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Blockchain concepts and the Hyperledger technologies are hot topics. Hyperledger is an open source project to create private blockchain applications for different domains including finance, banking, supply chain, IoT and much more. This book will be an easy reference to explore and build blockchain networks using Hyperledger services. This book will start with explaining the blockchain evolution and then proceed to an overview of technologies like Ethereum, R3 Corda, Coco, and Hyperledger. We will learn how to set up and launch Hyperledger Fabric in Bluemix. We will look into the architecture and the components of Hyperledger Fabric which are used to build private blockchain applications. Later we will delve into how we can interact with Hyperledger Fabric blockchain to build private networks from scratch covering all the required principles such as chaincode, smart contracts, cryptocurrencies and much more on the Hyperledger network. By the end of this book, you will be able to build and deploy your own decentralized applications using Hyperledger addressing key pain points encountered in blockchain lifecycle.

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Professional Java Development with the Spring Framework Table of Contents Professional Java Development with the Spring Framework Introduction Chapter 1 - Introducing the Spring Framework Chapter 2 - The Bean Factory and Application Context Chapter 3 - Advanced Container Concepts Chapter 4 - Spring and AOP Chapter 5 - DAO Support and JDBC Framework Chapter 6 - Transaction and Resource Management Chapter 7 - Object/Relational Mapping Chapter 8 - Lightweight Remoting Chapter 9 - Supporting Services Chapter 10 - Acegi Security System for Spring Chapter 11 - Spring and EJB Chapter 12 - Web MVC Framework Chapter 13 - Web View Technologies Chapter 14 - Integrating with Other Web Frameworks Chapter 15 - The Sample Application Chapter 16 - Conclusion Appendix A - Requirements for the Sample Application Index List of Figures List of Sidebars

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Linux Driver Development for Embedded Processors – Second Edition 版本: Learn to develop Linux embedded drivers with kernel 4.9 LTS The flexibility of Linux embedded, the availability of powerful, energy efficient processors designed for embedded computing and the low cost of new processors are encouraging many industrial companies to come up with new developments based on embedded processors. Current engineers have in their hands powerful tools for developing applications previously unimagined, but they need to understand the countless features that Linux offers today. This book will teach you how to develop device drivers for Device Tree Linux embedded systems. You will learn how to write different types of Linux drivers, as well as the appropriate APIs (Application Program Interfaces) and methods to interface with kernel and user spaces. This is a book is meant to be practical, but also provides an important theoretical base. More than twenty drivers are written and ported to three different processors. You can choose between NXP i.MX7D, Microchip SAMA5D2 and Broadcom BCM2837 processors to develop and test the drivers, whose implementation is described in detail in the practical lab sections of the book. Before you start reading, I encourage you to acquire any of these processor boards whenever you have access to some GPIOs, and at least one SPI and I2C controllers. One of the boards used to implement the drivers is the famous Raspberry PI 3 Model B board. You will learn how to develop drivers, from the simplest ones that do not interact with any external hardware, to drivers that manage different kind of devices: accelerometers, DACs, ADCs, RGB LEDs, Multi-Display LED controllers, I/O expanders, and Buttons. You will also develop DMA drivers, drivers that manage interrupts, and drivers that write/read on the internal registers of the processor to control external devices. To easy the development of some of these drivers, you will use different types of Frameworks:

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I am from the days when computer engineers and scientists had to write assembly language on IBM mainframes to develop high-performance programs. Programs were written on punch cards and compilation was a one-day process; you dropped o your punch-code written program and picked up the results the next day. If there was an error, you did it again. In those days, a good programmer had to understand the underlying machine hardware to produce good code. I get a little nervous when I see computer science students being taught only at a high abstraction level and languages like Ruby. Although abstraction is a beautiful thing to develop things without getting bogged down with unnecessary details, it is a bad thing when you are trying to develop super high performance code. Since the introduction of the rst CPU, computer architects added incredible features into CPU hardware to \forgive" bad programming skills; while you had to order the sequence of machine code instructions by hand two decades ago, CPUs do that in hardware for you today (e.g., out of order processing). A similar trend is clearly visible in the GPU world. Most of the techniques that were taught asperformance improvement techniquesin GPU programming ve years ago (e.g., thread divergence, shared memory bank conicts, and reduced usage of atomics) are becoming less relevant with the improved GPU architectures because GPU architects are adding hardware features that are improving these previous ineciencies so much that it won’t even matter if a programmer is sloppy about it within another 5{10 years. However, this is just a guess. What GPU architects can do depends on their (i)transistor budget, as well as (ii) their customers’ demands. When I saytransistor budget, I am referring to how many transistors the GPU manufacturers can cram into an Integrated Circuit (IC), aka a \chip." When I saycustomer demands, I mean that even if they can implement a feature, the applications that their customers are using might not