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Modern Data Access with Entity Framework Core: Database Programming Techniques for .NET, .NET Core, UWP, and Xamarin with C#
by Holger SchwichtenbergC# developers, here’s your opportunity to learn the ins-and-outs of Entity Framework Core, Microsoft’s recently redesigned object-relational mapper. Benefit from hands-on learning that will teach you how to tackle frustrating database challenges, such as workarounds to missing features in Entity Framework Core, and learn how to optimize the performance of your applications, head-on!Modern Data Access with Entity Framework Core teaches best practices, guidance, and shortcuts that will significantly reduce the amount of resources you internally dedicate to programming data access code. The proven methods and tools taught in this book, such as how to get better performance, and the ability to select the platform of your choice, will save you valuable time and allow you to create seamless data access. Dive into succinct guidance that covers the gamut-- from installing Entity Framework Core, reverse engineering, forward engineering (including schema migrations), and data reading and modification with LINQ, Dynamic LINQ, SQL, Stored Procedures, and Table Valued Functions- to using third-party products such as LINQPad, Entity Developer, Entity Framework Profiler, EFPlus, and AutoMapper. You'll also appreciate excerpts of conceptual software architecture discussion around Entity Framework Core that might otherwise take years to learn.What You'll LearnUnderstand the core concepts of Entity Framework Core, as well process models for existing databases (reverse engineering) and the generation of database schemas from object models (forward engineering)Study real-world case studies for hands-on EF Core instructionGet up to speed with valuable database access scenarios and code samplesDiscover workarounds to augment missing features in Entity Framework CoreUse Entity Framework Core to write mobile appsBonus online appendix covers Entity Framework Core 2.1 release updatesWho This Book Is ForSoftware developers who have basic experience with .NET and C#, as well as some understanding of relational databases. Knowledge of predecessor technologies such as ADO.NET and the classic ADO.NET Entity Framework is not necessary to learn from this book.
Modern Data Analytics in Excel: Using Power Query, Power Pivot, and More for Enhanced Data Analytics
by George MountIf you haven't modernized your data cleaning and reporting processes in Microsoft Excel, you're missing out on big productivity gains. And if you're looking to conduct rigorous data analysis, more can be done in Excel than you think. This practical book serves as an introduction to the modern Excel suite of features along with other powerful tools for analytics.George Mount of Stringfest Analytics shows business analysts, data analysts, and business intelligence specialists how to make bigger gains right from your spreadsheets by using Excel's latest features. You'll learn how to build repeatable data cleaning workflows with Power Query, and design relational data models straight from your workbook with Power Pivot. You'll also explore other exciting new features for analytics, such as dynamic array functions, AI-powered insights, and Python integration.Learn how to build reports and analyses that were previously difficult or impossible to do in Excel. This book shows you how to:Build repeatable data cleaning processes for Excel with Power QueryCreate relational data models and analysis measures with Power PivotPull data quickly with dynamic arraysUse AI to uncover patterns and trends from inside ExcelIntegrate Python functionality with Excel for automated analysis and reporting
Modern Data Architecture on Azure: Design Data-centric Solutions on Microsoft Azure
by Sagar LadThis book is an exhaustive guide to designing and implementing data solutions on Azure. It covers the process of managing data from end to end, starting from data collection all the way through transformation, distribution, and consumption. Modern Data Architecture on Azure begins with an introduction to the fundaments of data management, followed by a demonstration of how to build relational and non-relational data solutions on Azure. Here, you will learn data processing for complex analysis and how to work with CSV and JSON files. Moving forward, you will learn the foundational concepts of big data architecture, along with data management patterns and technology options offered by Azure. From there, you’ll be walked through the data architecture process, including data consortium on Azure, enterprise data governance, and much more. The book culminates with a deep dive into data architecture frameworks with data modeling. After reading this book, you will have a thorough understanding of data design and analytics using Azure, allowing you to collect and analyze massive amounts of data to optimize business performance, forecast future results, and more. What Will You Learn Understand the fundamentals of data architecture including data management, data handling ethics, data governance, and metadata managementAnalyze and understand business needs to choose the right Azure services and make informed business decisionsUnderstand Azure Cloud Data design patterns for relational and non-relational data, batch real-time processing, and ETL/ELT pipelinesModernize data architecture using Azure to leverage data and AI to enable digital transformation by securing and optimizing overall data lifecycle management Who Is This Book For: Data solution architects, data engineers, and IT consultants who want to gain a better understanding of modern data architecture design and implementation on Azure.
Modern Data Engineering with Apache Spark: A Hands-On Guide for Building Mission-Critical Streaming Applications
by Scott HainesLeverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow.Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compile reusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes.Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production. What You Will LearnSimplify data transformation with Spark Pipelines and Spark SQLBridge data engineering with machine learningArchitect modular data pipeline applications Build reusable application components and libraries Containerize your Spark applications for consistency and reliabilityUse Docker and Kubernetes to deploy your Spark applicationsSpeed up application experimentation using Apache Zeppelin and Docker Understand serializable structured data and data contracts Harness effective strategies for optimizing data in your data lakes Build end-to-end Spark structured streaming applications using Redis and Apache Kafka Embrace testing for your batch and streaming applications Deploy and monitor your Spark applications Who This Book Is ForProfessional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness and use Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world
Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
by Timothy MastersDiscover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You’ll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are:Forward selection component analysis Local feature selection Linking features and a target with a hidden Markov model Improvements on traditional stepwise selection Nominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets. Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts. C++ and CUDA C experience is highly recommended. However, this book can be used as a framework using other languages such as Python.
Modern Data Protection: Ensuring Recoverability Of All Modern Workloads
by W. Curtis PrestonGive your organization the data protection it deserves without the uncertainty and cost overruns experienced by your predecessors or other companies. System and network administrators have their work cut out for them to protect physical and virtual machines in the data center and the cloud; mobile devices including laptops and tablets; SaaS services like Microsoft 365, Google Workspace, and Salesforce; and persistent data created by Kubernetes and container workloads.To help you navigate the breadth and depth of this challenge, this book presents several solutions so you can determine which is right for your company. You'll learn the unique requirements that each workload presents, then explore various categories of commercial backup hardware, software, and services available to protect these data sources, including the advantages and disadvantages of each approach.Learn the workload types that your organization should be backing upExplore the hardware, software, and services you can use to back up your systemsUnderstand what's wrong with your current data protection systemPair your backed-up workloads to the appropriate backup systemLearn the adjustments that will make your backups better, without wasting money
Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)
by Benjamin S. Baumer Daniel T. Kaplan Nicholas J. HortonModern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions. <P><P> Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.
Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)
by Benjamin S. Baumer Daniel T. Kaplan Nicholas J. HortonFrom a review of the first edition: "Modern Data Science with R… is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician).Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
Modern Data Strategy
by Mike Fleckenstein Lorraine FellowsThis book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.
Modern Data Visualization with R (Chapman & Hall/CRC The R Series)
by Robert KabacoffModern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the mapping of geographic data. Both static and interactive graphics are described and the use of color, shape, shading, grouping, annotation, and animations are covered in detail. The book moves from a default look and feel for graphs, to graphs with customized colors, fonts, legends, annotations, and organizational themes.Features Contains a wide breadth of graph types including newer and less well-known approaches Connects each graph type to the characteristics of the data and the goals of the analysis Moves the reader from simple graphs describing one variable to building visualizations that describe complex relationships among many variables Provides newer approaches to creating interactive web graphics via JavaScript libraries Details how to customize each graph type to meet users’ needs and those of their audiences Gives methods for creating visualizations that are publication ready for print (in color or black and white) and the web Suggests best practices Offers examples from a wide variety of fields The book is written for those new to data analysis as well as the seasoned data scientist. It can be used for both teaching and research, and will particularly appeal to anyone who needs to describe data visually and wants to find and emulate the most appropriate method quickly. The reader should have some basic coding experience, but expertise in R is not required. Some of the later chapters (e.g., visualizing statistical models) assume exposure to statistical inference at the level of analysis of variance and regression.
The Modern Data Warehouse in Azure: Building with Speed and Agility on Microsoft’s Cloud Platform
by Matt HowBuild a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast—fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business.Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision making that drives your business forward toward a pattern of success.This book teaches you how to employ the Azure platform in a strategy to dramatically improve implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure such as choosing which type of SQL engine (from at least three options) best meets the needs of your organization. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based implementations.What You Will LearnChoose the appropriate Azure SQL engine for implementing a given data warehouseDevelop smart, reusable ETL/ELT processes that are resilient and easily maintainedAutomate mundane development tasks through tools such as PowerShellEnsure consistency of data by creating and enforcing data contractsExplore streaming and event-driven architectures for data ingestionCreate advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouseWho This Book Is ForData warehouse or ETL/ELT developers who wish to implement a data warehouse project in the Azure cloud, and developers currently working in on-premise environments who want to move to the cloud, and for developers with Azure experience looking to tighten up their implementation and consolidate their knowledge
Modern Database Management
by Jeffrey A. Hoffer Mary B. Prescott Fred R. McfaddenEverything you ever wanted to know about database design and management.
Modern Database Management (Twelfth Edition)
by Jeffrey A. Hoffer Ramesh Venkataraman Heikki TopiThis text is designed to be used with an introductory course in database management. Such a course is usually required as part of an information systems curriculum in business schools, computer technology programs, and applied computer science departments.
Modern Deep Learning Design and Application Development: Versatile Tools to Solve Deep Learning Problems
by Andre YeLearn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You’ll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you’ll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You’ll learn not only to understand and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today’s difficult problems. What You’ll Learn Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization.Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches. Who This Book Is For Data scientists with some familiarity with deep learning to deep learning engineers seeking structured inspiration and direction on their next project. Developers interested in harnessing modern deep learning methods to solve a variety of difficult problems.
Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems
by Andre Ye Zian WangDeep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain – tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data – an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs – Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks – through both their ‘default’ usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability. Each chapter comes with extensive visualization, code, and relevant research coverage. Modern Deep Learning for Tabular Data is one of the first of its kind – a wide exploration of deep learning theory and applications to tabular data, integrating and documenting novel methods and techniques in the field. This book provides a strong conceptual and theoretical toolkit to approach challenging tabular data problems.What You Will LearnImportant concepts and developments in modern machine learning and deep learning, with a strong emphasis on tabular data applications.Understand the promising links between deep learning and tabular data, and when a deep learning approach is or isn’t appropriate.Apply promising research and unique modeling approaches in real-world data contexts.Explore and engage with modern, research-backed theoretical advances on deep tabular modelingUtilize unique and successful preprocessing methods to prepare tabular data for successful modelling. Who This Book Is ForData scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security.
Modern DevOps Practices: Implement and secure DevOps in the public cloud with cutting-edge tools, tips, tricks, and techniques
by Gaurav AgarwalEnhance DevOps workflows by integrating the functionalities of Docker, Kubernetes, Spinnaker, Ansible, Terraform, Flux CD, CaaS, and more with the help of practical examples and expert tipsKey FeaturesGet up and running with containerization-as-a-service and infrastructure automation in the public cloudLearn container security techniques and secret management with Cloud KMS, Anchore Grype, and Grafeas KritisLeverage the combination of DevOps, GitOps, and automation to continuously ship a package of softwareBook DescriptionContainers have entirely changed how developers and end-users see applications as a whole. With this book, you'll learn all about containers, their architecture and benefits, and how to implement them within your development lifecycle.You'll discover how you can transition from the traditional world of virtual machines and adopt modern ways of using DevOps to ship a package of software continuously. Starting with a quick refresher on the core concepts of containers, you'll move on to study the architectural concepts to implement modern ways of application development. You'll cover topics around Docker, Kubernetes, Ansible, Terraform, Packer, and other similar tools that will help you to build a base. As you advance, the book covers the core elements of cloud integration (AWS ECS, GKE, and other CaaS services), continuous integration, and continuous delivery (GitHub actions, Jenkins, and Spinnaker) to help you understand the essence of container management and delivery. The later sections of the book will take you through container pipeline security and GitOps (Flux CD and Terraform).By the end of this DevOps book, you'll have learned best practices for automating your development lifecycle and making the most of containers, infrastructure automation, and CaaS, and be ready to develop applications using modern tools and techniques.What you will learnBecome well-versed with AWS ECS, Google Cloud Run, and KnativeDiscover how to build and manage secure Docker images efficientlyUnderstand continuous integration with Jenkins on Kubernetes and GitHub actionsGet to grips with using Spinnaker for continuous deployment/deliveryManage immutable infrastructure on the cloud with Packer, Terraform, and AnsibleExplore the world of GitOps with GitHub actions, Terraform, and Flux CDWho this book is forIf you are a software engineer, system administrator, or operations engineer looking to step into the world of DevOps within public cloud platforms, this book is for you. Existing DevOps engineers will also find this book useful as it covers best practices, tips, and tricks to implement DevOps with a cloud-native mindset. Although no containerization experience is necessary, a basic understanding of the software development life cycle and delivery will help you get the most out of the book.
Modern Digital Design and Switching Theory
by Eugene D. FabriciusModern Digital Design and Switching Theory is an important text that focuses on promoting an understanding of digital logic and the computer programs used in the minimization of logic expressions. Several computer approaches are explained at an elementary level, including the Quine-McCluskey method as applied to single and multiple output functions, the Shannon expansion approach to multilevel logic, the Directed Search Algorithm, and the method of Consensus. Chapters 9 and 10 offer an introduction to current research in field programmable devices and multilevel logic synthesis. Chapter 9 covers more advanced topics in programmed logic devices, including techniques for input decoding and Field-Programmable Gate Arrays (FPGAs). Chapter 10 includes a discussion of boolean division, kernels and factoring, boolean tree structures, rectangle covering, binary decision diagrams, and if-then-else operators. Computer algorithms covered in these two chapters include weak division, iterative weak division, and kernel extraction by tabular methods and by rectangle covering theory. Modern Digital Design and Switching Theory is an excellent textbook for electrical and computer engineering students, in addition to a worthwhile reference for professionals working with integrated circuits.
Modern Digital Halftoning (Signal Processing and Communications #1)
by Daniel L. Lau Gonzalo R. ArceThe late 1980s, revolutionary advances in digital halftoning enabled inkjet printers to achieve much higher image fidelity. The rapid rate of progress has resulted in numerous breakthroughs scattered throughout the literature, rendering old technologies obsolete and renewing the need for a centralized source on the current state of the art. Entirely revised and updated, Modern Digital Halftoning, Second Edition provides an integrated and up-to-date treatment of stochastic halftoning and digital printing. Using full-color illustrations to enhance the text, this edition incorporates new topics as well as updated models, algorithms, and methods used to construct and improve the quality of green-noise, blue-noise, and multitone images. Following a review of various halftoning techniques, this edition now covers amplitude modulated dither arrays, adapting to human visual models, direct binary search, and handling stochastic moiré problems. It also presents a new chapter on lenticular printing, a means for printing color holographic images. Accompanying downloadable resources contain MATLAB® software files and illustrated examples employing algorithms, statistics, and other key concepts from the book. Documenting the development of digital printing since the first edition, Modern Digital Halftoning, Second Edition offers a well-rounded and accurate perspective on the technological capabilities of digital printing and provides all the necessary tools for continuing research in the field.
Modern Digital Radio Communication Signals and Systems
by Sung-Moon Michael YangThis book serves as an easily accessible reference for wireless digital communication systems. Topics are presented with simple but non-trivial examples and then elaborated with their variations and sophistications. It includes numerous examples and exercises to illustrate key points. The book emphasizes both practical problem solving and a thorough understanding of fundamentals, aiming to realize the complementary relationship between practice and theory. Though the author emphasize wireless radio channels, the fundamentals that are covered are useful to different channels - digital subscriber line, coax, power lines, optical fibers, and even Gigabit serial interconnection. This book is the outgrowth of the author’s hands-on experience in the telecommunication systems industry as a research and development engineer. It is written primarily for practitioners of wireless digital communication systems – engineers and technical leaders and managers – and for digital communication systems in general including new comers like graduate students and upper-division undergraduate students. The material in chapters 5(OFDM), 6(Channel coding), 7(Synchronization) and 8(Transceivers) contains something new, not explicitly available in typical textbooks, and useful in practice. For example, in Chapter 5, all known orthogonal frequency division multiplex signals are formulated based on pulse shape and thus flexible, e.g., unlike currently predominant symbol block transmission, it can be serial transmission. In Chapter 6, we emphasize practical applications of powerful error coding such as LDPC to higher order modulations, fading, and non-linearity problem. In Chapter 7, new digital timing detectors are suggested for small access bandwidth shaping pulse, and a digital quadrature imbalance correction is also included along with digital carrier phase recovery. In Chapter 8, low IF digital image cancelling transceiver is treated in detail so that practical implementation can be readily done with advantages.
Modern Digital Radio Communication Signals and Systems
by Sung-Moon Michael YangThis book serves as an easily accessible reference for wireless digital communication systems. Topics are presented with simple but non-trivial examples and then elaborated with their variations and sophistications. The book includes numerous examples and exercises to illustrate key points. For this new edition, a set of problems at the end of each chapter is added, for a total of 298 problems. The book emphasizes both practical problem solving and a thorough understanding of fundamentals, aiming to realize the complementary relationship between practice and theory. Though the author emphasizes wireless radio channels, the fundamentals that are covered here are useful to different channels - digital subscriber line, coax, power lines, optical fibers, and even Gigabit serial connections. The material in chapters 5 (OFDM), 6 (Channel coding), 7 (Synchronization), and 8 (Transceivers) contains new and updated information, not explicitly available in typical textbooks, and useful in practice. For example, in chapter 5, all known orthogonal frequency division multiplex signals are derived from its digitized analog FDM counterparts. Thus, it is flexible to have different pulse shape for subcarriers, and it can be serial transmission as well as block transmission. Currently predominant cyclic prefix based OFDM is a block transmission using rectangular pulse in time domain. This flexibility may be useful in certain applications. For additional information, consult the book support website: https://baycorewireless.com
Modern Directional Statistics: Modern Methods And Case Studies (Chapman & Hall/CRC Interdisciplinary Statistics)
by Christophe Ley Thomas VerdeboutModern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory. <P><P> The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods. <P><P>Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein’s Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics. <P><P> Thomas Verdebout is professor of mathematical statistics at Université libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.
Modern Electronics Devices and Communication Systems: Select Proceedings of MEDCOM 2021 (Lecture Notes in Electrical Engineering #948)
by Dinesh Kumar Singh Rajeev Agrawal Chandramani Kishore Singh Ayush GoyalThis book presents select and peer-reviewed proceedings of the International Conference on Smart Communication and Imaging Systems (MEDCOM 2021). The contents explore the recent technological advances in the field of next-generation electronics devices and communication systems. The topics include the design and development of smart, secure, and reliable future communication networks; satellite, radar, and microwave techniques for intelligent communication. The book also covers methods and applications of GIS and remote sensing; medical image analysis and its applications in smart health. This book can be useful for students, researchers, and professionals working in the field of communication systems and image processing.
Modern Enterprise Architecture: Using DevSecOps and Cloud-Native in Large Enterprises
by Jeroen MulderEnterprise Architecture (EA) frameworks such as TOGAF and Zachman are still valid, but enterprise architects also need to adapt to the new reality of agile, DevOps, and overall disruption through digital transformation. This book will help do just that. The Change to Modern Enterprise Architecture will teach you how to use known frameworks in the new world of digital transformation. Over the course of the book, you'll learn how modern EA is helping drive strategic business decisions, create continuous and agile (“floating”) architecture for scalability, and how to address quality and speed in architecture using and integrating DevSecOps frameworks in EA. This book is divided into three parts: the first explains what modern enterprise architecture is and why it’s important to any business. It covers the different EA frameworks and explains what they are. In the second part, you will learn how to integrate modern development frameworks into EA, and why this knowledge will enable you to deftly respond to various business challenges. The final section of the book is all about scaling the enterprise using modern enterprise architecture. You will also see how the role of the enterprise architect is changing and how to remain in control of your architecture. Upon completing this book, you'll understand why the enterprise architect is no longer just a role overseeing the architecture strategy of a business, but has become more of a leader in driving engineering excellence. What You'll Learn Integrate DevSecOps as artifact to modern EAUse Enterprise Architecture to scale up your businessUnderstand the changing role of the enterprise architectDefine a floating architecture to enhance business agility Who This Book Is For Enterprise architects, IT architects, lead engineers, business architects, business leaders, product managers.
Modern Fortran: style and usage
by Norman S. Clerman Walter SpectorFortran is one of the oldest high-level languages and remains the premier language for writing code for science and engineering applications. Modern Fortran: Style and Usage is a book for anyone who uses Fortran, from the novice learner to the advanced expert. It describes best practices for programmers, scientists, engineers, computer scientists, and researchers who want to apply good style and incorporate rigorous usage in their own Fortran code or to establish guidelines for a team project. The presentation concentrates primarily on the characteristics of Fortran 2003, while also describing methods in Fortran 90/95 and valuable new features in Fortran 2008. The authors draw on more than a half century of experience writing production Fortran code to present clear succinct guidelines on formatting, naming, documenting, programming, and packaging conventions and various programming paradigms such as parallel processing (including OpenMP, MPI, and coarrays), OOP, generic programming, and C language interoperability. Programmers working with legacy code will especially appreciate the section on updating old programs.
Modern Fortran: Building efficient parallel applications
by Milan CurcicModern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you&’ll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light.Summary Using Fortran, early and accurate forecasts for hurricanes and other major storms have saved thousands of lives. Better designs for ships, planes, and automobiles have made travel safer, more efficient, and less expensive than ever before. Using Fortran, low-level machine learning and deep learning libraries provide incredibly easy, fast, and insightful analysis of massive data. Fortran is an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science, and industry. And it's come a long, long way since starting life on IBM mainframes in 1956. Modern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or re-discover—Fortran.. About the technology For over 60 years Fortran has been powering mission-critical scientific applications, and it isn't slowing down yet! Rock-solid reliability and new support for parallel programming make Fortran an essential language for next-generation high-performance computing. Simply put, the future is in parallel, and Fortran is already there. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. What's inside Fortran's place in the modern world Working with variables, arrays, and functions Module development Parallelism with coarrays, teams, and events Interoperating Fortran with C About the reader For developers and computational scientists. No experience with Fortran required. About the author Milan Curcic is a meteorologist, oceanographer, and author of several general-purpose Fortran libraries and applications. Table of Contents PART 1 - GETTING STARTED WITH MODERN FORTRAN 1 Introducing Fortran 2 Getting started: Minimal working app PART 2 - CORE ELEMENTS OF FORTRAN 3 Writing reusable code with functions and subroutines 4 Organizing your Fortran code using modules 5 Analyzing time series data with arrays 6 Reading, writing, and formatting your data PART 3 - ADVANCED FORTRAN USE 7 Going parallel with Fortan coarrays 8 Working with abstract data using derived types 9 Generic procedures and operators for any data type 10 User-defined operators for derived types PART 4 - THE FINAL STRETCH 11 Interoperability with C: Exposing your app to the web 12 Advanced parallelism with teams, events, and collectives