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Yaw Sensor MR587733 - Precise Angular Measurement for Robotics, UAVs, and Automobiles
Yaw Sensor MR587733 - Precise Angular Measurement for Robotics, UAVs, and Automobiles
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Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem.At all times, pertinent real-world applications are provided.This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
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Industrial Automation and Robotics : Techniques and Applications
This book discusses the radical technological changes occurring due to Industry 4.0, with a focus on offering a better understanding of the Fourth Industrial Revolution.It also presents a detailed analysis of interdisciplinary knowledge, numerical modeling and simulation, and the application of cyber–physical systems, where information technology and physical devices create synergic systems leading to unprecedented efficiency.The book focuses on industrial applications of automation and robotics.It covers recent developments and trends occurring in both computer-aided manufacturing techniques, as well as computer-aided assembly techniques.Robots using embedded systems and artificial intelligence applications are also covered.Industrial Automation and Robotics: Techniques and Applications offers theoretical results, practical solutions, and guidelines that are valuable for both researchers and those working in the area of engineering.
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Robotics and Automation in Healthcare : Advanced Applications
The recent pandemic has forced researchers to adapt technologies such as robotics and AI in the healthcare field.This book, Robotics and Automation in Healthcare: Advanced Applications, explores these new technologies by focusing on important issues related to the employment of robotics and automation in healthcare, such as in medical diagnosis, treatment, and surgery. The volume reviews wireless charging of implantable pacemakers, considers smart bot design for library building of medical colleges, and discusses strain distribution in biomechanical systems.Other topics included in the book are medical imaging, drone technology, 3D printing, and image processing techniques.The application and importance of actuators in medical devices, especially during surgery, is discussed, as are wearable devices for pre-identification of seizure development.The volume also looks at a decision support system for detection of suitable robots and early detection of diseases with the support of image processing techniques.The application of nano-robots in healthcare is also explored. Providing advanced information and insight into robotics, wearable devices, and applications of image processing in healthcare field, this volume will be helpful to those in communications and electronics engineering as well as those at the forefront of smart technology in healthcare.
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What is a linear mapping?
A linear mapping, also known as a linear transformation, is a function between two vector spaces that preserves the operations of vector addition and scalar multiplication. In other words, it maps a vector from one space to another in a way that maintains the structure of the vector space. Linear mappings are fundamental in linear algebra and are used to describe various mathematical concepts and relationships in a geometrically meaningful way. They play a crucial role in solving systems of linear equations, studying eigenvalues and eigenvectors, and understanding the properties of matrices.
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To what extent does this sentence make sense: Linear algebra, linear mapping?
The sentence "Linear algebra, linear mapping" makes sense to a certain extent. It suggests a connection between linear algebra and linear mapping, both of which are fundamental concepts in mathematics. Linear algebra deals with vector spaces, linear transformations, and systems of linear equations, while linear mapping refers to the process of transforming one vector space into another in a linear manner. Therefore, the sentence hints at the relationship between these two concepts within the broader field of mathematics.
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Are linear functions a mapping or not?
Yes, linear functions are a type of mapping. A linear function is a mathematical relationship between two variables that can be represented by a straight line on a graph. Each input value (x) is mapped to a unique output value (y) based on a specific equation. This mapping allows us to predict the output value for any given input value within the defined range of the function.
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What does the notation linear mapping of polynomials mean?
The notation "linear mapping of polynomials" refers to a function that takes a polynomial as input and produces another polynomial as output, while satisfying the properties of linearity. This means that the function preserves addition and scalar multiplication, i.e., f(a*p(x) + b*q(x)) = a*f(p(x)) + b*f(q(x)), where p(x) and q(x) are polynomials, and a and b are scalars. In other words, a linear mapping of polynomials is a function that behaves like a linear transformation on the space of polynomials.
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Food Engineering Automation with Robotics and AI
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Foundations of Linear and Generalized Linear Models
A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models.The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables.Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data.Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methodsAn overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problemsNumerous examples that use R software for all text data analysesMore than 400 exercises for readers to practice and extend the theory, methods, and data analysisA supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
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Aegean Linear Script(s) : Rethinking the Relationship Between Linear A and Linear B
When does a continuum become a divide? This book investigates the genetic relationship between Linear A and Linear B, two Bronze Age scripts attested on Crete and Mainland Greece and understood to have developed one out of the other.By using an interdisciplinary methodology, this research integrates linguistic, epigraphic, palaeographic and archaeological evidence, and places the writing practice in its sociohistorical setting.By challenging traditional views, this work calls into question widespread assumptions and interpretative schemes on the relationship between these two scripts, and opens up new perspectives on the ideology associated with the retention, adaptation and transmission of a script, and how identity was negotiated at a moment of closer societal interaction between Cretans and Greek-speaking Mainlanders in the Late Bronze Age.By delving deeper into the structure and inner workings of these two writing systems, this book will make us rethink the relationship between Linear A and B.
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Robots, Automation and the Innovation Economy
Cascades of new technologies and innovations are entering our lives so fast that it is difficult for us to adapt to one innovation before the next becomes embedded into our everyday lives.What happens when the changes brought by technology are so profound that they affect all aspects of our lives?This book explores the potential impact of artificial intelligence (AI) and intelligent robots on individuals, organizations and society, specifically examining the impact on jobs and workplaces in the future.It provides an understanding of how we can adapt to changes that appear like flocks of black swans. Five key areas are unpacked in the book: automation, AI, (the significance of AI technology), innovation, competence transformation, and the fact that the pace of change is so rapid that it outstrips our ability to adapt to consecutive changes.The main objective is to show how AI will change society and how we as individuals and society must adapt in order to survive what the author terms ‘robot shock’, together with its consequences and after-effects.It offers a greater understanding of resistance to change and how we need to adopt strategies for adapting to major changes.Each of the book’s six chapters also contains policy inputs, framed as propositions, that are intended specifically for decision-makers.The book concludes by offering possible strategies for overcoming the negative effects of ‘robot shock’. The book intends to send a message to leaders of institutions, decision-makers and anyone attempting to understand and explain how we – as a social system – can succeed in tackling the many major challenges and crises faced by humanity.
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How can one define a linear mapping with three vectors?
A linear mapping with three vectors can be defined as a function that takes three vectors from one vector space and maps them to three vectors in another vector space, while preserving the properties of linearity. In other words, for a linear mapping to be defined with three vectors, it must satisfy the properties of additivity and homogeneity. Additivity means that the mapping of the sum of two vectors is equal to the sum of their individual mappings, and homogeneity means that the mapping of a scalar multiple of a vector is equal to the scalar multiple of the vector's mapping. These properties ensure that the linear mapping preserves the structure of the vector space.
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How do you determine the mapping rule of a linear function?
To determine the mapping rule of a linear function, you need to identify the slope and the y-intercept of the function. The slope is the rate at which the function is increasing or decreasing, and it can be calculated by finding the change in y divided by the change in x for any two points on the line. The y-intercept is the point where the line intersects the y-axis, and it can be found by setting x=0 and solving for y. Once you have the slope and y-intercept, you can write the mapping rule in the form y = mx + b, where m is the slope and b is the y-intercept.
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Can you ask me a question about linear mapping in mathematics?
Sure! Can you explain the concept of a linear mapping and provide an example of how it is used in real-world applications?
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Is it linear or non-linear?
The relationship between the variables is non-linear.
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