Relevant bibliographies by topics / Data surveillance

Academic literature on the topic 'Data surveillance'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Data surveillance"

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Matzner, Tobias. "Beyond data as representation: The performativity of Big Data in surveillance." Surveillance & Society 14, no. 2 (September 21, 2016): 197–210. http://dx.doi.org/10.24908/ss.v14i2.5831.

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The article discusses problems of representative views of data and elaborates a concept of the performativity of data. It shows how data used for surveillance contributes in creating suspect subjectivities. In particular, the article focuses on the inductive or explorative processing of data and on the decoupling of data generation and analysis that characterize current use of data for surveillance. It lines out several challenges this poses to established accounts of surveillance: David Lyon’s concept of surveillance as social sorting and Haggerty and Ericson’s “surveillant assemblage”. These problems are attributed to a representationalist view, which focuses on the veracity of data. This can lead to ignoring problematic consequences of surveillance procedures and the full scope of affected persons. Building on an idea by Rita Raley, an alternative account of data as performative is proposed. Using Judith Butler’s concept of “citationality,” this account shows how surveillance is entangled with the production of subjects through data in general. Surveillance is reformulated as a particular way in which subjects are produced that is parasitical to other forms of subjectivation.
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Steeves, Valerie. "Big Data Surveillance." Journal of Intelligence, Conflict, and Warfare 4, no. 2 (November 23, 2021): 95–98. http://dx.doi.org/10.21810/jicw.v4i2.3751.

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On October 21, 2021, Dr. Valerie Steeves presented Big Data Surveillance: Privacy and Trust Implications at the October 2021 CASIS Digital Roundtable event. This presentation was followed by a question and answer period, where CASIS Executives and attendees were given the opportunity to discuss the presentation with Dr. Steeves. The discussion topics included building an understanding of data surveillance for young people and safeguarding their privacy through building trusting relations.
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Brayne, Sarah. "The Criminal Law and Law Enforcement Implications of Big Data." Annual Review of Law and Social Science 14, no. 1 (October 13, 2018): 293–308. http://dx.doi.org/10.1146/annurev-lawsocsci-101317-030839.

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Law enforcement agencies increasingly use big data analytics in their daily operations. This review outlines how police departments leverage big data and new surveillant technologies in patrol and investigations. It distinguishes between directed surveillance—which involves the surveillance of individuals and places under suspicion—and dragnet surveillance—which involves suspicionless, unparticularized data collection. Law enforcement's adoption of big data analytics far outpaces legal responses to the new surveillant landscape. Therefore, this review highlights open legal questions about data collection, suspicion requirements, and police discretion. It concludes by offering suggestions for future directions for researchers and practitioners.
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Andrejevic, Mark, and Kelly Gates. "Big Data Surveillance: Introduction." Surveillance & Society 12, no. 2 (May 9, 2014): 185–96. http://dx.doi.org/10.24908/ss.v12i2.5242.

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Southey, Hugh, and Adam Straw. "Surveillance, Data and Privacy." Judicial Review 18, no. 4 (December 20, 2013): 440–45. http://dx.doi.org/10.1080/10854681.2013.11426812.

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Wesseling, Mara, Marieke de Goede, and Louise Amoore. "DATA WARS BEYOND SURVEILLANCE." Journal of Cultural Economy 5, no. 1 (February 2012): 49–66. http://dx.doi.org/10.1080/17530350.2012.640554.

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Smith, Gavin J. D. "Surveillance, Data and Embodiment." Body & Society 22, no. 2 (January 21, 2016): 108–39. http://dx.doi.org/10.1177/1357034x15623622.

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Boulanger, Virginie, Étienne Poirier, Anne MacLaurin, and Caroline Quach. "Divergences between healthcare-associated infection administrative data and active surveillance data in Canada." Canada Communicable Disease Report 48, no. 1 (January 26, 2022): 4–16. http://dx.doi.org/10.14745/ccdr.v48i01a02.

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Background: Although Canada has both a national active surveillance system and administrative data for the passive surveillance of healthcare-associated infections (HAI), both have identified strengths and weaknesses in their data collection and reporting. Active and passive surveillance work independently, resulting in results that diverge at times. To understand the divergences between administrative health data and active surveillance data, a scoping review was performed. Method: Medline, Embase and Cumulative Index to Nursing and Allied Health Literature along with grey literature were searched for studies in English and French that evaluated the use of administrative data, alone or in comparison with traditional surveillance, in Canada between 1995 and November 2, 2020. After extracting relevant information from selected articles, a descriptive summary of findings was provided with suggestions for the improvement of surveillance systems to optimize the overall data quality. Results: Sixteen articles met the inclusion criteria, including twelve observational studies and four systematic reviews. Studies showed that using a single source of administrative data was not accurate for HAI surveillance when compared with traditional active surveillance; however, combining different sources of data or combining administrative with active surveillance data improved accuracy. Electronic surveillance systems can also enhance surveillance by improving the ability to detect potential HAIs. Conclusion: Although active surveillance of HAIs produced the most accurate results and remains the gold-standard, the integration between active and passive surveillance data can be optimized. Administrative data can be used to enhance traditional active surveillance. Future studies are needed to evaluate the feasibility and benefits of potential solutions presented for the use of administrative data for HAI surveillance and reporting in Canada.
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Sell, Randall L. "LGBTQ Health Surveillance: Data = Power." American Journal of Public Health 107, no. 6 (June 2017): 843–44. http://dx.doi.org/10.2105/ajph.2017.303798.

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Busch, Thorsten, Christoph Schank, Ulrich Leicht-Deobald, Antoinette Weibel, Simon Daniel Schafheitle, Isabelle Wildhaber, and Gabriel Kasper. "Workplace Surveillance & Big Data." Academy of Management Proceedings 2018, no. 1 (August 2018): 14411. http://dx.doi.org/10.5465/ambpp.2018.14411abstract.

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Dissertations / Theses on the topic "Data surveillance"

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Wang, Simi. "Surveillance video data fusion." Thesis, Kingston University, 2016. http://eprints.kingston.ac.uk/35593/.

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The overall objective under consideration is the design of a system capable of automatic inference about events occurring in the scene under surveillance. Using established video processing techniques. low level inferences are relatively straightforward to establish as they only determine activities of some description. The challenge is to design a system that is capable of higher-level inference, that can be used to notify stakeholders about events having semantic importance. It is argued that re-identification of the entities present in the scene (such as vehicles and pedestrians) is an important intermediate objective, to support many of the types of higher level interference required. The input video can be processed in a number of ways to obtain estimates of the attributes of the objects and events in the scene. These attributes can then be analysed, or 'fused', to enable the high-level inference. One particular challenge is the management of the uncertainties, which are associated with the estimates, and hence with the overall inferences. Another challenge is obtaining accurate estimates of prior probabilities, which can have a significant impact on the final inferences. This thesis makes the following contributions. Firstly, a review of the nature of the uncertainties present in a visual surveillance system and quantification of the uncertainties associated with current techniques. Secondly, an investigation into the benefits of using a new high resolution dataset for the problem of pedestrain re-identification under various scenarios including occlusoon. This is done by combining state-of-art techniques with low level fusion techniques. Thirdly, a multi-class classification approach to solve the classification of vehicle manufacture logos. The approach uses the Fisher Discriminative classifier and decision fusion techniques to identify and classify logos into its correct categories. Fourthly, two probabilistic fusion frameworks were developed, using Bayesian and Evidential Dempster-Shafer methodologies, respectively, to allow inferences about multiple objectives and to reduce the uncertainty by combining multiple information sources. Fifthly, an evaluation framework was developed, based on the Kelly Betting Strategy, to effectively accommodate the additional information offered by the Dempster-Shafer approach, hence allowing comparisons with the single probabilistic output provided by a Bayesian analysis.
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Clarke, Roger Anthony, and Roger Clarke@xamax com au. "Data Surveillance: Theory, Practice & Policy." The Australian National University. Faculty of Engineering and Information Technology, 1997. http://thesis.anu.edu.au./public/adt-ANU20031112.124602.

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Data surveillance is the systematic use of personal data systems in the investigation or monitoring of the actions or communications of one or more persons. This collection of papers was the basis for a supplication under Rule 28 of the ANU's Degree of Doctor of Philosophy Rules. The papers develop a body of theory that explains the nature, applications and impacts of the data processing technologies that support the investigation or monitoring of individuals and populations. Literature review and analysis is supplemented by reports of field work undertaken in both the United States and Australia, which tested the body of theory, and enabled it to be articulated. The research programme established a firm theoretical foundation for further work. It provided insights into appropriate research methods, and delivered not only empirically-based descriptive and explanatory data, but also evaluative information relevant to policy-decisions. The body of work as a whole provides a basis on which more mature research work is able to build.
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Meeyai, Aronrag. "The analysis of influenza surveillance data." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501077.

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Adams, Andrew J. "Multispectral persistent surveillance /." Online version of thesis, 2008. http://hdl.handle.net/1850/7070.

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Flach, James D. "River basin surveillance using remotely sensed data." Thesis, Aston University, 1989. http://publications.aston.ac.uk/14296/.

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This thesis describes the development of an operational river basin water resources information management system. The river or drainage basin is the fundamental unit of the system; in both the modelling and prediction of hydrological processes, and in the monitoring of the effect of catchment management policies. A primary concern of the study is the collection of sufficient and sufficiently accurate information to model hydrological processes. Remote sensing, in combination with conventional point source measurement, can be a valuable source of information, but is often overlooked by hydrologists, due to the cost of acquisition and processing. This thesis describes a number of cost effective methods of acquiring remotely sensed imagery, from airborne video survey to real time ingestion of meteorological satellite data. Inexpensive micro-computer systems and peripherals are used throughout to process and manipulate the data. Spatial information systems provide a means of integrating these data with topographic and thematic cartographic data, and historical records. For the system to have any real potential the data must be stored in a readily accessible format and be easily manipulated within the database. The design of efficient man-machine interfaces and the use of software enginering methodologies are therefore included in this thesis as a major part of the design of the system. The use of low cost technologies, from micro-computers to video cameras, enables the introduction of water resources information management systems into developing countries where the potential benefits are greatest.
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Laxhammar, Rikard. "Anomaly detection in trajectory data for surveillance applications." Licentiate thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-17235.

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Abnormal behaviour may indicate important objects and events in a wide variety of domains. One such domain is intelligence and surveillance, where there is a clear trend towards more and more advanced sensor systems producing huge amounts of trajectory data from moving objects, such as people, vehicles, vessels and aircraft. In the maritime domain, for example, abnormal vessel behaviour, such as unexpected stops, deviations from standard routes, speeding, traffic direction violations etc., may indicate threats and dangers related to smuggling, sea drunkenness, collisions, grounding, hijacking, piracy etc. Timely detection of these relatively infrequent events, which is critical for enabling proactive measures, requires constant analysis of all trajectories; this is typically a great challenge to human analysts due to information overload, fatigue and inattention. In the Baltic Sea, for example, there are typically 3000–4000 commercial vessels present that are monitored by only a few human analysts. Thus, there is a need for automated detection of abnormal trajectory patterns. In this thesis, we investigate algorithms appropriate for automated detection of anomalous trajectories in surveillance applications. We identify and discuss some key theoretical properties of such algorithms, which have not been fully addressed in previous work: sequential anomaly detection in incomplete trajectories, continuous learning based on new data requiring no or limited human feedback, a minimum of parameters and a low and well-calibrated false alarm rate. A number of algorithms based on statistical methods and nearest neighbour methods are proposed that address some or all of these key properties. In particular, a novel algorithm known as the Similarity-based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) is proposed. This algorithm is based on the theory of Conformal prediction and is unique in the sense that it addresses all of the key properties above. The proposed algorithms are evaluated on real world trajectory data sets, including vessel traffic data, which have been complemented with simulated anomalous data. The experiments demonstrate the type of anomalous behaviour that can be detected at a low overall alarm rate. Quantitative results for learning and classification performance of the algorithms are compared. In particular, results from reproduced experiments on public data sets show that SNN-CAD, combined with Hausdorff distance  for measuring dissimilarity between trajectories, achieves excellent classification performance without any parameter tuning. It is concluded that SNN-CAD, due to its general and parameter-light design, is applicable in virtually any anomaly detection application. Directions for future work include investigating sensitivity to noisy data, and investigating long-term learning strategies, which address issues related to changing behaviour patterns and increasing size and complexity of training data.
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Hu, Jun. "Privacy-Preserving Data Integration in Public Health Surveillance." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19994.

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With widespread use of the Internet, data is often shared between organizations in B2B health care networks. Integrating data across all sources in a health care network would be useful to public health surveillance and provide a complete view of how the overall network is performing. Because of the lack of standardization for a common data model across organizations, matching identities between different locations in order to link and aggregate records is difficult. Moreover, privacy legislation controls the use of personal information, and health care data is very sensitive in nature so the protection of data privacy and prevention of personal health information leaks is more important than ever. Throughout the process of integrating data sets from different organizations, consent (explicitly or implicitly) and/or permission to use must be in place, data sets must be de-identified, and identity must be protected. Furthermore, one must ensure that combining data sets from different data sources into a single consolidated data set does not create data that may be potentially re-identified even when only summary data records are created. In this thesis, we propose new privacy preserving data integration protocols for public health surveillance, identify a set of privacy preserving data integration patterns, and propose a supporting framework that combines a methodology and architecture with which to implement these protocols in practice. Our work is validated with two real world case studies that were developed in partnership with two different public health surveillance organizations.
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Teo, Wan Ching. "Privacy in the European Union data surveillance context." Thesis, University of Reading, 2018. http://centaur.reading.ac.uk/77849/.

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This study asks: how has European Union (EU) law protected privacy in the context of data surveillance used by the EU to address terrorist threats? It argues that the manner in which privacy is protected has a profound impact on the interests that privacy serves to protect -importantly, having a private sphere free from intrusion where the individual can think, express, explore and act; interests that are crucial for human dignity, autonomy and the maintenance of a democratic society. It conceptualises the different notions of privacy derived from legal and philosophical theorists and underscores that privacy harm can result from intrusion into the private sphere. The study adopts De Hert and Gutwirth's view that privacy is an opacity tool, acting to shield the private sphere by prohibiting interference. Privacy is thus distinguished from data protection; the latter serving as a transparency tool to regulate power and manage interferences. The thesis outlines the legal framework for the protection of privacy in the EU, critically evaluating the influence of data protection frameworks. The study asserts that data surveillance threatens privacy because it intrudes into the private sphere of the individual, monitoring and tracking activities and communications in a systematic, indiscriminate manner. It uses two case studies of air passenger profiling and data retention to study how privacy has been protected. The central thesis is that the approach taken to protecting privacy in the EU data surveillance context has largely been framed through the prism of data protection. In view of the different goals that data protection serves, the study finds that a data protection approach to privacy's protection has led to the proceduralisation of privacy, legitimising and bureaucratising intrusions into the private sphere so long as data collected are adequately secured. The implications are that interferences with the private sphere are de facto legitimised and data surveillance accommodated.
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Abghari, Shahrooz, and Samira Kazemi. "Open Data for Anomaly Detection in Maritime Surveillance." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4807.

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Context: Maritime Surveillance (MS) has received increased attention from a civilian perspective in recent years. Anomaly detection (AD) is one of the many techniques available for improving the safety and security in the MS domain. Maritime authorities utilize various confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new sources of data for MS. These newly identified data sources, which provide publicly accessible data, are the open data sources. Taking advantage of the open data sources in addition to the traditional sources of data in the AD process will increase the accuracy of the MS systems. Objectives: The goal is to investigate the potential open data as a complementary resource for AD in the MS domain. To achieve this goal, the first step is to identify the applicable open data sources for AD. Then, a framework for AD based on the integration of open and closed data sources is proposed. Finally, according to the proposed framework, an AD system with the ability of using open data sources is developed and the accuracy of the system and the validity of its results are evaluated. Methods: In order to measure the system accuracy, an experiment is performed by means of a two stage random sampling on the vessel traffic data and the number of true/false positive and negative alarms in the system is verified. To evaluate the validity of the system results, the system is used for a period of time by the subject matter experts from the Swedish Coastguard. The experts check the detected anomalies against the available data at the Coastguard in order to obtain the number of true and false alarms. Results: The experimental outcomes indicate that the accuracy of the system is 99%. In addition, the Coastguard validation results show that among the evaluated anomalies, 64.47% are true alarms, 26.32% are false and 9.21% belong to the vessels that remain unchecked due to the lack of corresponding data in the Coastguard data sources. Conclusions: This thesis concludes that using open data as a complementary resource for detecting anomalous behavior in the MS domain is not only feasible but also will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.
This thesis investigated the potential open data as a complementary resource for Anomaly Detection (AD) in the Maritime Surveillance (MS) domain. A framework for AD was proposed based on the usage of open data sources along with other traditional sources of data. According to the proposed AD framework and the algorithms for implementing the expert rules, the Open Data Anomaly Detection System (ODADS) was developed. To evaluate the accuracy of the system, an experiment on the vessel traffic data was conducted and an accuracy of 99% was obtained for the system. There was a false negative case in the system results that decreased the accuracy. It was due to incorrect AIS data in a special situation that was not possible to be handled by the detection rules in the scope of this thesis. The validity of the results was investigated by the subject matter experts from the Swedish Coastguard. The validation results showed that the majority of the ODADS evaluated anomalies were true alarms. Moreover, a potential information gap in the closed data sources was observed during the validation process. Despite the high number of true alarms, the number of false alarms was also considerable that was mainly because of the inaccurate open data. This thesis provided insights into the open data as a complement to the common data sources in the MS domain and is concluded that using open data will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.
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Eriksson, Pontus, Carl Nordström, and Alexander Troshin. "Surveillance Using Facial Recognition and Social Media Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385696.

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People share more and more on social media, aware that they are being surveilled but unaware of the scope and the ways that their data is processed. Large amounts of resources are dedicated to performing the surveillance, both in terms of labor and computation. This project explores the scope of data collection and processing by showing that it is possible to gather, process, and store data from the social media platforms Twitter and Reddit in real-time using only a personal computer. The focus was to use facial recognition to find specific individuals in the stream of data, but the data collected can be used for other purposes. We have also explored the ethical concerns regarding both the collection and processing of such data.
Människor delar mer och mer på social medier medvetna om att de blir övervakade, men omedvetna om i vilken utsträckning och på vilka sätt datan är processad. Idag används mycket resurser för att urföra dessa uppgifter. Med det här projektet visar vi att det är möjligt att samla in, processa och spara data från sociala medierna Reddit och Twitter i realtid genom att enbart använda en persondator. Vårat fokus har varit att använda ansiktsigenkänning för att identifiera specifika individer från en dataström, men datan kan användas för andra syften. Vi har också kollat på de etiska dilemman som dyker upp i samband med insamling och processning av sådan data.
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Books on the topic "Data surveillance"

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Program, Hawaii Health Surveillance. 1986 survey data. Honolulu, Hawaii (P.O. Box 3378, Honolulu 96801-9990): Hawaii State Dept. of Health, Research and Statistics Office, Office of Health Monitoring, 1989.

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Flach, James Dominic. River basin surveillance using remotely sensed data. Birmingham: Aston University. Department of Civil Engineering, 1989.

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Wilson, Sue. Assessing the utility of cancer surveillance data. Birmingham: University of Birmingham, 1997.

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Canada, Public Health Agency of. Inventory of injury data sources and surveillance activities. Ottawa, Ont: Public Health Agency of Canada, 2005.

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Xinyu, Wu, Xu Yangsheng, and SpringerLink (Online service), eds. Intelligent Surveillance Systems. Dordrecht: Springer Science+Business Media B.V., 2011.

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Levin, Robert E. Space Surveillance Network: Appropriate controls needed over data access. Washington, D.C: United States General Accounting Office., 2002.

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Fraser, Graham, and Gianfranco Spiteri. Annual epidemiological report reporting on 2009 surveillance data and 2010 epidemic intelligence data. Stockholm: European Centre for Disease Prevention and Control, 2011.

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Mathisen, Johan. Using the balance sheet approach in surveillance: Framework, data sources, and data availability. [Washington, D.C.]: International Monetary Fund, Policy Development and Review Dept. and Statistics Dept, 2006.

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Unit, Great Britain Consumer Safety. Home accident surveillance system: Ninth annual report - 1985 data. London: The Unit, 1986.

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Basile, Kathleen C. Sexual violence surveillance: Uniform definitions and recommended data elements. Atlanta, Ga: Centers for Disase Control and Prevention, National Center for Injury Prevention and Control, 2009.

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Book chapters on the topic "Data surveillance"

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Lyon, David. "Surveillance capitalism, surveillance culture and data politics 1." In Data Politics, 64–77. Abingdon, Oxon ; New York, NY : Routledge, 2019. | Series: Routledge studies in international political sociology: Routledge, 2019. http://dx.doi.org/10.4324/9781315167305-4.

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Appice, Annalisa, Anna Ciampi, Fabio Fumarola, and Donato Malerba. "Sensor Data Surveillance." In Data Mining Techniques in Sensor Networks, 73–88. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5454-9_4.

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Yan, Wei Qi. "Surveillance Data Analytics." In Texts in Computer Science, 75–126. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10713-0_4.

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Yan, Wei Qi. "Surveillance Data Analytics." In Introduction to Intelligent Surveillance, 59–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28515-3_4.

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Galli, Federico. "Data-Driven Surveillance." In Law, Governance and Technology Series, 47–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13603-0_3.

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Yan, Wei Qi. "Surveillance Data Analytics." In Introduction to Intelligent Surveillance, 65–106. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60228-8_4.

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Martin, Kirsten. "Surveillance and Power." In Ethics of Data and Analytics, 157–60. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003278290-24.

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Yan, Wei Qi. "Surveillance Data Secure Transmissions." In Introduction to Intelligent Surveillance, 37–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28515-3_3.

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Yan, Wei Qi. "Surveillance Data Secure Transmissions." In Introduction to Intelligent Surveillance, 41–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60228-8_3.

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Yan, Wei Qi. "Surveillance Data Secure Transmissions." In Texts in Computer Science, 45–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10713-0_3.

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Conference papers on the topic "Data surveillance"

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Garzon-Alfonso, Cristian C., and Manuel Rodriguez-Martinez. "Twitter Health Surveillance (THS) System." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622504.

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Whitman, Dave. "Surveillance Data Object (Revisited)." In 2010 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2010. http://dx.doi.org/10.1109/icnsurv.2010.5503341.

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Teague, Ralph. "Surveillance data management system." In Optomechatronic Systems III, edited by Toru Yoshizawa. SPIE, 2002. http://dx.doi.org/10.1117/12.469989.

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Tourassi, Georgia. "Deep learning enabled national cancer surveillance." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258411.

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Itano, Ryuya, Tomoya Nohara, and Takahiro Koita. "Crowd-Aided Anomaly Detection in Surveillance Videos." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020914.

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Remillard, Smith, and Donovan. "Surveillance data network transition strategy." In 22nd Digital Avionics Systems Conference. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/dasc.2003.1245839.

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JUDD, THOMAS. "Automatic dependent surveillance data transfer." In Digital Avionics Systems Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1988. http://dx.doi.org/10.2514/6.1988-3997.

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Jain, Akshat, Shraddha Basantwani, Owais Kazi, and Yogita Bang. "Smart surveillance monitoring system." In 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI). IEEE, 2017. http://dx.doi.org/10.1109/icdmai.2017.8073523.

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Kantharaj, Amulya, and Alireza Abdoli. "Improving Aviation and Flight Safety By Analyzing Surveillance Data." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671600.

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Anderson, Sandra. "Data communications." In 2012 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2012. http://dx.doi.org/10.1109/icnsurv.2012.6218489.

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Reports on the topic "Data surveillance"

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Craig, Samuel. Passive Surveillance Data Processor/Analyzer. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada169141.

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Bisping, L. E. Hanford Site Environmental Surveillance Data 1997. Office of Scientific and Technical Information (OSTI), August 1998. http://dx.doi.org/10.2172/948.

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Kam, F. B. K. Surveillance data bases, analysis, and standardization program. Office of Scientific and Technical Information (OSTI), September 1990. http://dx.doi.org/10.2172/6559883.

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Tobak, John P. Automated Acquisition of Copperhead Stockpile Surveillance Test Data. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada231387.

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Bhattacharya, Tanmoy. Data-aware distributed scientific computing for big-data problems in bio-surveillance. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1092438.

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McElroy, W. LWR pressure vessel surveillance dosimetry improvement program: LWR power reactor surveillance physics-dosimetry data base compendium. Office of Scientific and Technical Information (OSTI), August 1985. http://dx.doi.org/10.2172/5241512.

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Sekmen, Aki, and Fenghui Yao. Multi-Sensor Vision Data Fusion for Smart Airborne Surveillance. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada499525.

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Frome E.L., Watkins J. P. ,. Ellis E. D. Poisson Regression Analysis of Illness and Injury Surveillance Data. Office of Scientific and Technical Information (OSTI), December 2012. http://dx.doi.org/10.2172/1060522.

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Malani, Anup, Satej Soman, Sabareesh Ramachandran, Alice Chen, and Darius Lakdawalla. Vaccine Allocation Priorities Using Disease Surveillance and Economic Data. Cambridge, MA: National Bureau of Economic Research, January 2022. http://dx.doi.org/10.3386/w29682.

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Blasing, T. J., K. L. Daniels, P. Y. Goldberg, B. M. Horwedel, I. L. McCollough, A. E. Osbourne-Lee, C. K. Valentine, and D. A. Wolf. Environmental surveillance data report for the first quarter of 1988. Office of Scientific and Technical Information (OSTI), August 1988. http://dx.doi.org/10.2172/7188940.

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