Relevant bibliographies by topics / Magnetic anomaly detection sensor

Contents

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

Academic literature on the topic 'Magnetic anomaly detection sensor'

Author: Grafiati
Published: 21 April 2022
Last updated: 18 July 2025

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Journal articles on the topic "Magnetic anomaly detection sensor"

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Hu, Bo Zhou, Meng Chun Pan, Peng Jiang, Wu Gang Tian, Jia Fei Hu, and Jing Hua Hu. "The Research on Magnetic Target Detection Technology Based on Wireless Sensor Network." Applied Mechanics and Materials 644-650 (September 2014): 1213–17. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1213.

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In the conditions of magnetic dipole model, this paper proposed forward a centroid localization algorithm on magnetic anomaly target based on wireless sensor network node which distribution are random and the improved the weighted centroid localization algorithm based on magnetic induction intensity. According to the fluctuation of magnetic field intensity which detected by magnetic sensors, that can detect the existence of magnetic anomaly target and its location. Established an experimental system of the wireless sensor network for magnetic anomaly detection whose core designs including the
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Tianhan, Yang, and Li Shizhong. "The Impact of Magnet Structures on Target Magnetic Anomaly Detection." Journal of Physics: Conference Series 2891, no. 12 (2024): 122009. https://doi.org/10.1088/1742-6596/2891/12/122009.

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Abstract To address the limitation of short detection ranges in conventional magnetic sensors, excitation magnetic fields can be used to enhance the target’s magnetic anomaly and thereby extend the detection range. By arranging permanent magnets to create excitation magnetic fields around the magnetic sensor, targets are magnetized, producing significant magnetic anomalies that can be detected by the sensor. Four different magnet configurations were tested in simulations to compare the response characteristics of the magnetic sensor using a typical iron rod as the target. Based on these simula
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Li, Hangcheng, Jiaming Luo, Jiajun Zhang, et al. "Determinants of Maximum Magnetic Anomaly Detection Distance." Sensors 24, no. 12 (2024): 4028. http://dx.doi.org/10.3390/s24124028.

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The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are estab
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Liu, Yong, Yu Fang Zhang, and Hong Yi. "The New Magnetic Survey Method for Underwater Pipeline Detection." Applied Mechanics and Materials 239-240 (December 2012): 338–43. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.338.

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Magnetic survey has been applied to detect underwater pipeline location, tracking and depth more and more nowadays. In order to detect such smaller targets, besides the application of high-precision magnetometer detector, an effective method especial for underwater pipeline is needed. This paper is to research the magnetic survey method for detecting the underwater pipeline. In terms of its magnetic anomaly characteristic, magnetic sensor array and magnetic anomaly gradient method which can eliminate geomagnetic diurnal variation are adopted to detect the location, tracking and depth of underw
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Ege, Yavuz, Mustafa Çoramık, Murat Kabadayı, et al. "Anomaly detection with low magnetic flux: A fluxgate sensor network application." Measurement 81 (March 2016): 43–56. http://dx.doi.org/10.1016/j.measurement.2015.12.004.

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Page, Brian R., Reeve Lambert, Nina Mahmoudian, David H. Newby, Elizabeth L. Foley, and Thomas W. Kornack. "Compact Quantum Magnetometer System on an Agile Underwater Glider." Sensors 21, no. 4 (2021): 1092. http://dx.doi.org/10.3390/s21041092.

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This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduct
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Ge, Jiahao, Jinwu Xiang, and Daochun Li. "Integrated Low Electromagnetic Interference Design Method for Small, Fixed-Wing UAVs for Magnetic Anomaly Detection." Drones 8, no. 8 (2024): 347. http://dx.doi.org/10.3390/drones8080347.

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Unmanned aerial vehicles (UAVs) equipped with magnetic airborne detectors (MADs) represent a new combination for underground or undersea magnetic anomaly detection. The electromagnetic interference (EMI) generated by a UAV platform affects the acquisition of weak magnetic signals by the MADs, which brings unique conceptual design difficulties. This paper proposes a systematic and integrated low-EMI design method for small, fixed-wing UAVs. First, the EMI at the MAD is analyzed. Second, sensor layout optimization for a single UAV is carried out, and the criteria for the sensor layout are given.
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Yang, Ruiping, Hongpeng Wang, Huan Liu, Wang Luo, Jian Ge, and Haobin Dong. "A new digital single-axis fluxgate magnetometer according to the cobalt-based amorphous effects." Review of Scientific Instruments 93, no. 3 (2022): 035104. http://dx.doi.org/10.1063/5.0084376.

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Fluxgate sensors are currently widely used for weak magnetic field measurement because of their relatively great performance, such as resolution, power consumption, and measurement of vector magnetic fields directly. Since the analog fluxgate sensor has some drawbacks, e.g., it would be influenced by the noise of the analog circuit. Hence, in recent years, the analog circuit is gradually inclined to be realized by digital processing in which the software parameter adjustment is employed to replace the analog components, which can greatly improve the flexibility of the design. This paper propos
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Shen, Ying, Jiazeng Wang, Jiedong Shi, Shuxiang Zhao, and Junqi Gao. "Interpretation of signature waveform characteristics for magnetic anomaly detection using tunneling magnetoresistive sensor." Journal of Magnetism and Magnetic Materials 484 (August 2019): 164–71. http://dx.doi.org/10.1016/j.jmmm.2019.04.016.

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Naghibi, Javad, Kamyar Mehran, and Martin P. Foster. "High-Frequency Non-Invasive Magnetic Field-Based Condition Monitoring of SiC Power MOSFET Modules." Energies 14, no. 20 (2021): 6720. http://dx.doi.org/10.3390/en14206720.

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Current distribution anomaly can be used to indicate the onset of package-related failures modes in Silicon Carbide power MOSFET modules. In this paper, we propose to obtain the wire bond’s magnetic field profile using an array of Tunnel Magneto-Resistance (TMR) sensors, and characterise the small changes in the current density distribution to find the onset of the wire bond degradation processes, including wire bond lift-off, wire bond cracking, and wire bond fracture. We propose a novel condition monitoring technique where a non-galvanic high-bandwidth sensing and a reliability model monitor
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Dissertations / Theses on the topic "Magnetic anomaly detection sensor"

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Gameiro, Gonçalo. "Design modifications of a UAV wing for optimal integration of a magnetic anomaly detection sensor." Master's thesis, Academia da Força Aérea; Instituto Superior Técnico, 2018. http://hdl.handle.net/10400.26/40254.

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Supervisors: Prof. Afzal Suleman. Examination Committee: Chairperson: Prof. Filipe Szolnoky Ramos Pinto Cunha; Supervisor: Prof. Afzal Suleman; Member of the Committee: Major Dr. Luís Filipe da Silva Félix<br>This work describes the conceptual design of a Unmanned Air Vehicle (UAV) wing with a Magnetic Anomaly Detection (MAD) sensor for submarine detection operations. Nowadays, underwater marine vessels are able to evade conventional detection methods such as sonar. Therefore, it is necessary to integrate MAD sensors in modern Anti-Submarine Warfare theatres. UAVs typically generate a magn
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Torres, John C. "Geomagnetic Compensation for Low-Cost Crash Avoidance Project." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/492.

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The goal of this work was to compensate for the effects of the Earth’s magnetic field in a vector field magnetic sensor. The magnetic sensor is a part of a low-cost crash avoidance system by Stephane Roussel where the magnetic sensor was used to detect cars passing when it was mounted to a test vehicle. However, the magnetic sensor’s output voltage varied when it changed orientation with respect to the Earth’s magnetic field. This limited the previous work to only analyze detection rates when the test vehicle travelled a single heading. Since one of the goals of this system is to be low-co
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Cao, Yichen. "Anomaly Detection on Embedded Sensor Processing Platform." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290893.

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Embedded platforms are often used as a sensor data processing node to collect data and transmit the data to the remote server. Due to the poor performance and power limitation, data processing was often left to the remote server. With the improvement of the computation ability, it is becoming possible to do some partial data processing on the embedded platforms, which would reduce the power and time consumption on the data transmission. Moreover, processing the data locally on the embedded platforms could reduce the dependence on the network. The platform could even do some tasks offline. This
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Conde, Erick F. "Environmental Sensor Anomaly Detection Using Learning Machines." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/1050.

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The problem of quality assurance/quality control (QA/QC) for real-time measurements of environmental and water quality variables has been a field explored by many in recent years. The use of in situ sensors has become a common practice for acquiring real-time measurements that provide the basis for important natural resources management decisions. However, these sensors are susceptible to failure due to such things as human factors, lack of necessary maintenance, flaws on the transmission line or any part of the sensor, and unexpected changes in the sensors' surrounding conditions. Two types
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Garcia, Font Víctor. "Anomaly detection in smart city wireless sensor networks." Doctoral thesis, Universitat Oberta de Catalunya, 2017. http://hdl.handle.net/10803/565607.

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Aquesta tesi proposa una plataforma de detecció d’intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s’identifiquen els diversos passos necessaris des de la recollida de dades fins a l’execució de les tècniques de detecció d’intrusio
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Menglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.

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Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the appli
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Vignisson, Egill. "Anomaly Detection in Streaming Data from a Sensor Network." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257507.

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In this thesis, the use of unsupervised and semi-supervised machine learning techniques was analyzed as potential tools for anomaly detection in the sensor network that the electrical system in a Scania truck is comprised of. The experimentation was designed to analyse the need for both point and contextual anomaly detection in this setting. For the point anomaly detection the method of Isolation Forest was experimented with and for contextual anomaly detection two different recurrent neural network architectures using Long Short Term Memory units was relied on. One model was simply a many to
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Abuaitah, Giovani Rimon. "ANOMALIES IN SENSOR NETWORK DEPLOYMENTS: ANALYSIS, MODELING, AND DETECTION." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1376594068.

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Dudek, Denise Miriam [Verfasser]. "Lightweight Anomaly Detection for Wireless Sensor Networks / Denise Miriam Dudek." München : Verlag Dr. Hut, 2015. http://d-nb.info/1075409012/34.

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Zhao, Jingjun. "A Two-phase Security Mechanism for Anomaly Detection in Wireless Sensor Networks." Diss., North Dakota State University, 2013. https://hdl.handle.net/10365/26498.

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Wireless Sensor Networks (WSNs) have been applied to a wide range of application areas, including battle fields, transportation systems, and hospitals. The security issues in WSNs are still hot research topics. The constrained capabilities of sensors and the environments in which sensors are deployed, such as hostile and non-reachable areas, make the security more complicated. This dissertation describes the development and testing of a novel two-phase security mechanism for hierarchical WSNs that is capable of defending both outside and inside attacks. For the outside attacks, the attackers a
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Books on the topic "Magnetic anomaly detection sensor"

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Baus, Wolfgang W. Magnetic anomaly detection using conventional and superconductive sensors with respect to vehicle monitoring. Brockmeyer, 1995.

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Forrest, R. N. A program to compute magnetic anomaly detection probabilities. Naval Postgraduate School, 1988.

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Forrest, R. N. A program to compute magnetic anomaly detection probabilities. 2nd ed. Naval Postgraduate School, 1990.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7467-7.

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Liotta, Antonio, Hedde Bosman, and Giovanni Iacca. Smart Anomaly Detection for Sensor Systems: Computational Intelligence Techniques for Sensor Networks and Applications. Springer London, Limited, 2020.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer, 2019.

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Usman, Muhammad, Vallipuram Muthukkumarasamy, Xin-Wen Wu, and Surraya Khanum. Mobile Agent-Based Anomaly Detection and Verification System for Smart Home Sensor Networks. Springer, 2018.

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Gearhart, Robert. Archaeological Interpretation of Marine Magnetic Data. Edited by Ben Ford, Donny L. Hamilton, and Alexis Catsambis. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199336005.013.0004.

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Interpreting remote sensing data is one of the most important tasks of archaeologists working in submerged environments. Researchers rely on remote-sensing technologies to aid their search for historic shipwrecks of interest. Magnetometers are essential for detection of buried shipwrecks. The main goal of magnetic interpretation has been to distinguish shipwrecks from debris, usually resulting in an archaeological assessment of each anomaly concerning its potential for historic significance. The past two decades have seen improvement in archaeologists' abilities to detect shipwreck anomalies.
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Book chapters on the topic "Magnetic anomaly detection sensor"

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Xu, W., Z. Y. Guo, P. Wu, and Z. X. Liu. "Detection Optimization of Single-Axis Magnetic Anomaly Sensor for Pig Clog Locating." In Proceedings of the International Field Exploration and Development Conference 2018. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7127-1_177.

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Shukla, Mayank, Sneha Yadav, Abhay Pratap Singh, Fizza Rizvi, and Surya Vikram Singh. "Anomaly detection in wireless sensor network." In Emerging Trends in Computer Science and Its Application. CRC Press, 2025. https://doi.org/10.1201/9781003606635-100.

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Farruggia, Alfonso, Giuseppe Lo Re, and Marco Ortolani. "Probabilistic Anomaly Detection for Wireless Sensor Networks." In AI*IA 2011: Artificial Intelligence Around Man and Beyond. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23954-0_44.

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Fang, Yi, Olufemi A. Omitaomu, and Auroop R. Ganguly. "Incremental Anomaly Detection Approach for Characterizing Unusual Profiles." In Knowledge Discovery from Sensor Data. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12519-5_11.

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Chang, Marcus, Andreas Terzis, and Philippe Bonnet. "Mote-Based Online Anomaly Detection Using Echo State Networks." In Distributed Computing in Sensor Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02085-8_6.

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Salem, Osman, Alexey Guerassimov, Ahmed Mehaoua, Anthony Marcus, and Borko Furht. "Anomaly Detection Scheme for Medical Wireless Sensor Networks." In Handbook of Medical and Healthcare Technologies. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8495-0_8.

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Li, Han, Bin Yu, and Ting Zhao. "An Anomaly Pattern Detection Method for Sensor Data." In Web Information Systems and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_28.

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Zhu, Qianwen, Jinyu Zhou, Shiyu Zhao, and Wei Wang. "Graph-Based Anomaly Detection of Wireless Sensor Network." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9423-3_18.

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Rabatel, Julien, Sandra Bringay, and Pascal Poncelet. "SO_MAD: SensOr Mining for Anomaly Detection in Railway Data." In Advances in Data Mining. Applications and Theoretical Aspects. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03067-3_16.

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Chatterjee, Aditi, and Kiranmoy Das. "State Estimation and Anomaly Detection in Wireless Sensor Networks." In Emerging Wireless Communication and Network Technologies. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0396-8_16.

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Conference papers on the topic "Magnetic anomaly detection sensor"

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Hu, Xinyue. "Influence of Attitude Changes on Magnetic Measurement Accuracy in UAV Magnetic Anomaly Detection." In 2025 5th International Conference on Sensors and Information Technology (ICSI). IEEE, 2025. https://doi.org/10.1109/icsi64877.2025.11009300.

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Roignant, Timothée, Nicolas Le Josse, Abdel Boudraa, Jean-Jacques Szkolnik, Paul Penven, and Hugues Henocq. "Magnetic Anomaly Detection Using Noise-Optimized Orthonormalized Functions on Dual Magnetometric Sensor Signals." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715193.

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Yuan, Zi-Fan, Xin-Gen Liu, and Ming-Yao Xia. "CBAM-Based Residual Network for Magnetic Anomaly Target Detection." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641654.

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Das, Kaushik, Sankhapani Neog, Priyanuj Bora, Digbijoy Chettry, and Rishab Bora. "Anomaly Detection in Sensor Data using Machine Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724176.

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Sheng, Xun, Min Hu, Gang Yu, Li Teng, and Donghua Su. "Anomaly Detection of Sensor Data Based on Similarity." In 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869813.

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Chenevas-Paule, C., S. Zozor, L. L. Rouve, O. J. J. Michel, O. Pinaud, and R. Kukla. "On an Analytical Orthonormal Multipolar Basis for Magnetic Anomaly Detection." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715174.

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Lv, Yumeng, Congyu Liao, and Huan Liu. "Frequency Domain-Based Low-Rank Approximation for Magnetic Anomaly Detection." In 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2024. https://doi.org/10.1109/icsmd64214.2024.10920537.

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Xiao, Tianshun, and Feng Zeng. "AnomalyTK: A Multivariate Time Series Anomaly Detection Method." In 2025 International Conference on Sensor-Cloud and Edge Computing System (SCECS). IEEE, 2025. https://doi.org/10.1109/scecs65243.2025.11065321.

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Neto, Almir, Luis Gomes, and Zita Vale. "Virtual Sensor for Door Opening Detection and Anomaly Detection Using Machine Learning." In 2024 22nd International Conference on Intelligent Systems Applications to Power Systems (ISAP). IEEE, 2024. http://dx.doi.org/10.1109/isap63260.2024.10744280.

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Kaliappan, Vishnu Kumar, Calvin Abel Mathews, Kevin Samuel C, and Dharani Jaganathan. "Temporal-Spatial based Deep Anomaly Detection (TS-DAD) Model : A Steering Sensor Anomaly Detection in Unmanned Ground Vehicles." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11035346.

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Reports on the topic "Magnetic anomaly detection sensor"

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Forrest, R. N. A Program to Compute Magnetic Anomaly Detection Probabilities. Revision 2. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada225427.

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Sadler, Laurel C., Robert Winkler, and Niranjan Suri. Anomaly Detection for Data Reduction in an Unattended Ground Sensor (UGS) Field. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609445.

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Smith, Carl H., and Robert W. Schneider. Very Dense Magnetic Sensor Arrays for Precision Measurement and Detection. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada453763.

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Griffin, Stephen M., and L. L. Helms. Sub-Audio Magnetics: Miniature Sensor Technology for Simultaneous Magnetic and Electromagnetic Detection of UXO. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada578948.

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Nestleroth. L52298 Augmenting MFL Tools With Sensors that Assess Coating Condition. Pipeline Research Council International, Inc. (PRCI), 2009. http://dx.doi.org/10.55274/r0010396.

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External coatings are routinely used to protect transmission pipelines from corrosion; however, coatings may degrade or disbond over time enabling corrosion to occur. Transmission pipeline operators often use magnetic flux leakage (MFL) in-line inspection tools to detect metal loss corrosion defects. Rather than finding the cause of a problem, failure of the coating within a corrosive environment, MFL corrosion surveys only find the result of the problem, corrosion defects that may permanently alter the pressure carrying capacity of the pipeline. Stress corrosion cracking (SCC) can be detected
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