Relevant bibliographies by topics / Detection

Contents

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

Academic literature on the topic 'Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Detection"

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Qian, Sen. "What Is Detection?" Detection 02, no. 02 (2014): 7–9. http://dx.doi.org/10.4236/detection.2014.22002.

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Sugiura, Hiroki, Shinichi Demura, Yoshinori Nagasawa, Shunsuke Yamaji, Tamotsu Kitabayashi, Shigeki Matsuda, Takayoshi Yamada, and Ning Xu. "Relationship between Extent of Coffee Intake and Recognition of Its Effects and Ingredients." Detection 01, no. 01 (2013): 1–6. http://dx.doi.org/10.4236/detection.2013.11001.

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Brandão, Marcelo Luiz Lima, Carla de Oliveira Rosas, Silvia Maria Lopes Bricio, Valéria de Mello Medeiros, Juliana de Castro Beltrão da Costa, Rodrigo Rollin Pinheiro, Paola Cardarelli-Leite, Marcus Henrique Campino de La Cruz, and Armi Wanderley da Nóbrega. "Preparation of Reference Material for Proficiency Test for Enumeration of Coliforms in Cheese Matrix." Detection 01, no. 01 (2013): 7–12. http://dx.doi.org/10.4236/detection.2013.11002.

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Zhang, Jianyong, Xiao Cai, and Xiaohu Mo. "On Two Cryogenic Systems of High Purity Germanium Detector." Detection 01, no. 02 (2013): 13–20. http://dx.doi.org/10.4236/detection.2013.12003.

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Chuto, Maneenuch, Sudkate Chaiyo, Weena Siangproh, and Orawon Chailapakul. "A Rapid Separation and Highly Determination of Paraben Species by Ultra-Performance Liquid Chromatography —Electrochemical Detection." Detection 01, no. 02 (2013): 21–29. http://dx.doi.org/10.4236/detection.2013.12004.

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Cardone, Fabio, Giovanni Cherubini, Walter Perconti, Andrea Petrucci, and Alberto Rosada. "Neutron Imaging by Boric Acid." Detection 01, no. 02 (2013): 30–35. http://dx.doi.org/10.4236/detection.2013.12005.

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Weng, Binbin, Jijun Qiu, Lihua Zhao, Caleb Chang та Zhisheng Shi. "Theoretical D* Optimization of N+-p Pb<sub>1-x</sub>Sn<sub>x</sub>Se Long-Wavelength (8 - 11 μm) Photovoltaic Detector at 77 K". Detection 02, № 01 (2014): 1–6. http://dx.doi.org/10.4236/detection.2014.21001.

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Żak, Dariusz, Jarosław Jureńczyk, and Janusz Kaniewski. "Zener Phenomena in InGaAs/InAlAs/InP Avalanche Photodiodes." Detection 02, no. 02 (2014): 10–15. http://dx.doi.org/10.4236/detection.2014.22003.

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Wu, Jun, Hao Fu, and Xiashi Zhu. "Separation/Analysis Rhodamine B by Anion Surfactant/Ionic Liquid Aqueous Two-Phase Systems Coupled with Ultraviolet Spectrometry." Detection 02, no. 03 (2014): 17–25. http://dx.doi.org/10.4236/detection.2014.23004.

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Saleh, Tawfik A. "Detection: From Electrochemistry to Spectroscopy with Chromatographic Techniques, Recent Trends with Nanotechnology." Detection 02, no. 04 (2014): 27–32. http://dx.doi.org/10.4236/detection.2014.24005.

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Dissertations / Theses on the topic "Detection"

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Kapoor, Prince. "Shoulder Keypoint-Detection from Object Detection." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38015.

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This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentation, we decided to explore an object detection task using a particular model architecture which maintains a sweet spot between computational cost and accuracy. The model architecture which we had used is LSTM-Decoder. The model had been experimented with different CNN feature extractor and found their pros and cons in variant scenarios. The results which we had obtained on different datasets elucidates that CNN plays a major role in obtaining higher accuracy and we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection Dataset. In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as follows: using the detected annotation from object detection, a small cropped image is generated which would be feed into a small cascade network which was trained for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently, we had generated our results for shoulder keypoint detection. However, this idea could be extended to full-body pose Estimation by modifying the cascaded network for pose estimation purpose and this had become an important topic of discussion for the future work of this thesis.
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Laxhammar, Rikard. "Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications." Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.

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Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic.
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Albrektsson, Fredrik. "Detecting Sockpuppets in Social Media with Plagiarism Detection Algorithms." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208553.

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As new forms of propaganda and information control spread across the internet, the need for novel ways of identifying them increases as well. One increasingly popular method of spreading false messages on microblogs like Twitter is to disseminate them from seemingly ordinary, but centrally controlled and coordinated user accounts – sockpuppets. In this paper we examine a number of potential methods for identifying these by way of applying plagiarism detection algorithms for text, and evaluate their performance against this type of threat. We identify one type of algorithm in particular – that using vector space modeling of text – as particularly useful in this regard.<br>Allteftersom  nya  former  av  propaganda  och  informationskontroll  sprider sig över internet krävs också nya sätt att identifiera dessa. En  allt mer populär metod för att sprida falsk information på mikrobloggar  som  Twitter  är  att  göra  det  från  till  synes  ordinära,  men  centralt  kontrollerade och koordinerade användarkonton – på engelska kända  som “sockpuppets”. I denna undersökning testar vi ett antal potentiella  metoder  för  att  identifiera  dessa  genom  att  applicera  plagiatkontrollalgoritmer  ämnade  för  text,  och  utvärderar  deras prestanda mot denna sortens hot. Vi identifierar framför allt en typ av  algoritm  –  den  som  nyttjar  vektorrymdsmodellering  av  text  –  som speciellt användbar i detta avseende.
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Le, Anhtuan. "Intrusion Detection System for detecting internal threats in 6LoWPAN." Thesis, Middlesex University, 2017. http://eprints.mdx.ac.uk/21958/.

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6LoWPAN (IPv6 over Low-power Wireless Personal Area Network) is a standard developed by the Internet Engineering Task Force group to enable the Wireless Sensor Networks to connect to the IPv6 Internet. This standard is rapidly gaining popularity for its applicability, ranging extensively from health care to environmental monitoring. Security is one of the most crucial issues that need to be considered properly in 6LoWPAN. Common 6LoWPAN security threats can come from external or internal attackers. Cryptographic techniques are helpful in protecting the external attackers from illegally joining the network. However, because the network devices are commonly not tampered-proof, the attackers can break the cryptography codes of such devices and use them to operate like an internal source. These malicious sources can create internal attacks, which may downgrade significantly network performance. Protecting the network from these internal threats has therefore become one of the centre security problems on 6LoWPAN. This thesis investigates the security issues created by the internal threats in 6LoWPAN and proposes the use of Intrusion Detection System (IDS) to deal with such threats. Our main works are to categorise the 6LoWPAN threats into two major types, and to develop two different IDSs to detect each of this type effectively. The major contributions of this thesis are summarised as below. First, we categorise the 6LoWPAN internal threats into two main types, one that focuses on compromising directly the network performance (performance-type) and the other is to manipulate the optimal topology (topology-type), to later downgrade the network service quality indirectly. In each type, we select some typical threats to implement, and assess their particular impacts on network performance as well as identify performance metrics that are sensitive in the attacked situations, in order to form the basis detection knowledge. In addition, on studying the topology-type, we propose several novel attacks towards the Routing Protocol for Low Power and Lossy network (RPL - the underlying routing protocol in 6LoWPAN), including the Rank attack, Local Repair attack and DIS attack. Second, we develop a Bayesian-based IDS to detect the performance-type internal threats by monitoring typical attacking targets such as traffic, channel or neighbour nodes. Unlike other statistical approaches, which have a limited view by just using a single metric to monitor a specific attack, our Bayesian-based IDS can judge an abnormal behaviour with a wiser view by considering of different metrics using the insightful understanding of their relations. Such wiser view helps to increase the IDS’s accuracy significantly. Third, we develop a Specification-based IDS module to detect the topology-type internal threats based on profiling the RPL operation. In detail, we generalise the observed states and transitions of RPL control messages to construct a high-level abstract of node operations through analysing the trace files of the simulations. Our profiling technique can form all of the protocol’s legal states and transitions automatically with corresponding statistic data, which is faster and easier to verify compare with other manual specification techniques. This IDS module can detect the topology-type threats quickly with a low rate of false detection. We also propose a monitoring architecture that uses techniques from modern technologies such as LTE (Long-term Evolution), cloud computing, and multiple interface sensor devices, to expand significantly the capability of the IDS in 6LoWPAN. This architecture can enable the running of both two proposed IDSs without much overhead created, to help the system to deal with most of the typical 6LoWPAN internal threats. Overall, the simulation results in Contiki Cooja prove that our two IDS modules are effective in detecting the 6LoWPAN internal threats, with the detection accuracy is ranging between 86 to 100% depends on the types of attacks, while the False Positive is also satisfactory, with under 5% for most of the attacks. We also show that the additional energy consumptions and the overhead of the solutions are at an acceptable level to be used in the 6LoWPAN environment.
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Olsson, Jonathan. "Detecting Faulty Piles of Wood using Anomaly Detection Techniques." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83061.

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The forestry and the sawmill industry have a lot of incoming and outgoing piles of wood. It's important to maintain quality and efficiency. This motivates an examination of whether machine learning- or more specifically, anomaly detection techniques can be implemented and used to detect faulty shipments. This thesis presents and evaluates some computer vision techniques and some deep learning techniques. Deep learning can be divided into groups; supervised, semi-supervised and unsupervised. In this thesis, all three groups were examined and it covers supervised methods such as Convolutional Neural Networks, semi-supervised methods such as a modified Convolutional Autoencoder (CAE) and lastly, an unsupervised technique such as Generative Adversarial Network (GAN) was being tested and evaluated.  A version of a GAN model proved to perform best for this thesis in terms of the accuracy of faulty detecting shipments with an accuracy rate of 68.2% and 79.8\% overall, which was satisfactory given the problems that were discovered during the progress of the thesis.
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Prevot, Yohan. "Arterial perfusion detection method by synchronous detection." [Tampa, Fla] : University of South Florida, 2005. http://purl.fcla.edu/usf/dc/et/SFE0001385.

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Chau, Sam. "Investigation of silicon PIN-detector for laser pulse detection." Thesis, Linköping University, Department of Science and Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-325.

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<p>This report has been written at SAAB Bofors Dynamics (SBD) AB in Gothenburg at the department of optronic systems.</p><p>In military observation operations, a target to hit is chosen by illumination of a laser designator. From the targetpoint laser radiation is reflected on a detector that helps identify the target. The detector is a semiconductor PIN-type that has been investigated in a laboratory environment together with a specially designed laser source. The detector is a photodiode and using purchased components, circuits for both the photodiode and the laserdiode has been designed and fabricated. The bandwidth of the op-amp should be about 30 MHz, in the experiments a bandwidth of 42 MHz was used. Initially the feedback network, which consists of a 5.6 pF capacitor in parallel with a 1-kohm resistor determined the bandwidth. To avoid the op-amp saturate under strong illuminated laser radiation the feedback network will use a 56-pF capacitor and a 100-ohm resistor respectively.</p><p>The laser should be pulsed with 10-20 ns width, 10 Hz repetition frequency, about 800 nm wavelength and a maximum output power of 80 mW. To avoid electrical reflection signals at measurement equipment connections, the laser circuit includes a resistor of about 50 ohm, that together with the resistance in the laserdiode forms the right termination that eliminate the reflection signals. The wire-wound type of resistor shall be avoided in this application and instead a surface mounted type was beneficial with much lower inductance. The detector showed a linear behaviour up to 40-mW optical power. Further investigation was hindered by the breakdown of the laserdiodes. The function generator limits the tests to achieve 80 mW in light power. In different experiments the responsivity of the photodiode is different from the nominal value, however it would have required more time to investigate the causes.</p>
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Chang, Pi-Jung. "Double Chooz neutrino detector: neutron detection systematic errors and detector seasonal stability." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16861.

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Doctor of Philosophy<br>Department of Physics<br>Glenn Horton-Smith<br>In March 2012, the Double Chooz reactor neutrino experiment published its most precise result so far: sin[superscript]2 2theta13 = 0.109 +/- 0.030(stat.) +/- 0.025(syst.). The statistical significance is 99.8% away from the no-oscillation hypothesis. The systematic uncertainties from background and detection efficiency are smaller than the first publication of the Double Chooz experiment. The neutron detection efficiency, one of the biggest contributions in detection systematic uncertainties, is a primary topic of this dissertation. The neutron detection efficiency is the product of three factors: the Gd-capture fraction, the efficiency of time difference between prompt and delayed signals, and the efficiency of energy containment. [superscript]252 Cf is used to determine the three factors in this study. The neutron detection efficiency from the [superscript]252 Cf result is confirmed by the electron antineutrino data and Monte Carlo simulations. The systematic uncertainty from the neutron detection efficiency is 0.91% used in the sin[superscript]2 2theta13 analysis. The seasonal variation in detector performance and the seasonal variations of the muon intensity are described in detail as well. The detector stability is confirmed by observation of two phenomena: 1) the [electron antineutrino] rate, which is seen to be uncorrelated with the liquid scintillator temperature, and 2) the daily muon rate, which has the expected correspondence with the effective atmospheric temperature. The correlation between the muon rate and effective atmospheric temperature is further analyzed in this thesis to determine the ratio of kaon to pion in the local atmosphere. An upper limit on instability of the neutron detection efficiency is established in the final chapter. The systematic error, 0.13%, from the relative instability is the deviation of the calibration runs. This thesis concludes with the potential systematic errors of neutron detection efficiency and estimation of how these potential systematic errors affect the result of sin[superscript]2 2theta13.
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Wang, Jinghui. "Evaluation of GaN as a Radiation Detection Material." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343316898.

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Frascarelli, Antonio Ezio. "Object Detection." Thesis, Mälardalens högskola, Inbyggda system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28259.

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During the last two decades the interest about computer vision raised steadily with multiple applications in fields like medical care, automotive, entertainment, retail, industrial, and security. Objectdetection is part of the recognition problem, which is the most important scope of the computervision environment.The target of this thesis work is to analyse and propose a solution for object detection in a real timedynamic environment. RoboCup@Home will be the benchmarking event for this system, which willbe equipped on a robot competing in the 2018 event. The system has to be robust and fast enoughto allow the robot to react to each environment change in a reasonable amount of time.The input hardware used to achieve such system comprise of a Microsoft Kinect, which providesan high definition camera and fast and reliable 3D scanner. Through the study and analysis ofstate-of-the-art algorithms regarding machine vision and object recognition, the more suitable oneshave been tested to optimise the execution on the targeted hardware. Porting of the application toan embedded platform is discussed.
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Books on the topic "Detection"

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G, Greene Douglas, ed. Detection by Gaslight: 14 Victorian Detective Stories. Mineola, N.Y., USA: Dover Publications, 1997.

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Lee, Wenke, Cliff Wang, and David Dagon, eds. Botnet Detection. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-68768-1.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. Iterative Detection. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6.

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Verschuere, Bruno, Gershon Ben-Shakhar, and Ewout Meijer, eds. Memory Detection. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511975196.

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Christodorescu, Mihai, Somesh Jha, Douglas Maughan, Dawn Song, and Cliff Wang, eds. Malware Detection. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-44599-1.

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Capineri, Lorenzo, and Eyüp Kuntay Turmuş, eds. Explosives Detection. Dordrecht: Springer Netherlands, 2019. http://dx.doi.org/10.1007/978-94-024-1729-6.

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Sengupta, Nandita, and Jaya Sil. Intrusion Detection. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2716-6.

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Gaspar, Imre, ed. RNA Detection. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7213-5.

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Lane, Brian. Crime & detection. New York: DK Pub., 2005.

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Heyer, Georgette. Detection Unlimited. Bath: Chivers Press, 1993.

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Book chapters on the topic "Detection"

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Bzik, Thomas J. "Detecting the Detection Limit." In Detection Limits in Air Quality and Environmental Measurements, 1–15. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2019. http://dx.doi.org/10.1520/stp161820180117.

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Ascari, Maurizio. "Detection before Detection." In A Counter-History of Crime Fiction, 17–40. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230234536_2.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Overview of Non-Iterative Detection." In Iterative Detection, 1–76. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_1.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Principles of Iterative Detection." In Iterative Detection, 77–191. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_2.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Iterative Detection for Complexity Reduction." In Iterative Detection, 193–238. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_3.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Adaptive Iterative Detection." In Iterative Detection, 239–71. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_4.

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Chugg, Keith M., Achilleas Anastasopoulos, and Xiaopeng Chen. "Applications in Two Dimensional Systems." In Iterative Detection, 273–313. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_5.

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Beerel, Peter A. "Implementation Issues: A Turbo Decoder Design Case Study." In Iterative Detection, 315–40. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1699-6_6.

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Sengupta, Nandita, and Jaya Sil. "Introduction." In Intrusion Detection, 1–25. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2716-6_1.

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Sengupta, Nandita, and Jaya Sil. "Discretization." In Intrusion Detection, 27–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2716-6_2.

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Conference papers on the topic "Detection"

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Ulyanov, N. A., S. V. Yaskevich, and P. A. Dergach. "DETECTION OF RECORDS OF WEAK LOCAL EARTHQUAKES USING MACHINE LEARNING." In All-Russian Youth Scientific Conference with the Participation of Foreign Scientists Trofimuk Readings - 2021. Novosibirsk State University, 2021. http://dx.doi.org/10.25205/978-5-4437-1251-2-76-78.

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In this paper, a detection method is proposed that is able to outperform deterministic methods in detecting events on seismograms, successfully detecting the same or more events with fewer false detections.
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Gauch, Anja, and Richard Diurba. "Prototyping the Light Detection of the DUNE Near Detector." In Prototyping the Light Detection of the DUNE Near Detector. US DOE, 2023. http://dx.doi.org/10.2172/2283763.

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van Nuland, Marjolein. "Neutron Detection in the DUNE Liquid Argon Near Detector." In Neutron Detection in the DUNE Liquid Argon Near Detector. US DOE, 2024. https://doi.org/10.2172/2479539.

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Beck, M., M. E. Anderson, and M. G. Raymer. "Imaging through Scattering Media Using Pulsed Homodyne Detection." In Advances in Optical Imaging and Photon Migration. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/aoipm.1994.ci.257.

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In this paper we demonstrate high resolution imaging of a target embedded in a scattering medium using a pulsed laser source. The imaging is performed by interfering the light emerging from the scattering medium with a 200 fs duration pulsed local oscillator beam, and detecting the interfered light with a balanced homodyne detector. We show that the data collected by our detector can be analyzed in two ways. One method strongly suppresses the detection of scattered light emerging from the medium, leaving almost exclusively the ballistic (unscattered) component of the signal. Using the other data analysis technique, both the scattered and ballistic components of the signal are readily detected. This shows that despite the fact that coherent (homodyne) detection is used, the system can be optimized for the detection of either coherent (unscattered) or incoherent (scattered) light.
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Strotov, V. V., and P. E. Zhgutov. "Combining Several Algorithmsto Increase the Accuracy of Pedestrian Detection and Localization." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-628-635.

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In this paper, the problem of developing an algorithm for detecting pedestrians in a sequence of images is considered. The concept of a combined algorithm combining a pedestrian detector with an aggregation function of several channels, together with a motion detection algorithm and a tracking function is proposed. The paper describes the stages of the algorithm, as well as experimental studies that were conducted on a set of test videos. During the study of the combined algorithm, the quality of detection of the combined algorithm and the pedestrian detector was compared separately. The study of the dependence of the results of the algorithms on changes in the detector parameters was carried out. Based on the results of experimental studies, a conclusion was made about the best indicators of accuracy and quality of detection in a combined algorithm that can find application in computer vision systems.
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Bedard, Alfred J. "The Infrasound Network (ISNet) as an 88D Adjunct Tornado Detection Tool: The Status of Infrasonic Tornado Detection." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-10480.

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After a background of operation through three years (2003–2005), the purpose of this paper is to summarize the status of our efforts to evaluate the potential of NOAA’s Infrasonic Detection Network (ISNet) for tornado detection and warning. The network implementation has involved parallel efforts at hardware design, detection algorithm development, designing displays, application of a fully compressible numerical model to understand infrasound generation processes, and modeling propagation using a 3-D acoustic ray tracing program. In combination, these efforts are placing us in a position to realistically assess the potential of infrasound to help with tornado detection and warning. One of our initial expectations, (that a 3-station network separated by about 200Km would robustly triangulate on acoustic tornadic sources) was shattered. Only at longer ranges (typically &amp;gt; 200Km because of an upper atmospheric wave guide) did the 3 stations (at Boulder, CO, Pueblo, CO, and Goodland, KS) monitor the same source with clear detections. On the other hand, individual stations showed regional tornado detection skill with different stations at times simultaneously detecting different regional tornadoes. Ray trace simulations have explained these regional detection differences, indicating that there is a need for closer network station spacing. Examples of both good and failed detections of tornadic storms are presented and recommendations are made for optimum network spacing. The needs for further evaluation are reviewed; including implementation of a denser network, improved verification (network stations can contribute guidance to field programs), improved display options, and timeliness. An intriguing aspect of our observations is that storms often produce infrasound about 30 minutes prior to a first tornado report. Numerical simulations completed and in progress should help illuminate the infrasonic source processes. Figure 1 below indicates the important elements involved with infrasonic tornado detection.
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Pacaldo, Joren Mundane, Chi Wee Tan, Wah Pheng Lee, Dustin Gerard Ancog, and Haroun Al Raschid Christopher Macalisang. "Utilizing Synthetically-Generated License Plate Automatic Detection and Recognition of Motor Vehicle Plates in Philippines." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1022.

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We investigated the potential use of synthetic data for automatic license plate detection and recognition by detecting and clustering each of the characters on the license plates. We used 36 cascading classifiers (26 letters + 10 numbers) as an individual character to detect synthetically generated license plates. We trained our cascade classifier using a Local Binary Pattern (LBP) as the visual descriptor. After detecting all the characters individually, an investigation has been established in identifying and utilizing a clustering algorithm in grouping these characters for valid license plate recognition. Two clustering algorithms have been considered including Hierarchical and K-means. Investigation results revealed that the hierarchical clustering algorithm approach produces better results in clustering the detecting characters than the K-means. Inaccuracy in the actual detection and recognition of license plates is largely attributed to the false detections in some of the 36 classifiers used in the study. To improve the precision in the detection of plate numbers, it is recommended to have a good classifier for each character detection and utilization of a good clustering algorithm. The proponents concluded that detecting and clustering each character was not an effective approach, however the use of synthetic data in training the classifiers shows promising results. Keywords: Cascading Classifiers, Synthetic Data, Local Binary Pattern, License Plate Recognition
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Khalid, Anna. "Kilonova Detection." In Kilonova Detection. US DOE, 2023. http://dx.doi.org/10.2172/1995266.

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Mitrevski, Jovan. "Low Energy LArTPC Signal Detection Using Anomaly Detection." In Low Energy LArTPC Signal Detection Using Anomaly Detection. US DOE, 2023. http://dx.doi.org/10.2172/2204657.

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Kolodziej, Joanna, Mateusz Krzyszton, and Pawel Szynkiewicz. "Anomaly Detection In TCP/IP Networks." In 37th ECMS International Conference on Modelling and Simulation. ECMS, 2023. http://dx.doi.org/10.7148/2023-0542.

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Intrusion Detection Systems (IDS) should be capable of quickly detecting attacks and network traffic anomalies to reduce the damage to the network components. They may efficiently detect threats based on prior knowledge of attack characteristics and the potential threat impact ('known attacks'). However, IDS cannot recognize threats, and attacks ('unknown attacks') usually occur when using brand-new technologies for system damage. This paper presents two security services -- Net Anomaly Detector (NAD) and a signature-based PGA Filter for detecting attacks and anomalies in TCP/IP networks. Both services are modules of the cloud-based GUARD platform developed in the H2020 GUARD project. Such a platform was the main component of the simulation environment in the work presented in this paper. The provided experiments show that both modules achieved satisfactory results in detecting an unknown type of DoS attacks and signatures of DDoS attacks.
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Reports on the topic "Detection"

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Bussiere, Matthew, Shawn Smith, and Stephane Bussiere. DTPH56-15-T-00004 Framework for Verifying and Validating External Leak Detection Systems on Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2015. http://dx.doi.org/10.55274/r0011847.

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A leak detection evaluation framework has been developed for use pipe pipeline operators to identify and evaluate candidate external leak detection systems intended for possible deployment on onshore transmission pipelines and assist operators in making an informal technology selection. The framework focusses on leak detection systems that are deployed or that operate external to the pipeline. Such systems are reportedly capable of detecting small releases that fall below the detection threshold of traditional leak detection systems, such as computation pipeline monitoring systems, and can therefore serve to complement existing leak detection infrastructure by extending the overall detection range.
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Broder, Bruce, and Stuart Schwartz. Quickest Detection Procedures and Transient Signal Detection. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada230068.

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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping: Linköping University Electronic Press, March 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were tested for narrowing the detections down to pests of a certain size. The results indicates that with further refinement and more training images this approach might hold potential for fungus gnat and rape beetles. Further, this thesis also investigates detection performance of Mask R-CNN and YOLOv5 on different insects in fields for the purpose of automating the data gathering process. The models showed promise for detection of rape beetles. YOLOv5 also showed promise as a multi-class detector of different insects, where sizes ranged from small rape beetles to larger bumblebees.
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Nestleroth, Dr J. Bruce. PR-3-823-R01 Remote Field Eddy Current Detection of Stress-Corrosion Cracks. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 1990. http://dx.doi.org/10.55274/r0011870.

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The feasibility of detecting stress-corrosion cracks (SCC) using the Remote Field Eddy Current (RFEC) technique was demonstrated. The RFEC technique interrogates the entire thickness of the pipe and is applicable for in-line inspection. If it can be shown that the RFEC technique is effective in detecting SCC, then the technique is an ideal method for detecting the defects of interest. A defect detection model is proposed for explaining the mechanism for crack detection. For axially orientated, closed cracks, such as SCC, the conventional defect detection model proved to be too simplistic and not applicable. Therefore, a new detection model that examines the flow of circumferential eddy currents was developed based on experimental results. This model, though not rigorous, provides a general understanding of the applicability of the RFEC technique for finding SCC. The data from the cracks and various artificial defects are presented in three formats: isometric projections, pseudocolor images, and line-of-sight data. Though only two cracks were found, the experimental results correlate well with the circumferential eddy current theory. A theoretical analysis of the effects of motion on the output signal of the receiver is presented. This analysis indicates that the inspection speed of simple implementations may be limited to a few miles per hour.
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Guan, Yawen, Deborah Sulsky, J. Tucker, and Christian Sampson. Feature Detection. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769711.

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Wendelberger, James G. Pit and Crack Detection Summary Report Crack Detection. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1504667.

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Lukow, Steven, Ross Lee, Jonathan Gigax, and David Grow. Improving Non-Destructive Detection Technology Through SAVY Feature Detection. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1884728.

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Siebenaler. PR-015-084510-R01 Evaluation of External Leak Detection Systems for Liquid Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2009. http://dx.doi.org/10.55274/r0010674.

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A project was conducted to determine the viability of various external leak detection systems for use on liquid pipelines.� This project defined external leak detection as a means of detecting unintended pipeline discharge without the use of pressure or flow modeling. Specifically, this project was focused on the ability to detect leaks that are too small for most computational pipeline monitoring (CPM) systems to detect.� A list of possible technologies was assembled and a tradeoff study was conducted to rank the technologies.
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Va'vra, Jaroslav. Single Electron Detection in Quadruple-GEM Detector with Pad Readout. Office of Scientific and Technical Information (OSTI), March 2001. http://dx.doi.org/10.2172/784889.

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Barnett, R. M., K. Einsweiler, and I. Hinchliffe. Higgs detection via decays to leptons with the SDC detector. Office of Scientific and Technical Information (OSTI), November 1990. http://dx.doi.org/10.2172/6053256.

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