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Статті в журналах з теми "Spatiotemporal identification"
Lakumarapu, Srikanth, and Rashmi Agarwal. "Cramming Identification through Spatiotemporal Data." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 693–701. http://dx.doi.org/10.26438/ijcse/v6i6.693701.
Повний текст джерелаVoss, H., M. Bünner, and M. Abel. "Identification of continuous, spatiotemporal systems." Physical Review E 57, no. 3 (March 1998): 2820–23. http://dx.doi.org/10.1103/physreve.57.2820.
Повний текст джерелаPAN, Y., and S. A. BILLINGS. "THE IDENTIFICATION OF COMPLEX SPATIOTEMPORAL PATTERNS USING COUPLED MAP LATTICE MODELS." International Journal of Bifurcation and Chaos 18, no. 04 (April 2008): 997–1013. http://dx.doi.org/10.1142/s021812740802080x.
Повний текст джерелаPan, J. B., S. C. Hu, H. Wang, Q. Zou, and Z. L. Ji. "PaGeFinder: quantitative identification of spatiotemporal pattern genes." Bioinformatics 28, no. 11 (April 6, 2012): 1544–45. http://dx.doi.org/10.1093/bioinformatics/bts169.
Повний текст джерелаConkling, Tara J., James A. Martin, Jerrold L. Belant, and Travis L. DeVault. "Spatiotemporal Dynamics in Identification of Aircraft–Bird Strikes." Transportation Research Record: Journal of the Transportation Research Board 2471, no. 1 (January 2015): 19–25. http://dx.doi.org/10.3141/2471-03.
Повний текст джерелаPan, Y., and S. A. Billings. "Neighborhood Detection for the Identification of Spatiotemporal Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 3 (June 2008): 846–54. http://dx.doi.org/10.1109/tsmcb.2008.918571.
Повний текст джерелаNing, Hanwen, Xingjian Jing, and Li Cheng. "Identification of non-linear stochastic spatiotemporal dynamical systems." IET Control Theory & Applications 7, no. 17 (November 21, 2013): 2069–83. http://dx.doi.org/10.1049/iet-cta.2013.0150.
Повний текст джерелаKrakover, Shaul. "Identification of Spatiotemporal Paths of Spread and Backwash." Geographical Analysis 15, no. 4 (September 3, 2010): 318–29. http://dx.doi.org/10.1111/j.1538-4632.1983.tb00790.x.
Повний текст джерелаEllison, Adrian B., Richard B. Ellison, Asif Ahmed, Dean Rance, and Stephen P. Greaves. "Spatiotemporal Identification of Trip Stops from Smartphone Data." Applied Spatial Analysis and Policy 12, no. 1 (May 4, 2016): 27–43. http://dx.doi.org/10.1007/s12061-016-9188-0.
Повний текст джерелаDong, Xunde, and Cong Wang. "Identification of the Gray–Scott Model via Deterministic Learning." International Journal of Bifurcation and Chaos 31, no. 04 (March 30, 2021): 2150051. http://dx.doi.org/10.1142/s0218127421500516.
Повний текст джерелаДисертації з теми "Spatiotemporal identification"
Townsend, Rory George. "Spatiotemporal patterns in neural population activity: Identification, dynamics, and function." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18039.
Повний текст джерелаPan, Yi. "The identification and analysis of spatiotemporal systems using coupled map lattice." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500125.
Повний текст джерелаLiu, Gang. "Spatiotemporal Sensing and Informatics for Complex Systems Monitoring, Fault Identification and Root Cause Diagnostics." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5727.
Повний текст джерелаWimberly, Brent. "Identification of spatiotemporal nutrient patterns and associated ecohydrological trends in the tampa bay coastal region." Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/642.
Повний текст джерелаB.S.C.E.
Bachelors
Engineering and Computer Science
Civil Engineering
Alford, Lea Marie. "Identification and Spatiotemporal Control of the Asymmetrical Membrane Cortex in Cleavage Stage Sea Urchin Embryos." Thesis, Boston College, 2009. http://hdl.handle.net/2345/978.
Повний текст джерелаPolarity established by the first cleavages in sea urchin embryos was investigated in this thesis revealing precocious embryonic polarity. Studies of embryonic polarity have focused on protostomes such as C. elegans, and those on deuterostomes have focused on later developmental stages. I find asymmetries in the sea urchin membrane cell cortex as early as the first division after fertilization as a result of new membrane addition in the cleavage furrow. Membrane domains and the polarity determinants Par6, aPKC, and Cdc42 are polarized to the apical, or free, cell surface, while the cell-cell contact site remains distinct. Using immunofluorescence, fluorescence recovery after photobleaching (FRAP), and specific inhibitor treatments, myosin filaments were identified as the major regulator of membrane cortex polarity. However, membrane domains and cortical polarity determinants are differentially regulated with respect to blastomere dissociation. These asymmetries are required for proper spindle alignment and cleavage plane determination and are responsible for polarized fluid phase endocytosis. The work in this thesis and future studies addressing the connection between the membrane cortex and myosin filaments has and will lead to a greater understanding of the maintenance of embryonic polarity in cleavage stage sea urchin embryos
Thesis (PhD) — Boston College, 2009
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Biology
Al, Jord Adel. "Centriole amplification in brain multiciliated cells : high resolution spatiotemporal dynamics and identification of regulatory mechanisms." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066706/document.
Повний текст джерелаMulticiliated mammalian cells play a crucial role in the propulsion of physiological fluids. Their dysfunction causes severe chronic diseases. In contrast to the strict centriole number control in cycling cells, multiciliated cell differentiation is marked by the production of up to several hundred centrioles, each nucleating a motile cilium. The mechanisms of centriole amplification or centriole number control in these cells were unknown and new centrioles were thought to appear de novo in the cytoplasm. First, videomicroscopy combined with correlative super-resolution and electron microscopy has enabled us to determine that all procentrioles are generated via runs of nucleation from the pre-existing progenitor cell centrosome. We show that the daughter centriole of the centrosome is the primary nucleation site for 95% of the new centrioles in multiciliated cells and thus refute the de novo hypothesis. Then, we provide evidence of an activation of the mitosis regulatory network during the centriole dynamic. With single cell live imaging and pharmacological modulation of mitosis regulators, we show that the mitosis machinery orchestrates the spatiotemporal progression of centriole amplification in terminally differentiating multiciliated cell progenitors. The fine-tuning of Cdk1 activity prevents mitosis while allowing the timely coordination of centriole number, growth, and disengagement through checkpoint-like phase transitions necessary for subsequent functional motile ciliation. This PhD provides a new paradigm for studying multiciliated cell differentiation, cilia-related diseases and pathological centriole amplification associated with cancer and microcephaly
Pruzinsky, Nina. "Identification and spatiotemporal dynamics of tuna (Family: Scombridae; Tribe: Thunnini) early life stages in the oceanic Gulf of Mexico." Thesis, NSUWorks, 2018. https://nsuworks.nova.edu/occ_stuetd/472.
Повний текст джерелаBerro, Soumaya. "Identification of muscle activation schemes by inverse methods applied on HD-sEMG signals." Electronic Thesis or Diss., Compiègne, 2022. http://www.theses.fr/2022COMP2708.
Повний текст джерелаFast or real-time identification of the spatiotemporal activation of Motor Units (MUs), functional units of the neuromuscular system, is fundamental in applications as prosthetic control and rehabilitation guidance but often dictates expensive computational times. Therefore, the thesis work was devoted to providing an algorithm that enables the real-time identification of MU spatial and temporal activation strategies by applying inverse methods on HD-sEMG (high-density surface electromyogram) signals from a grid placed over the Biceps Brachii (BB). For this purpose, we propose an innovative approach, that involves the use of the classical minimum norm inverse method and a 3D fitting curve interpolation, namely CFB-MNE approach. This method, based on inverse identification (minimum norm estimation) coupled to simulated motor unit action potential (MUAP) dictionary from a recent model and tested on simulations, allowed the real time localization of simulated individual motor units. A robustness analysis (anatomical, physiological, and instrumental modifications) was then performed to verify the efficiency of the proposed algorithm. Finally, the proposed algorithm was tested on MUs with realistic recruitment patterns giving promising results in both spatial and temporal identification. To conclude, a door to future perspectives was opened, according to the obtained promising results, suggesting the use of machine learning and artificial intelligence (AI) to further boost the performance of the proposed algorithm
Ndione, Méry. "Dynamique et identification des sources de contamination fécale dans un espace littoral connaissant des pratiques de tourisme et de loisirs : l’exemple de la baie d’Aytré." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS006.
Повний текст джерелаThe microbiological quality of bathing water is progressively decreasing from year to year, and can constitute a major public health problem. Thus, sanitary monitoring of the microbiological quality of bathing waters is carried out in accordance with the European directive (2006/7/EC) to ensure the sanitary safety of bathers and preserve the image of these recreational ecosystems. For many years, the bay of Aytré (Charente Maritime, France), has been classified as "poor quality" and this beach is prohibited for bathing since 2018. The health issues and the preponderant role of this beach on the tourism development and the local economy led to investigate the origin and the spatiotemporal determinism of this fecal contamination. This thesis presents an integrated approach to the analysis of the fecal contamination of Aytré Bay through a multidisciplinary study of different hypotheses analysed since the beginning of the 2000s by the local authorities. The level of fecal contamination of the bathing water during a year was relatively low with a notable seasonal variation in the abundance of the fecal contamination indicators Escherichia coli and enterococci. Exceedances of the regulatory thresholds on 24% and 32% of the water samples from Platin Nord and Platin Sud, the two bathing sites in Aytré Bay, were mainly due to enterococci. The microbiological quality of the sediment over time showed that the sediment was not a diffuse source of contamination in the water. The combined analysis of protein, genetic and chemical markers revealed the presence of enterococci species of environmental origin on the one hand, and on the other hand, a contamination of mainly animal origin and a small contribution from human origin. The analytical strategy and tools developed during this study will help to improve the sanitary surveillance methods of bathing waters
Ghrissi, Amina. "Ablation par catheter de fibrillation atriale persistante guidée par dispersion spatiotemporelle d’électrogrammes : Identification automatique basée sur l’apprentissage statistique." Thesis, Université Côte d'Azur, 2021. http://www.theses.fr/2021COAZ4026.
Повний текст джерелаCatheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent patient-tailored AF ablation therapy, giving 95% of procedural success rate, is based on the use of a multipolar mapping catheter called PentaRay. It targets areas of spatiotemporal dispersion (STD) in the atria as potential AF drivers. STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous leads.In practice, interventional cardiologists localize STD sites visually using the PentaRay multipolar mapping catheter. This thesis aims to automatically characterize and identify ablation sites in STD-based ablation of persistent AF using machine learning (ML) including deep learning (DL) techniques. In the first part, EGM recordings are classified into STD vs. non-STD groups. However, highly imbalanced dataset ratio hampers the classification performance. We tackle this issue by using adapted data augmentation techniques that help achieve good classification. The overall performance is high with values of accuracy and AUC around 90%. First, two approaches are benchmarked, feature engineering and automatic feature extraction from a time series, called maximal voltage absolute values at any of the bipoles (VAVp). Statistical features are extracted and fed to ML classifiers but no important dissimilarity is obtained between STD and non-STD categories. Results show that the supervised classification of raw VAVp time series itself into the same categories is promising with values of accuracy, AUC, sensi-tivity and specificity around 90%. Second, the classification of raw multichannel EGM recordings is performed. Shallow convolutional arithmetic circuits are investigated for their promising theoretical interest but experimental results on synthetic data are unsuccessful. Then, we move forward to more conventional supervised ML tools. We design a selection of data representations adapted to different ML and DL models, and benchmark their performance in terms of classification and computational cost. Transfer learning is also assessed. The best performance is achieved with a convolutional neural network (CNN) model for classifying raw EGM matrices. The average performance over cross-validation reaches 94% of accuracy and AUC added to an F1-score of 60%. In the second part, EGM recordings acquired during mapping are labeled ablated vs. non-ablated according to their proximity to the ablation sites then classified into the same categories. STD labels, previously defined by interventional cardiologists at the ablation procedure, are also aggregated as a prior probability in the classification task.Classification results on the test set show that a shallow CNN gives the best performance with an F1-score of 76%. Aggregating STD label does not help improve the model’s performance. Overall, this work is among the first attempts at the application of statistical analysis and ML tools to automatically identify successful ablation areas in STD-based ablation. By providing interventional cardiologists with a real-time objective measure of STD, the proposed solution offers the potential to improve the efficiency and effectiveness of this fully patient-tailored catheter ablation approach for treating persistent AF
Книги з теми "Spatiotemporal identification"
Dicecco, Nico. The Aura of Againness. Edited by Thomas Leitch. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199331000.013.35.
Повний текст джерелаЧастини книг з теми "Spatiotemporal identification"
Zhao, Guoying, and Matti Pietikäinen. "Visual Speaker Identification with Spatiotemporal Directional Features." In Lecture Notes in Computer Science, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39094-4_1.
Повний текст джерелаLi, Ye, Guangqiang Yin, Shaoqi Hou, Jianhai Cui, and Zicheng Huang. "Spatiotemporal Feature Extraction for Pedestrian Re-identification." In Wireless Algorithms, Systems, and Applications, 188–200. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23597-0_15.
Повний текст джерелаZhong, Xian, Meng Feng, Wenxin Huang, Zheng Wang, and Shin’ichi Satoh. "Poses Guide Spatiotemporal Model for Vehicle Re-identification." In MultiMedia Modeling, 426–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_35.
Повний текст джерелаFang, Zhixiang, Shih-Lung Shaw, Wei Tu, and Qingquan Li. "Spatiotemporal Critical Opportunity and Link Identification for Joint Participation Scheduling." In Space-Time Integration in Geography and GIScience, 109–26. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9205-9_7.
Повний текст джерелаChen, Wei, and Wangchongyu Peng. "Spatiotemporal Quantification and Identification of Urban Development and Its Characteristics." In Digital Analysis of Urban Structure and Its Environment Implication, 49–80. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6641-5_3.
Повний текст джерелаHuo, Yonghua, Hongwu Ge, Libin Jiao, Bowen Gao, and Yang Yang. "Encrypted Traffic Identification Method Based on Multi-scale Spatiotemporal Feature Fusion Model with Attention Mechanism." In Proceedings of the 11th International Conference on Computer Engineering and Networks, 857–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6554-7_92.
Повний текст джерелаSpurney, Ryan, Michael Schwartz, Mariah Gobble, Rosangela Sozzani, and Lisa Van den Broeck. "Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification." In Modeling Transcriptional Regulation, 47–65. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_4.
Повний текст джерелаEshel, Gidon. "Regression and Least Squares." In Spatiotemporal Data Analysis. Princeton University Press, 2011. http://dx.doi.org/10.23943/princeton/9780691128917.003.0009.
Повний текст джерелаBhowmick, Sutanu, and Satish Nagarajaiah. "Structural System Identification Using Vision-Based Full-Field Spatiotemporal Measurements." In Recent Developments in Structural Health Monitoring and Assessment — Opportunities and Challenges, 375–405. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811243011_0013.
Повний текст джерелаBetancourt, Ramón J., Ramón Daniel Rodríguez-Soto, Antonio Concha Sánchez, and Emilio Barocio Espejo. "Identification of source harmonics in electrical networks using spatiotemporal approaches." In Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques, 163–89. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-32-399904-5.00013-2.
Повний текст джерелаТези доповідей конференцій з теми "Spatiotemporal identification"
Khan, Muhammad Hassan, Muhammad Shahid Farid, and Marcin Grzegorzek. "Person identification using spatiotemporal motion characteristics." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296264.
Повний текст джерелаBrandstrom, Gary, Dennis W. Ruck, Steven K. Rogers, and Bruce E. Stribling. "Space object identification using spatiotemporal pattern recognition." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers and Dennis W. Ruck. SPIE, 1996. http://dx.doi.org/10.1117/12.235937.
Повний текст джерелаGkentsidis, K., T. Pistola, N. Mitianoudis, and N. V. Boulgouris. "Deep Person Identification Using Spatiotemporal Facial Motion Amplification." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191281.
Повний текст джерелаLi, Shuang, Slawomir Bak, Peter Carr, and Xiaogang Wang. "Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00046.
Повний текст джерелаLi, Weihong, and Yeben Chen. "Risk factor identification and spatiotemporal diffusion path during the dengue outbreak." In 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2016. http://dx.doi.org/10.1109/eorsa.2016.7552827.
Повний текст джерелаYang, Xu, Bin Zhang, Yuan Dong, Fengye Xiong, and Hongliang Bai. "Spatiotemporal Attention on Sliced Parts for Video-based Person Re-identification." In 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2018. http://dx.doi.org/10.1109/vcip.2018.8698653.
Повний текст джерелаZheng, Chong, Ping Wei, and Nanning Zheng. "A Duplex Spatiotemporal Filtering Network for Video-based Person Re-identification." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412371.
Повний текст джерелаMohseni, Hamid R., and Saeid Sanei. "A new method for spatiotemporal identification of event-related potential subcomponents." In 2009 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5332547.
Повний текст джерелаCastells, F., R. Ruiz, J. J. Rieta, and J. Millet. "An integral atrial wave identification based on spatiotemporal source separation: clinical validation." In Computers in Cardiology, 2003. IEEE, 2003. http://dx.doi.org/10.1109/cic.2003.1291256.
Повний текст джерелаWang, Liwei, Xuedong Yan, Deqi Chen, Xiaobing Liu, and Tong Liu. "Identification and Classification of Spatiotemporal Traffic Congestion Based on Floating Car Data." In 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482933.009.
Повний текст джерела