التفاصيل البيبلوغرافية
| العنوان: |
From Compression of Wearable-based Data to Effortless Indoor Positoning |
| المؤلفون: |
Klus, Lucie |
| Thesis Advisors: |
Granell, Carlos, Nurmi, Jari, Lohan, Elena Simona |
| المصدر: |
TDX (Tesis Doctorals en Xarxa) |
| بيانات النشر: |
2023. |
| سنة النشر: |
2023 |
| وصف مادي: |
212 p. |
| مصطلحات موضوعية: |
Wearable-based data, Wearable Applications, Ciències |
| الوصف: |
Cotutela: Universidad de defensa de la tesis doctoral Tampere University Doctorat Internacional |
| Description (Translated): |
The dissertation focuses on boosting the energy efficiency of IoT and wearable devices by implementing lossy compression techniques onto sensor-based time-series data and into indoor localization paradigms. The thesis deals with lossy compression mechanisms that can be implemented for energy-e¿cient, delay-sensitive wearable data gathering, transfer, and storage. The novel DLTC compression method ensures optimal compression ratio and reconstruction error trade-off, with minimum complexity and delay. In the scope of indoor positioning, the proposed bit-level, feature-wise, and sample-wise reduction of the radio map supports accurate positioning while saving resources in data storage and transfer. The work implements a multi-dimensional compression of the radio map to boost the performance e¿ciency of the positioning system and proposes a cascade model to compensate for k-NN¿s drawback of computationally expensive prediction on voluminous datasets. |
| نوع الوثيقة: |
Dissertation/Thesis |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| DOI: |
10.6035/14124.2023.45900046 |
| URL الوصول: |
http://hdl.handle.net/10803/688947 |
| Rights: |
L'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-sa/4.0/ |
| رقم الانضمام: |
edstdx.10803.688947 |
| قاعدة البيانات: |
TDX |