Relevant bibliographies by topics / Pattern recognition; Radar data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Pattern recognition; Radar data'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Pattern recognition; Radar data"

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Trafalis, Theodore B., and Anderson White. "Data Mining Techniques for Pattern Recognition: Tornado Signatures in Doppler Weather Radar Data." International Journal of Smart Engineering System Design 5, no. 4 (2003): 347–59. http://dx.doi.org/10.1080/10255810390224107.

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Bianco, Laura, Daniel Gottas, and James M. Wilczak. "Implementation of a Gabor Transform Data Quality-Control Algorithm for UHF Wind Profiling Radars." Journal of Atmospheric and Oceanic Technology 30, no. 12 (2013): 2697–703. http://dx.doi.org/10.1175/jtech-d-13-00089.1.

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Abstract In this paper a Gabor transform–based algorithm is applied to identify and eliminate intermittent signal contamination in UHF wind profiling radars, such as that produced by migrating birds. The algorithm is applied in the time domain, and so it can be used to improve the accuracy of UHF radar wind profiler data in real time—an essential requirement if these wind profiler data are to be assimilated into operational weather forecast models. The added value of using a moment-level Weber–Wuertz pattern recognition scheme that follows the Gabor transform processing is demonstrated.
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Ahmed, Shahzad, and Sung Ho Cho. "Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier." Sensors 20, no. 2 (2020): 564. http://dx.doi.org/10.3390/s20020564.

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The emerging integration of technology in daily lives has increased the need for more convenient methods for human–computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man–machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstra
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Maas, Christian, and Jörg Schmalzl. "Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar." Computers & Geosciences 58 (August 2013): 116–25. http://dx.doi.org/10.1016/j.cageo.2013.04.012.

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Chu, Chen-Chau, N. Nandhakumar, and J. K. Aggarwal. "Image segmentation using laser radar data." Pattern Recognition 23, no. 6 (1990): 569–81. http://dx.doi.org/10.1016/0031-3203(90)90035-j.

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Li, Jia, Xiumin Chu, Wei He, Feng Ma, Reza Malekian, and Zhixiong Li. "A Generalised Bayesian Inference Method for Maritime Surveillance Using Historical Data." Symmetry 11, no. 2 (2019): 188. http://dx.doi.org/10.3390/sym11020188.

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In practice, maritime monitoring systems rely on manual work to identify the authenticities, risks, behaviours and importance of moving objects, which cannot be obtained directly through sensors, especially from marine radar. This paper proposes a generalised Bayesian inference-based artificial intelligence that is capable of identifying these patterns of moving objects based on their dynamic attributes and historical data. First of all, based on dependable prior data, likelihood information about objects of interest is obtained in terms of dynamic attributes, such as speed, direction and posi
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Kim, Seong-Hoon, Zong Woo Geem, and Gi-Tae Han. "A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor." Sensors 19, no. 15 (2019): 3340. http://dx.doi.org/10.3390/s19153340.

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Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal d
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Austin, G. L., A. Bellon, M. Riley, and E. Ballantyne. "Navigation by Computer Processing of Marine Radar Images." Journal of Navigation 38, no. 3 (1985): 375–83. http://dx.doi.org/10.1017/s0373463300032744.

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The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.
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LIAO, XUEJUN, and ZHENG BAO. "RADAR TARGET RECOGNITION BASED ON PARAMETERIZED HIGH RESOLUTION RANGE PROFILES." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 07 (2000): 979–86. http://dx.doi.org/10.1142/s0218001400000623.

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A new scheme of radar target recognition based on parameterized high resolution range profiles (PHRRP) is presented in this paper. A novel criterion called generalized-weighted-normalized correlation (GWNC) is proposed for measuring the similarity between PHRRP's. By properly choosing the parameter of the mainlobe width in GWNC, aspect sensitivity of PHRRP's can be reduced without sacrificing their discriminative power. Performance of the scheme is evaluated using a dataset of three scaled aircraft models. The experimental results show that by using GWNC, only a small number of most dominant s
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Milisavljević, Nada, Isabelle Bloch, Sebastiaan van den Broek, and Marc Acheroy. "Improving mine recognition through processing and Dempster–Shafer fusion of ground-penetrating radar data." Pattern Recognition 36, no. 5 (2003): 1233–50. http://dx.doi.org/10.1016/s0031-3203(02)00251-0.

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Dissertations / Theses on the topic "Pattern recognition; Radar data"

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Dixon, Jason Herbert. "Pattern-theoretic automatic target recognition for infrared and laser radar data." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54404.

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Pattern theory, a mathematical framework for representing knowledge of complex patterns developed by applied mathematician Ulf Grenander, has been shown to have potential uses in automatic target recognition (ATR). Prior research performed in the mid-1990s at Washington University in St. Louis resulted in ATR algorithms based on concepts in pattern theory for forward-looking infrared (FLIR) and laser radar (LADAR) imagery, but additional work was needed to create algorithms that could be implemented in real ATR systems. This was due to performance barriers and a lack of calibration between ta
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Da, Silveira Reinaldo Bomfim. "Recognition of clutter in weather radars using polarization diversity information and artificial neural networks." Thesis, University of Essex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265022.

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Mat, Lela Mohamed Said bin. "The integration of remotely sensed data using Landsat and radar imagery with ancillary information for forest management." Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314550.

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Scott, Michael L. "Automated Characterization of Bridge Deck Distress Using Pattern Recognition Analysis of Ground Penetrating Radar Data." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/28624.

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Many problems are involved with intspecting and evaluating the condition of bridges in the United States. Concrete bridge deck inspection and evaluation presents one of the largest problems. The deterioration of these concrete decks progresses more rapidly than any other bridge component, which leads to early concrete deck replacements that must be done before the bridge superstructure needs to be replaced. The primary cause of deterioration in these concrete bridge decks is corrosion-induced concrete cracking, which frequently results in delaminations. Delamination distress increas
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Tang, Shijun. "Investigation on Segmentation, Recognition and 3D Reconstruction of Objects Based on LiDAR Data Or MRI." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801920/.

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Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I
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Dover, Kathryn. "Pattern Recognition in Stock Data." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/105.

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Finding patterns in high dimensional data can be difficult because it cannot be easily visualized. There are many different machine learning methods to fit data in order to predict and classify future data but there is typically a large expense on having the machine learn the fit for a certain part of a dataset. We propose a geometric way of defining different patterns in data that is invariant under size and rotation. Using a Gaussian Process, we find that pattern within stock datasets and make predictions from it.
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Choudhury, Sabyasachy. "Hierarchical Data Structures for Pattern Recognition." Thesis, Indian Institute of Science, 1987. http://hdl.handle.net/2005/74.

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Pattern recognition is an important area with potential applications in computer vision, Speech understanding, knowledge engineering, bio-medical data classification, earth sciences, life sciences, economics, psychology, linguistics, etc. Clustering is an unsupervised classification process corning under the area of pattern recognition. There are two types of clustering approaches: 1) Non-hierarchical methods 2) Hierarchical methods. Non-hierarchical algorithms are iterative in nature and. perform well in the context of isotropic clusters. Time-complexity of these algorithms is order of (0 (n)
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Dannenberg, Matthew. "Pattern Recognition in High-Dimensional Data." Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/76.

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Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of
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Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.

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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic
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Xu, Weiping. "Non-Euclidean dissimilarity data in pattern recognition." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/3848/.

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This thesis addresses problems in dissimilarity (proximity) learning, particularly focusing on identifying the sources and rectifying the non-Euclidean dissimilarity in pattern recog- nition. We aim to develop a framework for analyzing the non-Euclidean dissimilarity by combining the methods from differential geometry and manifold learning theory. The algorithms are applied to objects represented by the dissimilarity measures. In Chapter 3 we describe how to reveal the origins of the non-Euclidean behaviors of the dissimilarity matrix for the purpose of rectifying the dissimilarities. We com-
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Books on the topic "Pattern recognition; Radar data"

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Kadar, Ivan. Signal processing, sensor fusion, and target recognition XIX: 5-7 April 2010, Orlando, Florida, United States. SPIE, 2010.

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Singh, Sameer, Maneesha Singh, Chid Apte, and Petra Perner, eds. Pattern Recognition and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188.

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Basu, Mitra, and Tin Kam Ho, eds. Data Complexity in Pattern Recognition. Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-172-3.

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1967-, Koutroumbas Konstantinos, and ScienceDirect (Online service), eds. Pattern recognition. 4th ed. Elsevier/Academic Press, 2009.

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Chemical pattern recognition. Research Studies Press, 1986.

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Cakmakov, Dusan. Feature selection for pattern recognition. Informa, 2002.

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Stańczyk, Urszula, and Lakhmi C. Jain, eds. Feature Selection for Data and Pattern Recognition. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45620-0.

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Schachter, Bruce J. Automatic target recognition. SPIE, 2016.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31537-4.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5.

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Book chapters on the topic "Pattern recognition; Radar data"

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Marques de Sá, Joaquim P. "Data Clustering." In Pattern Recognition. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56651-6_3.

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Margara, Alessandro. "Pattern Recognition." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_189.

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Margara, Alessandro. "Pattern Recognition." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_189-1.

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Güler, S., G. Garcia, L. Gülen, and M. N. Toksöz. "The Detection of Geological Fault Lines in Radar Images." In Pattern Recognition Theory and Applications. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_16.

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Vázquez-Bautista, R. F., L. J. Morales-Mendoza, R. Ortega-Almanza, and A. Blanco-Ortega. "Adaptive Algorithm-Based Fused Bayesian Maximum Entropy-Variational Analysis Methods for Enhanced Radar Imaging." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15992-3_17.

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Jin, Xin, Le Wu, Xinghui Zhou, et al. "Predicting Aesthetic Radar Map Using a Hierarchical Multi-task Network." In Pattern Recognition and Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03335-4_4.

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Everitt, Brian S., and Graham Dunn. "Discrimination, Classification and Pattern Recognition." In Applied Multivariate Data Analysis. John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch11.

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Shekhar, Shashi, and Hui Xiong. "Pattern Recognition in Spatial Data." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_963.

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Rocha, Juan C., and Stefan Daume. "Data mining and pattern recognition." In The Routledge Handbook of Research Methods for Social-Ecological Systems. Routledge, 2021. http://dx.doi.org/10.4324/9781003021339-21.

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Abufadel, Amer, Tony Yezzi, and Ronald W. Schafer. "4D Segmentation of Cardiac Data Using Active Surfaces with Spatiotemporal Shape Priors." In Applied Pattern Recognition. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76831-9_4.

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Conference papers on the topic "Pattern recognition; Radar data"

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Ma, Ning, Wei Yan, and Qingdong Wang. "Integer wavelet transform for weather radar data compression." In Multispectral Image Processing and Pattern Recognition, edited by Jun Tian, Tieniu Tan, and Liangpei Zhang. SPIE, 2001. http://dx.doi.org/10.1117/12.442917.

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Wang, Chaoqun. "Data alignment techniques for radar/IIR dual-sensor integration seeker." In Multispectral Image Processing and Pattern Recognition, edited by Jun Shen, Sharatchandra Pankanti, and Runsheng Wang. SPIE, 2001. http://dx.doi.org/10.1117/12.441619.

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Chang, Liu, and Shi Xiaofei. "Study of Data Fusion of AIS and Radar." In 2009 International Conference of Soft Computing and Pattern Recognition. IEEE, 2009. http://dx.doi.org/10.1109/socpar.2009.133.

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Vasile, Alexandru, Frederick R. Waugh, Daniel Greisokh, and Richard M. Heinrichs. "Automatic Alignment of Color Imagery onto 3D Laser Radar Data." In 35th Applied Imagery Pattern Recognition Workshop (AIPR 2006). IEEE, 2006. http://dx.doi.org/10.1109/aipr.2006.16.

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Zhang, Peng, Mingbao Hu, Ning Ma, and Chun He. "Weather radar data denoising using improved wavelet threshold method." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Yongji Wang, Jun Li, Bangjun Lei, and Jingyu Yang. SPIE, 2007. http://dx.doi.org/10.1117/12.749050.

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Liao, Yanping, and Xinyu Chen. "Working Pattern Recognition of Airborne Fire Control Radar for Unbalanced Data." In ICDSP 2020: 2020 4th International Conference on Digital Signal Processing. ACM, 2020. http://dx.doi.org/10.1145/3408127.3408186.

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Shkvarko, Yuriy V., Josue A. Lopez, Stewart R. Santos, and Guillermo Garcia-Torales. "Intelligent neural computing-based way for multi-sensor imaging radar data fusion." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899726.

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Stephan, Michael, Thomas Stadelmayer, Avik Santra, Georg Fischer, Robert Weigel, and Fabian Lurz. "Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412858.

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Chamseddine, Mahdi, Jason Rambach, Didier Stricker, and Oliver Wasenmuller. "Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413247.

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Li, Zhixi, and Ram M. Narayanan. "Data Level Fusion of Multilook Inverse Synthetic Aperture Radar (ISAR) Images." In 35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06). IEEE, 2006. http://dx.doi.org/10.1109/aipr.2006.21.

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Reports on the topic "Pattern recognition; Radar data"

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Foster, Thomas. Application of Pattern Recognition Techniques for Early Warning Radar (EWR) Discrimination. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada298895.

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Kamath, C., and R. Musick. Scalable pattern recognition for large-scale scientific data mining. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/310913.

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Baldwin, C., C. Kamath, and R. Musick. An LLNL perspective on ASCI data mining and pattern recognition requirements. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/9659.

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MARTINEZ, RUBEL F. A Visual Empirical Region of Influence Pattern Recognition Tool for Leave-One-Out Data Analysis. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/793409.

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Kamath, C. LDRD 99-ERI-010 Final Report: Sapphire: Scalable Pattern Recognition for Large-Scale Scientific Data Mining. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/15003138.

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Burr, T., J. Doak, J. A. Howell, D. Martinez, and R. Strittmatter. Knowledge fusion: Time series modeling followed by pattern recognition applied to unusual sections of background data. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/215313.

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Hoon Sohn, Charles Farrar, Norman Hunter, and Keith Worden. Applying the LANL Statistical Pattern Recognition Paradigm for Structural Health Monitoring to Data from a Surface-Effect Fast Patrol Boat. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/780916.

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Gribok, Andrei V. Performance of Advanced Signal Processing and Pattern Recognition Algorithms Using Raw Data from Ultrasonic Guided Waves and Fiber Optics Transducers. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1495185.

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