Improving Clustering and Cluster Validation with Missing Data Using Distance Estimation Methods

2021 ◽  
pp. 123-133
Author(s):  
Marko Niemelä ◽  
Tommi Kärkkäinen
Keyword(s):  
Missing Data ◽  
Distance Estimation ◽  
Estimation Methods ◽  
Cluster Validation

Inter-vehicle distance estimation considering camera attitude angles based on monocular vision

2020 ◽  
pp. 095440702094139
Author(s):  
Jun Liu ◽  
Rui Zhang ◽  
Shihao Hou
Keyword(s):  
Distance Estimation ◽  
Estimation Methods ◽  
Vanishing Point ◽  
Estimation Model ◽  
The Road ◽  
Attitude Angle ◽  
Driving Assistance ◽  
Lane Line ◽  
Texture Orientation ◽  
On The Road

Perceiving the distance between vehicles is a crucial issue for advanced driving assistance systems. However, most vision-based distance estimation methods do not consider the influence of the change in camera attitude angles during driving or only use the vanishing point detected by lane lines to correct the pitch angle. This paper proposed an improved pinhole distance estimation model based on the road vanishing point without the lane line information. First, the road vanishing point is detected based on the dominant texture orientation, and the yaw and pitch angles of the camera are estimated. Then, a distance estimation model considering attitude angle compensation is established. Finally, the experimental results show that the proposed method can effectively correct the influence of the camera attitude angle on the distance estimation results.

scholarly journals Two-hop distance estimation in wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Estimation Method ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Distance Information ◽  
Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

scholarly journals Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices

2019 ◽  
Author(s):  
Ananya Bhattacharjee ◽  
Md. Shamsuzzoha Bayzid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Missing Data ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Missing Values ◽  
Gene Tree ◽  
Estimation Methods ◽  
Learning Technique ◽  
Distance Matrices

AbstractBackgroundDue to the recent advances in sequencing technologies and species tree estimation methods capable of taking gene tree discordance into account, notable progress has been achieved in constructing large scale phylogenetic trees from genome wide data. However, substantial challenges remain in leveraging this huge amount of molecular data. One of the foremost among these challenges is the need for efficient tools that can handle missing data. Popular distance-based methods such as neighbor joining and UPGMA require that the input distance matrix does not contain any missing values.ResultsWe introduce two highly accurate machine learning based distance imputation techniques. One of our approaches is based on matrix factorization, and the other one is an autoencoder based deep learning technique. We evaluate these two techniques on a collection of simulated and biological datasets, and show that our techniques match or improve upon the best alternate techniques for distance imputation. Moreover, our proposed techniques can handle substantial amount of missing data, to the extent where the best alternate methods fail.ConclusionsThis study shows for the first time the power and feasibility of applying deep learning techniques for imputing distance matrices. The autoencoder based deep learning technique is highly accurate and scalable to large dataset. We have made these techniques freely available as a cross-platform software (available at https://github.com/Ananya-Bhattacharjee/ImputeDistances).

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

scholarly journals Cross-Sectional HIV Incidence Estimation with Missing Biomarkers

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Doug Morrison ◽  
Oliver Laeyendecker ◽  
Jacob Konikoff ◽  
Ron Brookmeyer
Keyword(s):  
Missing Data ◽  
Estimation Methods ◽  
Hiv Incidence ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Window Period ◽  
Subtype B ◽  
Incidence Estimation ◽  
Multiple Biomarkers ◽  
The Mean

Abstract Considerable progress has been made in the development of approaches for HIV incidence estimation based on a cross-sectional survey for biomarkers of recent infection. Multiple biomarkers when used in combination can increase the precision of cross-sectional HIV incidence estimates. Multi-assay algorithms (MAAs) for cross-sectional HIV incidence estimation are hierarchical stepwise algorithms for testing the biological samples with multiple biomarkers. The objective of this paper is to consider some of the statistical challenges for addressing the problem of missing biomarkers in such testing algorithms. We consider several methods for handling missing biomarkers for (1) estimating the mean window period, and (2) estimating HIV incidence from a cross sectional survey once the mean window period has been determined. We develop a conditional estimation approach for addressing the missing data challenges and compare that method with two naïve approaches. Using MAAs developed for HIV subtype B, we evaluate the methods by simulation. We show that the two naïve estimation methods lead to biased results in most of the missing data scenarios considered. The proposed conditional approach protects against bias in all of the scenarios.

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