El presente texto pretende servir de elemento base para la adquisición de los fundamentos, conceptos, componentes y enfoques del componente lógico de dichos sistemas.

Las imágenes representan una de las formas de transmitir información con mayor potencial. En esta obra se presentan los conceptos y técnicas fundamentales del procesamiento digital de imágenes, esto es, se expone la teoria y la practica de temas basicos y avanzados de los sistemas de vision artificial, todo esto con el fin de que: Conozca los conceptos y tecnicas de analisis del procesamiento digital de imagenes. Aprenda las tecnicas algoritmicas asociadas al procesamiento digital de imagenes y su programacion en MATLAB. Aplique el conocimiento y ejecucion de los algoritmos usados para la manipulacion, mejoramiento, realzado y deteccion de imagenes. Erik Cuevas es Ingeniero en Comunicaciones y Electronica por parte de la Universidad de Guadalajara, Maestro en Electronica Industrial por el ITESO y Doctor en Inteligencia Artificial por la Universidad Libre de Berlin (FU-Berlin) en Alemania; actualmente, es miembro del Sistema Nacional de Investigadores Nivel II en el area del computo evolutivo y sus aplicaciones al procesamiento de imagenes. Margarita Diaz Cortes es Ingeniera Biomedica y Maestra en Ciencias de la Ingenieria Electronica y Computacion por parte de la Universidad de Guadalajara; actualmente, realiza su doctorado en Ciencias Computacionales en la Universidad Libre de Berlin. Jose Octavio Camarena Mendez es Ingeniero Electronico con especialidad en Instrumentacion Biomedica por parte del Instituto Tecnologico de Morelia (ITM) y Maestro en Ciencias en Ingenieria Electronica y Computacion por la Universidad de Guadalajara; actualmente, estudia el doctorado en Ciencias de la Electronica y Computacion en el Centro Universitario de Ciencias Exactas e Ingenierias.

Explore the practices and cutting-edge research on the new and exciting topic of paradata Paradata are measurements related to the process of collecting survey data. Improving Surveys with Paradata: Analytic Uses of Process Information is the most accessible and comprehensive contribution to this up-and-coming area in survey methodology. Featuring contributions from leading experts in the field, Improving Surveys with Paradata: Analytic Uses of Process Information introduces and reviews issues involved in the collection and analysis of paradata. The book presents readers with an overview of the indispensable techniques and new, innovative research on improving survey quality and total survey error. Along with several case studies, topics include: Using paradata to monitor fieldwork activity in face-to-face, telephone, and web surveys Guiding intervention decisions during data collection Analysis of measurement, nonresponse, and coverage error via paradata Providing a practical, encompassing guide to the subject of paradata, the book is aimed at both producers and users of survey data. Improving Surveys with Paradata: Analytic Uses of Process The book also serves as an excellent resource for courses on data collection, survey methodology, and nonresponse and measurement error.

This book covers new and significant research related to the mathematical modelling of engineering and environmental processes, manufacturing, and industrial systems.It includes heat transfer, fluid mechanics, CFD, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and MHD; reliability modelling and system optimization; finite volume, finite element, and boundary element procedures; decision sciences in an industrial and manufacturing context; civil engineering systems and structures; mineral and energy resources; relevant software engineering issues associated with CAD and CAE; and materials and metallurgical engineering.

Adiabatic quantum computation (AQC) is an alternative to the better-known gate model of quantum computation. The two models are polynomially equivalent, but otherwise quite dissimilar: one property that distinguishes AQC from the gate model is its analog nature. Quantum annealing (QA) describes a type of heuristic search algorithm that can be implemented to run in the "native instruction set" of an AQC platform. D-Wave Systems Inc. manufactures {quantum annealing processor chips} that exploit quantum properties to realize QA computations in hardware. The chips form the centerpiece of a novel computing platform designed to solve NP-hard optimization problems. Starting with a 16-qubit prototype announced in 2007, the company has launched and sold increasingly larger models: the 128-qubit D-Wave One system was announced in 2010 and the 512-qubit D-Wave Two system arrived on the scene in 2013. A 1,000-qubit model is expected to be available in 2014. This monograph presents an introductory overview of this unusual and rapidly developing approach to computation.We start with a survey of basic principles of quantum computation and what is known about the AQC model and the QA algorithm paradigm. Next we review the D-Wave technology stack and discuss some challenges to building and using quantum computing systems at a commercial scale. The last chapter reviews some experimental efforts to understand the properties and capabilities of these unusual platforms. The discussion throughout is aimed at an audience of computer scientists with little background in quantum computation or in physics.s

Focuses on a recent approach to adiabatic perturbation theory, which emphasizes the role of effective equations of motion and the separation of the adiabatic limit from the semiclassical limit. A detailed introduction gives an overview of the subject and makes the later chapters accessible also to readers less familiar with the material. Although the general mathematical theory based on pseudodifferential calculus is presented in detail, there is an emphasis on concrete and relevant examples from physics. Applications range from molecular dynamics to the dynamics of electrons in a crystal and from the quantum mechanics of partially confined systems to Dirac particles and nonrelativistic QED.

To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook DescriptionDeep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.Youll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. Youll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, youll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. Youll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, youll move your NN model to production on the AWS Cloud.By the end of this book, youll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.What You Will LearnTrain a NN from scratch with NumPy and PyTorch Implement 2D and 3D multi-object detection and segmentation Generate digits and DeepFakes with autoencoders and advanced GANs Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN Combine CV with NLP to perform OCR, image captioning, and object detection Combine CV with reinforcement learning to build agents that play pong and self-drive a car Deploy a deep learning model on the AWS server using FastAPI and Docker Implement over 35 NN architectures and common OpenCV utilitiesWho this book is forThis book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, youll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

Language Engineering examines the processes involved in dictionary-making, parsing, spell-checking, lexical semantics, machine translation, question answering and text attribution. The dictionary-building and natural language processing techniques examined are applicable to most languages, and the book includes extensive examples in English, French and German.This book is the first detailed description of morphology, syntax and semantics using the C(C++) programming language, and as such should be essential reading for researchers in natural language processing and computational linguistics.

Cellular automata have been used in computer science since the 1940s. A cellular automaton consists of many identical simple processing units that interact with each other in a local way and in discrete time. Cellular automata have been applied to a wide variety of tasks and, due to the growing interest of the scientific community, a good number of relevant journals and conferences are dedicated to their study and development. One of the main characteristics of cellular automata is their simplicity, which makes them relatively easy to program. Nonetheless, despite their simplicity, cellular automata are able to generate complex results in many domains, even in the context of artificial life. Another remarkable characteristic of cellular automata is that they are suitable for parallel implementation. The present book deals with the fundamentals of cellular automata. Additionally, a new approach to extending cellular automata with the use of message passing is introduced. Each of the seven chapters of this book includes a number of figures, bibliographic references, and exercises of interest to the reader. The book offers students, practitioners and researchers a concise but broad coverage of the main aspects of cellular automata. The author of the book is an associate professor in the Department of Artificial Intelligence at UNED (Spanish Open University). Since the middle 1990s, he has performed teaching and research activities within the field of artificial intelligence, mainly in the areas of Bayesian networks and evolutionary computation. This book is the result of a long journey that started when the author became interested in NetLogo. NetLogo is an agent]based programming environment well suited for modeling and inspecting complex systems developing over time.

Engineering Analysis with SOLIDWORKS Simulation 2024 goes beyond the standard software manual. Its unique approach concurrently introduces you to the SOLIDWORKS Simulation 2024 software and the fundamentals of Finite Element Analysis (FEA) through hands-on exercises. A number of projects are presented using commonly used parts to illustrate the analysis features of SOLIDWORKS Simulation. Each chapter is designed to build on the skills, experiences and understanding gained from the previous chapters.

Thermal Analysis with SOLIDWORKS Simulation 2022 goes beyond the standard software manual. It concurrently introduces the reader to thermal analysis and its implementation in SOLIDWORKS Simulation using hands-on exercises. A number of projects are presented to illustrate thermal analysis and related topics. Each chapter is designed to build on the skills and understanding gained from previous exercises. Thermal Analysis with SOLIDWORKS Simulation 2022 is designed for users who are already familiar with the basics of Finite Element Analysis (FEA) using SOLIDWORKS Simulation or who have completed the book Engineering Analysis with SOLIDWORKS Simulation 2022. Thermal Analysis with SOLIDWORKS Simulation 2022 builds on these topics in the area of thermal analysis. Some understanding of FEA and SOLIDWORKS Simulation is assumed.

An Introduction to SOLIDWORKS Flow Simulation 2024 takes you through the steps of creating the SOLIDWORKS part for the simulation followed by the setup and calculation of the SOLIDWORKS Flow Simulation project. The results from calculations are visualized and compared with theoretical solutions and empirical data. Each chapter starts with the objectives and a description of the specific problems that are studied. End of chapter exercises are included for reinforcement and practice of what has been learned. The eighteen chapters of this book are directed towards first-time to intermediate level users of SOLIDWORKS Flow Simulation. It is intended to be a supplement to undergraduate Fluid Mechanics and Heat Transfer related courses. This book can also be used to show students the capabilities of fluid flow and heat transfer simulations in freshman and sophomore courses such as Introduction to Engineering. Both internal and external flow problems are covered and compared with experimental results and analytical solutions. Covered topics include airfoil flow, boundary layers, compressible flow, flow meters, heat exchanger, natural and forced convection, pipe flow, rotating flow, tube bank flow and valve flow. Covers these features of SOLIDWORKS Flow Simulation 2024:

Developing many of the major, exciting, pre- and post-millennium developments from the ground up, this book is an ideal entry point for graduate students into quantum information theory. Significant attention is given to quantum mechanics for quantum information theory, and careful studies of the important protocols of teleportation, superdense coding, and entanglement distribution are presented. In this new edition, readers can expect to find over 100 pages of new material, including detailed discussions of Bell's theorem, the CHSH game, Tsirelson's theorem, the axiomatic approach to quantum channels, the definition of the diamond norm and its interpretation, and a proof of the ChoiKraus theorem. Discussion of the importance of the quantum dynamic capacity formula has been completely revised, and many new exercises and references have been added. This new edition will be welcomed by the upcoming generation of quantum information theorists and the already established community of classical information theorists.

This largely self-contained book on the theory of quantum information focuses on precise mathematical formulations and proofs of fundamental facts that form the foundation of the subject. It is intended for graduate students and researchers in mathematics, computer science, and theoretical physics seeking to develop a thorough understanding of key results, proof techniques, and methodologies that are relevant to a wide range of research topics within the theory of quantum information and computation. The book is accessible to readers with an understanding of basic mathematics, including linear algebra, mathematical analysis, and probability theory. An introductory chapter summarizes these necessary mathematical prerequisites, and starting from this foundation, the book includes clear and complete proofs of all results it presents. Each subsequent chapter includes challenging exercises intended to help readers to develop their own skills for discovering proofs concerning the theory of quantum information.

A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples. The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approachwith hundreds of examples and exercises using NetLogoenables readers to begin constructing models immediately, regardless of experience or discipline.