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Based on their own experiences of in-depth case studies of software projects in international corporations, in this book the authors present detailed practical guidelines on the preparation, conduct, design and reporting of case studies of software engineering.  This is the first software engineering specific book on the case study research method.

Pseudo-random sequences are essential ingredients of every modern digital communication system including cellular telephones, GPS, secure internet transactions and satellite imagery. Each application requires pseudo-random sequences with specific statistical properties. This book describes the design, mathematical analysis and implementation of pseudo-random sequences, particularly those generated by shift registers and related architectures such as feedback-with-carry shift registers. The earlier chapters may be used as a textbook in an advanced undergraduate mathematics course or a graduate electrical engineering course; the more advanced chapters provide a reference work for researchers in the field. Background material from algebra, beginning with elementary group theory, is provided in an appendix.

Covering point process theory, random geometric graphs and coverage processes, this rigorous introduction to stochastic geometry will enable you to obtain powerful, general estimates and bounds of wireless network performance and make good design choices for future wireless architectures and protocols that efficiently manage interference effects. Practical engineering applications are integrated with mathematical theory, with an understanding of probability the only prerequisite. At the same time, stochastic geometry is connected to percolation theory and the theory of random geometric graphs and accompanied by a brief introduction to the R statistical computing language. Combining theory and hands-on analytical techniques with practical examples and exercises, this is a comprehensive guide to the spatial stochastic models essential for modelling and analysis of wireless network performance.

Written by industry experts, this book aims to provide you with an understanding of how to design and work with wearable sensors.Together these insights provide the first single source of information on wearable sensors that would be a valuable addition to the library of any engineer interested in this field.Wearable Sensors covers a wide variety.

Dr Donald Bailey starts with introductory material considering the problem of embedded image processing, and how some of the issues may be solved using parallel hardware solutions. Field programmable gate arrays (FPGAs) are introduced as a technology that provides flexible, fine-grained hardware that can readily exploit parallelism within many image processing algorithms. A brief review of FPGA programming languages provides the link between a software mindset normally associated with image processing algorithms, and the hardware mindset required for efficient utilization of a parallel hardware design. The design process for implementing an image processing algorithm on an FPGA is compared with that for a conventional software implementation, with the key differences highlighted. Particular attention is given to the techniques for mapping an algorithm onto an FPGA implementation, considering timing, memory bandwidth and resource constraints, and efficient hardware computational techniques. Extensive coverage is given of a range of low and intermediate level image processing operations, discussing efficient implementations and how these may vary according to the application. The techniques are illustrated with several example applications or case studies from projects or applications the author has been involved with. Issues such as interfacing between the FPGA and peripheral devices are covered briefly, as is designing the system in such a way that it can be more readily debugged and tuned. <ul type=\"disc\"> <li>Provides a bridge between algorithms and hardware</li> <li>Demonstrates how to avoid many of the potential pitfalls</li> <li>Offers practical recommendations and solutions</li> <li>Illustrates several real-world applications and case studies</li> <li>Allows those with software backgrounds to understand efficient hardware implementation</li> </ul> <p><i>Design for Embedded Image Processing on FPGAs</i>&#160;is ideal for researchers and engineers in the vision or image processing industry, who are looking at smart sensors, machine vision, and robotic vision, as well as FPGA developers and application engineers.</p> <p>The book can also be used by graduate students studying imaging systems, computer engineering, digital design, circuit design, or computer science. It can also be used as supplementary text for courses in advanced digital design, algorithm and hardware implementation, and digital signal processing and applications.</p> <p>Lecture slides for instructors available at:</p> <p>www.wiley.com/go/bailey/fpga</p>

VHDL, the IEEE standard hardware description language for describing digital electronic systems, allows engineers to describe the structure and specify the function of a digital system as well as simulate and test it before manufacturing. In addition, designers use VHDL to synthesize a more detailed structure of the design, freeing them to concentrate on more strategic design decisions and reduce time to market. Adopted by designers around the world, the VHDL family-of-standards have recently been revised to address a range of issues, including portability across synthesis tools. This best-selling comprehensive tutorial for the language and authoritative reference on its use in hardware design at all levels, from system to gates, has been revised to reflect the new IEEE standard, VHDL-2001. The author presents the entire description language and builds a modeling methodology based on successful software engineering techniques. This second edition updates the first, retaining the author’s unique ability to teach this complex subject to a broad audience of students and practicing professionals.

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and moreDiscover modern causal inference techniques for average and heterogenous treatment effect estimationExplore and leverage traditional and modern causal discovery methods Book Description Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. What you will learn Master the fundamental concepts of causal inferenceDecipher the mysteries of structural causal modelsUnleash the power of the 4-step causal inference process in PythonExplore advanced uplift modeling techniquesUnlock the secrets of modern causal discovery using PythonUse causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.

The IT industry is obsessed with new technologies. Courses, books, and magazines mostly focus on what is new. Starting with what a legacy system looks like to applying various techniques for maintaining and securing these systems, this book gives you all the knowledge you need to maintain a legacy system.

This is one of the first books to provide a comprehensive description of OPNET IT Guru and Modeler software. The book explains how to use the software for simulating and modeling computer networks and includes laboratory projects that help readers learn different aspects of the software in a hands-on way. The authors illustrate how to develop and configure models for every layer of the TCP/IP reference model. They also offer extensive examples that show how to set up and configure many nontrivial features of OPNET software.

\"This book is intended to be a sequel from the author's other title with Kluwer \"Hyperspectral Imaging: Techniques for Spectral Detection and Classification\". It contains five major parts. Part I is new aspects of OSP including 7 chapters, OSP revisit, generalized OSP, FPGA designs for OSP and CEM, Kalman filter-based linear unmixing, least squares fully constrained linear mixture analysis, exploitation-based hyperspectral data compression and size estimation of supixel targets, Part II is interference rejection for linear unmixing composed of three chapters, signal-composed interference-annihilated theory, interference-annihilated noise-adjusted theory and information-processed matched filter theory; Part III is nonlinear non-literal techniques for linear unmixing consisting of 3 chapters, convex cone analysis, information theoretic criterion-based project pursuit and nonlinear mixing model analysis; Part IV is spectral coding comprising of three chapters, progressive spectral coding, spectral binary coding and spectral coding for band selection; Part V is applications made up of two chapters, applications to magnetic resonance imaging and landmine detection\"--