Literatura científica selecionada sobre o tema "Magnetic heading"
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Artigos de revistas sobre o assunto "Magnetic heading"
Iqbal, Muhammad, Masood Ur Rehman, Umar Iqbal Bhatti e Najam Abbas Naqvi. "Magnetometer heading estimation through online calibration for land navigation applications". Natural and Applied Sciences International Journal (NASIJ) 2, n.º 1 (16 de dezembro de 2021): 56–69. http://dx.doi.org/10.47264/idea.nasij/2.1.5.
Texto completo da fonteLi, Ziyaun, Yanmin Zhang e Wentie Yang. "The Effect of Vehicles Attitude Angle Error on Magnetic Compass Heading Estimation". Journal of Physics: Conference Series 2718, n.º 1 (1 de março de 2024): 012048. http://dx.doi.org/10.1088/1742-6596/2718/1/012048.
Texto completo da fonteHu, Hao. "A Vehicle Heading and Attitude Measurement System Based on Earth Magnetic Field Sensor". Advanced Materials Research 588-589 (novembro de 2012): 1140–43. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1140.
Texto completo da fonteGang, Yin, Zhang Yingtang, Ren Guoquan, Li Zhining e Fan Hongbo. "Magnetic interferential signal compensation in magnetic heading measurement". Transactions of the Institute of Measurement and Control 38, n.º 9 (20 de julho de 2016): 1098–106. http://dx.doi.org/10.1177/0142331215579218.
Texto completo da fonteEl-Diasty, M. "An Accurate Heading Solution using MEMS-based Gyroscope and Magnetometer Integrated System (Preliminary Results)". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2 (11 de novembro de 2014): 75–78. http://dx.doi.org/10.5194/isprsannals-ii-2-75-2014.
Texto completo da fonteLiu, Gong-Xu, e Ling-Feng Shi. "Adaptive algorithm of magnetic heading detection". Measurement Science and Technology 28, n.º 11 (12 de outubro de 2017): 115101. http://dx.doi.org/10.1088/1361-6501/aa8257.
Texto completo da fonteFeng, Yi Bo, Xi Sheng Li e Xiao Juan Zhang. "Research on a Combined Directional Instrument of DMC and Gyro for Vehicles". Advanced Materials Research 490-495 (março de 2012): 2510–14. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.2510.
Texto completo da fonteKarimi, Mojtaba, Edwin Babaians, Martin Oelsch e Eckehard Steinbach. "Deep Fusion of a Skewed Redundant Magnetic and Inertial Sensor for Heading State Estimation in a Saturated Indoor Environment". International Journal of Semantic Computing 15, n.º 03 (setembro de 2021): 313–35. http://dx.doi.org/10.1142/s1793351x21400079.
Texto completo da fonteFoster, M. R. "Vehicle Navigation Using the Adaptive Compass". Journal of Navigation 39, n.º 2 (maio de 1986): 279–85. http://dx.doi.org/10.1017/s0373463300000138.
Texto completo da fonteChang, Ming, Lei Xu, Xin Pang, Jiawei Zhang, Houpu Li e Mingzhen Lin. "Characteristic analysis and blind area prediction of aeromagnetic scalar gradient detection method". AIP Advances 12, n.º 8 (1 de agosto de 2022): 085211. http://dx.doi.org/10.1063/5.0102139.
Texto completo da fonteTeses / dissertações sobre o assunto "Magnetic heading"
Daou, Andrea. "Real-time Indoor Localization with Embedded Computer Vision and Deep Learning". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR002.
Texto completo da fonteThe need to determine the location of individuals or objects in indoor environments has become an essential requirement. The Global Navigation Satellite System, a predominant outdoor localization solution, encounters limitations when applied indoors due to signal reflections and attenuation caused by obstacles. To address this, various indoor localization solutions have been explored. Wireless-based indoor localization methods exploit wireless signals to determine a device's indoor location. However, signal interference, often caused by physical obstructions, reflections, and competing devices, can lead to inaccuracies in location estimation. Additionally, these methods require access points deployment, incurring associated costs and maintenance efforts. An alternative approach is dead reckoning, which estimates a user's movement using a device's inertial sensors. However, this method faces challenges related to sensor accuracy, user characteristics, and temporal drift. Other indoor localization techniques exploit magnetic fields generated by the Earth and metal structures. These techniques depend on the used devices and sensors as well as the user's surroundings.The goal of this thesis is to provide an indoor localization system designed for professionals, such as firefighters, police officers, and lone workers, who require precise and robust positioning solutions in challenging indoor environments. In this thesis, we propose a vision-based indoor localization system that leverages recent advances in computer vision to determine the location of a person within indoor spaces. We develop a room-level indoor localization system based on Deep Learning (DL) and built-in smartphone sensors combining visual information with smartphone magnetic heading. To achieve localization, the user captures an image of the indoor surroundings using a smartphone, equipped with a camera, an accelerometer, and a magnetometer. The captured image is then processed using our proposed multiple direction-driven Convolutional Neural Networks to accurately predict the specific indoor room. The proposed system requires minimal infrastructure and provides accurate localization. In addition, we highlight the importance of ongoing maintenance of the vision-based indoor localization system. This system necessitates regular maintenance to adapt to changing indoor environments, particularly when new rooms have to be integrated into the existing localization framework. Class-Incremental Learning (Class-IL) is a computer vision approach that allows deep neural networks to incorporate new classes over time without forgetting the knowledge previously learned. In the context of vision-based indoor localization, this concept must be applied to accommodate new rooms. The selection of representative samples is essential to control memory limits, avoid forgetting, and retain knowledge from previous classes. We develop a coherence-based sample selection method for Class-IL, bringing forward the advantages of the coherence measure to a DL framework. The relevance of the methodology and algorithmic contributions of this thesis is rigorously tested and validated through comprehensive experimentation and evaluations on real datasets
Cheng, Yuang-Tung, e 鄭遠東. "Applications of Magnetic Sensors for Vessel Heading Determination". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/07140094483042235770.
Texto completo da fonte國立臺灣海洋大學
輪機工程系
94
Abstract For hundreds of years now ships have capitalized on the field generated by the earth’s magnetic poles to navigate around the world. The crude compass has evolved into a magnetic sensing device commonly know as a magnetometer. This directional tool has been used very successfully as a heading and orientation sensor on dynamic platforms. Magnetometers are sensor that detect both the signal and magnitude of the earth field as a voltage output. Using solid-state magnetic sensors and a tilt sensor , a low coast compass system can be realized. This paper covers part of the design vessel heading control systems. The paper presents the principle and design of magnetic sensors in direction control. HMR 3300 is a 3-axial magnetometer, that uses the RS232 converter. This paper presents the design method of the RS232 signal generator based on the programming language Visual C++. And it can generate different basic signals and chirp signals. It has a variety of functions including display of the signal for heading and linking another vessel electrical system .
Livros sobre o assunto "Magnetic heading"
Grossman, Charles B. Magnetic resonance imaging and computed tomography of the head and spine. Baltimore: Williams & Wilkins, 1990.
Encontre o texto completo da fonteGrossman, Charles B. Magnetic resonance imaging and computed tomography of the head and spine. 2a ed. Baltimore: Williams & Wilkins, 1996.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Magnetic heading"
Zhang, Wen, Tianzhi Huang e Zhenguo Sun. "Localization of Wall Climbing Robot on Cylinder-Shaped Steel". In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221179.
Texto completo da fonteAly, Abeer E. "Nd2Fe14B and SmCo5 a Permanent Magnet for Magnetic Data Storage and Data Transfer Technology". In Advanced Materials and Nano Systems: Theory and Experiment - Part 2, 120–78. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815049961122020012.
Texto completo da fonteBaldomir, D., e P. Hammond. "Electromagnetic radiation". In Geometry of Electromagnetic Systems, 123–44. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198591870.003.0006.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Magnetic heading"
Qi Zhang, Liang-shui Lei, Jiang Fan e Song Liu. "Autocalibration of a magnetic compass without heading reference". In 2010 2nd Conference on Environmental Science and Information Application Technology (ESIAT). IEEE, 2010. http://dx.doi.org/10.1109/esiat.2010.5567331.
Texto completo da fonteSushchenko, O., F. Yanovsky, Y. Bezkorovainyi e O. Melaschenko. "Influencing UAV Electric Motors on Magnetic Heading Deviation". In 2020 IEEE 6th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC). IEEE, 2020. http://dx.doi.org/10.1109/msnmc50359.2020.9255621.
Texto completo da fonteDuan, Fengyang, Baixiang Sun e Lijing Zhang. "High Accuracy Acquisition of the Magnetic Heading Signal". In 2007 International Conference on Mechatronics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icma.2007.4303912.
Texto completo da fonteSun, BaoJiang, e Yue Xu. "Heading Measurement based on Ultrasonic and Magnetic Compass". In 2nd International Conference On Systems Engineering and Modeling. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icsem.2013.18.
Texto completo da fonteAfzal, Muhammad Haris, Valerie Renaudin e Gerard Lachapelle. "Magnetic field based heading estimation for pedestrian navigation environments". In 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2011. http://dx.doi.org/10.1109/ipin.2011.6071947.
Texto completo da fonteWang, Kai, Kingshing Yip, Chengchun Shien, Xinan Wang e Guangyi Shi. "Research on Dynamic Heading Calculation of Complex Magnetic Disturbance". In 2019 IEEE 2nd International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS). IEEE, 2019. http://dx.doi.org/10.1109/nsens49395.2019.9293999.
Texto completo da fonteMa, Ming, Qian Song, Yang-huan Li e Zhi-min Zhou. "Magnetic field aided heading estimation for indoor pedestrian positioning". In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2017. http://dx.doi.org/10.1109/itnec.2017.8284870.
Texto completo da fonteRen, Yan, Jiancheng Fang, Duan Xu e Fangling Qin. "Identification and elimination of outliers in magnetic heading information measurement". In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6469924.
Texto completo da fonteLi, Li-Jin, e Ji-Hui Pan. "Research on Dynamic Error Characteristics of Strapdown Magnetic Heading Measurement System". In 2018 International Conference on Sensor Networks and Signal Processing (SNSP). IEEE, 2018. http://dx.doi.org/10.1109/snsp.2018.00098.
Texto completo da fonteLi, Yuanyuan, Tong Zhu e Ruyi Li. "Analysis of Attitude and Heading Computer System in Magnetic Disturbance State". In 2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM). IEEE, 2022. http://dx.doi.org/10.1109/aiam57466.2022.00045.
Texto completo da fonte