Academic literature on the topic 'Surveillance signal processing'
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Journal articles on the topic "Surveillance signal processing"
Ksendzuk, A. V., A. A. Kanatchikov, and P. A. Gerasimov. "SPACE SURVEILLANCE SYSTEM’S SAR SIGNAL DETECTION RESULTS." Issues of radio electronics, no. 3 (March 20, 2018): 63–68. http://dx.doi.org/10.21778/2218-5453-2018-3-63-68.
Full textK. Abdul-Hussein, Mohammad, Oleksii Strelnytskyi, Ivan Obod, Iryna Svyd, and Haider Th Salim Alrikabi. "Evaluation of the Interference’s Impact of Cooperative Surveillance Systems Signals Processing for Healthcare." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 03 (March 8, 2022): 43–59. http://dx.doi.org/10.3991/ijoe.v18i03.28015.
Full textFerguson, Brian. "Signal processing for acoustic surveillance of the land environment." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2298. http://dx.doi.org/10.1121/1.4744059.
Full textDimitropoulos, K., N. Grammalidis, H. Gao, C. Stockhammer, and A. Kurz. "Magnetic signal processing & analysis for airfield traffic surveillance." IEEE Aerospace and Electronic Systems Magazine 23, no. 1 (January 2008): 21–27. http://dx.doi.org/10.1109/aes-m.2008.4444485.
Full textYani, Kalfika, Fiky Y. Suratman, and Koredianto Usman. "Design and Implementation Pulse Compression for S-Band Surveillance Radar." Journal of Measurements, Electronics, Communications, and Systems 7, no. 1 (December 30, 2020): 20. http://dx.doi.org/10.25124/jmecs.v7i1.2631.
Full textWestphal, R. "Sensors, Medical Image and Signal Processing." Yearbook of Medical Informatics 16, no. 01 (August 2007): 70–71. http://dx.doi.org/10.1055/s-0038-1638528.
Full textХудов, Г. В., Сальман Рашід Оваід, В. М. Ліщенко, and В. О. Тютюнник. "Methods of signal processing in a multiradar system of the same type of two-coordinated surveillance radars." Системи обробки інформації, no. 3(162), (September 30, 2020): 65–72. http://dx.doi.org/10.30748/soi.2020.162.07.
Full textKsendzuk, A. V., and A. A. Kanatchikov. "SPACEBORNE SAR SIGNAL DETECTION AND PARAMETER ESTIMATION IN SPACE TRACKING AND SURVEILLANCE SYSTEM MODELING." Issues of radio electronics, no. 3 (March 20, 2019): 31–35. http://dx.doi.org/10.21778/2218-5453-2019-3-31-35.
Full textFadeev, E. V. "RECIEVED SIGNAL PROCESSING ALGORITHMS IN AVIATION-BASED RADIOELECTRONIC SURVEILLANCE SYSTEMS." Bulletin of Russian academy of natural sciences 21, no. 4 (2021): 11–19. http://dx.doi.org/10.52531/1682-1696-2021-21-4-11-19.
Full textYan, He, Daiyin Zhu, Robert Wang, and Xinhua Mao. "Practical signal processing algorithm for wide‐area surveillance‐GMTI mode." IET Radar, Sonar & Navigation 9, no. 8 (October 2015): 991–98. http://dx.doi.org/10.1049/iet-rsn.2014.0452.
Full textDissertations / Theses on the topic "Surveillance signal processing"
Adams, Andrew J. "Multispectral persistent surveillance /." Online version of thesis, 2008. http://hdl.handle.net/1850/7070.
Full textGórski, Tomasz. "Space-time adaptive signal processing for sea surveillance on-shore stationary radars." Télécom Bretagne, 2008. http://www.theses.fr/2008TELB0075.
Full textWortham, Cody. "Space-Time Processing for Ground Surveillance Radar." Thesis, Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14468.
Full textHallermeyer, Alexandre. "Traitement du Signal d’un LIDAR Doppler scannant dédié à la surveillance aéroportuaire." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLC007/document.
Full textAn algorithm was developed to estimate precisely wake vortices parameters (positions and circulations) using spectral data provided by a LIDAR. It is articulated in 3 main stages: The first one allows to detect the presence of vortices and to make a rough localization thanks to the method of the velocity envelopes. The second step is to refine the estimation of vortex positions using an optimization of the least squares criterion. This step also permits to make an first estimation of the vortices circulation. The third and final step focuses on estimating vortex circulations by maximizing the likelihood criterion. Estimates are becoming finer and more focused on the most critical parameters. The development of this algorithm required the use of several models (LIDAR, wake vortices, atmosphere) and to formulate a number of simplifying assumptions in order to reach a reasonable computational cost. The proposed algorithm was then subjected to a performance evaluation, the interest being focused on the robustness with respect to the different noises altering the measurement, particularly the one related to the atmospheric turbulence, and with respect to the model errors. This evaluation was carried out both on simulated data using simplified parametric models, and on Large Eddy Simulations.The instrumental parameters of LIDAR are potential degrees of freedom to improve the performance of the estimator, in particular for the most critical quantities, that is to say the circulation values. The calculation of the performance of the estimator requiring a significant computational cost, it lends itself poorly for optimization purposes. This is why a study of the influence of the LIDAR parameters on the Cramér-Rao Bound (CRB) was carried out. This study allowed to understand the influence of the instrumental parameters and to reach an optimal configuration for the CRB
Comstedt, Erik. "Effect of additional compression features on h.264 surveillance video." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-30901.
Full textSekak, Fatima. "Microwave radar techniques and dedicated signal processing for Vital Signs measurement." Thesis, Université de Lille (2018-2021), 2021. https://pepite-depot.univ-lille.fr/LIBRE/EDENGSYS/2021/2021LILUN033.pdf.
Full textIn the context of securing transportation systems, short-range monitoring of people's activity, in particular the driver's activity in a vehicle, is a major issue in the improvement of the driver assistance system. The application targeted in this work concerns mainly the railway domain.Respiratory and heart rates of the driver are key indicators for the evaluation of the physiological state. Conventional methods of measuring these vital signs rely on sensors operating in direct contact with the skin. Therefore, the intrusive character of these solutions is not suited for the transportation domain, especially because of the induced discomfort. In this work, a microwave radar solution operating at low power is proposed for the continuous measurement of respiratory and cardiac activity signals. In particular, physiological signals (heartbeat, mechanical movement of the rib cage) are indicators of human activity that can be detected at a distance (up to ten meters) using radiated microwave electromagnetic waves.Although the literature shows a growing interest in the development of radar techniques dedicated to the surveillance of people, there is no robust, sensitive and accurate commercial device available to date. A detailed analysis of the electrical and geometrical parameters of the radar technique is proposed in this work in order to identify the sources of uncertainties, to define the optimal parameters, to validate experimentally the proposed solution. An original signal processing, based on the cyclostationary approach, is implemented in order to extract the parameters of interest in reference or disturbed measurement environments. The proposed hardware solutions associated with an optimal signal processing allow to foresee radar architectures adapted to non-laboratory contingencies
Nyström, Axel. "Evaluation of Multiple Object Tracking in Surveillance Video." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666.
Full textFirla, Marcin. "Automatic signal processing for wind turbine condition monitoring. Time-frequency cropping, kinematic association, and all-sideband demodulation." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT006/document.
Full textThis thesis proposes a three signal-processing methods oriented towards the condition monitoring and diagnosis. In particular the proposed techniques are suited for vibration-based condition monitoring of rotating machinery which works under highly non-stationary operational condition as wind turbines, but it is not limited to such a usage. All the proposed methods are automatic and data-driven algorithms.The first proposed technique enables a selection of the most stationary part of signal by cropping time-frequency representation of the signal.The second method is an algorithm for association of spectral patterns, harmonics and sidebands series, with characteristic frequencies arising from kinematic of a system under inspection. This method features in a unique approach dedicated for rolling-element bearing which enables to overcome difficulties caused by a slippage phenomenon.The third technique is an all-sideband demodulation algorithm. It features in a multi-rate filter and proposes health indicators to facilitate an evaluation of the condition of the investigated system.In this thesis the proposed methods are validated on both, simulated and real-world signals. The presented results show good performance of all the methods
Song, Bi. "Scene analysis, control and communication in distributed camera networks." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3359910.
Full textIncludes abstract. Title from first page of PDF file (viewed January 27, 2010). Includes bibliographical references (p. 99-105). Issued in print and online. Available via ProQuest Digital Dissertations.
Kazemisaber, Mohammadreza. "Clutter Removal in Single Radar Sensor Reflection Data via Digital Signal Processing." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99874.
Full textBooks on the topic "Surveillance signal processing"
Hippenstiel, Ralph Dieter. Detection theory: Applications and digital signal processing. Boca Raton, Fla: CRC Press, 2002.
Find full textKlein, Lawrence A. Millimeter-wave and infrared multisensor design and signal processing. Boston: Artech House, 1997.
Find full textA, Velastin Sergio, Remagnino Paolo 1963-, and Institution of Electrical Engineers, eds. Intelligent distributed video surveillance systems. London: Institution of Electrical Engineers, 2006.
Find full textForesti, Gian Luca. Multisensor Surveillance Systems: The Fusion Perspective. Boston, MA: Springer US, 2003.
Find full textCalif.) Signal and Data Processing of Small Targets (Conference) (2013 San Diego. Signal and Data Processing of Small Targets 2013: 28-29 August 2013, San Diego, California, United States. Edited by Drummond Oliver E, Teichgraeber Richard D, and SPIE (Society). Bellingham, Washington, USA: SPIE, 2013.
Find full textIEEE National Radar Conference (1997 Syracuse, N.Y.). Proceedings of the 1997 IEEE National Radar Conference: May 13-15, 1997, Syracuse, New York. New York, N.Y: IEEE Aerospace and Electronic Systems Society, 1997.
Find full textIEEE, Radar Conference (1999 Waltham Mass ). The record of the 1999 IEEE Radar Conference: [RADARCON '99] : Radar into the next Millennium : held at the Westin Hotel, Waltham, Massachusetts, April 20-22, 1999. New York, N.Y: IEEE Aerospace and Electronic Systems Society, 1999.
Find full textIEEE International Conference on Advanced Video and Signal Based Surveillance (2005 Como, Italy). Advanced video and signal based surveillance: Proceedings of AVSS 2005 : Como, Italy, 15-16 September 2005. Piscataway, NJ: IEEE, 2005.
Find full textPoland) IEEE International Conference on Advanced Video and Signal Based Surveillance (10th 2013 Krakow. 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2013): Krakow, Poland, 27-30 August 2013. Piscataway, NJ: IEEE, 2013.
Find full textIEEE International Conference on Advanced Video and Signal Based Surveillance (8th 2011 Klagenfurt, Austria). 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance: (AVSS 2001), Klagenfurt, Austria, 30 August-2 September 2011. Piscataway, NJ: IEEE, 2011.
Find full textBook chapters on the topic "Surveillance signal processing"
Swami, Kedar, Bhardwaz Bhuma, Semanto Mondal, and L. Anjaneyulu. "Audio Surveillance System." In Machine Intelligence and Signal Processing, 351–60. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1366-4_28.
Full textChen, Wei-Gang. "Moving Shadow Detection in Video Surveillance Based on Multi-feature Analysis." In Multimedia and Signal Processing, 224–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_29.
Full textSevaldsen, Erik. "Underwater Surveillance — Concepts, Equipment and Results." In Acoustic Signal Processing for Ocean Exploration, 459–64. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1604-6_42.
Full textBondalapati, Anjanadevi, and D. H. Manjaiah. "Intelligent Video Surveillance Systems Using Deep Learning Methods." In Machine Learning in Signal Processing, 213–42. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003107026-9.
Full textMartínez, Fabio, Antoine Manzanera, and Eduardo Romero. "A Motion Descriptor Based on Statistics of Optical Flow Orientations for Action Classification in Video-Surveillance." In Multimedia and Signal Processing, 267–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35286-7_34.
Full textArunnehru, J., and M. Kalaiselvi Geetha. "Automatic Human Emotion Recognition in Surveillance Video." In Intelligent Techniques in Signal Processing for Multimedia Security, 321–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44790-2_15.
Full textAshwini and T. Kusuma. "Analysis of Global Motion Compensation and Polar Vector Median for Object Tracking Using ST-MRF in Video Surveillance." In Machine Intelligence and Signal Processing, 453–64. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1366-4_36.
Full textSivarathinabala, M., S. Abirami, and R. Baskaran. "A Study on Security and Surveillance System Using Gait Recognition." In Intelligent Techniques in Signal Processing for Multimedia Security, 227–52. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44790-2_11.
Full textNdungutse, Jean Bosco, Rui Xu, Gilbert Shyirambere, Fatana Jafari, and Shi-Jian Liu. "Deep Learning-Based Helmet Wearing Detection for Safety Surveillance." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 91–100. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1053-1_9.
Full textGokula Vishnu Kirti, D., C. R. Balaji, and A. Joshuva. "A Smart Delimit Scrutinization Droid for Defence Border Surveillance Through LIDAR." In Advances in Automation, Signal Processing, Instrumentation, and Control, 795–800. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_74.
Full textConference papers on the topic "Surveillance signal processing"
Tsuhan Chen. "A journey from signal processing to surveillance." In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007. IEEE, 2007. http://dx.doi.org/10.1109/avss.2007.4425272.
Full textLiu, Hanyu, Chong Tang, Shaoen Wu, and Honggang Wang. "Real-time video surveillance for large scenes." In Signal Processing (WCSP 2011). IEEE, 2011. http://dx.doi.org/10.1109/wcsp.2011.6096963.
Full textMisiurewicz, Jacek, Lukasz Maslikowuki, Artur Gromek, and Anna Kurowska. "MIMO Techniques for Space Surveillance Radar." In 2019 Signal Processing Symposium (SPSympo). IEEE, 2019. http://dx.doi.org/10.1109/sps.2019.8882084.
Full textLv, Shao-Zhong, Xiao-Ping Wang, and Li-Jie Zhang. "Fast and robust video foreground segmentation for indoor surveillance." In Signal Processing (WCSP 2009). IEEE, 2009. http://dx.doi.org/10.1109/wcsp.2009.5371622.
Full textDos Santos Fagundes, R., D. Lejeune, A. Mansour, F. Le Roy, and R. Lababidi. "Wideband high dynamic range surveillance." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362411.
Full textVergara, L., P. Bernabeu, and J. Igual. "Wide area fire surveillance by infrared digital signal processing." In Proceedings of the Third International Conference on Information Fusion. IEEE, 2000. http://dx.doi.org/10.1109/ific.2000.859859.
Full textGoriushkina, A. E., I. V. Svyd, G. E. Zavolodko, and G. V. Maistrenko. "Comparative analysis of signal processing methods secondary surveillance radar." In 2018 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo). IEEE, 2018. http://dx.doi.org/10.1109/ukrmico43733.2018.9047593.
Full textTurley, Mike D. E. "Signal processing techniques for maritime surveillance with skywave radar." In 2008 International Conference on Radar (Radar 2008). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4653925.
Full textKlock, Clemens, Volker Winkler, and Michael Edrich. "LTE-signal processing for passive radar air traffic surveillance." In 2017 18th International Radar Symposium (IRS). IEEE, 2017. http://dx.doi.org/10.23919/irs.2017.8008105.
Full textPetroviu, V., and B. Bondzulic. "Objective assessment of surveillance video quality." In Sensor Signal Processing for Defence (SSPD 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/ic.2012.0105.
Full textReports on the topic "Surveillance signal processing"
Rhody, Harvey, David Sher, and James Modestino. Intelligent Signal Processing Techniques for Multi-Sensor Surveillance Systems. Fort Belvoir, VA: Defense Technical Information Center, December 1989. http://dx.doi.org/10.21236/ada218890.
Full textVarshney, Pramod K. Multimodal Signal Processing for Personnel Detection and Activity Classification for Indoor Surveillance. Fort Belvoir, VA: Defense Technical Information Center, November 2013. http://dx.doi.org/10.21236/ada606602.
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