The Experts below are selected from a list of 70557 Experts worldwide ranked by ideXlab platform
Chunming Rong - One of the best experts on this subject based on the ideXlab platform.
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CloudCom-Asia - Time Series Similarity Evaluation Based on Spearman's Correlation Coefficients and Distance Measures
Cloud Computing and Big Data, 2020Co-Authors: Jiaqi Ye, Chengwei Xiao, Rui Maximo Esteves, Chunming RongAbstract:This paper evaluates the similarity between two time series generated by two sensors manufactured by different companies, trying to provide some valuable information upon choosing sensors of different brands. Spearman Correlation coefficient analysis and Euclidean distance measurement have been applied. Experiment is carried out on R. Visualization of the studied time series and results of similarity measured over time series by Spearman Correlation coefficient and Euclidean distance are presented. Besides, the consistency and inconsistency in the analysis results of two measurements have been discussed in this paper.
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time series similarity evaluation based on Spearman s Correlation coefficients and distance measures
International Conference on Cloud Computing, 2015Co-Authors: Jiaqi Ye, Chengwei Xiao, Rui Maximo Esteves, Chunming RongAbstract:This paper evaluates the similarity between two time series generated by two sensors manufactured by different companies, trying to provide some valuable information upon choosing sensors of different brands. Spearman Correlation coefficient analysis and Euclidean distance measurement have been applied. Experiment is carried out on R. Visualization of the studied time series and results of similarity measured over time series by Spearman Correlation coefficient and Euclidean distance are presented. Besides, the consistency and inconsistency in the analysis results of two measurements have been discussed in this paper.
Shoushun Chen - One of the best experts on this subject based on the ideXlab platform.
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A Hamming Distance and Spearman Correlation Based Star Identification Algorithm
IEEE Transactions on Aerospace and Electronic Systems, 2019Co-Authors: Mehta Deval Samirbhai, Shoushun ChenAbstract:This paper presents a novel star identification algorithm for a “lost-in-space” mode star tracker. The Spearman-Correlation approach provides reliable recognition even when the captured images are swayed and biased from the onboard star pattern database. The hamming distance approach provides a shortlisted list of star IDs. Thus, the proposed combination of hamming distance and Spearman Correlation provides a reliable and fast recognition. The achievable performance is evaluated by testing on simulated and real images.
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ICIP - A Spearman Correlation based star pattern recognition
2017 IEEE International Conference on Image Processing (ICIP), 2017Co-Authors: Deval Samirbhai Mehta, Shoushun ChenAbstract:High accuracy is required for determining the orientation of a satellite in space. Amongst the existing sensors, a star tracker provides a very high accuracy of attitude determination. When no prior attitude is available, it operates in the “Lost-In-Space (LIS)” mode. Star pattern recognition is the most crucial part of a star tracker in the LIS mode. In this paper, a novel star pattern recognition approach is proposed, which constructs a signal from the features extracted in the star image and utilizes Spearman Correlation for identifying the correct stars. The proposed technique achieves a high identification accuracy of 99.67%. The results from the simulations show that this technique is also highly recognition reliable to the cases of missing stars, deviation in star positions, magnitude uncertainty, and false stars compared to the existing star identification algorithms.
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A Spearman Correlation based star pattern recognition
2017 IEEE International Conference on Image Processing (ICIP), 2017Co-Authors: Deval Samirbhai Mehta, Shoushun ChenAbstract:High accuracy is required for determining the orientation of a satellite in space. Amongst the existing sensors, a star tracker provides a very high accuracy of attitude determination. When no prior attitude is available, it operates in the “Lost-In-Space (LIS)” mode. Star pattern recognition is the most crucial part of a star tracker in the LIS mode. In this paper, a novel star pattern recognition approach is proposed, which constructs a signal from the features extracted in the star image and utilizes Spearman Correlation for identifying the correct stars. The proposed technique achieves a high identification accuracy of 99.67%. The results from the simulations show that this technique is also highly recognition reliable to the cases of missing stars, deviation in star positions, magnitude uncertainty, and false stars compared to the existing star identification algorithms.
Jiaqi Ye - One of the best experts on this subject based on the ideXlab platform.
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CloudCom-Asia - Time Series Similarity Evaluation Based on Spearman's Correlation Coefficients and Distance Measures
Cloud Computing and Big Data, 2020Co-Authors: Jiaqi Ye, Chengwei Xiao, Rui Maximo Esteves, Chunming RongAbstract:This paper evaluates the similarity between two time series generated by two sensors manufactured by different companies, trying to provide some valuable information upon choosing sensors of different brands. Spearman Correlation coefficient analysis and Euclidean distance measurement have been applied. Experiment is carried out on R. Visualization of the studied time series and results of similarity measured over time series by Spearman Correlation coefficient and Euclidean distance are presented. Besides, the consistency and inconsistency in the analysis results of two measurements have been discussed in this paper.
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time series similarity evaluation based on Spearman s Correlation coefficients and distance measures
International Conference on Cloud Computing, 2015Co-Authors: Jiaqi Ye, Chengwei Xiao, Rui Maximo Esteves, Chunming RongAbstract:This paper evaluates the similarity between two time series generated by two sensors manufactured by different companies, trying to provide some valuable information upon choosing sensors of different brands. Spearman Correlation coefficient analysis and Euclidean distance measurement have been applied. Experiment is carried out on R. Visualization of the studied time series and results of similarity measured over time series by Spearman Correlation coefficient and Euclidean distance are presented. Besides, the consistency and inconsistency in the analysis results of two measurements have been discussed in this paper.
Keyang Cheng - One of the best experts on this subject based on the ideXlab platform.
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Multi-camera Background and Scene Activity Modelling Based on Spearman Correlation Analysis and Inception-V3 Network
2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), 2019Co-Authors: Keyang Cheng, Muhammad Saddam Khokhar, Rabia TahirAbstract:A novel approach for background and scene activity modelling with Spearman Correlation analysis and customized deep learning model is introduced in this paper. It detects and gives correlated analytics between casual and temporal regional activities on the basis of similarities and primary dissimilarities in the same scene captured by several cameras. The experiment implement on four overlapped videos that are captured inside the hall from four cameras. Detected and analyzed by our model, 17.32% correlated co-occurrences is actual Correlation among all videos. Rest of 82.68% of videos is background that shows similar and repetitive features in Spearman rank tied result. Simulation results demonstrate that the proposed method can detect high Correlation among all activities during the frame rate with tied features ability.
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ICDE Workshops - Multi-camera Background and Scene Activity Modelling Based on Spearman Correlation Analysis and Inception-V3 Network
2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), 2019Co-Authors: Keyang Cheng, Muhammad Saddam Khokhar, Rabia TahirAbstract:A novel approach for background and scene activity modelling with Spearman Correlation analysis and customized deep learning model is introduced in this paper. It detects and gives correlated analytics between casual and temporal regional activities on the basis of similarities and primary dissimilarities in the same scene captured by several cameras. The experiment implement on four overlapped videos that are captured inside the hall from four cameras. Detected and analyzed by our model, 17.32% correlated co-occurrences is actual Correlation among all videos. Rest of 82.68% of videos is background that shows similar and repetitive features in Spearman rank tied result. Simulation results demonstrate that the proposed method can detect high Correlation among all activities during the frame rate with tied features ability.
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Data Driven Processing Via Two-Dimensional Spearman Correlation Analysis (2D-SCA)
2019 13th International Conference on Mathematics Actuarial Science Computer Science and Statistics (MACS), 2019Co-Authors: Muhammad Saddam Khokhar, Keyang Cheng, Misbah AyoubAbstract:This paper introduces an algorithm two-dimensional Spearman Correlation analysis; the present algorithm is the extension of classical Spearman Correlation analysis through algebraic solution for multivariate two-dimensional monotonic (linear or non-linear) multi-media datasets. In a way, two different images with nonlinearity challenges like different dimensions are processed with correspondence techniques such as reshaping images into 1D or vectors. Further, it can reduce dimension reduction along with quadratic algorithm complexity. Due to segmentation of matrices and tied rank ability of Spearman Correlation analysis. The implementation of proposed algorithm performs on four remarkable dataset. The results demonstration is helpful for researchers to choose finger image impression dataset with algorithm performance and sensors related techniques.
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Multi-Dimension Projection for Non-Linear Data Via Spearman Correlation Analysis (MD-SCA)
2019 8th International Conference on Information and Communication Technologies (ICICT), 2019Co-Authors: Muhammad Saddam Khokhar, Keyang Cheng, Misbah Ayoub, Lubamba Kasangu EricAbstract:This paper introduces an algorithm of multidimensional informative projection or view of multiple variable and more than two random variables via Spearman Correlation analysis (SCA). The proposed algorithm is an extension of Spearman Correlation analysis to extract linear or nonlinear information of projections through pairwise Correlation analysis. These multi-dimensional informative projections used as common patterns in pattern recognition application. The proposed algorithm extends SCA through linear algebraic solution for the optimization problem, the problem of dual representation of high multi-dimensional data, and structural dilemma issues along with deep learning model. Additionally, the proposed method decreases the quadratic algorithm complexity among linear and non-linear data through Spearman rank ability. The demonstration of proposed approached performs on two-bench mark data set: Face96 and Yale Face Database.
Muhammad Saddam Khokhar - One of the best experts on this subject based on the ideXlab platform.
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Multi-camera Background and Scene Activity Modelling Based on Spearman Correlation Analysis and Inception-V3 Network
2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), 2019Co-Authors: Keyang Cheng, Muhammad Saddam Khokhar, Rabia TahirAbstract:A novel approach for background and scene activity modelling with Spearman Correlation analysis and customized deep learning model is introduced in this paper. It detects and gives correlated analytics between casual and temporal regional activities on the basis of similarities and primary dissimilarities in the same scene captured by several cameras. The experiment implement on four overlapped videos that are captured inside the hall from four cameras. Detected and analyzed by our model, 17.32% correlated co-occurrences is actual Correlation among all videos. Rest of 82.68% of videos is background that shows similar and repetitive features in Spearman rank tied result. Simulation results demonstrate that the proposed method can detect high Correlation among all activities during the frame rate with tied features ability.
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ICDE Workshops - Multi-camera Background and Scene Activity Modelling Based on Spearman Correlation Analysis and Inception-V3 Network
2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), 2019Co-Authors: Keyang Cheng, Muhammad Saddam Khokhar, Rabia TahirAbstract:A novel approach for background and scene activity modelling with Spearman Correlation analysis and customized deep learning model is introduced in this paper. It detects and gives correlated analytics between casual and temporal regional activities on the basis of similarities and primary dissimilarities in the same scene captured by several cameras. The experiment implement on four overlapped videos that are captured inside the hall from four cameras. Detected and analyzed by our model, 17.32% correlated co-occurrences is actual Correlation among all videos. Rest of 82.68% of videos is background that shows similar and repetitive features in Spearman rank tied result. Simulation results demonstrate that the proposed method can detect high Correlation among all activities during the frame rate with tied features ability.
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Data Driven Processing Via Two-Dimensional Spearman Correlation Analysis (2D-SCA)
2019 13th International Conference on Mathematics Actuarial Science Computer Science and Statistics (MACS), 2019Co-Authors: Muhammad Saddam Khokhar, Keyang Cheng, Misbah AyoubAbstract:This paper introduces an algorithm two-dimensional Spearman Correlation analysis; the present algorithm is the extension of classical Spearman Correlation analysis through algebraic solution for multivariate two-dimensional monotonic (linear or non-linear) multi-media datasets. In a way, two different images with nonlinearity challenges like different dimensions are processed with correspondence techniques such as reshaping images into 1D or vectors. Further, it can reduce dimension reduction along with quadratic algorithm complexity. Due to segmentation of matrices and tied rank ability of Spearman Correlation analysis. The implementation of proposed algorithm performs on four remarkable dataset. The results demonstration is helpful for researchers to choose finger image impression dataset with algorithm performance and sensors related techniques.
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Multi-Dimension Projection for Non-Linear Data Via Spearman Correlation Analysis (MD-SCA)
2019 8th International Conference on Information and Communication Technologies (ICICT), 2019Co-Authors: Muhammad Saddam Khokhar, Keyang Cheng, Misbah Ayoub, Lubamba Kasangu EricAbstract:This paper introduces an algorithm of multidimensional informative projection or view of multiple variable and more than two random variables via Spearman Correlation analysis (SCA). The proposed algorithm is an extension of Spearman Correlation analysis to extract linear or nonlinear information of projections through pairwise Correlation analysis. These multi-dimensional informative projections used as common patterns in pattern recognition application. The proposed algorithm extends SCA through linear algebraic solution for the optimization problem, the problem of dual representation of high multi-dimensional data, and structural dilemma issues along with deep learning model. Additionally, the proposed method decreases the quadratic algorithm complexity among linear and non-linear data through Spearman rank ability. The demonstration of proposed approached performs on two-bench mark data set: Face96 and Yale Face Database.
