Hallo! Tracked shipping to Netherlands with Delivery Duty Paid for just €7 

Ship to
Netherlands
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Machine Learning for Indoor Localization and Navigation
Type
Physical Book
Publisher
Author
Year
2023
Language
English
Pages
584
Format
Paperback
Dimensions
23.40 x 15.60 x 3.00 cm
ISBN13
9783031267130

Machine Learning for Indoor Localization and Navigation

Saideep Tiku;Sudeep Pasricha (Author) · Springer · Paperback

Machine Learning for Indoor Localization and Navigation - Saideep Tiku;Sudeep Pasricha

Cheaper New Book Imported to Netherlands
Delivery: 01 Sep - 08 Sep Shipping: 11 to 15 business days.
€ 58,67
Faster New Book Imported to Netherlands
Delivery: 20 Aug - 24 Aug Shipping: 4 to 5 business days.
€ 61,87
Import costs and 9% BTW included in the price ✅
€ 58,67

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Machine Learning for Indoor Localization and Navigation"

While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation. 
In particular, the book:Provides comprehensive coverage of the application of machine learning to the domain of indoor localization;Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization;Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.



Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews