Relevant bibliographies by topics / Video sensor based detection

Contents

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

Academic literature on the topic 'Video sensor based detection'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Video sensor based detection"

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Tzes, Anthony, and William R. McShane. "Development of Prototype Video-Based Sensor for Vehicle Detection from Stand-Still Images." Transportation Research Record: Journal of the Transportation Research Board 1570, no. 1 (1997): 202–10. http://dx.doi.org/10.3141/1570-23.

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The design, development, and testing of a prototype wide-area traffic detection system are described. The video-based sensor computes the approximate number of vehicles present within an a priori defined observation area from stand-still images. This sensor is mostly oriented toward the traffic detection in congested intersections, in which sensors using existing radar, acoustic, and video-based technology are faced with critical obstacles caused by the automobile stoppage. The prototype system has been tested and found to perform satisfactorily in field studies.
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Lin, Congtian, Jiangning Wang, and Liqiang Ji. "An AI-based Wild Animal Detection System and Its Application." Biodiversity Information Science and Standards 7 (September 11, 2023): e112456. https://doi.org/10.3897/biss.7.112456.

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Rapid accumulation of biodiversity data and development of deep learning methods bring the opportunities for detecting and identifying wild animals automatically, based on artificial intelligence. In this paper, we introduce an AI-based wild animal detection system. It is composed of acoustic and image sensors, network infrastructures, species recognition models, and data storage and visualization platform, which go through the technical chain learned from Internet of Things (IOT) and applied to biodiversity detection. The workflow of the system is as follows:Deploying sensors for different de
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Fadhil, Faris Rai, and Ari Purno Wahyu Wibowo. "SMOKE DETECTION ON CNN BASED VIDEO SURVEILLANCE SYSTEM." Jurnal Darma Agung 31, no. 1 (2023): 377. http://dx.doi.org/10.46930/ojsuda.v31i1.3010.

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Forest fires are a serious problem that can cause extensive forest land and plantation areas to be damaged, this damage not only disrupts the habitat but the ecosystems in the forest, several studies have made an experiment to prevent forest fires, one of which is by using the help of electronic sensors installed in forest areas, this sensor works chemically by detecting heat or a change in the composition of the atmosphere present in the air and room temperature, from these changes the data is sent to the central station and a fire will be predicted, this method has a weakness including the n
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Li, Sujuan, and Shichen Huang. "Remote medical video region tamper detection system based on Wireless Sensor Network." EAI Endorsed Transactions on Pervasive Health and Technology 8, no. 31 (2022): e3. http://dx.doi.org/10.4108/eetpht.v8i31.702.

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INTRODUCTION: A new telemedicine video tamper detection system based on wireless sensor network is proposed and designed in this paper. OBJECTIVES: This work is proposed to improve the performance of telemedicine video communication and accurately detect the tamper area in telemedicine video. METHODS: The sensor nodes in the sensing layer are responsible for collecting telemedicine video information and transmitting the information to the data layer. The data layer completes the storage of information and transmits it to the processing layer. The detection module of the processing layer detect
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Cai, Wen-Yu, Jia-Hao Guo, Mei-Yan Zhang, Zhi-Xiang Ruan, Xue-Chen Zheng, and Shuai-Shuai Lv. "GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction." Journal of Healthcare Engineering 2020 (June 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/8887340.

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Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sourc
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Ho, Chao Ching, and Dan Wen Kuo. "IEEE 1451-Based Sensor Interfacing and Data Fusion for Fire Smoke Detection." Key Engineering Materials 613 (May 2014): 219–27. http://dx.doi.org/10.4028/www.scientific.net/kem.613.219.

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The performance of a fire sensor has a significant effect on fire detection. Today’s fire alarm systems, such as smoke and heat sensors, however are generally limited to a close proximity to the fire; and cannot provide additional information about fire circumstances. Thus, it is essential to design a suite of low-cost networked sensors that provide the capability of performing distributed measurement and control in real time. In this work, a wireless sensor system was developed for fire detection. The purpose of this paper is to analyze the integration of traditional fire sensors into intelli
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Fu, Ting, Joshua Stipancic, Sohail Zangenehpour, Luis Miranda-Moreno, and Nicolas Saunier. "Automatic Traffic Data Collection under Varying Lighting and Temperature Conditions in Multimodal Environments: Thermal versus Visible Spectrum Video-Based Systems." Journal of Advanced Transportation 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/5142732.

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Vision-based monitoring systems using visible spectrum (regular) video cameras can complement or substitute conventional sensors and provide rich positional and classification data. Although new camera technologies, including thermal video sensors, may improve the performance of digital video-based sensors, their performance under various conditions has rarely been evaluated at multimodal facilities. The purpose of this research is to integrate existing computer vision methods for automated data collection and evaluate the detection, classification, and speed measurement performance of thermal
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Ding, Yiwei, Chaeyeon Han, Pavan Seshadri, et al. "Toward audio-based sensing for pedestrian detection." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A282. http://dx.doi.org/10.1121/10.0027509.

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The detection and counting of pedestrians plays a central role for the design of smart cities. Although the use of cameras for this task has been shown to have high accuracy, they come at a high cost and are susceptible to challenges such as poor lighting, fog, and obstructed views. Our study investigates audio-based pedestrian detection, combining potentially low cost sensors with advanced machine learning based audio analysis algorithms. With an audio sensor installed along the walkway, machine learning algorithms can tell from the audio whether there is a pedestrian or not, or how far the p
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Xu, Qichang. "Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model." Complexity 2021 (May 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/3909522.

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Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to con
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Li, Qian, Rangding Wang, and Dawen Xu. "A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise." Electronics 12, no. 6 (2023): 1362. http://dx.doi.org/10.3390/electronics12061362.

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Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, fo
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Dissertations / Theses on the topic "Video sensor based detection"

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Olgemar, Markus. "Camera Based Navigation : Matching between Sensor reference and Video image." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15952.

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<p>an Internal Navigational System and a Global Navigational Satellite System (GNSS). In navigational warfare the GNSS can be jammed, therefore are a third navigational system is needed. The system that has been tried in this thesis is camera based navigation. Through a video camera and a sensor reference the position is determined. This thesis will process the matching between the sensor reference and the video image.</p><p>Two methods have been implemented: normalized cross correlation and position determination through a homography. Normalized cross correlation creates a correlation matrix.
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Salim, Christian. "Data Reduction based energy-efficient approaches for secure priority-based managed wireless video sensor networks." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD052/document.

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L'énorme quantité de données dans les réseaux de capteurs vidéo sans fil (WVSN) pour les nœuds de capteurs de ressources limitées augmente les défis liés à la consommation d'énergie et à la consommation de bande passante. La gestion du réseau est l’un des défis de WMSN en raison de l’énorme quantité d’images envoyées simultanément par les capteurs au coordinateur. Dans cette thèse, pour surmonter ces problèmes, plusieurs contributions ont été apportées. Chaque contribution se concentre sur un ou deux défis, comme suit: Dans la première contribution, pour réduire la consommation d'énergie, une
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Wang, Ying, and Weiyi Lv. "Indoor video-based smoke detection." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9573.

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Traditional smoke detection methods need sensors close to the source of the smoke. In order to avoid this disadvantage, this thesis presents a method for indoor video based smoke detection. In addition, it helps to improve the success rate of detection, as well as reducing the false detection rate of suspected smoke. This method consists of two parts. First, we create a Gaussian mixture model to detect smoke color pixels. Then, we use the smoke’s dynamic features to detect the smoke area and extract it from the area found in the first step, and in that way we can find the real smoke area. Our
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Barrios, Núñez Juan Manuel. "Content-based video copy detection." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/115521.

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Doctor en Ciencias, Mención Computación<br>La cantidad y el uso de videos en Internet ha aumentado exponencialmente durante los últimos años. La investigación académica en tópicos de videos se ha desarrollado durante décadas, sin embargo la actual ubicuidad de los videos presiona por el desarrollo de nuevos y mejores algoritmos. Actualmente existen variadas necesidades por satisfacer y muchos problemas abiertos que requieren de investigación científica. En particular, la Detección de Copias de Video (DCV) aborda la necesidad de buscar los videos que son copia de un documento original. El proce
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Nyberg, Selma. "Video Recommendation Based on Object Detection." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-351122.

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In this thesis, various machine learning domains have been combined in order to build a video recommender system that is based on object detection. The work combines two extensively studied research fields, recommender systems and computer vision, that also are rapidly growing and popular techniques on commercial markets. To investigate the performance of the approach, three different content-based recommender systems have been implemented at Spotify, which are based on the following video features: object detections, titles and descriptions, and user preferences. These systems have then been 
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Wang, Jing. "Spatio-temporal volume-based video event detection." Thesis, University of Huddersfield, 2012. http://eprints.hud.ac.uk/id/eprint/17552/.

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Online and offline video clips provide rich information on dynamic events that occurred over a period of time, for example, human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last 3 decades on 2D image feature processing and their applications in areas such as face matching and objects recognition, video event detection still remains one of the most challenging fields in computer vision study due to the wide range of continuous and non-linear signals engaged by an imaging system, and the inherent semantic difficulties in ma
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Sun, Kailiang. "Fluorescence based optical sensor for protein detection." Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2010r/ksun.pdf.

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Williams, Chris Williams. "Knowledge-Based Video Compression for Robots and Sensor Networks." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3915.

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Robot and sensor networks are needed for safety, security, and rescue applicationssuch as port security and reconnaissance during a disaster. These applications rely on realtimetransmission of images, which generally saturate the available wireless networkinfrastructure. Knowledge-based Compression is a strategy for reducing the video frametransmission rate between robots or sensors and remote operators. Because images mayneed to be archived as evidence and/or distributed to multiple applications with differentpost processing needs, lossy compression schemes, such as MPEG, H.26x, etc., are not
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Au, Carmen E. "Compression-based anomaly detection for video surveillance applications." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98598.

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In light of increased demands for security, we propose a unique approach to automated video surveillance using anomaly detection. The success of this approach is dependent on the ability of the system to ascertain the novelty of a given image acquired by a video camera. We adopt a compression-based similarity measure to determine similarity between images in a video sequence. Images that are sufficiently similar to the previously-seen images are discarded; conversely, images that are sufficiently dissimilar are stored for comparison with future incoming images.<br>The use of a compression-base
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Thomas, Naveen Moham. "Motion based video object detection for event retrieval." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441380.

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Books on the topic "Video sensor based detection"

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Ahad, Md Atiqur Rahman, Sozo Inoue, Daniel Roggen, and Kaori Fujinami, eds. Sensor- and Video-Based Activity and Behavior Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0361-8.

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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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Vitoria, Lobo Niels da, ed. Visual event detection. Kluwer Academic Publishers, 2001.

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INTEGRATED CMOS-BASED BIOCHEMICAL SENSOR MICROSYSTEMS: Oxygen and Bacteria Detection. VDM Verlag Dr. Müller, 2011.

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EM-Based Mixture Models Applied to Video Event Detection. INTECH, 2012.

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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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On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification. Independently Published, 2019.

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Haering, Niels, and Niels da Vitoria Lobo. Visual Event Detection (The International Series in Video Computing). Springer, 2001.

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Inoue, Sozo, Kaori Fujinami, Daniel Roggen, and Atiqur Rahman Ahad. Sensor- and Video-Based Activity and Behavior Computing: Proceedings of 3rd International Conference on Activity and Behavior Computing. Springer Singapore Pte. Limited, 2022.

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Book chapters on the topic "Video sensor based detection"

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Hanada, Yoshinori, Tahera Hossain, Anna Yokokubo, and Guillaume Lopez. "BoxerSense: Punch Detection and Classification Using IMUs." In Sensor- and Video-Based Activity and Behavior Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0361-8_6.

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Greiffenhagen, Michael, and Visvanathan Ramesh. "Performance Analysis of Multi- Sensor Based Real-Time People Detection and Tracking System." In Multimedia Video-Based Surveillance Systems. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4327-5_19.

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Yoshida, Takuto, Kazuma Kano, Keisuke Higashiura, et al. "A Data-Driven Approach for Online Pre-impact Fall Detection with Wearable Devices." In Sensor- and Video-Based Activity and Behavior Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0361-8_8.

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John, Vijay, and Seiichi Mita. "RVNet: Deep Sensor Fusion of Monocular Camera and Radar for Image-Based Obstacle Detection in Challenging Environments." In Image and Video Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34879-3_27.

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Riess, Christian. "Physical Integrity." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_9.

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AbstractPhysics-based methods anchor the forensic analysis in physical laws of image and video formation. The analysis is typically based on simplifying assumptions to make the forensic analysis tractable. In scenes that satisfy such assumptions, different types of forensic analysis can be performed. The two most widely used applications are the detection of content repurposing and content splicing. Physics-based methods expose such cases with assumptions about the interaction of light and objects, and about the geometric mapping of light and objects onto the image sensor.
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Jensen, Matthew L., Thomas O. Meservy, Judee K. Burgoon, and Jay F. Nunamaker. "Video-Based Deception Detection." In Intelligence and Security Informatics. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69209-6_22.

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Tron, Roberto, Andreas Terzis, and René Vidal. "Distributed Consensus Algorithms for Image-Based Localization in Camera Sensor Networks." In Distributed Video Sensor Networks. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-127-1_20.

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Varshney, Pramod K., and Ioana L. Coman. "Distributed Multi-Sensor Surveillance: Issues and Recent Advances." In Video-Based Surveillance Systems. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4_20.

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He, Xinyu, Fei Yuan, and Yi Zhu. "Drowning Detection Based on Video Anomaly Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87361-5_57.

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Xu, Ming, and Tim Ellis. "Colour-Invariant Motion Detection under Fast Illumination Changes." In Video-Based Surveillance Systems. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4_8.

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Conference papers on the topic "Video sensor based detection"

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Talwalkar, Karan R., Kshitij Navale, Aditya Ashok, and Anindita Khade. "Design and Analytical Performance of a Hybrid Model Using Deep Learning Techniques for the Detection of Deepfake Videos." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882277.

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ZIANI, Intissar, Gueltoum Bendiab, Mourad Bouzenada, and Stavros Shiaeles. "Video-Based Abnormal Human Behaviour Detection for Video Forensics." In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2024. http://dx.doi.org/10.1109/csr61664.2024.10679423.

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Zajdel, W., J. D. Krijnders, T. Andringa, and D. M. Gavrila. "CASSANDRA: audio-video sensor fusion for aggression detection." In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE, 2007. http://dx.doi.org/10.1109/avss.2007.4425310.

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Tanner, Rudolf, Martin Studer, Adriano Zanoli, and Andreas Hartmann. "People Detection and Tracking with TOF Sensor." In 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2008. http://dx.doi.org/10.1109/avss.2008.18.

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Snidaro, Lauro, Ingrid Visentini, and Gian Luca Foresti. "Multi-sensor Multi-cue Fusion for Object Detection in Video Surveillance." In 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2009. http://dx.doi.org/10.1109/avss.2009.67.

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Fernando, Heshan, Vedang Chauhan, and Brian Surgenor. "Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20102.

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This paper presents the results of a comparative study that investigated the use of image-based and signal-based sensors for fault detection and fault isolation of visually-cued faults on an automated assembly machine. The machine assembles 8 mm circular parts, from a bulk-supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Common faults on the machine include part jams and ejected parts that occur at different locations on the machine. Two sensor systems are installed on the machine for detecting and isolating these faults: an image-based system consisting o
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Al Machot, Fadi, Kyandoghere Kyamakya, Bernhard Dieber, and Bernhard Rinner. "Real time complex event detection for resource-limited multimedia sensor networks." In 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2011. http://dx.doi.org/10.1109/avss.2011.6027378.

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Macior, Robert E., Jonathan P. Knauth, Sharon M. Walter, and Richard Evans. "Mitigating ground-based sensor failures with video motion detection." In SPIE Europe Security and Defence, edited by David A. Huckridge and Reinhard R. Ebert. SPIE, 2008. http://dx.doi.org/10.1117/12.799677.

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Al Machot, Fadi, Carlo Tasso, Bernhard Dieber, et al. "Smart resource-aware multimedia sensor network for automatic detection of complex events." In 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2011. http://dx.doi.org/10.1109/avss.2011.6027362.

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Ivanova, Iustina, Marina Andrić, Sadaf Moaveninejad, Andrea Janes, and Francesco Ricci. "Video and Sensor-Based Rope Pulling Detection in Sport Climbing." In MM '20: The 28th ACM International Conference on Multimedia. ACM, 2020. http://dx.doi.org/10.1145/3422844.3423058.

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Reports on the topic "Video sensor based detection"

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Kulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2127.

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In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentia
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t
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Greinert, Jens. Mine Monitoring in the German Baltic Sea 2020; Dumped munition monitoring AL548, 03rd – 16th November 2020, Kiel (Germany) – Kiel (Germany) „MineMoni-II 2020“. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2021. http://dx.doi.org/10.3289/cr_al548.

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ALKOR cruise AL548 took place as part of the EMFF (European Maritime and Fisheries Fund)-funded project BASTA (Boost Applied munition detection through Smart data inTegration and AI workflows; https://www.basta-munition.eu) and as continuation of the munition monitoring started within the BMBF-funded project UDEMM (Environmental Monitoring for the Delaboration of Munition in the Sea; https://udemm.geomar.de/). In October 2018, a first cruise (POS530 MineMoni2018) was conducted, to gather data for a broad baseline study in the German Baltic Sea. Results show a moderate contamination level on re
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Gottuk, Daniel T., Matthew Harrison, Joseph L. Scheffey, Susan L. Rose-Pehrsson, Frederick W. Williams, and John P. Farley. An Initial Evaluation of Video-Based Fire Detection Technologies. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada440353.

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Kang, Kyung. MicroCantilever (MC) based nanomechanical sensor for detection of molecular interactions. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1037743.

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Cooper, Robert Lee, Peter Marleau, and Patrick J. Griffin. Ground water and snow sensor based on directional detection of cosmogenic neutrons. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1177063.

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Loui, A., S. McCall, and J. Zumstein. Research and Development of Non-Spectroscopic MEMS-Based Sensor Arrays for Targeted Gas Detection. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1059450.

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Loui, A., and S. McCall. Research and Development of Non-Spectroscopic MEMS-Based Sensor Arrays for Targeted Gas Detection. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1035279.

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Kumar, Praveen. PR753-233900-R01 Enhanced Leak Detection Using Minimally Invasive Multi-Sensor Device Based Inspection. Pipeline Research Council International, Inc. (PRCI), 2024. http://dx.doi.org/10.55274/r0000078.

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Abstract:
The project team investigated the feasibility of identifying pipeline leaks using Novel sensing approaches that have been recently gaining popularity in the "Pipeline Integrity assessment" realm (such as multi-Sensor inline inspection tools) that incorporate sensors such as Audio, Magnetometry, Pressure etc. The flow loop setup at the PRCI TDC site was leveraged to create a customized test setup and a test execution methodology was developed and executed towards this end. Two Sensing equipment vendors (hereinafter referred to as Vendor A and Vendor B) were used to collect various sensor datase
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Brown, Nicholas, Thomas Schumacher, and Miguel Vicente. Evaluation of a Novel Remote Displacement Sensor Prototype Using Video and Laser-Based Technology for Civil Infrastructure Applications. Portland State University, 2019. http://dx.doi.org/10.15760/ccemp.48.

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