This tutorial text is for those who use special functions in their work or study but are not mathematicians. Traditionally, special functions arise as solutions to certain linear second-order differential equations with variable coefficients. This book introduces these differential equations, their solutions, and their applications in optical science and engineering.
The Proceedings of 3rd International Conference on Opto-Electronics and Applied Optics, OPTRONIX 2016 is an effort to promote and present the research works by scientists and researchers including students in India and abroad in the area of Green Photonics and other related areas as well as to raise awareness about the recent trends of research and development in the area of the related fields. The book has been organized in such a way that it will be easier for the readers to go through and find out the topic of their interests. The first part includes the Keynote addresses by Rajesh Gupta, Department of Energy Science and Engineering, Indian Institute of Technology, Bombay; P.T. Ajith Kumar, President and Leading Scientist Light Logics Holography and Optics, Crescent Hill, Trivandrum, Kerala; and K.K. Ghosh, Institute of Engineering & Management, Kolkata, India. The second part focuses on the Plenary and Invited Talks given by eminent scientists namely, Vasudevan Lakshminarayanan, University of Waterloo, Canada; Motoharu Fujigaki, University of Fukuii, Japan; Takeo Sasaki, Tokyo University of Science, Japan; Kehar Singh, Former Professor, Indian Institute of Technology, Delhi, India; Rajpal S. Sirohi, Tezpur University, India; Ajoy Kumar Chakraborty, Institute of Engineering & Management, India; Lakshminarayan Hazra, Emeritus Professor, Calcutta University, India; S.K. Bhadra, Emeritus Scientist, Indian Institute of Chemical Biology, India; Partha Roy Chaudhuri, Department of Physics, Indian Institute of Technology, Kharagpur, India; Navin Nishchal, Indian Institute of Technology, Patna, India; Tarun Kumar Gangopadhyay, CSIR-Central Glass and Ceramic Research Institute, India; Samudra Roy, Department of Physics, Indian Institute of Technology, Kharagpur, India; Kamakhya Ghatak, University of Engineering & Management, India. The subsequent parts focus on contributory papers in : Green Photonics; Fibre and Integrated Optics; Lasers, Interferometry; Optical Communication and Networks; Optical and Digital Data and Image Processing; Opto-Electronic Devices, Terahertz Technology; Nano-Photonics, Bio-Photonics, Bio-Medical Optics; Lasers, Quantum Optics and Information Technology; E. M. Radiation Theory and Antenna; Cryptography; Quantum and Non-Linear Optics, Opto-Electronic Devices; Non-Linear Waveguides; Micro-Electronics and VLSI; Interdisciplinary.
Optics is an enabling science that forms a basis for our technological civilization. Courses in optics are a required part of the engineering or physics undergraduate curriculum in many universities worldwide. The aim of Understanding Optics with Python is twofold: first, to describe certain basic ideas of classical physical and geometric optics; second, to introduce the reader to computer simulations of physical phenomena. The text is aimed more broadly for those who wish to use numerical/computational modeling as an educational tool that promotes interactive teaching (and learning). In addition, it offers an alternative to developing countries where the necessary equipment to carry out the appropriate experiments is not available as a result of financial constraints. This approach contributes to a better diffusion of knowledge about optics. The examples given in this book are comparable to those found in standard textbooks on optics and are suitable for self-study. This text enables the user to study and understand optics using hands-on simulations with Python. Python is our programming language of choice because of its open-source availability, extensive functionality, and an enormous online support. Essentials of programming in Python 3.x, including graphical user interface, are also provided. The codes in the book are available for download on the book’s website. Discusses most standard topics of traditional physical and geometrical optics through Python and PyQt5 Provides visualizations and in-depth descriptions of Python’s programming language and simulations Includes simulated laboratories where students are provided a "hands-on" exploration of Python software Coding and programming featured within the text are available for download on the book’s corresponding website. "Understanding Optics with Python by Vasudevan Lakshminarayanan, Hassen Ghalila, Ahmed Ammar, and L. Srinivasa Varadharajan is born around a nice idea: using simulations to provide the students with a powerful tool to understand and master optical phenomena. The choice of the Python language is perfectly matched with the overall goal of the book, as the Python language provides a completely free and easy-to-learn platform with huge cross-platform compatibility, where the reader of the book can conduct his or her own numerical experiments to learn faster and better." — Costantino De Angelis, University of Brescia, Italy "Teaching an important programming language like Python through concrete examples from optics is a natural and, in my view, very effective approach. I believe that this book will be used by students and appreciated greatly by instructors. The topic of modelling optical effects and systems where the students should already have a physical background provides great motivation for students to learn the basics of a powerful programming language without the intimidation factor that often goes with a formal computer science course." — John Dudley, FEMTO-ST Institute, Besançon, France
Modern engineering and physical science applications demand a thorough knowledge of applied mathematics, particularly special functions. These typically arise in applications such as communication systems, electro-optics, nonlinear wave propagation, electromagnetic theory, electric circuit theory, and quantum mechanics. This text systematically introduces special functions and explores their properties and applications in engineering and science.
The first textbook on mathematical methods focusing on techniques for optical science and engineering, this text is ideal for upper division undergraduate and graduate students in optical physics. Containing detailed sections on the basic theory, the textbook places strong emphasis on connecting the abstract mathematical concepts to the optical systems to which they are applied. It covers many topics which usually only appear in more specialized books, such as Zernike polynomials, wavelet and fractional Fourier transforms, vector spherical harmonics, the z-transform, and the angular spectrum representation. Most chapters end by showing how the techniques covered can be used to solve an optical problem. Essay problems based on research publications and numerous exercises help to further strengthen the connection between the theory and its applications.
Fourier analysis is one of the most important concepts when you apply physical ideas to engineering issues. This book provides a comprehensive understanding of Fourier transform and spectral analysis in optics, image processing, and signal processing. Written by a world renowned author, this book looks to unify the readers understanding of principles of optics, information processing and measurement. This book describes optical imaging systems through a linear system theory. The book also provides an easy understanding of Fourier transform and system theory in optics. It also provides background of optical measurement and signal processing. Finally, the author also provides a systematic approach to learning many signal processing techniques in optics. The book is intended for researchers, industry professionals, and graduate level students in optics and information processing.
Mathematical Methods in Science and Engineering: Applications in Optics and Photonics helps students build a conceptual appreciation for critical mathematical methods, as well as the physical feel and intuition for select mathematical ideas. Throughout the text, examples are provided from the field of optics and photonics to clarify key concepts. The book features 13 targeted chapters that begin with a brief introduction to the topical area and then dive direc
This work covers spatial frequency, spread function, wave aberration, and transfer function - and how these concepts are related in an optical system, how they are measured and calculated, and how they may be useful.
Classical Charged Particle Beam Optics used in the design and operation of all present-day charged particle beam devices, from low energy electron microscopes to high energy particle accelerators, is entirely based on classical mechanics. A question of curiosity is: How is classical charged particle beam optics so successful in practice though the particles of the beam, like electrons, are quantum mechanical? Quantum Mechanics of Charged Particle Beam Optics answers this question with a comprehensive formulation of ‘Quantum Charged Particle Beam Optics’ applicable to any charged particle beam device.
Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.
