E-Books →Machine Learning for Indoor Localization and Navigation

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 the accuracy, 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.

Thanks For BuyingPremiumFrom My Links ForSupport