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 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 The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Hsiuying Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Conventional Method ◽  
High Dimensional Data ◽  
Audio Signal ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Signal Recognition ◽  
Bayes Method ◽  
Generalized Bayes

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

scholarly journals A Method for Aero-Engine Gas Path Anomaly Detection Based on Markov Transition Field and Multi-LSTM

Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 374
Author(s):  
Langfu Cui ◽  
Chaoqi Zhang ◽  
Qingzhen Zhang ◽  
Junle Wang ◽  
Yixuan Wang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
High Dimensional ◽  
True Value ◽  
Markov Transition ◽  
Aero Engine ◽  
Transition Field

There are some problems such as uncertain thresholds, high dimension of monitoring parameters and unclear parameter relationships in the anomaly detection of aero-engine gas path. These problems make it difficult for the high accuracy of anomaly detection. In order to improve the accuracy of aero-engine gas path anomaly detection, a method based on Markov Transition Field and LSTM is proposed in this paper. The correlation among high-dimensional QAR data is obtained based on Markov Transition Field and hierarchical clustering. According to the correlation analysis of high-dimensional QAR data, a multi-input and multi-output LSTM network is constructed to realize one-step rolling prediction. A Gaussian mixture model of the residuals between predicted value and true value is constructed. The three-sigma rule is applied to detect outliers based on the Gaussian mixture model of the residuals. The experimental results show that the proposed method has high accuracy for aero-engine gas path anomaly detection.

scholarly journals POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION

2016 ◽  
Vol XLI-B7 ◽  
pp. 341-345
Author(s):  
S. Rouabah ◽  
M. Ouarzeddine ◽  
B. Azmedroub
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Urban Areas ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
Polarimetric Sar ◽  
Data Set ◽  
Feature Vectors ◽  
Sar Data

Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.

Regularized Gaussian Mixture Model for High-Dimensional Clustering

2019 ◽  
Vol 49 (10) ◽  
pp. 3677-3688 ◽  
Author(s):  
Yang Zhao ◽  
Abhishek K. Shrivastava ◽  
Kwok Leung Tsui
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
High Dimensional Clustering
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