Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Engineering
  4. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

211426 results in Engineering

Page 1 of 10572

Engineering Dynamics
  • 2nd edition
  • Jerry H. Ginsberg, Matthew S. Allen, Philip A. Voglewede
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • Fully revised and updated, the new edition of Engineering Dynamics provides a comprehensive, self-contained and accessible treatment of classical dynamics. All chapters have been reworked to enhance student understanding, and new features include a stronger emphasis on computational methods, including rich examples using both Matlab and Python; new capstone computational examples extend student understanding, including modelling the flight of a rocket and the unsteady rolling of a disk. The coverage of Lagrange's equations is improved, spanning simple systems and systems relevant to engineers. It provides students with clear, systematic methods for solving problems in dynamics, demonstrates how to solve equations of motion numerically, and explains all mathematical operators. Including over 150 real-world examples to motivate student learning, over 400 homework problems, and accompanied online by Matlab and Python repositories and supplemental material, the new edition of this classic is ideal for senior undergraduate and graduate students in engineering.

Elementary Cartesian Tensors

Microstructural Rheology of Complex Fluids
  • L. Gary Leal
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • Complex fluids can be found all around us, from molten plastics to mayonnaise, and understanding their highly non-linear dynamics is the subject of much research. This text introduces a common theoretical framework for understanding and predicting the flow behavior of complex fluids. This framework allows for results including a qualitative understanding of the relationship between a fluid's behavior at the microscale of particles or macromolecules, and its macroscopic, viscoelastic properties. The author uses a microstructural approach to derive constitutive theories that remain simple enough to allow computational predictions of complicated macroscale flows. Readers develop their intuition to learn how to approach the description of materials not covered in the book, as well as limits such as higher concentrations that require computational methods for microstructural analysis. This monograph's unique breadth and depth make it a valuable resource for researchers and graduate students in fluid mechanics.

A Student's Guide to the Laws of Thermodynamics
  • Justin W. Garvin
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • Using a welcoming and conversational style, this Student's Guide takes readers on a tour of the laws of thermodynamics, highlighting their importance for a wide range of disciplines. It will be a valuable resource for self-guided learners, students, and instructors working in physics, engineering, chemistry, meteorology, climatology, cosmology, biology, and other scientific fields. The book discusses thermodynamic properties such as temperature, internal energy, and entropy, and develops the laws through primarily observational means without extensive reference to atomic principles. This classical approach allows students to get a handle on thermodynamics as an experimental science and prepares them for more advanced study of statistical mechanics, which is introduced in the final chapter. Detailed practical examples are used to illustrate the theoretical concepts, with a selection of problems included at the end of each chapter to facilitate learning. Solutions to these problems can be found online along with additional supplemental materials.

Transmission Lines
  • Equivalent Circuits, Electromagnetic Theory, and Photons
  • 2nd edition
  • Richard Collier
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • This accessible text, now in its second edition, explains the key principles of transmission lines using straightforward mathematics, extensive illustrations and practical worked examples. The early chapters use a lossless equivalent circuit to derive the basic theory, demonstrating how pulses and sine waves perform in simple transmission line circuits. Later chapters develop this model by demonstrating the derivation of circuit parameters, and the use of Maxwell's equations to extend this theory to major transmission lines. The second edition has been revised and expanded to emphasise the role of electromagnetic waves and photons in all transmission lines, providing valuable insight into the fundamental physics. New topics include sine waves in the time domain, multiple junctions, and attenuation in the presence of standing waves. Covering DC to optical frequencies, this book is an invaluable resource for students, researchers and professionals in electrical, microwave and optical engineering as well as applied physics.

High-Dimensional Probability
  • An Introduction with Applications in Data Science
  • 2nd edition
  • Roman Vershynin
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • 'High-Dimensional Probability,' winner of the 2019 PROSE Award in Mathematics, offers an accessible and friendly introduction to key probabilistic methods for mathematical data scientists. Streamlined and updated, this second edition integrates theory, core tools, and modern applications. Concentration inequalities are central, including classical results like Hoeffding's and Chernoff's inequalities, and modern ones like the matrix Bernstein inequality. The book also develops methods based on stochastic processes – Slepian's, Sudakov's, and Dudley's inequalities, generic chaining, and VC-based bounds. Applications include covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, and machine learning. New to this edition are 200 additional exercises, alongside extra hints to assist with self-study. Material on analysis, probability, and linear algebra has been reworked and expanded to help bridge the gap from a typical undergraduate background to a second course in probability.

Materials Management
  • Sanjay Sharma
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    01 April 2027

Random Vibrations
  • A Primer
  • Debasish Roy, G. Visweswara Rao
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • This textbook chart out an easy-to-comprehend account of the methods of random vibrations, aided by modern yet basic concepts in probability theory and random processes. It starts with a quick review of certain elements of structural dynamics, thus setting the stage for their seamless continuation in developing techniques for response analyses of structures under random environmental loads, such as winds and earthquakes. The book also offers a few glimpses of the powerful tools of stochastic processes to kindle the spirit of scientific inquiry. By way of applications, it contains numerous illustrative examples and exercises, many of which relate to practical design problems of interest to the industry. A companion website provides solutions to all the problems in the exercises. For the benefit of the prospective instructors, a semester-long schedule for offering a course on Random Vibrations is also suggested.

Control Systems Theory
  • N Sukavanam
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    01 April 2027

Advances in Material Science and Applications
  • Bulk (Silicon) vs Nano (Graphene)
  • Volume 2, Bulk (Silicon) vs Nano (Graphene)
  • S S Islam
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    01 April 2027

Analytical Aircraft Flight
  • Dynamics, Performance, Stability, and Control
  • Hugh H.-T. Liu
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    31 January 2026
  • Master the principles of flight dynamics, performance, stability, and control with this comprehensive and self-contained textbook. A strong focus on analytical rigor, balancing theoretical derivations and case studies, equips students with a firm understanding of the links between formulae and results. Over 130 step-by-step examples and 130 end-of-chapter problems cement student understanding, with solutions available to instructors. Computational Matlab code is provided for all examples, enabling students to acquire hands-on understanding, and over 200 ground-up diagrams, from simple “paper plane” models through to real-world examples, draw from leading commercial aircraft. Introducing fundamental principles and advanced concepts within the same conceptual framework, and drawing on the author's over 20 years of teaching in the field, this textbook is ideal for senior undergraduate and graduate-level students across aerospace engineering.

Coding for Erasure Channels
  • Marco Chiani, Gianluigi Liva, Enrico Paolini, Balázs Matuz
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    31 January 2026
  • Understand how to make wireless communication networks, digital storage systems and computer networks robust and reliable in the first unified, comprehensive treatment of erasure correcting codes. Data loss is unavoidable in modern computer networks; as such, data recovery can be crucial and these codes can play a central role. Through a focused, detailed approach, you will gain a solid understanding of the theory and the practical knowledge to analyze, design and implement erasure codes for future computer networks and digital storage systems. Starting with essential concepts from algebra and classical coding theory, the book provides specific code descriptions and efficient design methods, with practical applications and advanced techniques stemming from cutting-edge research. This is an accessible and self-contained reference, invaluable to both theorists and practitioners in electrical engineering, computer science and mathematics.

Advances in Material Science and Applications
  • Bulk (Silicon) vs Nano (Graphene)
  • Volume 1, Basic Physics
  • S. S. Islam
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    01 April 2027

Shipping Machine Learning Systems
  • A Practical Guide to Building, Deploying, and Scaling in Production
  • Mohamed El-Geish, Shabaz Patel, Anand Sampat
  • In collaboration with Hira Dangol
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    31 January 2026
  • This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.

The Stone Skeleton
  • Structural Engineering of Masonry Architecture
  • 2nd edition
  • Jacques Heyman
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • What is the timescale for the settlement and cracking of an old stone building? How do the elegant flying buttresses of a Gothic cathedral safely transfer thrust to the foundations? What is the effect of bell-ringing on a church tower? These and other questions pertinent to the upkeep of old stone structures are answered in this clear and authoritative guide, now revised in a new edition. With a firm scientific basis, but without the use of complex mathematics, the author provides a thorough and intuitive understanding of masonry structures. This new edition updates the text based on original research by the author, including sections on iconic structures such as St Peter's Basilica in Rome, the dome of St Paul's Cathedral and the vault of the Henry VII Chapel in London. An essential resource for structural engineers, architects, art historians and anyone passionate about the care and renovation of historic stone buildings.

Magnetism and Magnetic Materials
  • 2nd edition
  • J. M. D. Coey
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • The study of magnetism has driven progress in experimental science for centuries, and demonstrates how ground-breaking theoretical advances can be translated directly into essential, transformative technology. Now in an expanded second edition, this popular textbook provides comprehensive coverage of the theory and practical applications of magnetism and magnetic materials. The text has been updated throughout to address significant developments from the last decade, including new theoretical insights, advanced experimental probes, and thin film technology. A new chapter covers the important topic of transverse magnetotransport and effects of topology. The book is extensively illustrated with over 700 figures conveying important experimental data, concepts and applications, and each self-contained chapter concludes with a summary section, a list of further reading and a set of exercises. The text contains a wealth of useful information that will be of interest to graduate students and researchers in physics, materials science and engineering.

Energy Education in a Transitioning World
  • Edited by Valentini Pappa, Antje Danielson, Aaron S. Weber
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • Embed climate-focused energy awareness in every step of your educational program with this unique guide to specifying, designing, implementing, and evaluating educational energy initiatives. Discover how to design programs for different learner groups, and keep learners engaged; develop energy-focused project-based hands-on experiential teaching approaches; champion professional development; embed systems, modelling, and computational analysis within curricula; and address issues in justice and equity. This uniquely interdisciplinary approach spans engineering, the physical sciences, and the social sciences, supporting instructors in delivering programs that feed global demand for energy-related climate education, while highlighting ways to avoid the pitfalls of engineering-only energy programs. Ideal for academics involved in teaching and developing undergraduate and graduate courses in energy, academic educational program managers, and professionals in energy-related early career onboarding, this is your key to unlock an empowered energy-transition workforce.

Classical and Quantum Information Theory
  • Uncertainty, Information, and Correlation
  • Osvaldo Simeone
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • Discover the foundations of classical and quantum information theory in the digital age with this modern introductory textbook. Familiarise yourself with core topics such as uncertainty, correlation, and entanglement before exploring modern techniques and concepts including tensor networks, quantum circuits and quantum discord. Deepen your understanding and extend your skills with over 250 thought-provoking end-of-chapter problems, with solutions for instructors, and explore curated further reading. Understand how abstract concepts connect to real-world scenarios with over 400 examples, including numerical and conceptual illustrations, and emphasising practical applications. Build confidence as chapters progressively increase in complexity, alternating between classic and quantum systems. This is the ideal textbook for senior undergraduate and graduate students in electrical engineering, computer science, and applied mathematics, looking to master the essentials of contemporary information theory.

Introduction to Online Control
  • Elad Hazan, Karan Singh
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • This tutorial guide introduces online nonstochastic control, an emerging paradigm in control of dynamical systems and differentiable reinforcement learning that applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. In optimal control, robust control, and other control methodologies that assume stochastic noise, the goal is to perform comparably to an offline optimal strategy. In online control, both cost functions and perturbations from the assumed dynamical model are chosen by an adversary. Thus, the optimal policy is not defined a priori and the goal is to attain low regret against the best policy in hindsight from a benchmark class of policies. The resulting methods are based on iterative mathematical optimization algorithms and are accompanied by finite-time regret and computational complexity guarantees. This book is ideal for graduate students and researchers interested in bridging classical control theory and modern machine learning.

  • 10 - Support Vector Machine Classifier

  • Parteek Bhatia, Thapar University, India
  • Book:
    Machine Learning with Python
    Published online:
    22 February 2025
    Print publication:
    30 November 2025, pp 495-532

  • Summary

    Chapter Objectives

    • • To understand the working principle of support vector machine (SVM).

    • • To comprehend the rules for identification of correct hyperplane.

    • • To understand the concept of support vectors, maximized margin, positive and negative hyperplanes.

    • • To apply an SVM classifier for a linear and non-linear dataset.

    • • To understand the process of mapping data points to higher dimensional space.

    • • To comprehend the working principle of the SVM Kernel.

    • • To highlight the applications of SVM.

    10.1 Support Vector Machines

    Support vector machines (SVMs) are supervised machine learning (ML) models used to solve regression and classification problems. However, it is widely used for solving classification problems. The main goal of SVM is to segregate the n-dimensional space into labels or classes by defining a decision boundary or hyperplanes. In this chapter, we shall explore SVM for solving classification problems.

    10.1.1 SVM Working Principle

    SVM Working Principle | Parteek Bhatia, https://youtu.be/UhzBKrIKPyE

    To understand the working principle of the SVM classifier, we will take a standard ML problem where we want a machine to distinguish between a peach and an apple based on their size and color.

    Let us suppose the size of the fruit is represented on the X-axis and the color of the fruit is on the Y-axis. The distribution of the dataset of apple and peach is shown in Figure 10.1.

    To classify it, we must provide the machine with some sample stock of fruits and label each of the fruits in the stock as an “apple” or “peach”. For example, we have a labeled dataset of some 100 fruits with corresponding labels, i.e., “apple” or “peach”. When this data is fed into a machine, it will analyze these fruits and train itself. Once the training is completed, if some new fruit comes into the stock, the machine will classify whether it is an “apple” or a “peach”.

    Most of the traditional ML algorithms would learn by observing the perfect apples and perfect peaches in the stock, i.e., they will train themselves by observing the ideal apples of stock (apples which are very much like apples in terms of their size and color) and the perfect peaches of stock (peaches which are very much like peaches in terms of their size and color). These standard samples are likely to be found in the heart of stock. The heart of the stock is shown in Figure 10.2.

Page 1 of 10572