Allgemein
Learn Java for Android Development
Gain the essential Java language skills necessary for using the Android SDK platform to build Java-based Android apps. This book includes the latest Java SE releases that Android supports, and is geared towards the Android SDK version 10. It includes new content including JSON documents, functional programming, and lambdas as well as other language features important for migrating Java skills to Android development.Android is still the world's most popular mobile platform and because this technology is still mostly based on Java, you should first obtain a solid grasp of the Java language and its APIs in order to improve your chances of succeeding as an effective Android apps developer. Learn Java for Android Development, 4th Editionhelps you do that.Each of the book’s chapters provides an exercise section that gives you the opportunity to reinforce your understanding of the chapter’s material. Answers to the book’s more than 500 exercises are provided in an appendix. Once you finish, you will be ready to begin your Android app development journey using Java.WHAT YOU WILL LEARN* Discover the latest Java programming language features relevant to Android SDK development* Apply inheritance, polymorphism, and interfaces to Android development* Use Java collections, concurrency, I/O, networks, persistence, functional programming, and data access in Android apps* Parse, create, and transform XML and JSON documents* Migrate your Java skills for mobile development using the Android platformWHO THIS BOOK IS FORProgrammers with at least some prior Java programming experience looking to get into mobile Java development with the Android platform.PETER SPÄTH consults, trains/teaches, and writes books on various subjects, with a primary focus on software development. With a wealth of experience in Java-related languages, the release of Kotlin for building Android apps made him enthusiastic about writing books for Kotlin development in the Android environment. He also graduated in 2002 as a physicist and soon afterward became an IT consultant, mainly for Java-related projects.JEFF FRIESEN is a freelance tutor and software developer with an emphasis on Java (and now Android). In addition to authoring Learn Java for Android Development and co-authoring Android Recipes, Jeff has written numerous articles on Java and other technologies for JavaWorld, informIT, Java.net, and DevSource.1: Getting Started with JavaTalking about ART and licensing here2: Learning Language Fundamentals3: Discovering Classes and Objects4: Discovering Inheritance, Polymorphism, and Interfaces5: Mastering Advanced Language Features, Part 16: Mastering Advanced Language Features, Part 27: Exploring the Basic APIs, Part18: Exploring the Basic APIs, Part29: Functional Programming and Lambdas10: Exploring the Collections Framework11: Exploring the Concurrency Utilities12: Performing Classic I/O13: Accessing Networks14: Migrating to New I/O15: Accessing Databases16: Parsing, Creating, and Transforming XML Documents17: Working With JSON DocumentsA. Solutions to Exercises
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.KRISHNA KANT SINGH is an Associate Professor in Electronics and Communications Engineering in KIET Group of Institutions, Ghaziabad, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals. AKANSHA SINGH is an Associate Professor in Department of Computer Science Engineering in Amity University, Noida, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of neural network and remote sensing. She has authored more than 40 technical books and research papers in international conferences and SCIE journals. Her area of interest includes Mobile Computing, Artificial Intelligence, Machine Learning, Digital Image Processing. KORHAN CENGIZ received his PhD in Electronics Engineering from Kadir Has University, Istanbul, Turkey, in 2016. He has served as keynote speakers at many conferences. His research interests include wireless sensor networks, routing protocols, wireless communications, 5G systems, statistical signal processing, and spatial modulation. DAC-NHUONG LE has a MSc and PhD in computer science from Vietnam National University, Vietnam in 2009 and 2015, respectively. He is Associate Professor in Computer Science, Deputy-Head of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 12+ years with many publications in reputed international conferences, journals and online book chapters. His area of research includes: evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT and image processing in biomedical. Preface xiii1 MACHINE LEARNING ARCHITECTURE AND FRAMEWORK 1Nilanjana Pradhan and Ajay Shankar Singh1.1 Introduction 21.2 Machine Learning Algorithms 31.2.1 Regression 31.2.2 Linear Regression 41.2.3 Support Vector Machine 41.2.4 Linear Classifiers 51.2.5 SVM Applications 81.2.6 Naïve Bayes Classification 81.2.7 Random Forest 91.2.8 K-Nearest Neighbor (KNN) 91.2.9 Principal Component Analysis (PCA) 91.2.10 K-Means Clustering 101.3 Business Use Cases 101.4 ML Architecture Data Acquisition 141.5 Latest Application of Machine Learning 151.5.1 Image Identification 161.5.2 Sentiment Analysis 161.5.3 News Classification 161.5.4 Spam Filtering and Email Classification 171.5.5 Speech Recognition 171.5.6 Detection of Cyber Crime 171.5.7 Classification 171.5.8 Author Identification and Prediction 181.5.9 Services of Social Media 181.5.10 Medical Services 181.5.11 Recommendation for Products and Services 181.5.11.1 Machine Learning in Education 191.5.11.2 Machine Learning in Search Engine 191.5.11.3 Machine Learning in Digital Marketing 191.5.11.4 Machine Learning in Healthcare 191.6 Future of Machine Learning 201.7 Conclusion 22References 232 COGNITIVE COMPUTING: ARCHITECTURE, TECHNOLOGIES AND INTELLIGENT APPLICATIONS 25Nilanjana Pradhan, Ajay Shankar Singh and Akansha Singh2.1 Introduction 262.1 The Components of a Cognitive Computing System 272.3 Subjective Computing Versus Computerized Reasoning 282.4 Cognitive Architectures 292.5 Subjective Architectures and HCI 312.6 Cognitive Design and Evaluation 322.6.1 Architectures Conceived in the 1940s Can’t Handle the Data of 2020 412.7 Cognitive Technology Mines Wealth in Masses of Information 412.7.1 Technology is Only as Strong as Its Flexible, Secure Foundation 422.8 Cognitive Computing: Overview 432.9 The Future of Cognitive Computing 47References 493 DEEP REINFORCEMENT LEARNING FOR WIRELESS NETWORK 51Bharti Sharma, R.K Saini, Akansha Singh and Krishna Kant Singh3.1 Introduction 513.2 Related Work 543.3 Machine Learning to Deep Learning 553.3.1 Advance Machine Learning Techniques 563.3.1.1 Deep Learning 563.3.2 Deep Reinforcement Learning (DRL) 573.3.2.1 Q-Learning 583.3.2.2 Multi-Armed Bandit Learning (MABL) 583.3.2.3 Actor–Critic Learning (ACL) 583.3.2.4 Joint Utility and Strategy Estimation Based Learning 593.4 Applications of Machine Learning Models in Wireless Communication 593.4.1 Regression, KNN and SVM Models for Wireless 603.4.2 Bayesian Learning for Cognitive Radio 603.4.3 Deep Learning in Wireless Network 613.4.4 Deep Reinforcement Learning in Wireless Network 623.4.5 Traffic Engineering and Routing 633.4.6 Resource Sharing and Scheduling 643.4.7 Power Control and Data Collection 643.5 Conclusion 65References 664 COGNITIVE COMPUTING FOR SMART COMMUNICATION 73Poonam Sharma, Akansha Singh and Aman Jatain4.1 Introduction 744.2 Cognitive Computing Evolution 754.3 Characteristics of Cognitive Computing 764.4 Basic Architecture 774.4.1 Cognitive Computing and Communication 774.5 Resource Management Based on Cognitive Radios 784.6 Designing 5G Smart Communication with Cognitive Computing and AI 804.6.1 Physical Layer Design Based on Reinforcement Learning 824.7 Advanced Wireless Signal Processing Based on Deep Learning 844.7.1 Modulation 854.7.2 Deep Learning for Channel Decoding 864.7.3 Detection Using Deep Learning 874.8 Applications of Cognition-Based Wireless Communication 874.8.1 Smart Surveillance Networks for Public Safety 884.8.2 Cognitive Health Care Systems 884.9 Conclusion 89References 895 SPECTRUM SENSING AND ALLOCATION SCHEMES FOR COGNITIVE RADIO 91Amrita Rai, Amit Sehgal, T.L. Singal and Rajeev Agrawal5.1 Foundation and Principle of Cognitive Radio 925.2 Spectrum Sensing for Cognitive Radio Networks 945.3 Classification of Spectrum Sensing Techniques 955.4 Energy Detection 975.5 Matched Filter Detection 1005.6 Cyclo-Stationary Detection 1035.7 Euclidean Distance-Based Detection 1075.8 Spectrum Allocation for Cognitive Radio Networks 1085.9 Challenges in Spectrum Allocation 1185.9.1 Spectrum and Network Heterogeneity 1195.9.2 Issues and Challenges 1205.10 Future Scope in Spectrum Allocation 122References 1236 SIGNIFICANCE OF WIRELESS TECHNOLOGY IN INTERNET OF THINGS (IOT) 131Ashish Tripathi, Arun Kumar Singh, Pushpa Choudhary, Prem Chand Vashist and K. K. Mishra6.1 Introduction 1326.1.1 Internet of Things: A Historical Background 1336.1.2 Internet of Things: Overview, Definition, and Understanding 1336.1.3 Internet of Things: Existing and Future Scopes 1356.2 Overview of the Hardware Components of IoT 1366.2.1 IoT Hardware Components: Development Boards/Platforms 1366.2.1.1 Arduino 1366.2.1.2 Raspberry Pi 1376.2.1.3 BeagleBone 1376.2.2 IoT Hardware Components: Transducer 1386.2.2.1 Sensors 1386.2.2.2 Actuators 1386.3 Wireless Technology in IoT 1396.3.1 Topology 1396.3.1.1 Mesh Topology 1406.3.1.2 Star Topology 1416.3.1.3 Point-to-Point Topology 1416.3.2 IoT Networks 1426.3.2.1 Nano Network 1426.3.2.2 Near-Field Communication (NFC) Network 1436.3.2.3 Body Area Network (BAN) 1436.3.2.4 Personal Area Network (PAN) 1436.3.2.5 Local Area Network (LAN) 1436.3.2.6 Campus/Corporate Area Network (CAN) 1436.3.2.7 Metropolitan Area Network (MAN) 1446.3.2.8 Wide Area Network (WAN) 1446.3.3 IoT Connections 1446.3.3.1 Device-to-Device (D2D)/Machine-to-Machine (M2M) 1446.3.3.2 Machine-to-Gateway/Router (M2G/R) 1456.3.3.3 Gateway/Router-to-Data System (G/R2DS) 1456.3.3.4 Data System to Data System (DS2DS) 1456.3.4 IoT Protocols/Standards 1456.3.4.1 Network Protocols for IoT 1466.3.4.2 Data Protocols for IoT 1486.4 Conclusion 150References 1507 ARCHITECTURES AND PROTOCOLS FOR NEXT-GENERATION COGNITIVE NETWORKING 155R. Ganesh Babu, V. Amudha and P. Karthika7.1 Introduction 1567.1.1 Primary Network (Licensed Network) 1567.1.2 CR Network (Unlicensed Network) 1577.2 Cognitive Radio Network Technologies and Applications 1597.2.1 Classes of CR 1597.2.2 Next Generation (xG) of CR Applications 1627.3 Cognitive Computing: Architecture, Technologies, and Intelligent Applications 1637.3.1 CR Physical Architecture 1637.4 Functionalities of CR in NeXt Generation (xG) Networks 1647.5 Spectrum Sensing 1657.5.1 Spectrum Decision 1657.5.2 Spectrum Mobility 1657.5.3 CR Network Functions 1667.6 Cognitive Computing for Smart Communications 1677.6.1 CR Technologies 1677.7 Spectrum Allocation in Cognitive Radio 1697.8 Cooperative and Cognitive Network 1737.8.1 Cooperative Centralized Coordinated 1737.8.2 Cooperative Decentralized (Distributed) Coordinated and Uncoordinated 176References 1768 ANALYSIS OF PEAK-TO-AVERAGE POWER RATIO IN OFDM SYSTEMS USING COGNITIVE RADIO TECHNOLOGY 179Udayakumar Easwaran, Poongodi Palaniswamy and Vetrivelan Ponnusamy8.1 Introduction 1808.2 OFDM Systems 1818.3 Peak-to-Average Power Ratio 1838.4 Cognitive Radio (CR) 1848.5 Related Works 1868.6 Neural Network System Model 1938.7 Complexity Examination 1948.8 PAPR and BER Examination 1958.9 Performance Evaluation 1968.10 Results and Discussions 1968.11 Conclusion 200References 2009 A THRESHOLD-BASED OPTIMIZATION ENERGY-EFFICIENT ROUTING TECHNIQUE IN HETEROGENEOUS WIRELESS SENSOR NETWORKS 203Samayveer Singh9.1 Introduction 2049.2 Literature Review 2059.3 System Model 2079.3.1 Four-Level Heterogeneous Network Model 2089.3.2 Energy Dissipation Radio Model 2109.4 Proposed Work 2119.4.1 Optimum Cluster Head Election of the Proposed Protocol 2119.4.2 Information Congregation and Communication Process Based on Chaining System for Intra and Inter‑Cluster Communication 2149.4.3 The Complete Working Process of the Proposed Method 2149.5 Simulation Results and Discussions 2169.5.1 Network Lifetime and Stability Period 2179.5.2 Network Outstanding Energy 2199.5.3 Throughput 2199.5.4 Comparative Analysis of the Level-4 Network Protocols 2229.6 Conclusion 222References 22310 EFFICACY OF BIG DATA APPLICATION IN SMART CITIES 225Sudipta Sahana, Dharmpal Singh and Pranati Rakshit10.1 Introduction 22610.1.1 Characteristics of Big Data 22710.1.1.1 Velocity 22710.1.1.2 Volume 22710.1.1.3 Value 22810.1.1.4 Variety 22810.1.1.5 Veracity 22810.1.2 Definition of Smart Cities 22810.2 Types of Data in Big Data 22910.2.1 Structured Data 22910.2.2 Unstructured Data 23010.2.3 Semi-Structured Data 23010.3 Big Data Technologies 23110.3.1 Apache Hadoop 23110.3.2 HDFS 23110.3.3 Spark 23210.3.4 Microsoft HDInsight 23210.3.5 NoSQL 23310.3.6 Hive 23310.3.7 Sqoop 23410.3.8 R 23510.3.9 Data Lakes 23510.4 Data Source for Big Data 23510.4.1 Media 23610.4.2 Cloud 23610.4.3 The Web 23610.4.4 IOT 23610.4.5 Databases as a Big Data Source 23710.4.6 Hidden Big Data Sources 23710.4.6.1 Email 23710.4.6.2 Social Media 23810.4.6.3 Open Data 23810.4.6.4 Sensor Data 23810.4.7 Application-Oriented Big Data Source for a Smart City 23810.4.7.1 Healthcare 23810.4.7.2 Transportation 23910.4.7.3 Education 24010.5 Components of a Smart City 24110.5.1 Smart Infrastructure 24110.5.1.1 Intelligent Lighting 24110.5.1.2 Modern Parking Systems 24110.5.1.3 Associated Charging Points 24210.5.2 Smart Buildings and Belongings 24210.5.2.1 Safety and Security Systems 24210.5.2.2 Smart Sprinkler Systems for Gardens 24210.5.2.3 Smart Heating and Ventilation 24210.5.3 Smart Industrial Environment 24310.5.4 Smart City Services 24310.5.4.1 Smart Stalls 24310.5.4.2 Monitoring of Risky Areas 24410.5.4.3 Public Safety 24410.5.4.4 Fire/Explosion Management 24410.5.4.5 Automatic Health-Care Delivery 24410.5.5 Smart Energy Management 24410.5.5.1 Smart Grid 24510.5.5.2 Intelligent Meters 24510.5.6 Smart Water Management 24510.5.7 Smart Waste Management 24510.6 Challenge and Solution of Big Data for Smart City 24610.6.1 Challenge in Big Data for Smart City 24610.6.1.1 Data Integration 24610.6.1.2 Security and Privacy 24610.6.1.3 Data Analytics 24710.6.2 Solution of Challenge Smart City 24710.6.2.1 Conquering Difficulties with Enactment 24710.6.2.2 Making People Smarter—Education for Everyone 24810.7 Conclusion 248References 249Index 251
Von Data Mining bis Big Data
Von Datensalat zu DatenschatzEine wichtige Säule von Industrie 4.0 ist Big Data. Hierbei geht es um die intelligente Verwertung riesiger Datenmengen mit dem Ziel Prozesse besser zu beherrschen oder neue Geschäftsfelder zu finden. Big Data für sich zu erschließen bedeutet nichts anderes als einen Schatz zu heben, der in der Fülle von Informationen, die Sie in Ihrem Unternehmen anhäufen, verborgen liegt. Dieses Buch enthält die Schatzkarte. Hier erfahren Sie- wie mit Hilfe von Data Mining-Techniken unbekannte Zusammenhänge und Strukturen über den datenliefernden Prozess entdeckt werden können- wie mit den gewonnenen Erkenntnissen detaillierte Vorhersagen über das zukünftige Prozessverhalten und Strategien zur Optimierung ganzer Fabriken abgeleitet werden- welche Tools und Plattformen es gibt, um Big Data wirtschaftlich sinnvoll in Ihr Unternehmen einzuführen- wie andere Firmen aus verschiedensten Branchen mit Big Data erfolgreiche Effizienzsteigerungen erreicht habenWenn Sie sich im Rahmen der aktuell laufenden Digitalisierungswelle fragen, welche der modernen Techniken wofür genutzt werden können oder müssen, um den Anschluss nicht zu verpassen, dann ist dieses Buch genau das richtige für Sie. Der Autor hat über 20 JahreErfahrung bei der Entwicklung von Data Mining-Technologien und bei ihrem Einsatz in der Industrie. Prof. Dr. Otte leitet die Lehre im Bereich Künstliche Intelligenz im Masterstudium an der Hochschule Ulm
Komplexität von Algorithmen
Dieses Lehrbuch, entstanden aus einer Anfängervorlesung aus dem Informatik-Studiengang an der Leibniz Universität Hannover, bietet einen ersten Einstieg in den Bereich der Komplexitätstheorie.Der Leser wird mit den wichtigsten Begriffen und Resultaten aus diesem Bereich vertraut gemacht: Komplexitätsklassen, vollständige („schwierigste“) Probleme in einer Komplexitätsklasse – detailliert am Begriff der NP-Vollständigkeit und an vielen Beispielen ausgeführt – sowie Approximationsalgorithmen als Lösungsmöglichkeit für viele NP-vollständige Probleme.Außerdem enthält das Buch eine große Anzahl an Übungsaufgaben (mit vielen Lösungen) wie auch abschließend die Möglichkeit, sein erarbeitetes Wissen in zwei exemplarischen Klausuren zu prüfen.
Modulare Softwarearchitektur
MODULARE SOFTWAREARCHITEKTUR //- Lesen Sie Grundlegendes über die neue Schule der Softwarearchitektur- Nutzen Sie die konkreten Empfehlungen zum Bau langlebiger, weil modularer Enterprise- bzw. Makro-Architektur- Erfahren Sie, wie Sie Schritt für Schritt Ihr System zu einer modularen Architektur hin umbauen können- Lernen Sie die Prinzipien nachhaltiger und modularer Architektur anhand eines 16-teiligen Code-Tutorials kennen- Das 5C-Modell stellt eine Alternative zum Microservice-Hype dar und ist auch bei komplexen Architekturen anwendbarDie Softwarekrise hält die IT-Branche seit den 1960er-Jahren in Atem. Damals wurde heftig darüber debattiert, ob man nicht lieber auf die Verwendung des GOTO-Schlüsselworts und x-beliebiger Sprünge im Code verzichten sollte. Mit der zunehmenden Leistungsfähigkeit der Hardware wurde auch die darauf laufende Software immer komplexer, und man suchte nach Rezepten, um deren Wartbarkeit zu verbessern.Diese Entwicklung ist bis heute nicht abgeschlossen. Die digitale Transformation der Gesellschaft bringt für bestehende IT-Landschaften neue Herausforderungen mit sich. Enterprise-Architekturen, die sich oft durch ungeplant entstandene Komplexität auszeichnen, sollen plötzlich weiterwachsen. Da die klassischen Muster der Enterprise-Architektur dabei oft versagen, werden zurzeit neue Prinzipien und Muster entwickelt, welche genau diese Komplexität in geordnete Bahnen lenken sollen.Dieses Buch behandelt diese Themen und stellt den Bezug zu den guten alten Mustern und Prinzipien des Softwaredesigns her.AUS DEM INHALT //Über Softwarearchitektur/Migration von Legacy-Systemen/Domain Driven Design/Muster modularer Mikro-Architektur/Muster modularer Makro-Architektur bzw. langlebige Enterprise-Architektur/Monolithen, Microservices und Alternativen/Antipattern und Pitfalls modularer Architektur und wie man diese vermeidet/Umsetzung modularer Architektur in komplexen Organisationen Herbert Dowalil ist seit vielen Jahren als Softwareentwickler, -architekt, Trainer und inzwischen auch als Autor tätig. Dabei beschäftigt er sich mit der Frage, wie man durch den Entwurf effizienter Strukturen langfristig Produktivität und Flexibilität sicherstellt. Sein Hauptaugenmerk gilt dabei einer der Schwachstellen der IT-Branche, nämlich der modularen Enterprise- bzw. Makro-Architektur.
Visual Studio Extensibility Development
Learn the extensibility model of Visual Studio to enhance the Visual Studio integrated development environment (IDE). This book will cover every aspect, starting from developing an extension to publishing it and making it available to the end user.The book begins with an introduction to the basic concepts of Visual Studio including data structures and design patterns and moves forward with the fundamentals of the VS extensibility model. Here you will learn how to work on Roslyn - the .NET compiler platform - and load extensions in VS. Next, you will go through the extensibility model and see how various extensions, such as menus, commands, and tool windows, can be plugged into VS. Moving forward, you’ll cover developing VS extensions and configuring them, along with demonstrations on customizing extension by developing option pages. Further, you will learn to create custom code snippets and use a debugger visualizer. Next, you will go through creation of project and item templates including deployment of VS extensions using continuous integration (CI). Finally, you will learn tips and tricks for Visual Studio and its extensibility and integration with Azure DevOps.After reading Visual Studio Extensibility Development you will be able to develop, deploy, and customize extensions in Visual Studio IDE.WHAT YOU WILL LEARN* Discover the Visual Studio extensibility and automation model* Code Visual Studio extensions from scratch* Customize extensions by developing a tools option page for them* Create project templates, item templates, and code snippets.* Work with code generation using T4 templates* Code analysis and refactoring using Roslyn analyzers* Create and deploy a private extension gallery and upload the extensions* Upload a VS extension using CI* Ship your extension to Visual Studio MarketplaceWHO THIS BOOK IS FORDevelopers in Visual Studio IDE covering C#, Visual Basic (VB), JavaScript, and CSS.RISHABH VERMA is a Microsoft certified professional and works at Microsoft as a senior development consultant, helping the customers to design, develop, and deploy enterprise-level applications. An electronic engineer by education, he has 12+ years of hardcore development experience on the .NET technology stack. He is passionate about creating tools, Visual Studio extensions, and utilities to increase developer productivity. His interests are .NET Compiler Platform (Roslyn), Visual Studio extensibility, code generation and .NET Core. He is a member of .NET foundation (https://www.dotnetfoundation.org). He occasionally blogs at https://rishabhverma.net/. He has authored books on .NET Core 2.0 and .NET Core 3.1 prior to this title.His twitter id is @VermaRishabh and his linkedIn page is https://www.linkedin.com/in/rishabhverma/CHAPTER 1: BASICS PRIMERCHAPTER GOAL: The objective of this chapter is to introduce the basic concepts to the reader that would be required through-out this book, so that he gets comfortable in this learning journey.NO OF PAGES :50-60SUB -TOPICS1. What is a compiler?2. What is an SDK (Software Development Kit)?3. Recap of Tree Data structure1. Tree traversal,2. Abstract Syntax trees4. MEF (Managed Extensibility Framework) Basics.5. Visual Studio & its history6. XML & JSON7. Serialization & Deserialization.8. Revisiting Visitor, Abstract Factory and Factory design patterns.9. MSBuild basics10. Async-await.CHAPTER 2: GETTING STARTEDCHAPTER GOAL: With the fundamentals strongly in place, we are now good to get started with Visual Studio (VS) Extensibility model. We will do our setup in this chapter. This chapter would introduce the VS Extensibility, Roslyn to the reader. The reader would also learn to write and debug a VS extension.NO OF PAGES: 40SUB - TOPICS1. Prerequisites and installation of VS2. Anatomy of a VSIX3. How Visual Studio discovers and loads extensions.4. VSPackage5. Async Loading6. Writing your first simple templatized Visual Studio Extension.7. Roslyn - .NET Compiler platform fundamentalsCHAPTER 3: EXTENDING VISUAL STUDIOCHAPTER GOAL: This chapter would introduce the extensibility model and how various extensions can be plugged in VSas menus, commands, tool window, code window, solution explorer etcNO OF PAGES: 40-50SUB - TOPICS:1. The Visual Studio Extensibility model2. Tool Window extension3. Menus & commands,4. Code Window extension5. Solution explorer item extensionCHAPTER 4: DEVELOPING REAL WORLD EXTENSIONS - ICHAPTER GOAL: This chapter dives into developing useful real-world VS Extensions and shows how they can be made configurable by customizing UI and options page.We would also learn how to write to output window and manipulate documents and projects in this chapter.NO OF PAGES: 40-50SUB - TOPICS:1. VS Extension to search on MSDN/Bing/Google.2. VS Extension to generate HTTP Client proxy class for HTTP Web API using T4 templates.3. VS Extension to generate test data.4. Customizing extension by developing Tools option page.5. Customizing UI of extension.CHAPTER 5: DEVELOPING REAL WORLD EXTENSIONS - IICHAPTER GOAL: This chapter is the continuation of last chapter and continues the development of useful real-world VS extensions but this time using the .NET Compiler platform – Roslyn.NO OF PAGES: 40-50SUB - TOPICS:1. Rewrite VS Extension to generate HTTP Client proxy class for HTTP Web API using Roslyn.2. Developing a custom code analysis Visual Studio Extension.3. Developing a light bulb style code refactoring.4. Developing Roslyn based extension to generate unit tests using T4 template.CHAPTER 6: DO MORE WITH VS SDKCHAPTER GOAL: This chapter introduces the reader with famous Visual Studio Isolated and integrated Shell to develop applications that looks like Visual Studio and also develops handy productivity boosters like custom code snippets, debugger visualizers, modifying intellisense, debugging experience for developersNO OF PAGES: 40-50SUB - TOPICS:1. VS Isolated and Integrated Shell2. Developing applications that look like Visual Studio.3. Extending the debugger.4. Create custom code snippets.5. Create Debugger Visualizer for view data while debugging.6. Modifying intellisense.CHAPTER 7: TEMPLATES, DEBUGGING VS EXTENSIONSCHAPTER GOAL: This chapter explains how to create project and item templates. The chapter also shows a sample code lens extension then dives into debugging the extensionNO OF PAGES: 40-50SUB - TOPICS:6. Code lens sample extension.7. Creating Project and Item template.8. Debugging VS Extensions.CHAPTER 8: DEPLOYING VS EXTENSIONSCHAPTER GOAL: This chapter explains how to deploy VS extensions using continuous integration (CI). The chapter also explains how the extension can be made available to the world by uploading in marketplace. We also discuss how to make a private extension gallery and host your extension there.NO OF PAGES: 40-50SUB - TOPICS:9. Deploying a VS Extension using CI.10. Creating a private extension gallery/ Atom feed11. Hosting extension in private gallery.12. Sharing extension with the world using marketplace.CHAPTER 9: TIPS, TRICKS, EXTENSIONS AND WORDSCHAPTER GOAL: This chapter discusses few of the coolest tips and tricks for Visual Studio and its extensibility and shares few highly useful extensions. The chapter and book conclude with closing remarks on extensibility of Visual Studio Code and integration with Visual Studio Team Services (VSTS) or Azure DevOps.NO OF PAGES: 30-40SUB - TOPICS:1. Cool Tips and tricks2. Useful Extensions for C#, VB, JS, TS and CSS developers.3. A word on Visual Studio Code Extensibility4. Integration with VSTS or Azure DevOpsUseful Resources – 1 pageMore Reading – 1 pageCode Samples – Link to code samples from GitHub.
Computational Models for Cognitive Vision
LEARN HOW TO APPLY COGNITIVE PRINCIPLES TO THE PROBLEMS OF COMPUTER VISIONComputational Models for Cognitive Vision formulates the computational models for the cognitive principles found in biological vision, and applies those models to computer vision tasks. Such principles include perceptual grouping, attention, visual quality and aesthetics, knowledge-based interpretation and learning, to name a few. The author’s ultimate goal is to provide a framework for creation of a machine vision system with the capability and versatility of the human vision.Written by Dr. Hiranmay Ghosh, the book takes readers through the basic principles and the computational models for cognitive vision, Bayesian reasoning for perception and cognition, and other related topics, before establishing the relationship of cognitive vision with the multi-disciplinary field broadly referred to as “artificial intelligence”. The principles are illustrated with diverse application examples in computer vision, such as computational photography, digital heritage and social robots. The author concludes with suggestions for future research and salient observations about the state of the field of cognitive vision.Other topics covered in the book include:· knowledge representation techniques· evolution of cognitive architectures· deep learning approaches for visual cognitionUndergraduate students, graduate students, engineers, and researchers interested in cognitive vision will consider this an indispensable and practical resource in the development and study of computer vision.HIRANMAY GHOSH, PHD, was a Research Advisor to TATA Consultancy Services and an Adjunct Faculty Member with the National Institute of Technology Karnataka. During his long professional career, he has served several reputed organizations, including CMC, ECIL and C-DOT and TCS. He was an Adjunct Faculty Member with IIT Delhi, and with the National Institute of Technology Karnataka. He is a Senior Member of IEEE, Life Member of IUPRAI, and a Member of ACM. About the Author ixAcknowledgments xiPreface xiiiAcronyms xv1 INTRODUCTION 11.1 What Is Cognitive Vision 21.2 Computational Approaches for Cognitive Vision 31.3 A Brief Review of Human Vision System 41.4 Perception and Cognition 61.5 Organization of the Book 72 EARLY VISION92.1 Feature Integration Theory 92.2 Structure of Human Eye 102.3 Lateral Inhibition 132.4 Convolution: Detection of Edges and Orientations 142.5 Color and Texture Perception 172.6 Motion Perception 192.6.1 Intensity-Based Approach 192.6.2 Token-Based Approach 202.7 Peripheral Vision 212.8 Conclusion 243 BAYESIAN REASONING FOR PERCEPTION AND COGNITION 253.1 Reasoning Paradigms 263.2 Natural Scene Statistics 273.3 Bayesian Framework of Reasoning 283.4 Bayesian Networks 323.5 Dynamic Bayesian Networks 343.6 Parameter Estimation 363.7 On Complexity of Models and Bayesian Inference 383.8 Hierarchical Bayesian Models 393.9 Inductive Reasoning with Bayesian Framework 413.9.1 Inductive Generalization 413.9.2 Taxonomy Learning 453.9.3 Feature Selection 463.10 Conclusion 474 LATE VISION 514.1 Stereopsis and Depth Perception 514.2 Perception of Visual Quality 534.3 Perceptual Grouping 554.4 Foreground–Background Separation 594.5 Multi-stability 604.6 Object Recognition 614.6.1 In-Context Object Recognition 624.6.2 Synthesis of Bottom-Up and Top-Down Knowledge 644.6.3 Hierarchical Modeling 654.6.4 One-Shot Learning 664.7 Visual Aesthetics 674.8 Conclusion 695 VISUAL ATTENTION 715.1 Modeling of Visual Attention 725.2 Models for Visual Attention 755.2.1 Cognitive Models 755.2.2 Information-Theoretic Models 775.2.3 Bayesian Models 785.2.4 Context-Based Models 795.2.5 Object-Based Models 815.3 Evaluation 825.4 Conclusion 846 Cognitive Architectures 876.1 Cognitive Modeling 886.1.1 Paradigms for Modeling Cognition 886.1.2 Levels of Abstraction 916.2 Desiderata for Cognitive Architectures 926.3 Memory Architecture 946.4 Taxonomies of Cognitive Architectures 976.5 Review of Cognitive Architectures 996.5.1 STAR: Selective Tuning Attentive Reference 1006.5.2 LIDA: Learning Intelligent Distribution Agent 1026.6 Biologically Inspired Cognitive Architectures 1056.7 Conclusions 1067 KNOWLEDGE REPRESENTATION FOR COGNITIVE VISION 1097.1 Classicist Approach to Knowledge Representation 1097.1.1 First Order Logic 1117.1.2 Semantic Networks 1137.1.3 Frame-Based Representation 1147.2 Symbol Grounding Problem 1177.3 Perceptual Knowledge 1187.3.1 Representing Perceptual Knowledge 1197.3.2 Structural Description of Scenes 1207.3.3 Qualitative Spatial and Temporal Relations 1227.3.4 Inexact Spatiotemporal Relations 1247.4 Unifying Conceptual and Perceptual Knowledge 1277.5 Knowledge-Based Visual Data Processing 1287.6 Conclusion 1298 DEEP LEARNING FOR VISUAL COGNITION 1318.1 A Brief Introduction to Deep Neural Networks 1328.1.1 Fully Connected Networks 1328.1.2 Convolutional Neural Networks 1348.1.3 Recurrent Neural Networks 1378.1.4 Siamese Networks 1408.1.5 Graph Neural Networks 1408.2 Modes of Learning with DNN 1428.2.1 Supervised Learning 1428.2.1.1 Image Segmentation 1428.2.1.2 Object Detection 1448.2.2 Unsupervised Learning with Generative Networks 1448.2.3 Meta-Learning: Learning to Learn 1468.2.3.1 Reinforcement Learning 1488.2.3.2 One-Shot and Few-Shot Learning 1488.2.3.3 Zero-Shot Learning 1508.2.3.4 Incremental Learning 1508.2.4 Multi-task Learning 1528.3 Visual Attention 1548.3.1 Recurrent Attention Models 1558.3.2 Recurrent Attention Model for Video 1588.4 Bayesian Inferencing with Neural Networks 1598.5 Conclusion 1609 APPLICATIONS OF VISUAL COGNITION 1639.1 Computational Photography 1639.1.1 Color Enhancement 1649.1.2 Intelligent Cropping 1669.1.3 Face Beautification 1679.2 Digital Heritage 1689.2.1 Digital Restoration of Images 1689.2.2 Curating Dance Archives 1709.3 Social Robots 1729.3.1 Dynamic and Shared Spaces 1739.3.2 Recognition of Visual Cues 1749.3.3 Attention to Socially Relevant Signals 1759.4 Content Re-purposing 1779.5 Conclusion 17910 CONCLUSION 18110.1 “What Is Cognitive Vision” Revisited 18110.2 Divergence of Approaches 18310.3 Convergence on the Anvil? 185References 187Index 215
Beginning Unity Android Game Development
Master the art of programming games for Android using the Unity3D game engine. This book will help you understand basic concepts of game development in Unity. By the end of Beginning Unity Android Game Development, you will have the knowledge to confidently build an Android game.The book starts by explaining simple programming concepts to make beginners comfortable with the jargon. You will then learn to navigate around the Unity interface and use basic tools (hand, move, rotate, scale, and rect). You will also be acquainted with the creation of basic 3D objects in the game while understanding the purpose of several of Unity’s windows.In the last chapters, you will learn to create a simple game for Android using the concepts studied in the previous chapters. Scripts will be written to handle the behaviors of the player and enemies as well as to handle other aspects of the game. The author shares tips along the way to help improve in-game performance, such as switching to the universal rendering pipeline when targeting mobile platforms.At the end of the book, you will have a solid knowledge in making basic Android games that can be upgraded later to make more complex games.WHAT YOU WILL LEARN* Explore basic Unity and C# programming concepts and scripting for Android games* Navigate around the Unity interface and use its basic tools* Make the most of popular components and features of Unity* Write an Android game with optimizationsWHO THIS BOOK IS FORAbsolute beginners learning to program games for the Android platform using Unity3D. Basic knowledge of programming would be beneficial for the reader but is not required.Kishan started out by learning programming at a young age with Python. Finding a bigger interest in game development, he has been developing games using the Unity game engine for over four years now. He is also a Linux lover and has worked on his own distribution. Currently, he resides in his home country, Mauritius, where he often participates in major technical events and hackathons with Cyberstorm.mu while developing quality games and improving his portfolio with new skills.CHAPTER 1: PROGRAMMING CONCEPTSChapter Goal: This chapter is intended to make the reader feel comfortable with basic programming concepts and operations. It will make further topics about game dev scripting more accessible to those with no past programming experience.Sub -Topics:1. Fundamentals of programming2. Variables, constants, and types3. Arithmetic operations4. Boolean expressions5. Selection6. Iteration7. FunctionsCHAPTER 2: INTRODUCTION TO UNITYChapter Goal: This chapter provides an introduction to the Unity game engine and IDE. It shows how to navigate around, create basic objects and using transform tools to move, scale and rotate. The purpose of the Scene, Game, Hierarchy, Inspector, Project and Asset Store windows are also discussed.Sub -Topics:1. Creating a Unity account2. Downloading Unity and required add-ons3. Scene view4. Game view5. Hierarchy window6. Inspector window7. Using the transform tools8. Project window9. Asset store windowCHAPTER 3: GAMEOBJECTS, PREFABS, MATERIALS, AND COMPONENTSChapter Goal: We learn more about GameObjects, the benefits of making prefabs, and the use of several components. A small overview of the need to use materials is also provided.Sub -Topics:1. What are GameObjects and Prefabs2. Transform component3. Camera component4. Lighting component5. Renderer component6. Collider component7. Rigidbody component8. Audio source component9. Particle emitter component10. Trail renderer component11. MaterialsCHAPTER 4: USER INTERFACEChapter Goal: The Canvas component is introduced and the reader will learn about making a game more interactive using touch input.Sub -Topics:1. The Canvas component2. Text3. Image/RawImage4. Slider5. Input field6. Button7. Introduction to input axesCHAPTER 5: BUILDING OUR FIRST ANDROID GAME - SPHERE SHOOTERChapter Goal: After creating a new project, we learn about switching to a more lightweight rendering pipeline. The reader will learn how to create the game environment, first enemy, player tank and bullets. Scripts will also need to be written to handle player movement, shooting, enemy instantiation and behavior.Sub -Topics:1. The lightweight rendering pipeline2. Creating game terrain and adjusting lighting3. Making prefabs for the player, first enemy, and bullets4. Player movement5. Player shooting6. Spawning enemies7. Enemy movement8. Enemy destruction9. Game overCHAPTER 6: IMPROVING THE GAME - SPHERE SHOOTERChapter Goal: We will learn how to make the game more interesting by creating simple but elegant canvas elements, introduce concepts such as health and score, make two more types of enemies, introduce pickups, add more sound effects to the game along with particle systems, implement mobile controls and exporting a build ready to be played.Sub -Topics1. Fancy Menu when starting the game and dying2. Adding the concept of score3. Adding the concept of health4. Implementing particle systems5. Making a new faster enemy6. Making a new bigger enemy7. Creating a health pickup8. Adding sound effects9. Mobile joysticks10. Editing player settings and exporting11. What next?
Practical hapi
Understand the core concepts of hapi and learn to build RESTful APIs that are quick, useful, and productive. Created by the mobile team at Walmart Labs, hapi is a light Node.js framework that is perfect for building API servers, websites, and HTTP proxy applications.With this quick guide, you'll learn the basics of hapi and use those skills to build an application and a REST API with MySQL. You'll then wrap up with a Capstone project of industry relevance, understanding solution design, and how hapi fits into industry relevant projects for data driven apps.Used by companies such as PayPal and Mozilla, hapi is a key framework for anyone serious about enterprise web development. Practical hapi will ensure you focus your time on critical project tasks instead of building infrastructure.WHAT YOU'LL LEARN* Utilize the power of RESTful APIs and Node.js* Build your first hapi application based on its core concepts* Work with promises and asynchronous programming effectively* Use Sequelize for database connectivityWHO THIS BOOK IS FORAnyone with basic knowledge of JavaScript or Node.js who wants to learn to work with hapi. A primer for the relevant Node.js and JavaScript is provided so those with general programming experience can also use this book.KANIKA SUD has been working on the web for over 10 years now. Her work spans enterprise CMSes in JAVA, backend technologies in the LAMP stack and MEAN stack. She has also worked on open source e-commerce CMSes and UX strategy.1. Understanding RESTful APIs2. Beginning Node.js3. Asynchronous JavaScript4. Your First hapi Application5. Building on the Basics: Validation, Authentication, and Plugins6. Database Connectivity7. Capstone Project- REST API for Polling App8. Appendix
Webpack for Beginners
Learn how to use Webpack from installation to configuration without the hassle of complex examples. Webpack has become one of the most popular module bundlers in recent years; it’s widely used by developers, companies, and organizations of all sizes, and many web frameworks use it for the management of their assets. If you are serious about web development these days then you must learn and understand Webpack.You will begin by installing and configuring Webpack, and learn how to write modular code. You’ll then move onto understanding the usage of loaders and plugins with practical use cases, how to make aliases and resolve folders, cache busting, and installing third-party libraries such as jQuery, Bootstrap, QuillJS, and more. By the end of this book you will feel confident and ready to start using Webpack in your projects.Free from complex examples and intended to be as easy-to-follow as possible, this book is ideal for anyone who knows basic HTML, JavaScript, and how to work on the command line. Upgrade your developer skillset using Webpack for Beginners today.WHAT YOU WILL LEARN* Install and configure Webpack beyond the default settings* Efficiently work with plugins and loaders* Optimize Webpack for production* Use instant refreshing with the Webpack dev server and hot module replacement* Explore how to install some common JavaScript librariesWHO THIS BOOK IS FORThis book is conceived for beginners and newcomers to Webpack, and assumes you have some very basic knowledge in JavaScript, HTML and working on the command line. This step-by-step guide will help you understand and clarify everything you need to know to bundle your JavaScript hassle-free.Mohamed Bouzid has over 11 years' experience in technology and web development. From humble beginnings as a global freelancer, he has transitioned to the entrepreneurial world making products that people love and use every day. When not coding he can be found at the gym or at the coffee shop where he talks with friends about life, startups, and tech. 1. Webpack: First Steps2. Write Modular Code3. Loaders and Plugins4. Cache5. Resolving Folders6. Webpack DevServer7. Installing Third Party Libraries8. Conclusion
Dynamics 365 Essentials
Discover what the Common Data Service is and how Dynamics 365 fits in the Power Platform. Learn how to set up core Dynamics 365 Customer Engagement functionality and build more customized processes using model-driven apps. This book covers the Dynamics 365 Online system for sales, customer service, marketing, field service, and Outlook integration.In this second edition, core platform changes from the Common Data Service are included and you will know what this means for Dynamics 365. Updated features include processes, the latest form and view designer, and Business Process Flows. The book also includes new chapters on portals and power virtual agents.After reading DYNAMICS 365 ESSENTIALS, you will have mastered the core functionality available in Dynamics 365 CE and model-driven applications, and will be able to set it up for a range of different business scenarios.WHAT YOU WILL LEARN* Set up the core standard features of Dynamics 365 CE* Create model-driven apps within Dynamics 365 customized to specific business needs* Customize Dynamics 365 CE and leverage process automation functionality through the UI* Study the Common Data Service for AppsWHO THIS BOOK IS FORConsultants, business analysts, administrators, and project managers who are looking for more information about Dynamics 365SARAH CRITCHLEY is a Microsoft Dynamics Business Applications MVP, published author, and an experienced technical consultant who has worked on numerous business system implementations, now working as Lead Architect for Customer Service at Hitachi Solutions NA. She manages the strategy around all customer service technology, including Dynamics 365 Customer Service, Omni Channel Engagement, Forms Pro, Power Virtual Agent, and more. Having led software projects in numerous industries, including healthcare and the public sector, she works across all areas of the project life cycle: demonstrations, design, architecture, documentation, customization, and development. Sarah is heavily involved in community projects where she led and grew the Dynamics 365 & Power Platform UG in the UK for over three years, running technical events, and presenting on a variety of topics at conferences around the world.INTRODUCTION – PAGES - 5CHAPTER GOAL: Discuss what Dynamics 365 CE is, the latest news e.g. version 9.0 and the unified client (mention web refresh) , what the sections will cover. This book will be split into two – ‘Setup’ and ‘Configuration’ – the fact that you should be looking at best ways to do it and less technical debt.PART I - SETUPCHAPTER 1: CUSTOMER MANAGEMENTCHAPTER GOAL: For the reader to learn about how customers are used within Dynamics 365 CE, what they link to and how they operate in the context of Activities and Microsoft OutlookNO OF PAGES 20SUB -TOPICS1. Account and Contacts1. Outlook Integration for Dynamics 365 CE Online2. Timeline and Activity ManagementCHAPTER 2: CUSTOMER SERVICECHAPTER GOAL: For the reader to learn how to deliver a more omni channel customer service experience using the standard features of Dynamics 365 CENO OF PAGES: 40SUB - TOPICS1. Case Management (including Categories)2. Routing and Queues3. Knowledge Base Implementation and Feedback4. Microsoft Portals Configuration for Self-Service5. SLAs and Entitlements6. Social Engagement7. Setting up Cognitive Services for Knowledge Article SuggestionsCHAPTER 3: SALESCHAPTER GOAL: For the reader to learn what the standard sales lifecycle is, common customization points and how-to setup the standard Product CatalogNO OF PAGES : 25SUB - TOPICS:1. Product Catalog Setup and Management2. Leads to Invoice Sales Lifecycle with Product Catalog Integration2. Setting up Product Suggestion Model using Cognitive Services3. Creating Microsoft Word Templates for Quotes, Orders and InvoicesCHAPTER 4: SECURITY MODELCHAPTER GOAL: For readers to understand how the Dynamics 365 CE Security Model works and operatesNO OF PAGES:10SUB - TOPICS:1. 1.Security Model Overview2. Business Units, Teams and Users3. Access Teams4. Hierarchal and Positional SecurityCHAPTER 5: FIELD SERVICE AND RESOURCE SCHEDULINGCHAPTER GOAL: For readers to understand the basics of Field Service and Resourcing Capability which comes as standardNO OF PAGES: 15TBC. This is changing and will need to be updatedCHAPTER 6: MOBILE APPLICATIONCHAPTER GOAL: For readers to learn how to setup Dynamics 365 CE for MobileNO OF PAGES: 201. Setup3. Task Flows4. OfflineCHAPTER 7: REPORTINGCHAPTER GOAL: For readers to understand the capabilities of Dynamics 365 CE ReportingNO OF PAGES: 205. Views6. Dashboards7. Reports in Dynamics 365 Online8. ChartsPART II - CONFIGURATIONCHAPTER 8: PROCESSESCHAPTER GOAL: For readers to understand the capabilities for configuring Dynamics 365 CE through custom processesNO OF PAGES: 301. Business Process Flows2. Business Rules3. Workflows4. ActionsCHAPTER 9: UI CUSTOMIZATION AND RELATIONSHIPSCHAPTER GOAL: For readers to learn how to customize the user interface of Dynamics 365 and the relationshipsNO OF PAGES: 151. Forms2. Relationships3. Custom Controls4. MobileCHAPTER 10: APP FRAMEWORKCHAPTER GOAL: For readers to understand what the App Framework is and how to create ‘Apps’ within Dynamics 365NO OF PAGES: 151. App Framework Overview2. App Designer3. Site Map Designer4. ConfigurationCHAPTER 11: POWER AUTOMATIONCHAPTER GOAL: For readers to understand Microsoft Flow’s basic capabilitiesNO OF PAGES: 20This is changing and will need to be updatedCHAPTER 12: MANAGED VS UNMANAGED SOLUTIONSCHAPTER GOAL: Discussion on solutions design and layeringCHAPTER 13: COMMON DATA SERVICE FOR APPSCHAPTER GOAL: For readers to understand what the Common Data Service is and how to get started with integrations through the UI.NO OF PAGES: 20CHAPTER 14: GETTING STARTED WITH CDS PORTALSCHAPTER GOAL: To connect a portal to a CDS for Apps environment that does not have any Dynamics 365 applications (Sales, Service, or Marketing) preinstalledCHAPTER 15: DYNAMICS 365 FOR MARKETING
Getting Started with Advanced C#
Understand and work with the most important features of advanced C# in different programming environments. This book teaches you the fundamental features of advanced C# and how to incorporate them in different programming techniques using Visual Studio 2019.The book is divided into two parts. Part I covers the fundamentals and essentials of advanced programming in C#. You will be introduced to delegates and events and then move on to lambda expressions. Part II teaches you how to implement these features in different programming techniques, starting with generic programming. After that, you will learn about thread programming and asynchronous programming, to benefit from a multi-threaded environment. Finally, you will learn database programming using ADO.NET to connect to a MySQL database and you will know how to exercise SQL statements and stored procedures through your C# applications.WHAT YOU WILL LEARN* Use delegates, events, and lambda expressions in advanced programming* Make your application flexible by utilizing generics* Create a fast application with multi-threading and asynchronous programming* Work in Visual Studio Community Edition, which is the most common IDE for using C#* Understand alternative implementations along with their pros and consWho This Book Is ForDevelopers and programmers who are already working in C#VASKARAN SARCAR obtained his Master of Engineering in software engineering from Jadavpur University, Kolkata (India) and an MCA from Vidyasagar University, Midnapore (India). He was a National Gate Scholar (2007-2009) and has more than 12 years of experience in education and the IT industry. Vaskaran devoted his early years (2005-2007) to teaching at various engineering colleges and later he joined HP India PPS R&D Hub Bangalore and worked there until August, 2019. At the time of his retirement from the IT industry, he was a senior software engineer and a team lead at HP. To follow his dream and passion, Vaskaran is now a full-time author. Other Apress books by Vaskaran include: Interactive Object-Oriented Programming in Java (second edition), Java Design Patterns (second edition), Design Patterns in C#, Interactive C#, Interactive Object-Oriented Programming in Java, and Java Design Patterns.INTRODUCTIONPART-ICHAPTER 1: DELEGATESSubtopics:· What is a delegate?· How to create and use delegates?· What is a multicast delegate (Chaining of Delegates) and its uses?· Adding and removing methods from a multicast delegate.· Covariance and contravariance in non-generic delegates.CHAPTER 2: EVENTSSubtopics:· Events overview* Creation of events and example of simple events* Subscribing single and multiple events* Passing data to events* Discussion on event accessorsCHAPTER 3: ANONYMOUS FUNCTION AND LAMBDA EXP.Subtopics:· A quick introduction to anonymous function and lambda expression.* Lambda’s with and without parameters* Types and scopes of a lambda expressionExpression syntaxes with exampleCHAPTER 4: LINQSubtopics:* LINQ overview* Different case studies with simple and complicated query expressions* Retrieving customized data from a query expression* Comparing a method call syntax and a query syntax. PART-IICHAPTER 5: GENERIC PROGRAMMINGSubtopics:* Generics overviewComparing a generic program with its counterpart-a non-generic program * Self-referenced generics* Use of ‘default’ in a generic programHow to put constraints in a generic program * Covariance and contravariance in the context of a generic programCHAPTER 6: DATABASE PROGRAMMINGSubtopics:· How to connect to a database· Exercise simple queries to database and retrieving results from the database.CHAPTER 7: THREAD PROGRAMMINGSubtopics:Threads overview * Different case studies with multithreaded programs* Use of ParameterizedThreadStart delegate in a multithreaded environment * Passing multiple parameters to a thread* Discussion on Synchronization and deadlock with examplesCHAPTER 8: ASYNCHRONOUS PROGRAMMINGSubtopics:Brief overview * Different techniques to implement an asynchronous program(e.g. using async/await, thread, thread pool etc)CHAPTER 9: DYNAMIC PROGRAMMINGSubtopics:* DLR overview* Dynamic type and its uses* Dynamic type checking* Runtime look up etc.
Efficient Processing of Deep Neural Networks
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas. * Preface * Acknowledgments * Introduction * Overview of Deep Neural Networks * Key Metrics and Design Objectives * Kernel Computation * Designing DNN Accelerators * Operation Mapping on Specialized Hardware * Reducing Precision * Exploiting Sparsity * Designing Efficient DNN Models * Advanced Technologies * Conclusion * Bibliography * Authors' Biographies
Introducing Disaster Recovery with Microsoft Azure
Explore and learn the key building blocks of Microsoft Azure services and tools for implementing a disaster-recovery solution of any on-premises or cloud-based application. In this book, you will go through various aspects of planning, designing, and configuring a robust recovery solution on Azure.Introducing Disaster Recovery with Microsoft Azure starts by explaining the disaster-recovery landscape and how Azure disaster recovery is different from the traditional approach. You will learn how to leverage Azure site recovery and various Azure-based services to design and implement a recovery solution and much more. Moving forward, you will design and implement various scenarios such as on-premises to Azure, Azure to Azure, and on-premises to on-premises disaster recovery. You will also learn common considerations and technicalities of implementing recovery solutions for various multi-tier, monolithic, and modern micro-services enterprise applications. Finally, you will go through real-life examples, scenarios, and exercises.After reading this book, you will be able to design and implement disaster recovery on Azure in different scenarios. You will also look at a few real-world scenarios that will provide more practical insights.WHAT YOU WILL LEARN* Discover the fundamental building blocks of disaster recovery on Azure* Examine various application-specific considerations for disaster recovery* Leverage various PaaS capabilities to achieve maximum benefit* Design and implement a multi-regional Azure to Azure disaster recovery planWHO THIS BOOK IS FORConsultants, architects, and Azure administrators.BAPI CHAKRABORTY has over 14 years of IT experience in the field of on-premises and cloud infrastructure architecture, solution design, migration, deployment, and support practices. He has worked with customers and partners from various industries and understands their unique demands and requirements to achieve business goals. Bapi holds various industry and product certification including Microsoft, AWS, and IASA.YASHAJEET CHOWDHURY has over 17 years of IT experience in the field of On-premises and Cloud infrastructure architecture, solution design, migration, deployment, and support practices. He has deep technical hands-on experience on various Infrastructure services including Datacenter consolidation/migration, Virtualization, Cloud Computing & other Infrastructure offerings for various small and enterprise customers.Yash holds strong knowledge in Architecting, Designing, Implementation and many professional technical certifications including Microsoft and IBM.CHAPTER 1: DISASTER RECOVERY AND CLOUDCHAPTER GOAL: How cloud has changed the DR landscapeNO OF PAGES 20SUB -TOPICS1. How cloud has changed the DR landscape2. cost, security, storage, archival, maintenance, accessibility3. How traditional DR is different from new age DR4. Why Azure as a DR site5. options, storage, capabilities, pricing, integrated monitoring, tools, platform capability, competitive advantages6. benefits for existing customers, end to end orchestrationCHAPTER 2: INTRODUCING AZURE SITE RECOVERYCHAPTER GOAL: Understand Disaster recovery with Azure site recovery and Azure MigrateNO OF PAGES: 20SUB - TOPICS1. Supported scenarios including migration, supportability matrix,2. DR dependencies on Azure3. Storage, network, etc.4. Concepts of ASR for each scenarionetworking ER, Migration , tools, recovery plans, roles and RBAC, sites, config & process server etc.CHAPTER 3: DESIGNING AND IMPLEMENTING SIMPLE AZURE TO AZURE DR - SCENARIOCHAPTER GOAL: Understand Disaster recovery scenario for Azure to Azure scenarioNO OF PAGES : 20SUB - TOPICS:1. Questions to ask, decisions to make, data to collect, assess, Planning and designing the DR2. High level configurations3. Backup as a strategy4. Design and implement a multi regional A2A DR - scenario (5)5. High level configurations6. Common challenges and how to remediate themCHAPTER 4: ON-PREMISES TO AZURE DRCHAPTER GOAL: Understand Disaster recovery scenario for On-premises to Azure DR scenarioNO OF PAGES: 40SUB - TOPICS:1. Hyper-V (with SCVMM) to Azure2. Hyper-V (without SCVMM) to Azure3. Physical systems to Azure4. Azure Stack to Azure5. Planning6. Designing7. Configuring8. Common challenges and how to remediate themCHAPTER 5: ON-PREMISES TO ON-PREM USING ASRNO OF PAGES: 10CHAPTER GOAL: understand On-premises only scenario1. On-premises to On-prem using ASR2. Planning3. Designing4. Configuring5. Common challenges and how to remediate themCHAPTER 6: APP SPECIFIC DR SCENARIOSNO OF PAGES: 20CHAPTER GOAL: understand application specific DR scenario1. ADDS2. SQL3. Oracle4. RDS etc.CHAPTER 7: AUTOMATION AND MONITORINGNO OF PAGES: 10CHAPTER GOAL: understand automation and monitoring for Disaster recovery solution Common Automation and Monitoring scenarios for DR on AzureCHAPTER 8: SUMMARY, BEST PRACTICES AND EXERCISESNO OF PAGES: 10CHAPTER GOAL: Summary, Best Practices and Exercises for executing Azure Disaster Recovery1. Summary2. Best Practices3. Exercises
Overview of Some Windows and Linux Intrusion Detection Tools
The paper evaluates some the security tools. Top security tools can be found in http://sectools.org/. Most important vulnerabilities in Windows and Linux can be found in www.sans.org/top20/. The paper covers the installation and configuration of the following security tools:LANguardNessusSnortBASEACIDRmanSnortCenter.OSSECSguilI 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
Data Governance
Know-how für eine erfolgreiche Data Governance* umfassendes, anwendungsbezogenes Handbuch* den Fokus nicht nur auf fachliche und technische, sondern auch organisatorische Aspekte legen* mit vielen Fallbeispielen, die Inhalte und Umsetzung, Potenziale und mögliche Fallstricke verdeutlichenVor dem Hintergrund zunehmender regulatorischer Anforderungen sowie wachsender Komplexität der eingesetzten IT-Landschaften erlangt das Themengebiet "Data Governance" immer größere Bedeutung in den Unternehmen.Dieses Buch greift nach einer Einordnung und Abgrenzung des Themas die unterschiedlichen Kernaspekte der Data Governance umfassend auf. Anschließend werden spezielle Facetten und -Toolkategorien mit hoher praktischer Relevanz präsentiert, bevor die Darstellung spezifischer Unternehmenslösungen erfolgt.Prof. Dr. Peter Gluchowski leitet den Lehrstuhl für Wirtschaftsinformatik, insb. Systementwicklung und Anwendungssysteme, an der Technischen Universität in Chemnitz und konzentriert sich dort mit seinen Forschungsaktivitäten auf das Themengebiet Business Intelligence & Analytics. Er beschäftigt sich seit mehr als 25 Jahren mit Fragestellungen, die den praktischen Aufbau dispositiver bzw. analytischer Systeme zur Entscheidungsunterstützung betreffen. Seine Erfahrungen aus unterschiedlichsten Praxisprojekten sind in zahlreichen Veröffentlichungen zu diesem Themenkreis dokumentiert.
Smart Cities For Dummies
BECOME EMPOWERED TO BUILD AND MAINTAIN SMARTER CITIESAt its core, a smart city is a collection of technological responses to the growing demands, challenges, and complexities of improving the quality of life for billions of people now living in urban centers across the world.The movement to create smarter cities is still in its infancy, but ambitious and creative projects in all types of cities—big and small—around the globe are beginning to make a big difference. New ideas, powered by technology, are positively changing how we move humans and products from one place to another; create and distribute energy; manage waste; combat the climate crisis; build more energy efficient buildings; and improve basic city services through digitalization and the smart use of data.Inside this book you’ll find out:* What it really means to create smarter cities* How our urban environments are being transformed* Big ideas for improving the quality of life for communities* Guidance on how to create a smart city strategy* The essential role of data in building better cities* The major new technologies ready to make a difference in every communitySmart Cities For Dummies will give you the knowledge to understand this important topic in depth and be ready to be an agent of change in your community.DR. JONATHAN REICHENTAL is a multiple-award-winning technology and business leader whose career has spanned both the private and public sectors. He's been a senior software engineering manager, a director of technology innovation, and has served as chief information officer at both O'Reilly Media and the city of Palo Alto, California. He also creates online education for LinkedIn Learning and others. INTRODUCTION 1About This Book 2Foolish Assumptions 3Icons Used in This Book 3How This Book Is Organized 4Part 1: Making Cities Our Home 4Part 2: Building a Smarter City 4Part 3: Using Smart City Technologies 5Part 4: Planning for an Urban Future 5Part 5: The Part of Tens 5Part 6: Appendixes 6Beyond the Book 6Where to Go from Here 7PART 1: MAKING CITIES OUR HOME 9CHAPTER 1: COMPREHENDING THE PAST, PRESENT, AND FUTURE OF CITIES 11Discovering the Origin of Cities 11What is a city? 12Building the first cities 14Comprehending the Impact of the Industrial Revolutions 16The first industrial revolution 16The second industrial revolution 17The third industrial revolution 17The fourth industrial revolution 19Responding to population growth 19Urbanizing the Planet 21Changing landscapes resulting from urbanization 23Building megacities 23CHAPTER 2: DEFINING SMART CITIES 27Identifying Smart Cities 27What a smart city is 28What a smart city is not 32Working with digital infrastructures 34Building the Case for Smarter Cities 35Small cities versus large cities 36Smart nations and other smart things 37United Nations’ Sustainable Development Goals (SDGs) 39Examining Examples of Smart Cities 42Amsterdam, The Netherlands 42Konza Technopolis, Kenya 43CHAPTER 3: RESPONDING TO THE NEEDS AND CHALLENGES OF CITIES 45Mapping the Evolving Needs and Challenges of Cities 45Economic shifts 47Increasingly complex city requirements 47Interdependencies between systems 48Population changes 49Aging infrastructure 52Lifestyle choices 54Environment 55Health 56Water management 58Housing crisis 59Expecting Different Results 62Changing community behaviors and expectations 63Expanding community engagement 64Engaging in participatory design 65Transforming Urbanization 66Transportation 67Energy 70Buildings 71Telecommunications 73Sustainability 74PART 2: BUILDING A SMARTER CITY 77CHAPTER 4: STARTING FROM ZERO 79Establishing a Vision 79Identifying the role of city leadership 80Creating a vision 83Building a Smart City Team 85Identifying team members 85Creating a RACI chart 88Getting the team on the same page 89CHAPTER 5: CREATING A SMART CITY STRATEGY 91Building the Plan 92Developing a strategic plan 93Envisioning the envisioning process 95Converting your vision to action 98Codifying the Plan 100Identifying metrics 100Communicating the plan 102CHAPTER 6: ENABLING A SMART CITY STRATEGY 107Putting the Building Blocks in Place 108Developing policy 108Getting started 110Examining a few examples of smart city policies 111Establishing regulations 112Evaluating funding models 114Handling procurement issues 118Managing projects and carrying out business analyses 121Governing the Strategy 125Defining strategic governance 125Managing projects with project governance 126Regularly updating and reporting 128PART 3: USING SMART CITY TECHNOLOGIES 131CHAPTER 7: EMBRACING URBAN INNOVATION 133Defining Urban Innovation 134Relying on urban innovation networks 136Creating urban innovation labs 137Implementing Urban Innovation 139Examining the discovery process 141Running pilots and experiments 143Setting up living labs 144Engaging in hackathons 145Participating in urban challenges 149Open innovation versus closed innovation 151Sharing urban innovation 152Converting ideas into projects 153CHAPTER 8: ENABLING CHANGE THROUGH TECHNOLOGY 155Recognizing Technological Change in Modern Cities 156From analog to digital 157The fourth industrial revolution 160The fourth industrial revolution and cities 162The Internet of Things (IoT) 163Exploring a Variety of Urban Technologies 166Social media and communication tools 166Artificial intelligence (AI) 169Blockchain technology 171Autonomous vehicles (AVs) 175Drones 178Wireless communications 181Smart street lighting 184Smart grids and microgrids 189Smart water 192Digital twins 193Digital signage 196Application programming interfaces (APIs) 198CHAPTER 9: UNLEASHING THE POWER OF CITY DATA 205Becoming City-Data-Savvy 205Enabling data-driven decision-making 207Managing data 208Developing a data strategy 209Implementing data governance 211Working with City Data 214Securing data 214Opening data 215Making sense of data through analytics 218Using geographic information systems (GIS) 220Hiring a city chief data officer 223PART 4: PLANNING FOR AN URBAN FUTURE 225CHAPTER 10: BUILDING A SECURE FOUNDATION 227Securing Your Smart City 228Urban resilience 228Public safety 233Addressing Digital Security and Privacy 238Cybersecurity 239Privacy 241CHAPTER 11: IMAGINING THE CITY OF THE FUTURE 245Recognizing That the Best Is Yet to Come 246Green cities 247Inclusive cities 251Healthy cities 253Regenerative cities 256Envisioning Big Ideas 259Hyperloop 260Flying cars 262Cities without cars 264CHAPTER 12: ENGAGING IN YOUR CITY’S FUTURE 267Embracing an Urban Future 268An increase in civic engagement 270Continuous improvement in urban quality of life 274The difference between quality of life and standard of living 275Making a Better Tomorrow 278It’s your community — get involved 280Five things you can do tomorrow 281PART 5: THE PART OF TENS 283CHAPTER 13: TEN SMART CITY PITFALLS TO AVOID 285Making Your Smart City Project a Tech Program and Putting IT in Charge 286Garnering Insufficient Support and Engagement from Stakeholders 287Limiting Efforts to Your City Boundaries 288Paying Insufficient Attention to Inclusiveness Issues 289Moving Forward with Inadequate Governance 289Working with No Clear Vision for the Program 290Downplaying the Essential Roles of Security and Privacy 291Sharing Successes and Failures Too Narrowly 292Sticking Stubbornly to the Old Ways of Doing Things 293Thinking Too Short-Term 294CHAPTER 14: TEN WAYS CITIES WILL DEFINE OUR HUMAN FUTURE 295Most People Will Live, Work, and Play Their Entire Lives in Cities 296The Increasing Demands of Sustainability Will Shape Human Behavior 297City Interactions Will Increasingly Be Digital 298City Data Will Drive Community Decision-Making 299People Will Have Expanded Opportunities to Co-Create and Collaborate on Urban Solutions 300Crime May Be Reduced Significantly 301More Diversity Will Show Up in What Humans Do and How They Work 302The Way People and Goods Move Will Continue to Evolve 303The Delivery of Healthcare Will Be Transformed 305Everything Will Be Delivered 307PART 6: APPENDIXES 309APPENDIX A: SMART CITY STRATEGIES 311Africa 311Asia 312Australia 313Europe 315Middle East 317North America 319South America 321APPENDIX B: SMART CITY ORGANIZATIONS 323APPENDIX C: OPEN DATA PORTALS 333Africa 333Asia 334Australia and New Zealand 335Eastern Europe and Russia 336Western Europe 337Middle East 339North America 340South America 343APPENDIX D: SOLUTIONS BUILT ON OPEN DATA 345APPENDIX E: CITY PERFORMANCE DASHBOARDS 351Asia 351Australia and New Zealand 352Europe 353Middle East 354North America 354South America 356Index 357
Homeoffice und mobiles Arbeiten im Team effektiv umsetzen
Produktives Arbeiten im Homeoffice erfordert neben richtiger Ausstattung, guter Sprach- und Datenkommunikation und Zugriff auf Daten auch die – durch IT-Tools unterstützten – passenden Führungs- und Arbeitsmethoden. Dieser Ratgeber bietet konkrete Anleitungen, um ohne IT-Fachkenntnisse das weltweite Arbeiten im Team schnell und pragmatisch möglich zu machen.Peter Bruhn ist Diplom-Wirtschaftsinformatiker (TU Darmstadt) und zweifacher Master of Science (Computer Science, University of Illinois (USA) und Informationswissenschaft, Hochschule Darmstadt). Bereits 2000 spezialisierte er sich als Berater bei McKinsey & Company auf die Digitale Transformation. Anschließend baute er als Senior Manager im Innovationsbereich der Deutschen Telekom AG neue Geschäftsmodelle u.a. für die Digital Business Unit auf. Als Vice President Group Digital Transformation übernahm Bruhn 2016 die Verantwortung für die Digitale Agenda der TAKKT AG in Europa und den USA.Peter Bruhn ist ein Vorreiter für mobiles Arbeiten und hat als Führungskraft jahrelange Erfahrung in der Führung verteilter Teams auf Distanz. Einleitung.- Hardware.- Konnektivität.- Software.- Anwendungen für die Team-Zusammenarbeit.- IT-Sicherheit und Datenschutz.- Fazit und Empfehlung
Agile Artificial Intelligence in Pharo
Cover classical algorithms commonly used as artificial intelligence techniques and program agile artificial intelligence applications using Pharo. This book takes a practical approach by presenting the implementation details to illustrate the numerous concepts it explains.Along the way, you’ll learn neural net fundamentals to set you up for practical examples such as the traveling salesman problem and cover genetic algorithms including a fun zoomorphic creature example. Furthermore, Practical Agile AI with Pharo finishes with a data classification application and two game applications including a Pong-like game and a Flappy Bird-like game. This book is informative and fun, giving you source code to play along with. You’ll be able to take this source code and apply it to your own projects.WHAT YOU WILL LEARN* Use neurons, neural networks, learning theory, and more* Work with genetic algorithms * Incorporate neural network principles when working towards neuroevolution * Include neural network fundamentals when building three Pharo-based applicationsWHO THIS BOOK IS FORCoders and data scientists who are experienced programmers and have at least some prior experience with AI or deep learning. They may be new to Pharo programming, but some prior experience with it would be helpful.ALEXANDRE BERGEL, PH.D., is an associate professor in the Department of Computer Science (DCC) at the University of Chile and is a member of the Intelligent Software Construction laboratory (ISCLab). His research interests include software engineering, software performance, software visualization, programming environment, and machine learning. He is interested in improving the way we build and maintain software. His current hypotheses are validated using rigorous empirical methodologies. To make his research artifacts useful not only to stack papers, he co-founded Object Profile.1: Introduction2: The Perceptron Model3: Artificial Neuron4: Neural Networks5: Theory on Learning6: Data Classification7: A Matrix Library8: Matrix-Based Neural Network9: Genetic Algorithm10: Genetic Algorithm in Action11: Traveling Salesman Problem12: Exiting a Maze13: Building Zoomorphic Creatures14: Evolving Zoomorphic Creature15: Neuroevolution16: Neuroevolution with NEAT17: The MiniMario Video GameLast Words (Afterword)
Signal, Audio and Image Processing
This project shows some selected signal techniques, including image and audio processing, using the Matlab digital signal processing and image processing toolboxes. The project is divided to 3 parts.Part I includes design and implementation of different types of filters for filtering signal that has different sinusoidal frequency components or noise. The comparison was made between FIR low pass flter, butterworth filter, Chebycheve Type I low pass filter and Chebycheve Type II low pass filter. Then different types of IIR Butterworth filters were designed and implemented to filter a signal that has many harmonics components, including low pass filter, high pass filter, stop band filter and band pass filter.Part II examined audio filtering in the sense of specific frequency suppression and extraction. There are many different types of filters available for the construction of filters. We will specifically use the Butterworth filter. An audio signal was read and different types of filters, including low pass filter, high pass filter, stop band filter and band pass filter, were designed and implemented in order to filter the audio signal from some frequency bands. Then the discrete cosine transform compression examined on the audio signal at different compression rates: 50%, 75% , 87.5% Part III deals with image processing; the project shows examples in smoothing, sharpening, and edge detection. Other useful operations on the image were tested, including image cropping, image resizing, image, histogram equalization and altering image brightnessI 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
Optionen in der Energiewirtschaft
Optionen sind seit jeher fester Bestandteil der Finanzmärkte, finden jedoch gleichwohl zunehmend Einsatz in der Energiewirtschaft. Das vorliegende essential verschafft einen Überblick über dieses Instrument und dessen Einsatz. Dazu wird das Instrument der Option unter anderem über die resultierenden Zahlungsströme sowie darauf aufbauend Einsatzmöglichkeiten von Optionen in der Energiewirtschaft beschrieben. Den Abschluss bildet ein Ausblick auf die Bewertung dieser Instrumente. Ein theoretischer Ansatz zur Bewertung wird kurz umrissen. Aus dem theoretischen Vorgehen lassen sich einige Ansätze ableiten, die in der Praxis weit verbreitet sind und hier ebenfalls kurz vorgestellt werden.
Deep Reinforcement Learning
* ALLE WICHTIGEN METHODEN UND ALGORITHMEN PRAXISNAH ERLÄUTERT MIT CODEBEISPIELEN IN PYTHON* SELBSTSTÄNDIG LERNENDE AGENTEN PROGRAMMIEREN FÜR DIE STEUERUNG VON ROBOTERN, NLP IN INTERAKTIVEN SPIELEN, CHATBOTS UND MEHR* DEEP Q-NETWORKS, WERTITERATION, POLICY GRADIENTS, TRUST REGION POLICY OPTIMIZATION (TRPO), GENETISCHE ALGORITHMEN, MODERNE EXPLORATIONSVERFAHREN U.V.M.Reinforcement Learning ist ein Teilgebiet des Machine Learnings. Hierbei werden selbstständig lernende Agenten programmiert, deren Lernvorgang ausschließlich durch ein Belohnungssystem und die Beobachtung der Umgebung gesteuert wird.In diesem umfassenden Praxis-Handbuch zeigt Ihnen Maxim Lapan, wie Sie diese zukunftsweisende Technologie in der Praxis einsetzen. Sie lernen, wie Sie passende RL-Methoden für Ihre Problemstellung auswählen und mithilfe von Deep-Learning-Methoden Agenten für verschiedene Aufgaben trainieren wie zum Beispiel für das Lösen eines Zauberwürfels, für Natural Language Processing in Microsofts TextWorld-Umgebung oder zur Realisierung moderner Chatbots.Alle Beispiele sind so gewählt, dass sie leicht verständlich sind und Sie diese auch ohne Zugang zu sehr großer Rechenleistung umsetzen können. Unter Einsatz von Python und der Bibliothek PyTorch ermöglicht Ihnen der Autor so einen einfachen und praktischen Einstieg in die Konzepte und Methoden des Reinforcement Learnings wie Deep Q-Networks, Wertiteration, Policy Gradients, Trust Region Policy Optimization (TRPO), genetische Algorithmen und viele mehr.Es werden grundlegende Kenntnisse in Machine Learning und Deep Learning sowie ein sicherer Umgang mit Python vorausgesetzt.AUS DEM INHALT:* Implementierung komplexer Deep-Learning-Modelle mit RL in tiefen neuronalen Netzen* Ermitteln der passenden RL-Methoden für verschiedene Problemstellungen, darunter DQN, Advantage Actor Critic, PPO, TRPO, DDPG, D4PG und mehr* Bauen und Trainieren eines kostengünstigen Hardware-Roboters* NLP in Microsofts TextWorld-Umgebung für interaktive Spiele* Diskrete Optimierung für das Lösen von Zauberwürfeln* Trainieren von Agenten für Vier Gewinnt mittels AlphaGo Zero* Die neuesten Deep-RL-Methoden für Chatbots* Moderne Explorationsverfahren wie verrauschte Netze und Netz-DestillationMaxim Lapan ist Deep-Learning-Enthusiast und unabhängiger Forscher. Er hat langjährige Berufserfahrung mit Big Data und Machine Learning und beschäftigt sich derzeit insbesondere mit praktischen Anwendungen des Deep Learnings wie NLP und Deep Reinforcement Learning.
Ensemble Learning for AI Developers
Use ensemble learning techniques and models to improve your machine learning results.ENSEMBLE LEARNING FOR AI DEVELOPERS starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.WHAT YOU WILL LEARN* Understand the techniques and methods utilized in ensemble learning* Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias* Enhance your machine learning architecture with ensemble learningWHO THIS BOOK IS FORData scientists and machine learning engineers keen on exploring ensemble learningALOK KUMAR is an AI practitioner and innovation lead at Publicis Sapient. He has extensiveexperience in leading strategic initiatives and driving cutting-edge, fast-paced innovations. He won several awards and he is passionate about democratizing AI knowledge. He manages multiple non- profit learning and creative groups in NCR.MAYANK JAIN currently works as Manager Technology at the Publicis Sapient Innovation Lab Kepler as an AI/ML expert. He has more than 10 years of industry experience working on cutting-edge projects to make computers see and think using techniques such as deep learning, machine learning, and computer vision. He has written several international publications, holds patents in his name, and has been awarded multiple times for his contributions. Chapter 1: An Introduction to Ensemble LearningChapter Goal: This chapter will give you a brief overview of ensemble learningNo of pages - 10Sub -Topics Need for ensemble techniques in machine learning Historical overview of ensemble learning A brief overview of various ensemble techniquesChapter 2: Varying Training DataChapter Goal: In this chapter we will talk in detail about ensemble techniques where trainingdata is changed.No of pages: 30Sub – Topics: Use of bagging or bootstrap aggregating for making ensemble model Code samples Popular libraries support for bagging and best practices Introduction to random forests models Hands-on code examples for using random forest models Introduction to cross validation methods in machine learning Intro to K-Fold cross validation ensembles with code samples Other examples of varying data ensemble techniquesChapter 3: Varying CombinationsChapter Goal : In this chapter we will talk about in detail about techniques where models areused in combination with one another to getting an ensemble learning boost.No of pages: 40Sub – Topics: Boosting : We will talk in detail about various boosting techniques with historical examples Introduction to adaboost , with code examples , Industry best practices and useful state of the art libraries for adaboost Introduction to gradient boosting , with hands on code examples with useful libraries and industry best practices for gradient boosting Introduction to XGboost with hands on code examples with useful libraries and industry best practices for XGboost Stacking : We will talk in detail about various stacking techniques are used in machine learning world Stacking in practice: How stacking is used by Kagglers for improving for winning entries.Chapter 4: Varying ModelsChapter Goal: In this chapter we will talk about how ensemble learning models couldlead to better performance of your machine learning projectNo of pages: 30Sub - Topics: Training multiple model ensembles with code examples Hyperparameter tuning ensembles with code examples Horizontal voting ensembles Snapshot ensembles and its variants, Introduction to the cyclic learning rate. Code examples Use of ensembles in the deep learning world.Chapter 5: Ensemble Learning Libraries and How to Use ThemChapter Goal: In this chapter we will go into details about some very popular libraries used bydata science practitioners and Kagglers for ensemble learningNo of pages: 25Sub - Topics: Ensembles in Scikit-Learn Learning how to use ensembles in TensorFlow Implementing and using ensembles in PyTorch Using Boosting using Microsoft LightGBM Boosting using XGBoost Stacking using H2O library Ensembles in RChapter 6: Tips and Best PracticesChapter Goal: In this chapter we will learn what are the best practices around ensemble learning with real world examplesNo of pages: 25Sub - Topics: How to build a state of the art Image classifier using ensembles How to use ensembles in NLP with real-world examples Use of ensembles for structured data analysis Using ensembles for time series data Useful tips and pitfalls How to leverage ensemble learning in Kaggle competitions Useful examples and case studiesChapter 7 : The Path ForwardChapter goal – In this section we will cover recent advances in ensemble learningNo of pages: 10Sub - Topics: Recent trends and research in ensembles Use of ensembles in memory-constrained environments Use of ensembles in keeping eye of efficiency Useful resources
Quantum Computer Systems: Research for Noisy Intermediate-Scale Quantum Computers
THIS BOOK TARGETS COMPUTER SCIENTISTS AND ENGINEERS WHO ARE FAMILIAR WITH CONCEPTS IN CLASSICAL COMPUTER SYSTEMS BUT ARE CURIOUS TO LEARN THE GENERAL ARCHITECTURE OF QUANTUM COMPUTING SYSTEMS. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.* Preface* Acknowledgments* List of Notations* Introduction* Think Quantumly About Computing* Quantum Application Design* Optimizing Quantum Systems--An Overview* Quantum Programming Languages* Circuit Synthesis and Compilation* Microarchitecture and Pulse Compilation* Noise Mitigation and Error Correction* Classical Simulation of Quantum Computation* Concluding Remarks* Bibliography* Authors' Biographies