Signal conditioning and quality estimation method of Overhauser magnetometer

2021 ◽  
Author(s):  
Lidong Zheng ◽  
Haobin Dong ◽  
Wang Luo ◽  
Jian Ge ◽  
Huan Liu
Keyword(s):  
Estimation Method ◽  
Signal Conditioning ◽  
Quality Estimation

scholarly journals Link quality estimation based on over-sampling and weighted random forest

2021 ◽  
pp. 41-41
Author(s):  
Linlan Liu ◽  
Yi Feng ◽  
Shengrong Gao ◽  
Jian Shu
Keyword(s):  
Random Forest ◽  
Decision Trees ◽  
Estimation Method ◽  
Classification Performance ◽  
Link Quality ◽  
Estimation Methods ◽  
Quality Level ◽  
Estimation Model ◽  
Quality Estimation ◽  
Link Quality Estimation

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the proposed link quality estimation method has better performance with samples processed by K-means SMOTE. Furthermore, it has better estimation performance than the ones of Naive Bayesian, Logistic Regression and K-nearest Neighbour estimation methods.

scholarly journals Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles

2021 ◽  
Vol 15 ◽  
Author(s):  
Florian Kofler ◽  
Ivan Ezhov ◽  
Lucas Fidon ◽  
Carolin M. Pirkl ◽  
Johannes C. Paetzold ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Estimation Method ◽  
Quality Estimation ◽  
Clinical Routine ◽  
Diagnostic Decision ◽  
Binary Segmentation ◽  
Major Shortcoming ◽  
Special Assessment ◽  
Identification And Characterization

A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine.

Quality estimation method for gear hobbing based on attention and adversarial transfer learning

Measurement ◽  
2021 ◽  
pp. 110383
Author(s):  
Dayuan Wu ◽  
Ping Yan ◽  
Jie Pei ◽  
Yingtao Su ◽  
Han Zhou ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Estimation Method ◽  
Quality Estimation ◽  
Gear Hobbing

Quality Estimation for Facial Biometrics

2020 ◽  
pp. 298-320
Author(s):  
Sanjida Nasreen Tumpa ◽  
Andrei Dmitri Gavrilov ◽  
Omar Zatarain Duran ◽  
Fatema Tuz Zohra ◽  
Marina L. Gavrilova
Keyword(s):  
Face Recognition ◽  
Facial Recognition ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Recognition System ◽  
Quality Factors ◽  
Quality Estimation ◽  
Video Frames ◽  
Face Recognition System

Over past decade, behavioral biometric systems based on face recognition became leading commercial systems that meet the need for fast and efficient confirmation of a person's identity. Facial recognition works on biometric samples, like image or video frames, to recognize people. The performance of an automated face recognition system has a strong relationship with the quality of the biometric samples. In this chapter, the authors propose a quality estimation method based on a linear regression analysis to characterize the relationship between different quality factors and the performance of a face recognition system. The regression model can predict the overall quality of a facial sample which reflects the effects of various quality factors on that sample. The authors evaluated the quality estimation model on the Extended Yale Database B, finally formulating a data set of samples which will enable efficient implementation of biometric facial recognition.

Integrated Application of Eye Movement Analysis and Beauty Estimation in the Visual Landscape Quality Estimation of Urban Waterfront Park

2018 ◽  
Vol 32 (09) ◽  
pp. 1856010 ◽  
Author(s):  
Liang Sun ◽  
Hua Shao ◽  
Shuyang Li ◽  
Xiaoxun Huang ◽  
Wenyan Yang
Keyword(s):  
Eye Movement ◽  
Estimation Method ◽  
Movement Analysis ◽  
Eye Tracker ◽  
Quality Estimation ◽  
Movement Data ◽  
Visual Behaviors ◽  
Eye Movement Tracking ◽  
Landscape Quality ◽  
Urban Waterfront

Beauty estimation is a common method for landscape quality estimation, although it has some limitations. With eye tracker, the visual behaviors of the subjects during the estimation can be recorded. Through the analyses of heat maps, path maps and eye movement data, the psychological changes of the subjects and the underlying law of beauty aesthetic can be understood, which will provide supplementation to beauty estimation. This paper studied the beauty estimation of urban waterfront parks and proofed that the landscape quality estimation method focussing on beauty estimation and assisted by eye movement tracking is feasible. It can improve the objectiveness and accuracy of landscape quality estimation to some extent and provide a comprehensive understanding of the effects and combination law of landscape characteristic elements.

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