Auswahl der wissenschaftlichen Literatur zum Thema „Detection and response“

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Zeitschriftenartikel zum Thema "Detection and response"

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Soma, Venkata. "Threat Detection and Incident Response in the Cloud." International Journal of Science and Research (IJSR) 10, no. 11 (2021): 1578–81. http://dx.doi.org/10.21275/sr24822145029.

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Dobie, Robert A. "Objective Response Detection." Ear and Hearing 14, no. 1 (1993): 31–35. http://dx.doi.org/10.1097/00003446-199302000-00005.

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Courage, Ojo, Ayodeji Osoko Emmanuel, Nnenna Okolo Joy, and Jaji Mamudat. "Incident response: A structured model from detection to containment and recovery." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1401–7. https://doi.org/10.5281/zenodo.15032006.

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As cyber-attacks evolve in sophistication; organizations are under constant threat. This necessitates a cohesive approach to prioritize incident response (IR) capabilities and mitigate potential damages. This research paper explores integrating Information Security Management (ISM) and Incident Response (IR) functions; underlining the need for a unified strategy that leverages organizational learning theory. The study comprehensively analyzes the Incident Response Lifecycle; outlining the critical phases of preparation; detection and analysis; containment; eradication; recovery; and post-incid
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May, Susanne, and Carol Bigelow. "Modeling Nonlinear Dose-Response Relationships in Epidemiologic Studies: Statistical Approaches and Practical Challenges." Dose-Response 3, no. 4 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.004.

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Non-linear dose response relationships pose statistical challenges for their discovery. Even when an initial linear approximation is followed by other approaches, the results may be misleading and, possibly, preclude altogether the discovery of the nonlinear relationship under investigation. We review a variety of straightforward statistical approaches for detecting nonlinear relationships and discuss several factors that hinder their detection. Our specific context is that of epidemiologic studies of exposure-outcome associations and we focus on threshold and J-effect dose response relationsh
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Zhao, Rui, Yong Zheng Wen, Wei Ma, Jian Cheng Yang, and Xiao Mei Yu. "Specific Detection of Staphylococcus Enterotoxin B in Milk Using Piezoresistive Cantilever Aptasensor." Key Engineering Materials 645-646 (May 2015): 1298–302. http://dx.doi.org/10.4028/www.scientific.net/kem.645-646.1298.

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In this paper, an aptasensor with high sensitivity and rapid response was developed for the detection of staphylococcus enterotoxin B (SEB) by using thiol-modified piezoresistive cantilever. Thiol-modified aptamers, acting as the functionalized sensing elements, were immobilized on the nanogold-coated surface of the sensing cantilever as agents for detecting SEB. By using the functionalized aptasensors, different concentrations of SEB were detected with a wide detection range of 6-100 ng/mL and a quick response in milk. The experimental results indicated that the cantilever-based aptasensors h
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Tatineni, Sumanth. "AI-Infused Threat Detection and Incident Response in Cloud Security." International Journal of Science and Research (IJSR) 12, no. 11 (2023): 998–1004. http://dx.doi.org/10.21275/sr231113063646.

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Taniguchi, Kosuke, Kana Kuraguchi, and Yukuo Konishi. "Task Difficulty Makes ‘No’ Response Different From ‘Yes’ Response in Detection of Fragmented Object Contours." Perception 47, no. 9 (2018): 943–65. http://dx.doi.org/10.1177/0301006618787395.

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Two-alternative forced choice tasks are often used in object detection, which regards detecting an object as a ‘yes’ response and detecting no object as a ‘no’ response. Previous studies have suggested that the processing of yes/no responses arises from identical or similar processing. In this study, we investigated the difference of processing between detecting an object (‘yes’ response) and not detecting any object (‘no’ response) by controlling the task difficulty in terms of fragment length and stimulus duration. The results indicated that a ‘yes’ response depends on accurate and stable de
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Cohen, Fred. "Intrusion detection and response." Computers & Security 16, no. 6 (1997): 516. http://dx.doi.org/10.1016/s0167-4048(97)84669-x.

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Keller, Jörg, Wojciech Mazurczyk, Bela Genge, Lothar Fritsch, and Simon Vrhovec. "Cyberattack Detection and Response." JUCS - Journal of Universal Computer Science 25, no. (11) (2019): 1394–95. https://doi.org/10.3217/jucs-025-11.

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Abuabid, Ali, and Abdulrahman Aldeij. "Cyber Security Incident Response." Journal of Information Security and Cybercrimes Research 7, no. 1 (2024): 29–50. http://dx.doi.org/10.26735/pnob5534.

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In response to the growing cyber-attack threat, incident response teams have become a critical component of an organization's cybersecurity strategy. These teams are responsible for detecting, analyzing, and responding to security incidents promptly and effectively. However, detecting code injection attacks can be particularly challenging, as they can be difficult to detect and often go unnoticed until it is too late. Cybersecurity professionals use detection tools to detect and respond to DLL injection attacks that monitor system activity and detect unusual behavior. A large portion of the re
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Dissertationen zum Thema "Detection and response"

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Ibrahim, Tarik Mohamed Abdel-Kader. "Improving intrusion prevention, detection and response." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/479.

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In the face of a wide range of attacks, Intrusion Detection Systems (IDS) and other Internet security tools represent potentially valuable safeguards to identify and combat the problems facing online systems. However, despite the fact that a variety of commercial and open source solutions are available across a range of operating systems and network platforms, it is notable that the deployment of IDS is often markedly less than other well-known network security countermeasures and other tools may often be used in an ineffective manner. This thesis considers the challenges that users may face w
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Dincal, Selcuk. "Structural damage detection using frequency response functions." Thesis, Texas A&M University, 2005. http://hdl.handle.net/1969.1/3129.

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This research investigates the performance of an existing structural damage detection method (SDIM) when only experimentally-obtained measurement information can be used to calculate the frequency response functions used to detect damage. The development of a SDIM that can accurately identify damage while processing measurements containing realistic noise levels and overcoming experimental modeling errors would provide a robust method for identifying damage in the larger, more complex structures found in practice. The existing SDIM program, GaDamDet, uses an advanced genetic algorithm, along w
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Shakiba, Mohammad Hossein. "Analog Viterbi detection for partial-response signaling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ27719.pdf.

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Mason, S. M. "Objective waveform detection in electric response audiometry." Thesis, University of Nottingham, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.353922.

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Hu, Jiong. "An Automated Response Detection Procedure for Human Frequency Following Response Elicited by Voice Pitch." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1375179233.

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Hermansson, Albin. "View-Dependent Collision Detection and Response Using Octrees." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12751.

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Context. Collision is a basic necessity in most simulated environments, especially video games, which demand user interaction. Octrees are a way to divide the simulated environments into smaller, more manageable parts,and is a hierarchical tree-structure, where each node has eight children. Octrees and similar tree-structural methods have been used frequently to optimize collision calculations and partition the objects in the 3D space. Objectives. The aim of this thesis is to find a way to further improve upon the octree structure, by using a two-level octree structure, and simplify the collis
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ul, Haq M. Anwar. "White rot of onion-plant response and detection." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367806.

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Li, Wenhua. "Channel response and target detection in shallow water." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/42573.

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Pop-Fanea, Laura. "MRI techniques for detection of developing inflammatory response." Thesis, University of Aberdeen, 2005. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU197393.

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Since at least 1552 BC, inflammatory eye disease have been acknowledged as a major threat to sight. There have been a limited number of studies of <i>in vivo</i> inflammatory eye disease in humans and more information is required to improve image interpretation. The aim of this study was to develop and evaluate MRI techniques for the <i>in vivo</i> investigation of retinal damage and macrophage activity in the rat eye affected by experimental autoimmune uveitis (EAU), an inflammatory and autoimmune mediated eye disease. Rat eye structures affected by EAU were visualised <i>in vivo</i> in MR im
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Dorfman, Vladimir. "Detection and coding techniques for partial response channels /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3094619.

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Bücher zum Thema "Detection and response"

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Shakiba, Mohammad Hossein. Analog Viterbi detection for partial-response signaling. National Library of Canada = Bibliothèque nationale du Canada, 1997.

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Flammini, Francesco. Critical infrastructure security: Assessment, prevention, detection, response. WIT Press, 2012.

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Wayne, Jansen, and National Institute of Standards and Technology (U.S.), eds. Applying mobile agents to intrusion detection and response. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Wayne, Jansen, and National Institute of Standards and Technology (U.S.), eds. Applying mobile agents to intrusion detection and response. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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United States. Department of Homeland Security. Office of Inspector General. DHS' Domestic Nuclear Detection Office progress in integrating detection capabilities and response protocols. U.S. Dept. of Homeland Security, Office of Inspector General, 2007.

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Apikyan, Samuel, David Diamond, and Ralph Way, eds. Prevention, Detection and Response to Nuclear and Radiological Threats. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6658-0.

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Bullock, Barbara F. Surveillance and detection: A public health response to bioterrorism. USAF Counterproliferation Center, Air War College, Air University, 2002.

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NATO, Advanced Study Institute on Data Fusion for Situation Monitoring Incident Detection Alert and Response Management (2003 Yerevan Armenia). Data fusion for situation monitoring, incident detection, alert and response management. IOS Press, 2005.

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Zhou, Chengke. Novel approaches to alternator transient response prediction and rotor interturn fault detection. University of Manchester, 1994.

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Meijer, Rob R. Detection of advance item knowledge using response times in computer adaptive testing. Law School Admission Council, 2006.

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Buchteile zum Thema "Detection and response"

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May, Oliver, and Paul Curwell. "Detection and response." In Terrorist Diversion. Routledge, 2020. http://dx.doi.org/10.4324/9780429441929-9.

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Thompson, Eric C. "Detection and Identification of Events." In Cybersecurity Incident Response. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3870-7_7.

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McLaughlin, Kevin Lynn. "Detection, Response, and Remediation." In Cybersecurity Operations and Fusion Centers. CRC Press, 2023. http://dx.doi.org/10.1201/9781003259152-9.

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Stuyck, Tuur. "Collision Detection and Response." In Cloth Simulation for Computer Graphics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-02597-6_9.

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Johnson, Ross. "Observation Planning and Surveillance Detection." In Antiterrorism and Threat Response, 2nd ed. CRC Press, 2020. https://doi.org/10.4324/9781003374015-13.

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Epsi, Nusrat J., Sukanya Panja, and Antonina Mitrofanova. "CHAPTER 5. Big Data and Its Emerging Role in Precision Medicine and Therapeutic Response." In Detection Science. Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788019958-00088.

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Limongelli, Maria Pina, Emil Manoach, Said Quqa, et al. "Vibration Response-Based Damage Detection." In Structural Health Monitoring Damage Detection Systems for Aerospace. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72192-3_6.

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AbstractThis chapter aimed to present different data driven Vibration-Based Methods (VBMs) for Structural Health Monitoring (SHM). This family of methods, widely used for engineering applications, present several advantages for damage identification applications. First, VBMs provide continuous information on the health state of the structure at a global level without the need to access the damaged elements and to know their location. Furthermore, damage can be identified using the dynamic response of the structure measured by sensors non-necessarily located in the proximity of damage and witho
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Martelo, Liliana Marques, Lino Forte Marques, Hugh Douglas Burrows, and Mário Nuno Berberan-Santos. "Explosives Detection: From Sensing to Response." In Springer Series on Fluorescence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/4243_2019_9.

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Haber, Morey J., and Darran Rolls. "Identity Threat Detection and Response (ITDR)." In Identity Attack Vectors. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0233-1_9.

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An, Ning, Rui Ma, and Peng Dong. "Mean-Squared Response Sensitivity Method for Bridge Damage Detection." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6238-5_26.

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AbstractA new sensitivity approach for detecting bridge deterioration using the mean-squared response is proposed, in which the dynamic responses of the bridge are estimated using a vehicle-bridge dynamic interaction model. The sensitivity of the mean-squared response to bridge damage is investigated and matrices are generated by defining the stiffness variation of the bridge element as an index of damage factor. The mean-squared response sensitivity method detects local bridge damage with significantly fewer iteration times, and the identification accuracy is proportional to the number of mea
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Konferenzberichte zum Thema "Detection and response"

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Fan, Qingshan, Keqiang Li, Hanqing Zhang, and Jiang Huang. "Research on perovskite photodetectors with thickness controlling spectral response." In Tenth Symposium on Novel Optoelectronic Detection Technology and Applications, edited by Chen Ping. SPIE, 2025. https://doi.org/10.1117/12.3054278.

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Okae, Percy, and Hadijatu Mohamadu. "Car Accident Detection and Response System." In 2024 IEEE 9th International Conference on Adaptive Science and Technology (ICAST). IEEE, 2024. https://doi.org/10.1109/icast61769.2024.10856472.

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Kumar, CH Sai, N. Sai Jaswanth, V. Dinesh Reddy, and Thangam S. "IoT-Driven Hanging Detection and Response." In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2025. https://doi.org/10.1109/iciss63372.2025.11076295.

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Muayad, Muayad F., Sanarya K. Yahya, and Rozeha A. Rashid. "IOT Based Emergency Detection and Response System." In 2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies (ATNT). IEEE, 2024. http://dx.doi.org/10.1109/atnt61688.2024.10719072.

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Wang, Fuping, Yang Luo, and Guanzhuang Duan. "Checkerboard Subpixel Corner Detection Based on Local Area Intensity Response and LoG Response." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692987.

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Cai, Zhiping, Zhenyu Zhang, Weiming Chen, Xiang Luo, Yuan Gao, and Yanhui Zhang. "Capacitor health parameter detection based on frequency response." In 2024 3rd International Conference on Energy, Power and Electrical Technology (ICEPET). IEEE, 2024. http://dx.doi.org/10.1109/icepet61938.2024.10626356.

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Perone, Stefano, Luca Faramondi, Simone Guarino, et al. "ZADIG: A novel Extended Detection and Response System." In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2024. http://dx.doi.org/10.1109/csr61664.2024.10679420.

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Gadad, Tangevva Rudrappa, Varshitha R, Yoghana B. K, and Yumlembam Henba Singh. "Machine Learning in Cybersecurity: Threat Detection and Response." In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET). IEEE, 2025. https://doi.org/10.1109/icaet63349.2025.10932275.

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Conti, Antonia S., Carsten Dlugosch, and Klaus Bengler. "Detection response tasks." In the 4th International Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2390256.2390298.

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Cassidy, Derek J., John Healy, and John Sheridan. "Measuring the optical response of self-written waveguides within polymer sensors." In Optical Sensing and Detection VIII, edited by Francis Berghmans and Ioanna Zergioti. SPIE, 2024. http://dx.doi.org/10.1117/12.3014676.

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Berichte der Organisationen zum Thema "Detection and response"

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Lee, Wenke. Next-Generation Botnet Detection and Response. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada500564.

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Reilly, Mark E. Scalable Intrusion Detection and Response Framework. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408445.

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Myers, Steven Charles. Radiation Detection Overview for Nuclear Emergency Response. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1333119.

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Lakis, Rollin Evan. UAV Applications in Radiation Detection and Emergency Response. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1253522.

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Jansen, Wayne, Peter Mell, Tom Karygiannis, and Don Marks. Applying mobile agents to intrusion detection and response. National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6416.

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Chumfong, Isabelle T., Ann S. Yoshimura, Todd H. West, Heidi R. Ammerlahn, and Lynn I. Yang. Earlier detection in emergency response to an anthrax attack. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/883130.

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Tan, Pang-Ning, and Anil K. Jain. Information Assurance: Detection & Response to Web Spam Attacks. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535002.

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Inderieden, R. S., H. R. Everett, T. A. Heath-Pastore, and R. P. Smurlo. Overview of the Mobile Detection Assessment and Response System. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada422465.

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Ralston, James M. Impulse Response of Alternative Synthetic Apertures for Subsurface Detection. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada385875.

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Mukhopadhyay, Sanjoy, Richard Maurer, and Mark Biery. Networked radiation detection system for rapid tactical response [Slides]. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1832151.

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