Integration of Multivariate Gaussian Mixture Model for Enhanced PAM-4 Decoding Employing Basis Expansion

2018 ◽  
Author(s):  
Feng Lu ◽  
Peng-Chun Peng ◽  
Siming Liu ◽  
Mu Xu ◽  
Shuyi Shen ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Basis Expansion ◽  
Multivariate Gaussian

scholarly journals High-dimensional Probabilistic Fingerprinting in Wireless Sensor Networks based on a Multivariate Gaussian Mixture Model

2018 ◽  
Author(s):  
Yan Li ◽  
Simon Williams ◽  
Bill Moran ◽  
Allison Kealy ◽  
Guenther Retscher
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Low Cost ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Mobile Users ◽  
Prototype Model ◽  
Location Awareness ◽  
Multivariate Gaussian

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment, where three typical problems arise. First, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Second, heterogeneous devices record different received signal strength (RSS) levels due to the variations in chip-set and antenna attenuation. Third, APs are not necessarily visible in every scanning cycle leading to missing data. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. Considering the spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) is fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP has been investigated in this research, and demonstrated the efficiency of differentiation between cells. The proposed system is able to achieve comparable localization performance. The filed test results present a reliable 97% localization room level accuracy of multiple mobile users in a real university campus WiFi network without any prior knowledge of the environment.

scholarly journals High-Dimensional Probabilistic Fingerprinting in Wireless Sensor Networks Based on a Multivariate Gaussian Mixture Model

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2602 ◽  
Author(s):  
Yan Li ◽  
Simon Williams ◽  
Bill Moran ◽  
Allison Kealy ◽  
Guenther Retscher
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Low Cost ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Mobile Users ◽  
Prototype Model ◽  
Location Awareness ◽  
Multivariate Gaussian

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment in which three typical problems arise. Firstly, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Secondly, heterogeneous devices record different received signal strength (RSS) levels because of the variations in chip-set and antenna attenuation. Thirdly, APs are not necessarily visible in every scanning cycle leading to missing data issue. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. To account for spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) was fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP was investigated in this research, and demonstrated the efficiency as a beneficial information to differentiate between cells. The proposed system is able to achieve comparable localisation performance. Filed test results achieve a reliable 97% localisation room level accuracy of multiple mobile users in a real university campus Wi-Fi network.

scholarly journals Operational Anomaly Detection in Flight Data Using a Multivariate Gaussian Mixture Model

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Guoyi Li ◽  
Ashwin Rai ◽  
Hyunseong Lee ◽  
Aditi Chattopadhyay
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Random Noise ◽  
Gaussian Mixture ◽  
Detection Sensitivity ◽  
Weighted Averaging ◽  
Flight Data ◽  
Multivariate Gaussian ◽  
Aircraft System

This paper presents a robust real-time aircraft health monitoring framework using a machine learning based approach, specifically the multivariate Gaussian mixture model (mGMM), for the detection of in-air operational anomalies of an aircraft system. Sensor fusion and noise filtering algorithms have also been adopted to reduce dimensionality of the feature space while avoiding the elimination of useful information from the original flight data. Random noise in each feature, induced by the aircraft sensors and data acquisition system, is filtered out using a weighted averaging window while maintaining inherent variances. The filtered dataset is then fused according to the underlying physics of each sensed feature to reduce redundant features and subsequently trained using the mGMM. The methodology allows monitoring the behavior of each feature as well as correlations between features, significantly improving detection sensitivity. The high computational efficiency of this approach permits real-time monitoring of an aircraft system.

scholarly journals Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model

PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0161112 ◽  
Author(s):  
Jing Xiao ◽  
Qiongqiong Xu ◽  
Chuanli Wu ◽  
Yuexia Gao ◽  
Tianqi Hua ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Multivariate Gaussian
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