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Relevant bibliographies by topics / Sliding Window Correlation

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Journal articles
Dissertations / Theses
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Academic literature on the topic 'Sliding Window Correlation'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Sliding Window Correlation"

1

Shakil, Sadia, Jacob C. Billings, Shella D. Keilholz, and Chin-Hui Lee. "Parametric Dependencies of Sliding Window Correlation." IEEE Transactions on Biomedical Engineering 65, no. 2 (2018): 254–63. http://dx.doi.org/10.1109/tbme.2017.2762763.

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CHEN, XIANYAO, ZHAOHUA WU, and NORDEN E. HUANG. "THE TIME-DEPENDENT INTRINSIC CORRELATION BASED ON THE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 02 (2010): 233–65. http://dx.doi.org/10.1142/s1793536910000471.

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A Time-Dependent Intrinsic Correlation (TDIC) method is introduced. This new approach includes both auto- and cross-correlation analysis designed especially to analyze, capture and track the local correlations between nonlinear and nonstationary time series pairs. The approach is based on Empirical Mode Decomposition (EMD) to decompose the nonlinear and nonstationary data into their intrinsic mode functions (IMFs) and uses the instantaneous periods of the IMFs to determine a set of the sliding window sizes for the computation of the running correlation coefficients for multi-scale data. This n

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Mokhtari, Fatemeh, Milad I. Akhlaghi, Sean L. Simpson, Guorong Wu, and Paul J. Laurienti. "Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state." NeuroImage 189 (April 2019): 655–66. http://dx.doi.org/10.1016/j.neuroimage.2019.02.001.

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Vergara, Victor M., Anees Abrol, and Vince D. Calhoun. "An average sliding window correlation method for dynamic functional connectivity." Human Brain Mapping 40, no. 7 (2019): 2089–103. http://dx.doi.org/10.1002/hbm.24509.

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Li, Yinlong, and Tianshu Zhang. "A hybrid Hausdorff distance track correlation algorithm based on time sliding window." MATEC Web of Conferences 336 (2021): 07015. http://dx.doi.org/10.1051/matecconf/202133607015.

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In multi-sensor target tracking, track correlation is the key to the unification of global situation. Hausdorff distance has been applied to power fault elimination, point cloud data, medical measurement, image segmentation, vehicle trajectory recognition and other directions. To solve the problem of track correlation, a hybrid Hausdorff distance track correlation algorithm based on time sliding window is proposed. The hybrid Hausdorff distance based on position, speed and azimuth is defined, and the time sliding window is added on this basis. Simulation results show that the proposed algorith

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Malik, Jyoti, G. Sainarayanan, and Ratna Dahiya. "Sliding Window Based Fast Corner Matching Palmprint Authentication." International Journal of Computer Vision and Image Processing 1, no. 3 (2011): 16–34. http://dx.doi.org/10.4018/ijcvip.2011070102.

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Authentication time is the main and important part of the authentication system. Normally the response time should be fast but as the number of persons in the database increases, there is probability of more response time taken for authentication. The need of fast authentication system arises so that authentication time (matching time) is very less. This paper proposes a sliding window approach to make fast authentication system. The highlight of sliding window method is constant matching time, fast and can match translated images also. Several palmprint matching methods like match by correlat

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Hu, S. R., J. K. Zhang, K. Liang, and M. Bao. "Deflection Sensors Fault Time Locating Based on Kalman Filtering and Data Fusion." Applied Mechanics and Materials 743 (March 2015): 820–23. http://dx.doi.org/10.4028/www.scientific.net/amm.743.820.

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Traditional research on sensor fault are usually confined to fault space locating, however, it’s very necessary to determine the time that the fault occurrence for subsequent data processing and to guarantee the normal operation of the monitoring system. Therefore, this paper proposes a method of sensors fault time locating. First of all, use Kalman filter to process the sensor data, then define the support level of sliding window correlation to sample correlation, combining with multi sensors data fusion, so as to identify the accurate time point of fault. Combine the deflection sensors data

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Prokoph, A. "Fractal, multifractal and sliding window correlation dimension analysis of sedimentary time series." Computers & Geosciences 25, no. 9 (1999): 1009–21. http://dx.doi.org/10.1016/s0098-3004(99)00063-1.

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Gembris, D., J. G. Taylor, S. Schor, D. Suter, and S. Posse. "Functional MR Imaging in Real-Time using a sliding-window correlation technique." NeuroImage 7, no. 4 (1998): S613. http://dx.doi.org/10.1016/s1053-8119(18)31446-0.

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Ren, Jiansi, Ruoxiang Wang, Gang Liu, Yuanni Wang, and Wei Wu. "An SVM-Based Nested Sliding Window Approach for Spectral–Spatial Classification of Hyperspectral Images." Remote Sensing 13, no. 1 (2020): 114. http://dx.doi.org/10.3390/rs13010114.

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This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation

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Dissertations / Theses on the topic "Sliding Window Correlation"

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Chavan, Harshada Chandrakant. "Sliding Window Based Technique to Obtain Correlation Between Field Variables." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27844.

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In agriculture, finding correlation between field variables such as yield, NDVI, etc. is a classic problem. The most popular solution for finding correlation is to use regression analysis over complete field. In a field, there are numerous soil variables such as soil composition, water content, etc. that affect the correlation and it is not always possible to consider such factors in regression model. These factors adversely affect the accuracy of the correlation model. We demonstrate that it is incomplete and inaccurate to represent such correlation using single regression model for the compl

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Shakil, Sadia. "Windowing effects and adaptive change point detection of dynamic functional connectivity in the brain." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55006.

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Evidence of networks in the resting-brain reflecting the spontaneous brain activity is perhaps the most significant discovery to understand intrinsic brain functionality. Moreover, subsequent detection of dynamics in these networks can be milestone in differentiating the normal and disordered brain functions. However, capturing the correct dynamics is a challenging task since no ground truths' are present for comparison of the results. The change points of these networks can be different for different subjects even during normal brain functions. Even for the same subject and session, dynamics

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Thompson, Garth John. "Neural basis and behavioral effects of dynamic resting state functional magnetic resonance imaging as defined by sliding window correlation and quasi-periodic patterns." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49083.

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While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recent

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Grooms, Joshua Koehler. "Examining the relationship between BOLD fMRI and infraslow EEG signals in the resting human brain." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53957.

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Resting state functional magnetic resonance imaging (fMRI) is currently at the forefront of research on cognition and the brain’s large-scale organization. Patterns of hemodynamic activity that it records have been strongly linked to certain behaviors and cognitive pathologies. These signals are widely assumed to reflect local neuronal activity but our understanding of the exact relationship between them remains incomplete. Researchers often address this using multimodal approaches, pairing fMRI signals with known measures of neuronal activity such as electroencephalography (EEG). It has long

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Book chapters on the topic "Sliding Window Correlation"

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Fledel, Dor, and Avishai Wool. "Sliding-Window Correlation Attacks Against Encryption Devices with an Unstable Clock." In Selected Areas in Cryptography – SAC 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10970-7_9.

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Malik, Jyoti, G. Sainarayanan, and Ratna Dahiya. "Sliding Window-Based Fast Corner Matching Palmprint Authentication." In Intelligent Computer Vision and Image Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3906-5.ch012.

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Authentication time is the main and important part of the authentication system. Normally the response time should be fast but as the number of persons in the database increases, there is probability of more response time taken for authentication. The need of fast authentication system arises so that authentication time (matching time) is very less. This paper proposes a sliding window approach to make fast authentication system. The highlight of sliding window method is constant matching time, fast and can match translated images also. Several palmprint matching methods like match by correlation etc. are dependent upon the number of corners detected and so is the matching time. In sliding window method, matching time is constant as the numbers of matching operations are limited and the matching time is independent of the number of corners detected. The palmprint corner features extracted using two approaches Phase Congruency Corner Detector and Harris Corner Detector are binarized so that only useful information (features) is matched. The two approaches of Phase Congruency Corner Detector and Harris Corner Detector, when matched with hamming distance using sliding window can achieve recognition rate of 97.7% and 97.5% respectively.

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Conference papers on the topic "Sliding Window Correlation"

1

Hu, Jiafu, and Yongliang Dong. "Sliding Window Based Security Event Correlation." In 2007 International Conference on Networking, Architecture, and Storage (NAS 2007). IEEE, 2007. http://dx.doi.org/10.1109/nas.2007.50.

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Shakil, Sadia, Shella Dawn Keilholz, and Chin-Hui Lee. "On frequency dependencies of sliding window correlation." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359708.

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Thiruchelvam, Loshini, Vijanth S. Asirvadam, Sarat C. Dass, Hanita Daud, and Balvinder S. Gill. "Sliding window correlation analysis for dengue-climate variable relationship." In 4TH INTERNATIONAL CONFERENCE ON FUNDAMENTAL AND APPLIED SCIENCES (ICFAS2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4968149.

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Lorwongtrakool, Panida, and Phayung Meesad. "Correlation-Based Incremental Learning Network with Sliding Window for Perfume Classification." In IAIT2020: The 11th International Conference on Advances in Information Technology. ACM, 2020. http://dx.doi.org/10.1145/3406601.3406649.

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Liu, Wei, and Aiqun Hu. "Steady-state performance analyses for sliding window max-correlation matching adaptive algorithms." In Signal Processing (WCSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcsp.2010.5632579.

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Vilas-Boas, Vitor Mendes, Vitor Da Silva Jorge, and Cleison Daniel Silva. "Towards ideal time window for classifying motor imagery in brain-computer interfaces." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11961.

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Brain-Computer Interfaces (ICM) allow the control of devices by modulating brain activity. Commonly, when based on motor imagery (IM) these systems use the energy (de)synchronization in the electroencephalogram signal (EEG), voluntarily caused by the individual, to identify and classify their motor intention. Therefore, the EEG segment used in the training of the learning algorithms plays a fundamental role in the description of the characteristics and, consequently, in the recognition of patterns in the signal. In this context, the objective of this work is to demonstrate the correlation betw

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Chee, S. B., Ammar Al Shalabi, C. W. Chin, and B. F. Yousif. "On the Effect of Roller Materials on the Power Window Mechanism From a Tribological Perspective." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66112.

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This study serves to delineate the effects of material on the lifespan of a polymeric roller rubbing against a steel wire. Four materials, namely nylon, polyester, borosilicate glass and epoxy are the manipulated variables in conducting a simulation with a steel wire. A block-on-ring machine was used to conduct the tribo-experiments under dry contact condition. In concurrence with average operating conditions, the machine was set to 0.15 m/s sliding velocity, at an applied load of 10 N. Worn surfaces of the polymer were subsequently studied under optical microscopy. Frictional and wear resista

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Groche, Peter, and Norman Möller. "Tribological Investigation of Deep-Drawing Processes Using Servo Presses." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7292.

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Deep-drawing is one of the most important processes for the economic production of sheet metal parts, especially for high quantities. The forming forces during the process are a limiting factor for the producible shapes. A particular importance is associated with the tribology, because the percentage of friction forces on the total forces is relatively high. The friction between sheet metal and tool is influenced by several factors, such as surface characteristics of the work piece and the tool, lubrication conditions and process parameters, like the contact pressure and the sliding speed. Con

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Kolev, Boyan, Reza Akbarinia, Ricardo Jimenez-Peris, et al. "Parallel Streaming Implementation of Online Time Series Correlation Discovery on Sliding Windows with Regression Capabilities." In Special Session on Appliances for Data-Intensive and Time Critical Applications. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007843806810687.

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Kolev, Boyan, Reza Akbarinia, Ricardo Jimenez-Peris, et al. "Pipelined Implementation of a Parallel Streaming Method for Time Series Correlation Discovery on Sliding Windows." In Special Session on Appliances for Data-Intensive and Time Critical Applications. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008191304310436.

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