is responsive and Bootstrap continues to be at the forefront with web professionals. Learn by doing with Practical Bootstrap today.WHAT YOU WILL LEARN* Review how the grid system applies to Bootstrap* Create stunning cover pages that encompass a large background image* Build an admin dashboard page that changes its layout according to a device’s display width* Work with the modal HTML markup and its main parts* Customize modal behavior by setting various options using JavaScript* Integrate Bootstrap JavaScript libraries with your own HTML page* Add scroll spying functionality to your long-content pages* Adjust the offset and activation point of the scroll sectionsWHO THIS BOOK IS FORAnyone who wants to learn how to use Bootstrap. You should have knowledge of HTML, CSS and basic JavaScript.PANOS MATSINOPOULOS loves developing programs, both for web browsers and for mobile apps. He has been doing that for the last 25 years and has developed numerous applications. He also loves writing books, blogging and teaching computer programming. He has organized a lot of programming classes for kids, adults and elderly people.PRACTICAL BOOTSTRAP1. Getting Started2. Advanced Grid Techniques3. Target Project 14. Theme Reference: Part 15. Theme Reference: Part 26. Cover Page Project7. Admin Dashboard8. Forms9. Modal Dialogs10. ScrollSpy11. Tooltips and Popovers
Demystifying Azure AI
Explore artificial intelligence offerings by Microsoft Azure, along with its other services. This book will help you implement AI features in various Azure services to help build your organization and customers.The book starts by introducing you to the Azure Cognitive Search service to create and use an application. You then will learn the built-in automatic tuning intelligence mechanism in Azure SQL Database. This is an important feature you can use to enable Azure SQL Database to optimize the performance of your queries. Next, you will go through AI services with Azure Integration Platform service and Azure Logic Apps to build a modern intelligent workflow in your application. Azure functions are discussed as a part of its server-less feature. The book concludes by teaching you how to work with Power Automate to analyze your business workflow.After reading this book, you will be able to understand and work with different Azure Cognitive Services in AI.WHAT YOU WILL LEARN* Get started with Azure Cognitive Search service* Use AI services with Low Code – Power Automate* Use AI services with Azure Integration services* Use AI services with Azure Server-less offerings* Use automatic tuning in Azure SQL databaseWHO THIS BOOK IS FORAspiring Azure and AI professionalsKASAM SHAIKH, Cloud advocate, is a seasoned professional having 13 years of demonstrated industry experience working as Cloud Architect with one of the leading IT companies in Mumbai, INDIA. He is recognized as MVP by an online Tech community, also a Global AzureAI Speaker, and author of two best-selling books on Microsoft Azure and AI. He is the founder of Azure INDIA (az-INDIA) community, DearAzure, which is an online community for learning AzureAI. He owns a YouTube channel and shares his experience over his website www.kasamshaikh.comCHAPTER 1: AZURE SEARCH WITH AICHAPTER GOALS: In this chapter reader will learn about the only AI powered Cloud search offering by Microsoft, Azure Cognitive Search service. Using Cognitive Services with Azure Search with your web apps. Skip hiring search experts who know what an inverted index is. Don't worry about distributed systems expertise to scale your service to handle large amount of data. And forget about setting up, owning and managing the infrastructure. Let Azure Search do it all for you. When and How to use with step by step Demo creating an application.CHAPTER 2: AI AND BACKEND SERVICE OFFERINGCHAPTER GOALS: In this Chapter reader will learn the built-in automatic tuning intelligence mechanism in Azure SQL Database. Automatic tuning is a fully managed intelligent performance service that uses built-in intelligence to continuously monitor queries executed on a database, and it automatically improves their performance. Automatic tuning in Azure SQL Database might be one of the most important features that one can enable on Azure SQL Database to optimize the performance of your queries.CHAPTER 3: AI SERVICES WITH AZURE IPAASCHAPTER GOALS: In this Chapter, readers will explore the Azure Cognitive services, that can be leverage with Azure Integration Platform service, Azure Logic Apps. This gives developer a power to infuse an intelligent workflow in application. Will have a step by step demo to create a workflow with business use-case.CHAPTER 4: AI SERVICES WITH SERVERLESS OFFERINGCHAPTER GOALS: In this Chapter, readers will explore the Azure Cognitive services offerings, that can be leverage with Azure Serverless Offerings, Azure Functions. This gives developer a power to extend the scalable Functions with a smart and intelligent functional implementation in application. Will have a step by step demo on how to work with Cognitive extensions.CHAPTER 5: AI WITH NO CODECHAPTER GOALS: In this Chapter, readers will explore the Azure Cognitive services offerings, that can be leverage with No Code – PowerAutomate. This gives business an easy hand to present with a smart analysis in business workflow. Will have a step by step demo to create a workflow.
Understanding Hybrid Environments in SharePoint 2019
Design and develop hybrid scenarios with SharePoint Online and SharePoint Server 2019. This book will help you build hybrid environments from scratch, and covers basic to advanced scenarios.The book takes you through the setup and configuration of SharePoint 2019 on virtual machines in Microsoft Azure. It gives you an overview of the features of the SharePoint Server 2019 User Experience. Integration of SP 2019 with PowerApps and Microsoft Flow is covered, along with Power BI reporting. You will learn to manage the data gateway and understand how to publish the Power BI Report. Configuration of the SP 2019 home page is explained and you learn how to enable a hybrid experience via the SP Office 365 link setting. You will know how to register a public domain in Office 365 and configure it for SP hybrid. Cloud hybrid search with the PowerShell script approach is discussed as well as SP 2019 hybrid Self-Service Site Creation. Configuration of the hybrid taxonomy, content types, and SharePoint framework development with SP 2019 are covered. And the book ends on using the office online server for SharePoint Server 2019 along with SP Server 2019 upgrade scenarios.After reading this book, you will be able to build and manage different hybrid environments with SharePoint Online and SharePoint Server 2019.WHAT WILL YOU LEARN* Enlarge your hybrid terminology* Get familiar with the new and improved features of SharePoint Server 2019* Develop a hybrid experience with SharePoint Server 2019* Enable hybrid search* Understand the on-premises data gateway* Configure and integrate SharePoint Server 2019 with Power Platform, including Power BI, Power Apps, and Power AutomateWHO IS THIS BOOK FORSharePoint professionals who want to configure hybrid solutions in SharePointNANDDEEP SADANAND NACHAN is a Microsoft MVP (Office Apps and Services) and technology architect with experience in Microsoft technologies, including SharePoint, MS Azure, and .NET. He has been working with SharePoint for the last 15+ years and has worked with SharePoint versions starting with SharePoint 2007 (MOSS). He wrote the book Mastering SharePoint Framework. He organizes and speaks at industry seminars, conferences, and community events, including SPS, Global Microsoft 365 Developer Bootcamp, and Global Power Platform Bootcamp. He is an active contributor to Office 365 Dev Patterns and Practices on GitHub and writes articles on his blog. He is also a creative and technically sound photographer with experience in custom and specialized photography.SMITA SADANAND NACHAN is a SharePoint professional with 12+ years of experience in design, implementation, configuration, and maintenance of large-scale projects. She focuses on architectural design and implementation, website design and development, and complete application development cycles, with an intense focus on SharePoint and Office 365. She is a frequent speaker at various community events, including SPS, Global Microsoft 365 Developer Bootcamp, and Global Power Platform Bootcamp. She is a travel, fashion, and food blogger.CHAPTER 1, SETUP SHAREPOINT 2019 DEVELOPER VM IN MS AZUREChapter Goal:· Hardware and Software Requirements· Microsoft Azure VM Setup Options· Setup Server 2019 Trial VM in MS AzureCHAPTER 2, CONFIGURE SHAREPOINT SERVER 2019Chapter Goal:· Setup Active Directory· Setup AD Users· Configure SharePoint 2019 with AutoSPInstaller· Convert SharePoint Trial to RTM LicenseCHAPTER 3, SHAREPOINT SERVER 2019 USER EXPERIENCEChapter Goal:· SharePoint 2019 User Experience· Modern User ExperienceCHAPTER 4, SHAREPOINT SERVER 2019 FEATURES OVERVIEWChapter Goal:· SharePoint 2019 Focus Areas· Improved Features· Features Leaving Behind / Does Not make to On-PremiseCHAPTER 5, SHAREPOINT 2019 POWERAPPS INTEGRATIONChapter Goal:· PowerApps Overview· Install Gateway· Build PowerApps Canvas App· Gateway Windows Service· Publish the PowerApps AppCHAPTER 6, SHAREPOINT 2019 MS FLOW INTEGRATIONChapter Goal:· MS Flow Overview· On-premises Data Gateway· Configure MS Flow Connection· Create Microsoft Flow· Test the MS FlowCHAPTER 7, SHAREPOINT 2019 - POWER BI REPORTINGChapter Goal:· Power BI Overview· On-premises Data Gateway· Manage Gateway· Create Data Source· Install Personal Gateway· Power BI Reports· Publish the Power BI Report· Scheduled Refresh of DatasetsCHAPTER 8, SHAREPOINT 2019 - CONFIGURE HOME PAGEChapter Goal:· SharePoint Home· Features of Home Page· Supporting ServicesCHAPTER 9, SHAREPOINT 2019 - ENABLE HYBRID EXPERIENCEChapter Goal:· SharePoint Hybrid· Enable Hybrid Experience· SPO365 Link Settings Overview· Re-run SharePoint Hybrid Configuration Wizard· Configuration SummaryCHAPTER 10. REGISTER PUBLIC DOMAIN WITH OFFICE 365Chapter Goal:· Need for Public Domain· Buy a new domain· Use an existing domain· Set Default Office 365 Domain· Edit Active UsersCHAPTER 11, CONFIGURE OFFICE 365 FOR SHAREPOINT HYBRID· Prerequisites· Add UPN suffix to the Local AD· Manage Office 365 Directory from Azure Active Directory· Verify User Sync· Assign Licenses to UsersCHAPTER 12, SHAREPOINT 2019 - CLOUD HYBRID SEARCHChapter Goal:· Cloud Hybrid Search Overview· Enable Hybrid Search Experience· PowerShell Script Approach· On-Premises Cloud Search Service Application Configuration· Verify Hybrid SearchCHAPTER 13, SHAREPOINT 2019 HYBRID SELF-SERVICE SITE CREATIONChapter Goal:· Hybrid Self-Service Site Creation· Enable Hybrid Experience· Create Site Collection Page· Enable Hybrid Self-Service Site Creation from SharePoint Hybrid Configuration Wizard· Manage hybrid self-service site creation· Test Hybrid Self-Service Site CreationCHAPTER 14, SHAREPOINT 2019 CONFIGURE HYBRID TAXONOMY· SharePoint Hybrid Taxonomy· Copy SharePoint Server Taxonomy to SharePoint Online· Configure hybrid SharePoint taxonomy· Timer Job· Verify Taxonomy Groups ReplicationCHAPTER 15, SHAREPOINT 2019 CONFIGURE HYBRID CONTENT TYPESChapter Goal:· SharePoint Hybrid Content Types· Content Type Hub in SharePoint Online· Copy SharePoint Server Content Types to SharePoint Online· Configure hybrid SharePoint Content Types· Timer Job· Verify Content Types ReplicationCHAPTER 16, SHAREPOINT FRAMEWORK DEVELOPMENT WITH SHAREPOINT 2019· Decide Upon SharePoint Framework Version· Get SharePoint Server 2019 Ready for SPFx· Develop SharePoint Framework Web Part· Run the SPFx WebPart· TroubleshootingCHAPTER 17, OFFICE ONLINE SERVER FOR SHAREPOINT SERVER 2019Chapter Goal:· Introduction to Office Online Server· Download and Install Office Online Server· Install Office Online Server· Setup Office Online Farm· Verify the Office Online Server working· Connecting to SharePoint 2019 FarmCHAPTER 18, SHAREPOINT SERVER 2019 UPGRADE SCENARIOSChapter Goal:· Upgrade Scenarios· High Level Upgrade Scenarios
Design Thinking in Software and AI Projects
Learn the fundamentals of Design Thinking and how to apply Design Thinking techniques in defining software development and AI solutions. Design Thinking is an approach to innovation which identifies problems and generates solution ideas that can be rapidly proven through prototyping.This book provides a brief history of Design Thinking and an overview of the process. It then drills down into more detail regarding methods and tools used in a Design Thinking workshops leading to useful prototypes. Guidance is provided on:* Preparing for a Design Thinking Workshop * Uncovering potential business problems that might be solved* Prioritizing potential solutions* Identifying and characterizing stakeholders* Choosing the right prototypes for development* Limiting scope and best practices in prototype buildingThe book concludes with a discussion of best practices in operationalizing successful prototypes, and describes change management techniques critical for successful adoption. You can use the knowledge gained from reading this book to incorporate Design Thinking techniques in your software development and AI projects, and assure timely and successful delivery of solutions.WHAT YOU WILL LEARN* Gain foundational knowledge of what Design Thinking is and when to apply the technique* Discover preparation and facilitation techniques used in workshops* Know how ideas are generated and then validated through prototyping* Understand implementation best practices, including change management considerationsWHO THIS BOOK IS FORBusiness decision makers and project stakeholders as well as IT project owners who seek a method leading to fast development of successful software and AI prototypes demonstrating real business value. Also for data scientists, developers, and systems integrators who are interested in facilitating or utilizing Design Thinking workshops to drive momentum behind potential software development and AI projects.ROBERT STACKOWIAK works as an independent consultant, advisor, and author. He is a former data & artificial intelligence architect and technology business strategist at the Microsoft Technology Center in Chicago, and previously worked in similar roles at Oracle and IBM. He has conducted business discovery workshops, ideation workshops, and technology architecture sessions with many of North America’s leading-edge companies across a variety of industries and with government agencies. Bob has also spoken at numerous industry conferences internationally, served as a guest instructor at various universities, and is an author of several books. You can follow him on Twitter @rstackow and read his articles and posts on LinkedIn.TRACEY KELLY is Envisioning Lead with the Catalyst team at Microsoft. She has been leading the design thinking training through North America and Europe to help Microsoft technology-focused architects and business leadership transition and transform to customer-centric and business outcome solutions. Tracey is also on the board of the Women’s Technology Coalition and a former Women in Technology Director in Dallas. She leads design workshops and customer strategy sessions and has a long 20-year history of technology and design leadership at Fortune 500 companies to drive innovation.Chapter 1: Design Thinking Overview and History.- Chapter 2: Preparing for a Workshop.- Chapter 3: Problem Definition.- Chapter 4: Solution Definition.- Chapter 5: Prototype Creation.- Chapter 6: Production Development.- Chapter 7: Production Rollout.- Chapter 8. Appendix A: Sources.
Exploring C++20
Discover everything you need to know about C++ in a logical progression of small lessons that you can work through as quickly or as slowly as you need. This book divides C++ up into bite-sized chunks that will help you learn the language one step at a time. Fully updated to include C++20, it assumes no familiarity with C++ or any other C-based language.Exploring C++20 acknowledges that C++ can be a complicated language, so rather than baffle you with complex chapters explaining functions, classes, and statements in isolation you’ll focus on how to achieve results. By learning a little bit of this and a little of that you’ll soon have amassed enough knowledge to be writing non-trivial programs and will have built a solid foundation of experience that puts those previously baffling concepts into context.In this fully-revised third edition of Exploring C++, you’ll learn how to use the standard library early in the book. Next, you’ll work with operators, objects, and data-sources in increasingly realistic situations. Finally, you’ll start putting the pieces together to create sophisticated programs of your own design confident that you’ve built a firm base of experience from which to grow.WHAT YOU WILL LEARN* Grasp the basics, including compound statements, modules, and moreWork with custom types and see how to use them * Write useful algorithms, functions, and more* Discover the latest C++ 20 features, including concepts, modules, and ranges* Apply your skills to projects that include a fixed-point numbers and body-mass index applicationsCarry out generic programming and apply it in a practical project * Exploit multiple inheritance, traits/policies, overloaded functions, and metaprogrammingWHO THIS BOOK IS FORExperienced programmers who may have little or no experience with C++ who want an accelerated learning guide to C++20 so they can hit the ground running.Ray Lischner has a bachelor's degree in computer science from Caltech and a master's in computer science from Oregon State University. He worked as a software developer for a dozen years, at big and small companies across the US, using PL/I, C, C++, Delphi, Smalltalk, and various assembly languages on both large and small systems. He has been self-employed as a consultant, trainer, and author for the last ten years. Ray taught computer science at Oregon State University for several years and specialized in teaching introductory computer programming. He taught courses in C and C++ and software engineering.Part I: The Basics.-1. Honing your tools.-2. Reading C++ Code.-3. Integer Expressions.-4. Strings.-5. Simple Input.-6. Error Messages.-7. For Loops.-8. Formatted Output.-9. Arrays and Vectors.-10. Algorithms and Ranges.-11. Increment and Decrement.-12. Conditions and Logic.-13. Compound Statements.-14. Introduction to File I/O.-15. The Map Data Structure.-16. Type Synonyms.-17. Characters.-18. Character Categories.-19. Case-Folding.-20. Writing Functions.-21. Function Arguments.-22. Using Ranges.-23. Using Iterators.-24. Unnamed Functioins.-25. Overloading Function Names.-26. Big and Little Numbers.-27. Very Big and Very Little Numbers.-28. Documentation.- 29. Project 1: Body-Mass IndexPart II: Custom Types.-30. Custom Types.-31. Overloading Operators.-32. Custom I/O Operators.-33. Assignment and Initialization.-34. Writing Classes.- 35. More About Member Functions.-36. Access Levels.-37. Understanding Object-Oriented Programming.-38. Inheritance.-39. Virtual Functions.-40. Classes and Types.-41. Declarations and Definitions.- 42. Modules.-43. Old-Fashioned "Modules".-44. Function Objects.-45. Useful Algorithms.-46. More About Iterators.-47. Ranges, Views and Adaptors.-48. Exceptions.-49. More Operators.-50. Project 2: Fixed-Point Numbers.-Part III: Generic Programming.-51. Function Templates.-52. Class Templates.-53. Template Specialization.-54. Partial Template Specialization.-55. Template Constraints.-56. Names and Namespaces.-57. Containers.-58. Locales and Facets.-59. International Characters.-60. TextI/O.-61. Project3: Currency Type.-Part IV: Real Programming.-62. Pointers.-63. Regular Expressions.-64. Moving Data with Rvalue References.-65. Smart Pointers.-66. Files and File Names.-67. Working with Bits.-68. Enumerations.-69. Multiple Inheritance.-70. Concepts, Traits and Policies.-71. Names, Namespaces, and Templates.-72. Overloaded Functions and Operators.-73. Programming at Compile Time.-74. Project 4: Calculator.
Practical Numerical C Programming
Master the C code appropriate for numerical methods and computational modeling, including syntax, loops, subroutines, and files. Then, this hands-on book dives into financial applications using regression models, product moment correlation coefficients, and asset pricing.Next, Practical Numerical C Programming covers applications for engineering/business such as supermarket stock reordering simulation as well as flight information boards at airports and controlling a power plant. Finally, the book concludes with some physics including building simulation models for energy and pendulum motion. Along the way, you’ll learn center-of-mass calculations, Brownian motion, and more.After reading and using this book, you'll come away with pragmatic case studies of actual applications using C code at work. Source code is freely available and includes the latest C20 standard release.WHAT YOU WILL LEARN* Apply regression techniques to find the pattern for depreciation of the value of cars over a period of years* Work with the product moment correlation coefficient technique to illustrate the accuracy (or otherwise) of regression techniques* Use the past stock values of an asset to predict what its future values may be using Monte Carlo methods* Simulate the buying of supermarket stock by shoppers and check the remaining stock: if it is too low print a message to reorder the stock* Create a file of arrivals for an airport and send data to the airport’s display boards to show the current situation for the incoming flights* Simulate the patterns of particles moving in gases or solids WHO THIS BOOK IS FORProgrammers and computational modelers with at least some prior experience with programming in C as well as programming in general.Philip Joyce has 28 years experience as a software engineer – working on control of steel production, control of oil refineries, communications software (pre-Internet), office products (server software), and computer control of airports. Programming in Assembler, COBOL, Coral66, C, and C++. Mentor to new graduates in the company. He also has a MSc in computational physics (including augmented matrix techniques and Monte Carlo techniques using Fortran) - Salford University 1996. Chartered scientist, chartered physicist, member of the Institute of Physics (member of the higher education group).Chapter 1 Review of CReview of C and SDK with reference to the topics in this book.Reminds the reader of C syntax.Use loops, subroutines, file access.Create typical programs in C using SDK ExercisesPART 1 – FINANCIAL APPLICATIONSChapter 2 Regression:Use regression techniques to find the pattern for depreciation of the value of cars over a period of years.Program written will create graphical displays to illustrate the topic.ExercisesChapter 3 Product Moment Correlation Coefficient (PMCC):Use this technique to illustrate the accuracy (or otherwise) of regression techniques.ExercisesChapter 4 : Asset PricingUse the past stock values of an Asset to predict what its future values may be using Monte Carlo methods.Graphics displays to illustrate the topic.ExercisesPART 2 – ENGINEERING/INDUSTRIAL/COMMERCIAL APPLICATIONSChapter 5: Supermarket Stock Reordering SimulationCreate a file of stock for a supermarket. Simulate the buying of stock by shoppers. Check the remaining stock. If it is too low print a message to reorder the stock.ExercisesChapter 6: Flight Information Boards at AirportsCreate a file of arrivals for an airport. Send data to the airport’s display boards to show the current situation for the incoming flights. Update a flight and refresh the information to the display boards.ExercisesChapter 6 : Power Plant ControlProgram receives messages about pressures, temperatures, flow rates etc for a power plant. The program checks for values outside safety ranges and acts upon any problem values by sending messages to both the gauges and the managers responsible for them.ExercisesPART 3 – PHYSICS APPLICATIONSChapter 8 Potential Energy and Kinetic Energy SimulationUse formulas for Potential Energy and Kinetic Energy to show how one falls at the same rate as the other rises.ExercisesChapter 9 Pendulum Simulation Use formulas for the motion of a pendulum to create a graph to illustrate the mathematical relationship on a graph .ExercisesChapter 10: Centre of Mass CalculationCalculate the centre of mass of unusually-shaped objects.ExercisesChapter 11 : Brownian MotionSimulate the patterns of particles moving in gases or solids.Graphical displays to illustrate the topic.ExercisesChapter 12 Vacancy Model of Atoms Moving in SolidsDemonstrate the Vacancy Model of atoms moving in solids where they can move into empty sites within the solid. Graphical displays will show the movement of the atoms within a 2D site.ExercisesAPPENDICESA. C Programming Code GuideB. Answers to exercisesThese could be contained in an included CD which could also contain some data files the students could use in their examples
Alexa Tipps und Tricks für Dummies
Erfahren Sie, was Sie mit Alexa alles anstellen können - von der Soundanpassung des Lautsprechers mit der Equalizer-Funktion über das Freisprechen mit Drop In bis hin zum Automatisieren von Abläufen mit Routinen. So bringen Sie mit Alexa mehr Freude und mehr Intelligenz in Ihre Wohnung und Ihren Alltag. Dieses Buch zeigt Ihnen neben vielen Tipps und Tricks auch versteckte Funktionen und Top-Secrets, die nicht jeder kennt. Die Zahl der Anwendungen, auf die man mit Alexa zugreifen kann, steigt ständig: Ja, Sie können auch Ihr Smart Home über Alexa steuern. Das Buch enthält auch Hinweise zum Datenschutz. Benjy Thömmes ist Schüler und lebt in Gerolstein in der Eifel. Seit 2017 betreibt er einen eigenen Blog www.blog.yourecho.de, auf dem er regelmäßig über Alexa, den Amazon Echo und Smart-Home-Technologien schreibt.Über den Autor 9EINFÜHRUNG 19Über dieses Buch 19Törichte Annahmen über den Leser 20Was Sie nicht lesen müssen 20Wie dieses Buch aufgebaut ist 20Teil I: Im Grunde soll sie helfen 21Teil II: Mit Automatisierungen und Skills das Leben erleichtern 21Teil III: Mehr Funktionen für Suchtis 21Teil IV: Der Teil der Zehnen 21Symbole, die in diesem Buch verwendet werden 22TEIL I: IM GRUNDE SOLL SIE HELFEN 23KAPITEL 1 ALEXA, IHRE EIGENSCHAFTEN UND GRUNDEINSTELLUNGEN 25Die Bedeutung der verschiedenfarbigen Lichtringe 25Der blaue Lichtring 25Der rote Lichtring 26Der grüne Lichtring 26Der gelbe Lichtring 26Der lilafarbene Lichtring 26Die Grundeinstellungen von Alexa-Geräten 27Standort ändern 27Zeitzone ändern 28Maßeinheiten umstellen 28Sprache einstellen 28Aktivierungswort auswählen 29WLAN-Verbindung von Alexa ändern 29Gerät von Amazon-Konto abmelden 31KAPITEL 2 DER NEUE DJ: MUSIK HÖREN MIT ALEXA 33Alexa mit einem Musikdienst verbinden 33Standardmusikdienst festlegen 35Den Sound von Alexa mithilfe des Equalizers anpassen 36Konkurrenz für das Küchenradio 36Multiroom-Audio: Musik synchronauf mehreren Geräten abspielen 36Amazon Music: Musik über das Smartphone an Alexa senden 37KAPITEL 3 KALENDER: ALEXA ORGANISIERT IHREN TAG 39Kalender mit Alexa verbinden 39Den Draht zwischen Kalender und Alexa wieder trennen 41KAPITEL 4 SPRACHANTWORTEN: ALEXA ANTWORTET MAL ANDERS 43Der Alexa-Kurzmodus 43Der Alexa-Flüstermodus 44Die Geschwindigkeit ändern, in der Alexa spricht 45KAPITEL 5 IHR NEUES TELEFON HEIẞT ALEXA 47Anrufe und Nachrichten mit Alexa 47Skypen über Alexa 49Drop In 50Ankündigungen 51KAPITEL 6 ALEXA, NUN SEI DOCH MAL STILL 53Benachrichtigungen verwalten 53Der Bitte-nicht-stören-Modus 55Manuell: Selbst ein- und ausschalten 56Planmäßig: Alexa, du weißt, wann ich keine Zeit habe 56Den Benachrichtigungston deaktivieren 57KAPITEL 7 DAS SMART HOME ÜBER ALEXA STEUERN 59Alexa, schalte mein neues Gerät an! 59Geräte in Gruppen ordnen 61Smart-Home-Geräte aus der Alexa-App löschen 62Fire TV und den Fire-TV-Stick mit Alexa verbinden 63KAPITEL 8 WISSEN, WAS IN DER WELT PASSIERT 65Nachrichten über Alexa hören 65Alexa weiß, was Sie interessiert 66Neue Anbieter zur täglichen Zusammenfassung hinzufügen 66Die Reihenfolge der täglichen Zusammenfassung ändern 67Einen Nachrichtenanbieter wieder entfernen 67Kein Tor mehr verpassen 67Neue Mannschaften zum Update hinzufügen 68Neues Team und altes weg 69KAPITEL 9 ALLES ÜBER WECKER, TIMER UND ERINNERUNGEN 71Der Wecker kann mehr, als nur gestellt zu werden 71Wenn der Standard-Weckerton nervt 72Starten Sie mit Musik in den Tag 73Weg mit der Eieruhr, her mit Alexa! 73Erinnerungen 74KAPITEL 10 EINKAUFSLISTEN UND TO-DO-LISTEN 75Ihre Standardlisten über Alexa verwalten 75Listen über das Smartphone aufrufen und verwalten 76Eine neue Liste erstellen 77Ihre Alexa-Listen mit Drittanbieter-Apps synchronisieren 77KAPITEL 11 ALEXA, DIE KÜCHEN- UND EINKAUFSHILFE 81Alexa nach Rezepten suchen lassen 81Von zu Hause aus einkaufen 82Alexa, ich brauche Nudeln! 82Produkt leer, aber Verpackung noch vorhanden? 82Alexa bestellt? – Niemals! 83TEIL II: ALEXA AUTOMATISIEREN UND IHR WISSEN ERWEITERN 85KAPITEL 12 ALLES ÜBER SKILLS 87Wo gibt es diese Skills? 87Skills wieder deaktivieren 88Skills für die Kleinen 88In-Skill-Käufe in Skills für Kinder deaktivieren 89KAPITEL 13 ALEXA BLUEPRINTS: EIGENE SKILLS ERSTELLEN 91Die Grundlagen 91Wer ist dran? Alexa lässt den Zufall entscheiden! 92Ein Quiz erstellen 94Eigene Fragen und Antworten definieren 96KAPITEL 14 AUTOMATISIERUNGEN ÜBER ALEXA-ROUTINEN 99Die Grundlagen 99Eine Alexa-Routine erstellen 100Der Auslöser 100Die Aktionen 101KAPITEL 15 ZUSAMMEN GEHT (FAST) ALLES: IFTTT UND ALEXA 105TEIL III: NOCH MEHR FUNKTIONEN FÜR SUCHTIS 109KAPITEL 16 STIMMPROFILE: ALEXA ERKENNT, WER GERADE SPRICHT 111Alexa verraten, wer Sie sind 111Wenn Alexa Sie oft nicht erkennt 112KAPITEL 17 ALEXA IST AUF VIELEN GERÄTEN ZU HAUSE 115Alexa auch auf dem Handy nutzen 115Alexa als Standard-Sprachassistentin einstellen (nur Android!) 116Alexa ist überall, auch auf Ihrem Windows-10-Gerät 117Alexa auf Windows-10-Geräten installieren 117Die Alexa-App kann noch mehr! 118Fire TV und Alexa gehören zusammen 119Alexa geht fremd 120KAPITEL 18 DATENSCHUTZ UND ALEXA 121Alexa, vergiss, was ich gesagt habe! 121Verlauf von Smart-Home-Geräten löschen 122Sparsam mit Daten für Skills umgehen 124Es geht noch mehr 124KAPITEL 19 WAS KANN EINE ALEXA MIT DISPLAY MEHR? 127Videos, Filme und mehr – Alexa wird zum Fernseher 127Prime-Serienjunkies haben nun noch einfacher Zugriff auf Serien und Filme 128Musikvideos kostenlos über den Echo Show schauen 128Filmtrailer über den Echo Show schauen 128Durchs Web surfen 128Das Smart Home vom Display aus verwalten 129Smart-Home-Kameras und -Türklingeln immer im Blick 129Alexa wird zur Steuerzentrale 129Fotos anschauen 130Display-Hintergrund wählen 130Das war noch nicht alles 131TEIL IV: DER TOP-TEN-TEIL 133KAPITEL 20 DIE 10 BESTEN PRODUKTIVITÄTS-SKILLS FÜR DEN ALLTAG 135Abfallkalender 135TV Digital Fernsehprogramm 135wikiHow 136Deutsche Bahn 136Chefkoch 136Spritpreise 137Stundenplan 137Stoppuhr Deluxe 137Landkarte 138Wiki Deutschland 138KAPITEL 21 DIE BESTEN SPIELCHEN FÜR ALEXA 139Wahrheit oder Lüge 139Quizduell 139Burger Imperium 140Was singt Dave? Das Musikquiz 140Akinator 140Tag X 140Schätze den Preis 141Würdest du eher? 141Stadt, Land, Fluss 141Nervensäge 141KAPITEL 22 10 IDEEN FÜR ROUTINEN 143Mit Routine in den Tag starten 143Die Morgen-Routine als Wecker-Ersatz 144Nach dem Wecker noch mal ans Aufstehen erinnert werden 144Schlafenszeit, auch für dich, Alexa! 145Sonnenaufgang mit den Lampen simulieren 146Einen Befehl blockieren 147Natürlicher mit Alexa sprechen 148Hau drauf, Licht aus 148Sturzalarm, wenn keine Bewegung mehr erkannt wird 149Keiner mehr zu Hause 149KAPITEL 23 10 LÖSUNGEN FÜR 6 HÄUFIGE STÖRUNGEN 151Alexa fühlt sich immer angesprochen 151Alexa ist schwerhörig 152Die Smart-Home-Geräte funktionieren nicht 153Alexa, starte mal neu! 153Gerät umbenennen 154Router 24/7 online lassen 154Das 2,4-GHz-Band nicht deaktivieren 154Gerät neu erkennen lassen 155Alexa spielt keine Musik mehr! 155Der Bildschirm zeigt nichts mehr an 156Der Lichtring von Alexa leuchtet blau und dreht sich die ganze Zeit! 156Stichwortverzeichnis 159
Applied Machine Learning for Health and Fitness
Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more.Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley—who happens to be both a machine learning expert and a professional ski instructor—has written an insightful book that takes you on a journey of modern sport science and AI.Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the author’s practical expertise in both tech and sports is an undeniable asset for your learning process. Today’s data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space.WHAT YOU'LL LEARN* Use multiple data science tools and frameworks* Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition* Build and train neural networks, reinforcement learning models and more* Analyze multiple sporting activities with deep learning* Use datasets available today for model trainingUse machine learning in the cloud to train and deploy models* Apply best practices in machine learning and data scienceWHO THIS BOOK IS FORPrimarily aimed at data scientists, coaches, sports enthusiasts and athletes interested in connecting sports with technology and AI methods.Kevin Ashley is a Microsoft architect, IoT expert, and professional ski instructor. He is an author and developer of top sports and fitness apps and platforms such as Active Fitness and Winter Sports with a multi-million user audience. Kevin often works with sports scientists, Olympic athletes, coaches and teams to advance technology use in sports.IntroductionMachine Learning is fun with sensors and sports. Today’s data scientist is out there, on the ski slopes, or surfing the waves, and best way to apply machine learning is real life scenarios of sports. What can we do if we had the best, the ultimate model of our body and health monitoring us constantly? So, when we wanted to start a new sport, for example skiing or surfing, our personal body assistant could give us suggestions, like a personal coach. With machine learning and AI methods, imagine having a coach next to you 24/7.Part I: SensorsChapter 1: Getting StartedWhy are sensors important for health and fitness? For coaches, athletes and health professionals, they provide and objective picture of your activity. It’s often impossible to capture micro-movements and forces of a downhill racer, moving at 100 mph down a winding ski trail, but when equipped with sensors, every aspect of that movement can be captured, analyzed and studied. In this book we’ll use various IoT devices that can be used for sports data collection: inertial measurement units (IMUs), attitude and heading reference systems (AHRS), inertial navigation systems (INS/GPS), pressure sensors and others.1. Types of sensors and what they measurea. IMUs, AHRSb. INS/GPSc. Pressure sensorsd. Heart ratee. Vision and camera2. Sport science and dataa. Why is data frequency so important? A typical GPS device in your mobile phone works at 1Hz, that is one reading per second. Why isn’t this enough for most sports applications?b. Machine Learning really cares about data frequencies, as a rule of thumb we will use 100 Hz for most sensor data we collect3. How can Machine Learning help?a. Problems solved by machine learning for human movement, health and fitness applications4. Visualizing sports from sensor dataProject: First look at athlete movement analysis with a sample sensor data setChapter 2: Sensor HardwareIt turns out they don’t sell sensors with built in machine learning at convenience stores just yet! So, we made some. We go over some sport specific requirements for sensors, where and how sensors are placed on the body and equipment. In this chapter we will cover choices for sensor hardware, communication from sensors for data collection and data choices for IoT devices.1) Sensor IoT devices: IMU, AHRS, INS/GPS, Pressure, Proximity2) Sensor communication3) Data choices for IoT devicesProject: Learning to work with a sample SensorKit datasetChapter 3: Sensor SoftwareOur sensor is operating at a relatively high frequency of 100 samples per second (100 Hz). We need a special software to connect our sensor to the app. In this chapter we include a practical project on how to connect our sensor via a protocol like Bluetooth Low Energy to a mobile device and transfer data to the cloud.1) Sensor firmware2) Algorithms for sensor data processing3) Connecting with the app and the SDKProject: Writing the code to connect from sensor to the cloudChapter 4: 3D Printing SensorsProject: 3D printing is a fantastic technology for custom applications like sports! In this chapter I included a fun project on designing the case for our sensor, using 3D design software like Fusion 360 and 3D printing our sensor.1) Designing sensor casing model for sports2) Printing the sensor3) Every sport is different!Project: Designing a case and 3D printing our sensorPart II: Sensor DataSensors generate an enormous amount of data! In this part we learn about different types of sensor data, how to parse it, store it, transfer between IoT devices and the cloud.Chapter 5: Collecting sensor dataThis is where we sports scientists have most fun: data science on the ski slopes and surfing the waves! In this chapter I included a project.1) Sports and sensor placement2) Designing sports experiments3) Software and mobile devices for sports4) Sensor data for MLProject: Collecting dribble data from a basketball sensorChapter 6: Storing and parsing dataStoring sensor data is an interesting subject: at 100 Hz we have a lot of data from sports!1) Data frequency and aggregation decisions2) What to calculate on the sensors3) Sending data to the cloudProject: Writing code to parse and store sensor dataChapter 7: Managing and streaming IoT data in the cloudAn overview of modern IoT data technologies for the cloud, this chapter is about managing and streaming IoT data in the cloud.1) Non-relational databases for sensor data2) Streaming IoT data: (Spark, Kafka, Azure Stream Analytics)3) Data pipelines for IoTProject: Storing and streaming IoT data in the cloudPart III: Machine Learning for Health, Fitness and SportsFrom sensor data to physics of sports, movement analysis and machine learning models.Chapter 8: Physics of sportsSports scientists believe that each sport can be described mathematically with physics, let’s dive into sport science! In this chapter we’ll have a physics project to help us better understand the models.1) Physics of movement2) Sensors and physicsProject: Calculating forces for an athlete, using physicsChapter 9: Machine Learning modelsMachine Learning models for sports. This chapter defines reasoning behind various algorithms for machine learning in sports, as applied to sensor data.1) Raw sensor data2) Clean and transform the data3) Engineering features4) Supervised Learning5) Unsupervised Learning6) Reinforcement LearningProject: Creating a machine learning model from our experimentsChapter 10: Applying Machine Learning for various activitiesIn this chapter we look at some applications of sensors for sports, fitness and health.1) Skiing and snowboarding2) Basketball3) Tennis4) Diving5) Javelin6) SurfingPart III: Visualizing SensorsUsing computer vision and visualizing sports data in 3D and VR.Chapter 11: Computer visionComputer vision is an important way of tracking athletes in real time.1) Computer vision for sports overview2) 3D body rendering3) Problems with computer vision vs sensors (occlusion)4) Winning scenario: combining sensors with computer vision5) Project: using computer vision for athletic performanceProject: using computer vision for athletic performanceChapter 12: Visualizing athlete in 3D, Holograms and VRIn this chapter we’ll touch the holy grail of sports science: visualizing athlete in full 3D, as a holographic avatar.1) Methods and requirements for 3D visualization2) Using Unity to visualize dataChapter 13: Vision and SensorsThis chapter is about combining vision and sensors. Imagine, if we had to bring visual and sensor data together, then we have a tool that can provide both a near-real time visual feedback and video analysis.1) Combining sensor and video dataProject: Combining sensor and video data for analysisPart V: What the Coach needsFrom individual athletes to the team: this chapter would make the coach happy! Often, tracking an individual athlete with sensors is not enough: coaches or health professionals deal with teams they need to analyze.Chapter 14: Coach and team view on the dataWorking with coaches on US Olympic Team, WTA, WNBA, professional ski and snowboard instructors, I learned a lot about requirements that coaches have on the sensors, data, analytics and presentation of the data.1) Coaches and teams view2) Looking across the entire team3) Coach dashboard (PowerBI)Project: Creating a coach dashboard with PowerBIChapter 15: Connected sensors and sports teamsFrom individual athletes and sports, to connected experiences.1) Sensor data from the team prospective2) Connected teamConclusion: What’s nextThis book provides a toolkit, a foundation for a sports scientist or a data professional to use sensors and machine learning for insights about athlete performance and injury prevention.PROJECTS1) First look at athlete movement analysis with a sample sensor data set2) Learning to work with a sample sport dataset3) Writing the code to connect from sensor to the cloud4) Writing code to parse and store sensor data5) Storing and streaming IoT data in the cloud6) Designing a case and 3D printing our sensor7) Collecting dribble data from a basketball sensor8) Calculating forces for an athlete, using physics9) Creating a machine learning model from our experiments10) Using computer vision for athletic performance11) Combining sensor and video data for analysis12) Creating a coach dashboard with PowerBI for the team
Hands-on Time Series Analysis with Python
Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima.The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more.WHAT YOU'LL LEARN:· Explains basics to advanced concepts of time series· How to design, develop, train, and validate time-series methodologies· What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results· Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.· Univariate and multivariate problem solving using fbprophet.WHO THIS BOOK IS FORData scientists, data analysts, financial analysts, and stock market researchersVISHWAS B V is a Data Scientist, AI researcher and Sr. AI Consultant, Currently living in Bengaluru(INDIA). His highest qualification is Master of Technology in Software Engineering from Birla Institute of Technology & Science, Pilani, and his primary focus and inspiration is Data Warehousing, Big Data, Data Science (Machine Learning, Deep Learning, Timeseries, Natural Language Processing, Reinforcement Learning, and Operation Research). He has over seven years of IT experience currently working at Infosys as Data Scientist & Sr. AI Consultant. He has also worked on Data Migration, Data Profiling, ETL & ELT, OWB, Python, PL/SQL, Unix Shell Scripting, Azure ML Studio, Azure Cognitive Services, and AWS.ASHISH PATEL is a Senior Data Scientist, AI researcher, and AI Consultant with over seven years of experience in the field of AI, Currently living in Ahmedabad(INDIA). He has a Master of Engineering Degree from Gujarat Technological University and his keen interest and ambition to research in the following domains such as (Machine Learning, Deep Learning, Time series, Natural Language Processing, Reinforcement Learning, Audio Analytics, Signal Processing, Sensor Technology, IoT, Computer Vision). He is currently working as Senior Data Scientist for Cynet infotech Pvt Ltd. He has published more than 15 + Research papers in the field of Data Science with Reputed Publications such as IEEE. He holds Rank 3 as a kernel master in Kaggle. Ashish has immense experience working on cross-domain projects involving a wide variety of data, platforms, and technologiesChapter 1: Time Series and its CharacteristicsChapter 2: Data Wrangling and Preparation for Time SeriesChapter 3: Smoothing MethodsChapter 4: Regression Extension Techniques for Time SeriesChapter 5: Bleeding Edge TechniquesChapter 6: Bleeding Edge Techniques for Univariate Time SeriesChapter 7: Bleeding Edge Techniques for Multivariate Time SeriesChapter 8: Prophet
Practical Test Automation
Learn the principles behind test-driven development (TDD) and behavior-driven development (BDD) and see how Jasmine, RSpec and Cucumber can be used to your advantage. This book examines some of the leading technologies used for testing.You'll see how to use Jasmine’s features to work with a JavaScript application. You will learn how to use Mini Test and RSpec with Ruby and Rubymine. Finally, you’ll use Cucumber to develop your software using a BDD approach.Understanding test automation is a vital skill for any web developer. Practical Test Automation breaks down for you some of the important TDD and BDD technologies on the modern web.WHAT YOU'LL LEARN* Test an example JavaScript application with Jasmine* Use Jasmine with JS Bin* Work with Minitest for test-driven development* Test an example Ruby project with RSpec* Use Cucumber and Gherkin for behavior-driven development* Integrate Cucumber with RSpec WHO THIS BOOK IS FORThis book is for anyone who wants to learn test automation and more about test-driven development and behavior-driven development.PANOS MATSINOPOULOS loves developing programs, both for web browsers and for mobile apps. He has been doing that for the past 25 years and has developed numerous applications. He also loves writing books, blogging and teaching computer programming. He has organized a lot of programming classes for kids, adults and elderly people. You can read find him on Twitter @pmatsino.PRACTICAL TEST AUTOMATIONChapter 1. Introduction to JasmineChapter 2. Advanced JasmineChapter 3. Using MinitestChapter 4. Introduction to RSpecChapter 5. Useful RSpec ToolsChapter 6. Introduction to CucumberChapter 7. Advanced Cucumber
Hacking of Computer Networks
The objective of the book is to summarize to the user with main topics in computer networking hacking.The book consists of the following parts:Part 1: Lab SetupPart2: Foot printing and ReconnaissancePart 3: Scanning MethodologyPart 4: EnumerationPart 5:System HackingPart 6: Trojans and Backdoors and VirusesPart 7: Sniffer and Phishing HackingPart 8: Hacking Web ServersPart 9:Hacking Windows and Linux SystemsPart 10: Wireless HackingPart 11: Hacking Mobile ApplicationsI am Dr. Hidaia Mahmoud Mohamed Alassouli. I completed my PhD degree in Electrical Engineering from Czech Technical University by February 2003, and my M. Sc. degree in Electrical Engineering from Bahrain University by June 1995. I completed also one study year of most important courses in telecommunication and computer engineering courses in Islamic university in Gaza. So, I covered most important subjects in Electrical Engineering, Computer Engineering and Telecommunications Engineering during my study. My nationality is Palestinian from gaza strip.I obtained a lot of certified courses in MCSE, SPSS, Cisco (CCNA), A+, Linux.I worked as Electrical, Telecommunicating and Computer Engineer in a lot of institutions. I worked also as a computer networking administrator. I had considerable undergraduate teaching experience in several types of courses in many universities. I handled teaching the most important subjects in Electrical and Telecommunication and Computer Engineering. I could publish a lot of papers a top-tier journals and conference proceedings, besides I published a lot of books in Publishing and Distribution houses.I wrote a lot of important Arabic articles on online news websites. I also have my own magazine website that I publish on it all my articles: http:// www.anticorruption.000space.comMy personal website: www.hidaia-alassouli.000space.comEmail: hidaia_alassouli@hotmail.com