Rapid Beagleboard Prototyping With Matlab And Simulink

This book is a fast-paced guide with practical, hands-on recipes which will show you how to prototype Beagleboard-based audio/video applications using Matlab/Simlink and Sourcery Codebench on a Windows host.Beagleboard Embedded Projects is great for students and academic researchers who have practical ideas and who want to build a proof-of-concept system on an embedded hardware platform quickly and efficiently. It is also useful for product design engineers who want to ratify their applications and reduce the time-to-market. It is assumed that you are familiar with Matlab/Simulink and have some basic knowledge of computer hardware. Experience in Linux is favoured but not necessary, as our software development is purely on a Windows host.

This book is a fast-paced guide with practical, hands-on recipes which will show you how to prototype Beagleboard-based audio/video applications using Matlab/Simlink and Sourcery Codebench on a Windows host. Beagleboard Embedded Projects is great for students and academic researchers who have practical ideas and who want to build a proof-of-concept system on an embedded hardware platform quickly and efficiently. It is also useful for product design engineers who want to ratify their applications and reduce the time-to-market. It is assumed that you are familiar with Matlab/Simulink and have some basic knowledge of computer hardware. Experience in Linux is favoured but not necessary, as our software development is purely on a Windows host.

This book provides an overview of the kill chain approach to penetration testing, and then focuses on using Kali Linux to provide examples of how this methodology is applied in the real world. After describing the underlying concepts, step-by-step examples are provided that use selected tools to demonstrate the techniques.If you are an IT professional or a security consultant who wants to maximize the success of your network testing using some of the advanced features of Kali Linux, then this book is for you. This book will teach you how to become an expert in the pre-engagement, management, and documentation of penetration testing by building on your understanding of Kali Linux and wireless concepts.

The objective of this book is to provide the reader with a comprehensive coverage on the Robot Operating Systems (ROS) and latest related systems, which is currently considered as the main development framework for robotics applications. The book includes twenty-seven chapters organized into eight parts. Part 1 presents the basics and foundations of ROS. In Part 2, four chapters deal with navigation, motion and planning. Part 3 provides four examples of service and experimental robots. Part 4 deals with real-world deployment of applications. Part 5 presents signal-processing tools for perception and sensing. Part 6 provides software engineering methodologies to design complex software with ROS. Simulations frameworks are presented in Part 7. Finally, Part 8 presents advanced tools and frameworks for ROS including multi-master extension, network introspection, controllers and cognitive systems. This book will be a valuable companion for ROS users and developers to learn more ROS capabilities and features.

You can use MATLAB and Simulink for rapid prototyping of hardware designs. Some DSP System Toolbox algorithms support HDL code generation when used with HDL Coder. For digital filter designs in MATLAB, use Filter Design HDL Coder to generate HDL code and optimize filter implementation for hardware speed or area. Both HDL Coder and Filter Design HDL Coder generate target-independent synthesizable Verilog and VHDL code for FPGA programming or ASIC prototyping and design. They also generate scripts and test benches for use with third-party HDL simulators.

The aim of this book is to give the necessary knowledge about the implementation of MATLABʼ and Simulinkʼ in the development of embedded control systems. Together, MATLAB and Simulink present a sophisticated programming environment which may be used for the design as well as for the implementation of embedded control systems. In this book, the authors exploit the opportunity to generate automatically and embed control code from Simulink models which allows to develop quickly efficient and error free code. The automated code generation and the availability of powerful processors make possible the implementation of complex high-order controllers which achieve fast and high-performance closed-loop dynamics.

Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.