A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform

2021 ◽  
Author(s):  
Yuxiao Li ◽  
Santiago Mazuelas ◽  
Yuan Shen
Keyword(s):  
Supervised Learning ◽  
Learning Approach ◽  
Error Mitigation ◽  
Ranging Error

A Semi-Supervised Learning Approach for UWB Ranging Error Mitigation

2020 ◽  
pp. 1-1
Author(s):  
Tianyu Wang ◽  
Keke Hu ◽  
Zhihang Li ◽  
Kangbo Lin ◽  
Jian Wang ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Learning Approach ◽  
Error Mitigation ◽  
Ranging Error

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

scholarly journals Fingerprinting Mobile Devices Using Personalized Configurations

2016 ◽  
Vol 2016 (1) ◽  
pp. 4-19 ◽  
Author(s):  
Andreas Kurtz ◽  
Hugo Gascon ◽  
Tobias Becker ◽  
Konrad Rieck ◽  
Felix Freiling
Keyword(s):  
Supervised Learning ◽  
Mobile Devices ◽  
Real World ◽  
Third Party ◽  
Learning Approach ◽  
Total Accuracy ◽  
Over Time

Abstract Recently, Apple removed access to various device hardware identifiers that were frequently misused by iOS third-party apps to track users. We are, therefore, now studying the extent to which users of smartphones can still be uniquely identified simply through their personalized device configurations. Using Apple’s iOS as an example, we show how a device fingerprint can be computed using 29 different configuration features. These features can be queried from arbitrary thirdparty apps via the official SDK. Experimental evaluations based on almost 13,000 fingerprints from approximately 8,000 different real-world devices show that (1) all fingerprints are unique and distinguishable; and (2) utilizing a supervised learning approach allows returning users or their devices to be recognized with a total accuracy of 97% over time

Household Profile Identification for Behavioral Demand Response: A Semi-supervised Learning Approach Using Smart Meter Data

Energy ◽  
2021 ◽  
pp. 121728
Author(s):  
Fei Wang ◽  
Xiaoxing Lu ◽  
Xiqiang Chang ◽  
Xin Cao ◽  
Siqing Yan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Demand Response ◽  
Learning Approach ◽  
Smart Meter
Sign in / Sign up

Export Citation Format

Share Document