Michael Paluszek

Over the past several decades, hoping for accelerated scientific returns, the space industry has eagerly researched propulsion. This has resulted in an array of breakthrough technologies that would allow not only more rapid missions to further study the planets and asteroids within our solar system, but also ventures to distant destinations into deep space, or even enabling human settlements on the moon, Mars, and beyond. Advanced Space Propulsion: Technologies, Missions, and Computer Modeling includes coverage of all these technologies in an integrated manner. The volume, written by a team of sector-specific R&D experts, touches upon fundamentals cardinal to the understanding of [i] orbit theory and its implications for control, estimation, and optimization of trajectory and attitude and [ii] how each mission is designed (depending primarily on the mass of the payload, but also on how far from Earth the launch vehicle needs to go), and then succeeds in consolidating them with software and computer applications for simulations and modeling. It continues with a discussion on the complete gamut of in-space propulsion power sources and concludes with both human and robotic mission-related case studies and future implementation examples. The outcome is a carefully calibrated and self-contained resource that will prove to be invaluable for graduate and senior undergraduate students, researchers, scientists, and engineering professionals alike.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution.This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.What You Will LearnWrite code for machine learning, adaptive control, and estimation using MATLABUse MATLAB graphics and visualization tools for machine learningBecome familiar with neural netsBuild expert systemsUnderstand adaptive controlGain knowledge of Kalman FiltersWho This Book Is ForSoftware engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists.

ADCS - Spacecraft Attitude Determination and Control provides a complete introduction to spacecraft control. The book covers all elements of attitude control system design, including kinematics, dynamics, orbits, disturbances, actuators, sensors, and mission operations. Essential hardware details are provided for star cameras, reaction wheels, sun sensors, and other key components. The book explores how to design a control system for a spacecraft, control theory, and actuator and sensor details. Examples are drawn from the author’s 40 years of industrial experience with spacecraft such as GGS, GPS IIR, Mars Observer, and commercial communications satellites, and includes historical background and real-life examples.

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigationAn aircraft that lands on Titan, the moon of Saturn, using reinforcement learningStock market predictionNatural language processingMusic creation usng generative deep learningPlasma controlEarth sensor processing for spacecraftMATLAB Bluetooth data acquisition applied to dance physics What You Will LearnExplore deep learning using MATLAB and compare it to algorithmsWrite a deep learning function in MATLAB and train it with examplesUse MATLAB toolboxes related to deep learningImplement tokamak disruption predictionNow includes reinforcement learningWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

Learn from state-of-the-art examples in robotics, motors, detection filters, chemical processes, aircraft, and spacecraft. With this book you will review contemporary MATLAB coding including the latest MATLAB language features and use MATLAB as a software development environment including code organization, GUI development, and algorithm design and testing. Features now covered include the new graph and digraph classes for charts and networks; interactive documents that combine text, code, and output; a new development environment for building apps; locally defined functions in scripts; automatic expansion of dimensions; tall arrays for big data; the new string type; new functions to encode/decode JSON; handling non-English languages; the new class architecture; the Mocking framework; an engine API for Java; the cloud-based MATLAB desktop; the memoize function; and heatmap charts. MATLAB Recipes: A Problem-Solution Approach, Second Edition provides practical, hands-on code snippets and guidance for using MATLAB to build a body of code you can turn to time and again for solving technical problems in your work. Develop algorithms, test them, visualize the results, and pass the code along to others to create a functional code base for your firm. What You Will Learn Get up to date with the latest MATLAB up to and including MATLAB 2020bCode in MATLABWrite applications in MATLABBuild your own toolbox of MATLAB code to increase your efficiency and effectiveness Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples using MATLAB.