rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Tiny machine learning
    sub title: design principles and applications
  name:
    name part: Imoize, Agbotiname Lucky
    role:
      role term: editor.
  name:
    name part: Do, Dinh-Thuan
    name part: (Professor)
    role:
      role term: editor.
  name:
    name part: Song, Houbing
    role:
      role term: editor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: nju
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxxviii, 737 pages) : illustrations (chiefly color)
  abstract: An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.
  table of contents: Chapter 1 Introduction to TinyML -- Francisca Onyiyechi Nwokoma, Chidi Ukamaka Betrand, Juliet Nnenna Odii, Euphemia Chioma Nwokorie, and Euphemia Chioma Nwokorie -- Chapter 2 Learning Panorama Under TinyML -- Ikechukwu Ignatius Ayogu, Euphemia Chioma Nwokorie, Juliet Nnenna Odii, Francisca Onyiyechi Nwokoma, and Chidi Ukamaka Betrand -- Chapter 3 TinyML for Anomaly Detection -- Richard Govada Joshua, Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, and Samuel Oluwatobi Tofade -- Chapter 4 TinyML Power Consumption and Memory in IoT MCUs -- Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, Richard Govada Joshua, and Samuel Oluwatobi Tofade -- Chapter 5 Efficient Data Cleaning and Anomaly Detection in IoT Devices Using TinyCleanEDF -- Ilker Kara -- Chapter 6 TinyML devices and tools -- Abeeb Akorede Bello, Agbotiname Lucky Imoize, and Agbotiname Lucky Imoize -- Chapter 7 Privacy-Preserving Techniques in TinyML for IoT -- Oleksandr Kuznetsov, Emanuele Frontoni, Kateryna Kuznetsova, Marco Arnesano, and Pavlo Usik -- Chapter 8 Enhancing Cybersecurity in TinyML with Lightweight Cryptographic Algorithms -- Oleksandr Kuznetsov, Roman Minailenko, and Aigul Shaikhanova -- Chapter 9 Tiny Machine Learning for Enhanced Edge Intelligence -- Emmanuel Alozie, Agbotiname Lucky Imoize, Hawau I. Olagunju, Nasir Faruk, Salisu Garba, and Ayobami P. Olatunji.
  note: edited by Agbotiname Lucky Imoize, Dinh-Thuan Do, Houbing Herbert Song.
  note: Includes bibliographical references and index.
  subject:
    topic: Machine learning
  subject:
    topic: Embedded computer systems
  classification: Q325.5 .T56 2026
  classification: 006.3/1
  related item:
    title info:
      title: Tiny machine learning
    origin info:
      publisher: Hoboken, New Jersey : Wiley-IEEE Press, [2025]
    identifier: (DLC) 2025025132
  identifier: 9781394294572
  identifier: 1394294573
  identifier: 9781394294558
  identifier: 1394294557
  identifier: 9781394294565
  identifier: 1394294565
  identifier: 9781394294541
  identifier: 2025025133
  identifier: 11345183 IEEE
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 250613
    record change date: 20260215025741.6
    record identifier: in00005691349
    language of cataloging:
      language term: eng