This book will be a massive ally in troubleshooting your network using Wireshark, the world's most popular analyzer. Over 100 practical recipes provide a focus on real-life situations, helping you resolve your own individual issues.

OverviewPlace Wireshark in your network and configure it for effective network analysisConfigure capture and display filters to get the required dataUse Wireshark’s powerful statistical tools to analyze your network and its expert system to pinpoint network problems

In Detail

Is your network slow? Are your users complaining? Disconnections? IP Telephony problems? Video freezes? Network analysis is the process of isolating these problems and fixing them, and Wireshark has long been the most popular network analyzer for achieving this goal. Based on hundreds of solved cases, Network Analysis using Wireshark Cookbook provides you with practical recipes for effective Wireshark network analysis to analyze and troubleshoot your network.

"Network analysis using Wireshark Cookbook" highlights the operations of Wireshark as a network analyzer tool. This book provides you with a set of practical recipes to help you solve any problems in your network using a step-by-step approach.

"Network analysis using Wireshark Cookbook" starts by discussing the capabilities of Wireshark, such as the statistical tools and the expert system, capture and display filters, and how to use them. The book then guides you through the details of the main networking protocols, that is, Ethernet, LAN switching, and TCP/IP, and then discusses the details of application protocols and their behavior over the network. Among the application protocols that are discussed in the book are standard Internet protocols like HTTP, mail protocols, FTP, and DNS, along with the behavior of databases, terminal server clients, Citrix, and other applications that are common in the IT environment.

In a bottom-up troubleshooting approach, the book goes up through the layers of the OSI reference model explaining how to resolve networking problems. The book starts from Ethernet and LAN switching, through IP, and then on to TCP/UDP with a focus on TCP performance problems. It also focuses on WLAN security. Then, we go through application behavior issues including HTTP, mail, DNS, and other common protocols. The book finishes with a look at network forensics and how to search and find security problems that might harm the network.

What you will learn from this bookConfigure Wireshark for effective network troubleshootingSet up various display and capture filtersUse basic statistical tools that provide you with "who is talking" tables, conversations, and HTTP statisticsMaster both the standard and advanced features of IO graphsUse the expert system to pinpoint various types of events that might influence the behavior of your networkLearn about Wi-Fi testing and how to resolve problems related to wireless LANsExplore performance issues in TCP/IPExplore failures due to delays and jitters in the networkFind and resolve problems due to bandwidth, throughput, and packet lossIdentify and locate faults in communication applications including HTTP, FTP, mail, and various other applications – Microsoft OS problems, databases, voice, and video over IPIdentify and locate faults in detecting security failures and security breaches in the network

This Expert Guide gives you the techniques and technologies in digital signal processing (DSP) to optimally design and implement your embedded system. Written by experts with a solutions focus, this encyclopedic reference gives you an indispensable aid to tackling the day-to-day problems you face in using DSP to develop embedded systems.

With this book you will learn:

A range of development techniques for developing DSP code

Valuable tips and tricks for optimizing DSP software for maximum performance

The various options available for constructing DSP systems from numerous software components

The tools available for developing DSP applications

Numerous practical guidelines from experts with wide and lengthy experience of DSP application development

Features:

Several areas of research being done in advanced DSP technology

Industry case studies on DSP systems development

DSP software development tools available for download and evaluation

DSP for Embedded and Real-Time Systems is the reference for both the beginner and experienced, covering most aspects of using today's DSP techniques and technologies for designing and implementing an optimal embedded system. The only complete reference which explains all aspects of using DSP in embedded systems development making it a rich resource for every day useCovers all aspects of using today's DSP techniques and technologies for designing and implementing an optimal embedded systemEnables the engineer to find solutions to all the problems they will face when using DSP

This Expert Guide gives you the techniques and technologies in software engineering to optimally design and implement your embedded system. Written by experts with a solutions focus, this encyclopedic reference gives you an indispensable aid to tackling the day-to-day problems when using software engineering methods to develop your embedded systems.

With this book you will learn:

The principles of good architecture for an embedded system

Design practices to help make your embedded project successful

Details on principles that are often a part of embedded systems, including digital signal processing, safety-critical principles, and development processes

Techniques for setting up a performance engineering strategy for your embedded system software

How to develop user interfaces for embedded systems

Strategies for testing and deploying your embedded system, and ensuring quality development processes

Practical techniques for optimizing embedded software for performance, memory, and power

Advanced guidelines for developing multicore software for embedded systems

How to develop embedded software for networking, storage, and automotive segments

How to manage the embedded development process

Includes contributions from:

Frank Schirrmeister, Shelly Gretlein, Bruce Douglass, Erich Styger, Gary Stringham, Jean Labrosse, Jim Trudeau, Mike Brogioli, Mark Pitchford, Catalin Dan Udma, Markus Levy, Pete Wilson, Whit Waldo, Inga Harris, Xinxin Yang, Srinivasa Addepalli, Andrew McKay, Mark Kraeling and Robert Oshana. Road map of key problems/issues and references to their solution in the text

Review of core methods in the context of how to apply them

Examples demonstrating timeless implementation details

Short and to- the- point case studies show how key ideas can be implemented, the rationale for choices made, and design guidelines and trade-offs

Mathematics plays a key role in computer science, some researchers would consider computers as nothing but the physical embodiment of mathematical systems. And whether you are designing a digital circuit, a computer program or a new programming language, you need mathematics to be able to reason about the design — its correctness, robustness and dependability. This book covers the foundational mathematics necessary for courses in computer science.

The common approach to presenting mathematical concepts and operators is to define them in terms of properties they satisfy, and then based on these definitions develop ways of computing the result of applying the operators and prove them correct. This book is mainly written for computer science students, so here the author takes a different approach: he starts by defining ways of calculating the results of applying the operators and then proves that they satisfy various properties. After justifying his underlying approach the author offers detailed chapters covering propositional logic, predicate calculus, sets, relations, discrete structures, structured types, numbers, and reasoning about programs. The book contains chapter and section summaries, detailed proofs and many end-of-section exercises — key to the learning process. The book is suitable for undergraduate and graduate students, and although the treatment focuses on areas with frequent applications in computer science, the book is also suitable for students of mathematics and engineering.

Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics.

R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets. R is also becoming adopted into commercial tools such as Oracle Database. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for statistical exploration and research.

Covers the freely-available R language for statisticsShows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and moreTakes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done

What you’ll learn Acquire and install RImport and export data and scriptsGenerate basic statistics and graphicsProgram in R to write custom functionsUse R for interactive statistical explorationsImplement simulations and other advanced techniquesWho this book is for

Beginning R: An Introduction to Statistical Programming is an easy-to-read book that serves as an instruction manual and reference for working professionals, professors, and students who want to learn and use R for basic statistics. It is the perfect book for anyone needing a free, capable, and powerful tool for exploring statistics and automating their use. Table of Contents Part I. Learning the R Language

1. Getting R and Getting Started

2. Programming in R

3. Writing Reusable Functions

4. Summary Statistics

Part II. Using R for Descriptive Statistics

5. Creating Tables and Graphs

6. Discrete Probability Distributions

7. Computing Standard Normal Probabilities

Part III. Using R for Inferential Statistics

8. Creating Confidence Intervals

9. Performing t Tests 

10.  Implementing One-Way ANOVA

11.  Implementing Advanced ANOVA

12. Simple Correlation and Regression in R

13. Multiple Correlation and Regression in R

14. Logistic Regression

15. Performing Chi-Square Tests

16. Working in Nonparametric Statistics

Part IV. Taking R to the Next Level

17. Using R for Simulation

18. Resampling and Bootstrapping

19. Creating R Packages

20. Executing R Packages

Design and implement your own computer vision applications with the Raspberry PiAbout This Book Explore the vast opportunities of computer vision with the Raspberry Pi and PythonDesign and develop webcam and Raspberry Pi based applicationsThe book features ample screenshots that will offer a play-by-play account on how to implement computer vision fundamentals and applicationsWho This Book Is For

This book is intended for novices, as well as seasoned Raspberry Pi and Python enthusiasts, who would like to explore the area of computer vision. Readers with very little programming or coding/scripting experience can create wonderful image processing and computer vision applications with relatively fewer lines of code in Python. What You Will Learn Set up your Raspberry Pi and master computer vision with OpenCVWork with images, videos, webcams, the Pi camera, and create amazing timelapse videosBlend images and create artistic effects such as image transitioningTransform images, change colorspaces, and track objects based on colorsUse various high- and low-pass filters to remove noise from the imageFind contours and segments in images and detect edges, lines, and circlesInstall another simple yet powerful library, SimpleCV, and with its help create real-life applicationsIn Detail

This book will provide you with the skills you need to successfully design and implement your own Raspberry Pi and Python-based computer vision projects.

From the beginning, this book will cover how to set up your Raspberry Pi for computer vision applications, exploring the basics of OpenCV, and how to design and implement real-life computer vision applications on your own. By sequentially working through the steps in each chapter, you will quickly be able to master the features of OpenCV. In the end of the book, you will also be introduced to SimpleCV, which is another powerful computer vision library for Python. Featuring plenty of coding examples and exercises, this book offers you an unparalleled learning experience.

Write your own Digital Image Processing programs with the use of pillow, scipy.ndimage, and matplotlib in Python 3 with Raspberry Pi 3 as the hardware platform. This concise quick-start guide provides working code examples and exercises. Learn how to interface Raspberry Pi with various image sensors.
What You'll Learn
Understand Raspberry Pi concepts and setup
Understand digital image processing concepts
Study pillow, the friendly PIL fork
Explore scipy.ndimage and matplotlib
Master use of the Pi camera and webcam

Who This Book Is For
Raspberry Pi and IoT enthusiasts, digital image processing enthusiasts, Python and Open Source enthusiasts and professionals

Build an inexpensive cluster of multiple Raspberry Pi computers and install all the required libraries to write parallel and scientific programs in Python 3. This book covers setting up your Raspberry Pis, installing the necessary software, and making a cluster of multiple Pis. 
Once the cluster is built, its power has to be exploited by means of programs to run on it. So, Raspberry Pi Supercomputing and Scientific Programming teaches you to code the cluster with the MPI4PY library of Python 3. Along the way, you will learn the concepts of the Message Passing Interface (MPI) standards and will explore the fundamentals of parallel programming on your inexpensive cluster. This will make this book a great starting point for supercomputing enthusiasts who want to get started with parallel programming. 
The book finishes with details of symbolic mathematics and scientific and numerical programming in Python, using SymPi, SciPy, NumPy, and Matplotlib. You’ll see how to process signals and images, carry out calculations using linear algebra, and visualize your results, all using Python code. With the power of a Raspberry Pi supercomputer at your fingertips, data-intensive scientific programming becomes a reality at home.
What You Will Learn  Discover the essentials of supercomputing
Build a low-cost cluster of Raspberry Pis at home
Harness the power of parallel programming and the Message Passing Interface (MPI)
Use your Raspberry Pi for symbolic, numerical, and scientific programming

Who This Book Is For
Python 3 developers who seek the knowledge of parallel programming, Raspberry Pi enthusiasts, researchers, and the scientific Python community.

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning.

The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results.
Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology.
The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book.

What you'll learn:An overview of the field of machine learning
Commercial and open source packages in MATLAB
How to use MATLAB for programming and building machine learning applications
MATLAB graphics for machine learning
Practical real world examples in MATLAB for major applications of machine learning in big data

Who is this book for:
The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.

Programmers run into parsing problems all the time. Whether it's a data format like JSON, a network protocol like SMTP, a server configuration file for Apache, a PostScript/PDF file, or a simple spreadsheet macro language—ANTLR v4 and this book will demystify the process. ANTLR v4 has been rewritten from scratch to make it easier than ever to build parsers and the language applications built on top. This completely rewritten new edition of the bestselling Definitive ANTLR Reference shows you how to take advantage of these new features.

Build your own languages with ANTLR v4, using ANTLR's new advanced parsing technology. In this book, you'll learn how ANTLR automatically builds a data structure representing the input (parse tree) and generates code that can walk the tree (visitor). You can use that combination to implement data readers, language interpreters, and translators.

You'll start by learning how to identify grammar patterns in language reference manuals and then slowly start building increasingly complex grammars. Next, you'll build applications based upon those grammars by walking the automatically generated parse trees. Then you'll tackle some nasty language problems by parsing files containing more than one language (such as XML, Java, and Javadoc). You'll also see how to take absolute control over parsing by embedding Java actions into the grammar.

You'll learn directly from well-known parsing expert Terence Parr, the ANTLR creator and project lead. You'll master ANTLR grammar construction and learn how to build language tools using the built-in parse tree visitor mechanism. The book teaches using real-world examples and shows you how to use ANTLR to build such things as a data file reader, a JSON to XML translator, an R parser, and a Java class->interface extractor. This book is your ticket to becoming a parsing guru!

What You Need:

ANTLR 4.0 and above. Java development tools. Ant build system optional (needed for building ANTLR from source)

ANTLR v3 is the most powerful, easy-to-use parser generator built to date, and represents the culmination of more than 15 years of research by Terence Parr. This book is the essential reference guide to using this completely rebuilt version of ANTLR, with its amazing new LL() parsing technology, tree construction facilities, StringTemplate code generation template engine, and sophisticated ANTLRWorks GUI development environment. Learn to use ANTLR directly from the author!

ANTLR is a parser generator-a program that generates code to translate a specified input language into a nice, tidy data structure. You might think that parser generators are only used to build compilers. But in fact, programmers usually use parser generators to build translators and interpreters for domain-specific languages such as proprietary data formats, common network protocols, text processing languages, and domain-specific programming languages.

Domain-specific languages are important to software development because they represent a more natural, high fidelity, robust, and maintainable means of encoding a problem than simply writing software in a general-purpose language. For example, NASA uses domain-specific command languages for space missions to improve reliability, reduce risk, reduce cost, and increase the speed of development. Even the first Apollo guidance control computer from the 1960s used a domain-specific language that supported vector computations.

This book is the definitive guide to using the completely rebuilt ANTLR v3 and describes all features in detail, including the amazing new LL() parsing technology, tree construction facilities, StringTemplate code generation template engine, and sophisticated ANTLRWorks GUI development environment. You'll learn all about ANTLR grammar syntax, resolving grammar ambiguities, parser fault tolerance and error reporting, embedding actions to interpret or translate languages, building intermediate-form trees, extracting information from trees, generating source code, and how to use the ANTLR Java API.

Knowing how to create domain-specific languages (DSLs) can give you a huge productivity boost. Instead of writing code in a general-purpose programming language, you can first build a custom language tailored to make you efficient in a particular domain.

The key is understanding the common patterns found across language implementations. Language Design Patterns identifies and condenses the most common design patterns, providing sample implementations of each.

The pattern implementations use Java, but the patterns themselves are completely general. Some of the implementations use the well-known ANTLR parser generator, so readers will find this book an excellent source of ANTLR examples as well. But this book will benefit anyone interested in implementing languages, regardless of their tool of choice. Other language implementation books focus on compilers, which you rarely need in your daily life. Instead, Language Design Patterns shows you patterns you can use for all kinds of language applications.

You'll learn to create configuration file readers, data readers, model-driven code generators, source-to-source translators, source analyzers, and interpreters. Each chapter groups related design patterns and, in each pattern, you'll get hands-on experience by building a complete sample implementation. By the time you finish the book, you'll know how to solve most common language implementation problems.