Relevant bibliographies by topics / Local detection

Academic literature on the topic 'Local detection'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Local detection"

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Ulyanov, N. A., S. V. Yaskevich, Dergach P. A., and A. V. YablokovAV. "Detection of records of weak local earthquakes using neural networks." Russian Journal of Geophysical Technologies, no. 2 (January 13, 2022): 13–23. http://dx.doi.org/10.18303/2619-1563-2021-2-13.

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Manual processing of large volumes of continuous observations produced by local seismic networks takes a lot of time, therefore, to solve this problem, automatic algorithms for detecting seismic events are used. Deterministic methods for solving the problem of detection, which do an excellent job of detecting intensive earthquakes, face critical problems when detecting weak seismic events (earthquakes). They are based on principles based on the calculation of energy, which causes multiple errors in detection: weak seismic events may not be detected, and high-amplitude noise may be mistakenly detected as an event. In our work, we propose a detection method capable of surpassing deterministic methods in detecting events on seismograms, successfully detecting a similar or more events with fewer false detections.
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Fleming, A. D., S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp. "Automated microaneurysm detection using local contrast normalization and local vessel detection." IEEE Transactions on Medical Imaging 25, no. 9 (September 2006): 1223–32. http://dx.doi.org/10.1109/tmi.2006.879953.

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Sakr, Mohamed, Walid Atwa, and Arabi Keshk. "Genetic-based Summarization for Local Outlier Detection in Data Stream." International Journal of Intelligent Systems and Applications 13, no. 1 (February 8, 2021): 58–68. http://dx.doi.org/10.5815/ijisa.2021.01.05.

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Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data.
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Hollocou, Alexandre, Thomas Bonald, and Marc Lelarge. "Multiple Local Community Detection." ACM SIGMETRICS Performance Evaluation Review 45, no. 3 (March 20, 2018): 76–83. http://dx.doi.org/10.1145/3199524.3199537.

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Ni, Li, Wenjian Luo, Wenjie Zhu, and Bei Hua. "Local Overlapping Community Detection." ACM Transactions on Knowledge Discovery from Data 14, no. 1 (February 4, 2020): 1–25. http://dx.doi.org/10.1145/3361739.

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Wu, Yubao, Ruoming Jin, Jing Li, and Xiang Zhang. "Robust local community detection." Proceedings of the VLDB Endowment 8, no. 7 (February 2015): 798–809. http://dx.doi.org/10.14778/2752939.2752948.

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Oliveira, Leonardo D., Fernando Ciriaco, Taufik Abrão, and Paul Jean E. Jeszensky. "Local search multiuser detection." AEU - International Journal of Electronics and Communications 63, no. 4 (April 2009): 259–70. http://dx.doi.org/10.1016/j.aeue.2008.01.009.

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Rigolin, G., and C. O. Escobar. "Local detection of entanglement." European Physical Journal D 37, no. 2 (November 16, 2005): 291–96. http://dx.doi.org/10.1140/epjd/e2005-00301-8.

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Cidon, I., and J. M. Jaffe. "Local distributed deadlock detection by knot detection." ACM SIGCOMM Computer Communication Review 16, no. 3 (August 1986): 377–84. http://dx.doi.org/10.1145/1013812.18214.

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Xiang, Ju, Zhi-Zhong Wang, Hui-Jia Li, Yan Zhang, Shi Chen, Cui-Cui Liu, Jian-Ming Li, and Li-Juan Guo. "Comparing local modularity optimization for detecting communities in networks." International Journal of Modern Physics C 28, no. 06 (May 7, 2017): 1750084. http://dx.doi.org/10.1142/s012918311750084x.

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Community detection is one important problem in network theory, and many methods have been proposed for detecting community structures in the networks. Given quality functions for evaluating community structures, community detection can be considered as one kind of optimization problem, such as modularity optimization, therefore, optimization of quality functions has been one of the most popular strategies for community detection. In this paper, we introduced two kinds of local modularity functions for community detection, and the self-consistent method is introduced to optimize the local modularity for detecting communities in the networks. We analyze the behaviors of the modularity optimizations, and compare the performance of them in community detection. The results confirm the superiority of the local modularity in detecting community structures, especially on large-size and heterogeneous networks.
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Dissertations / Theses on the topic "Local detection"

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Marín, Tur Javier. "Pedestrian Detection based on Local Experts." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/120187.

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Al llarg dels darrers anys, els sistemes de detecció humana basats en visió per computador han començat a exercir un paper clau en diverses aplicacions lligades a l’assisténcia a la conducció, la videovigilància, la robòtica i la domòtica. Detectar persones és, sens cap dubte, una de les tasques més difícils en el camp de la Visió per Computador. Aixó es deu principalment al grau de variabilitat en l’aparenc¸a humana associada a la roba, postura, forma i grandària. A més, altres factors com escenaris amb molts elements, oclusions parcials o condicions ambientals poden fer que la tasca de detecció sigui encara més difícil. Els mètodes més prometedors a l’estat de la q¨uestió es basen en models d’aprenentatge discriminatius que són entrenats amb exemples positius (vianants) i negatius (no vianants). El conjunt d’entrenament és un dels elements més rellevants a l’hora de construir un detector que faci front a la citada gran variabilitat. Per tal de crear el conjunt d’entrenament es requereix supervisió humana. L’inconvenient en aquest punt és el gran esforc¸ que suposa haver d’anotar, així com la tasca de cercar l’esmentada variabilitat. En aquesta tesi abordem dos problemes recurrents a l’estat de la q¨uestió. En la primera etapa, es pretén reduir l’esforc¸ d’anotar mitjanc¸ant l’ús de gràfics per computador. Més concretament, desenvolupemun escenari urbà permés endavant generar un conjunt d’entrenament. Tot seguit, entrenem un detector usant aquest conjunt, i finalment, avaluem si aquest detector pot ser aplicat amb èxit en un escenari real. En la segona etapa, ens centrem en millorar la robustesa dels nostres detectors en el cas en que els vianants es trobin parcialment ocluids. Més concretament, presentem un nou mètode de tractament d’oclusions que consisteix en millorar la detecció de sistemes holístics en cas de trobar un vianant parcialment ocluid. Per dur a terme aquesta millora, fem ús de classificadors (experts) locals a través d’un mètode anomenat random subspace method (RSM). Si el sistema holístic infereix que hi ha un vianant parcialment ocluid, aleshores s’aplica el RSM, el qual ha estat entrenat prèviament amb un conjunt que contenia vianants parcialment ocluids. L’últim objectiu d’aquesta tesi és proposar un detector de vianants fiable basat en un conjunt d’experts locals. Per aconseguir aquest objectiu, utilitzem el mètode anomenat random forest, a on els arbres es combinen per classificar i cada node és un expert local. En particular, cada expert local es centra en realitzar una classificació robusta de zones del cos. Cal remarcar, a més, que el nostre mètode presenta molta menys complexitat a nivell de disseny que altres mètodes de l’estat de la q¨uestió, alhora que ofereix una eficiència computacional raonable i una major precisió.
During the last decade vision-based human detection systems have started to play a key role in multiple applications linked to driver assistance, surveillance, robot sensing and home automation. Detecting humans is by far one of the most challenging tasks in Computer Vision. This is mainly due to the high degree of variability in the human appearance associated to the clothing, pose, shape and size. Besides, other factors such as cluttered scenarios, partial occlusions, or environmental conditions can make the detection task even harder. Most promising methods of the state-of-the-art rely on discriminative learning paradigms which are fed with positive and negative examples. The training data is one of the most relevant elements in order to build a robust detector as it has to cope the large variability of the target. In order to create this dataset human supervision is required. The drawback at this point is the arduous effort of annotating as well as looking for such claimed variability. In this PhD thesis we address two recurrent problems in the literature. In the first stage, we aim to reduce the consuming task of annotating, namely, by using computer graphics. More concretely, we develop a virtual urban scenario for later generating a pedestrian dataset. Then, we train a detector using this dataset, and finally we assess if this detector can be successfully applied in a real scenario. In the second stage, we focus on increasing the robustness of our pedestrian detectors under partial occlusions. In particular, we present a novel occlusion handling approach to increase the performance of block-based holistic methods under partial occlusions. For this purpose, we make use of local experts via a RandomSubspaceMethod (RSM) to handle these cases. If the method infers a possible partial occlusion, then the RSM, based on performance statistics obtained from partially occluded data, is applied. The last objective of this thesis is to propose a robust pedestrian detector based on an ensemble of local experts. To achieve this goal, we use the random forest paradigm, where the trees act as ensembles an their nodes are the local experts. In particular, each expert focus on performing a robust classification of a pedestrian body patch. This approach offers computational efficiency and far less design complexity when compared to other state-of-the-artmethods, while reaching better accuracy.
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Ahlgren, Filip. "Local And Network Ransomware Detection Comparison." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18291.

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Background. Ransomware is a malicious application encrypting important files on a victim's computer. The ransomware will ask the victim for a ransom to be paid through cryptocurrency. After the system is encrypted there is virtually no way to decrypt the files other than using the encryption key that is bought from the attacker. Objectives. In this practical experiment, we will examine how machine learning can be used to detect ransomware on a local and network level. The results will be compared to see which one has a better performance. Methods. Data is collected through malware and goodware databases and then analyzed in a virtual environment to extract system information and network logs. Different machine learning classifiers will be built from the extracted features in order to detect the ransomware. The classifiers will go through a performance evaluation and be compared with each other to find which one has the best performance. Results. According to the tests, local detection was both more accurate and stable than network detection. The local classifiers had an average accuracy of 96% while the best network classifier had an average accuracy of 89.6%. Conclusions. In this case the results show that local detection has better performance than network detection. However, this can be because the network features were not specific enough for a network classifier. The network performance could have been better if the ransomware samples consisted of fewer families so better features could have been selected.
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Aytekin, Caglar. "Geo-spatial Object Detection Using Local Descriptors." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613488/index.pdf.

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There is an increasing trend towards object detection from aerial and satellite images. Most of the widely used object detection algorithms are based on local features. In such an approach, first, the local features are detected and described in an image, then a representation of the images are formed using these local features for supervised learning and these representations are used during classification . In this thesis, Harris and SIFT algorithms are used as local feature detector and SIFT approach is used as a local feature descriptor. Using these tools, Bag of Visual Words algorithm is examined in order to represent an image by the help of histograms of visual words. Finally, SVM classifier is trained by using positive and negative samples from a training set. In addition to the classical bag of visual words approach, two novel extensions are also proposed. As the first case, the visual words are weighted proportional to their importance of belonging to positive samples. The important features are basically the features occurring more in the object and less in the background. Secondly, a principal component analysis after forming the histograms is processed in order to remove the undesired redundancy and noise in the data, reduce the dimension of the data to yield better classifying performance. Based on the test results, it could be argued that the proposed approach is capable to detecting a number of geo-spatial objects, such as airplane or ships, for a reasonable performance.
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Saigo, Hiroto. "Local alignment kernels for protein homology detection." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/135936.

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Beare, Richard. "Image segmentation based on local motion detection /." Title page, contents and abstract only, 1997. http://web4.library.adelaide.edu.au/theses/09PH/09phb3684.pdf.

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Donnelley, Martin, and martin donnelley@gmail com. "Computer Aided Long-Bone Segmentation and Fracture Detection." Flinders University. Engineering, 2008. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20080115.222927.

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Medical imaging has advanced at a tremendous rate since x-rays were discovered in 1895. Today, x-ray machines produce extremely high-quality images for radiologists to interpret. However, the methods of interpretation have only recently begun to be augmented by advances in computer technology. Computer aided diagnosis (CAD) systems that guide healthcare professionals to making the correct diagnosis are slowly becoming more prevalent throughout the medical field. Bone fractures are a relatively common occurrence. In most developed countries the number of fractures associated with age-related bone loss is increasing rapidly. Regardless of the treating physician's level of experience, accurate detection and evaluation of musculoskeletal trauma is often problematic. Each year, the presence of many fractures is missed during x-ray diagnosis. For a trauma patient, a mis-diagnosis can lead to ineffective patient management, increased dissatisfaction, and expensive litigation. As a result, detection of long-bone fractures is an important orthopaedic and radiologic problem, and it is proposed that a novel CAD system could help lower the miss rate. This thesis examines the development of such a system, for the detection of long-bone fractures. A number of image processing software algorithms useful for automating the fracture detection process have been created. The first algorithm is a non-linear scale-space smoothing technique that allows edge information to be extracted from the x-ray image. The degree of smoothing is controlled by the scale parameter, and allows the amount of image detail that should be retained to be adjusted for each stage of the analysis. The result is demonstrated to be superior to the Canny edge detection algorithm. The second utilises the edge information to determine a set of parameters that approximate the shaft of the long-bone. This is achieved using a modified Hough Transform, and specially designed peak and line endpoint detectors. The third stage uses the shaft approximation data to locate the bone centre-lines and then perform diaphysis segmentation to separate the diaphysis from the epiphyses. Two segmentation algorithms are presented and one is shown to not only produce better results, but also be suitable for application to all long-bone images. The final stage applies a gradient based fracture detection algorithm to the segmented regions. This algorithm utilises a tool called the gradient composite measure to identify abnormal regions, including fractures, within the image. These regions are then identified and highlighted if they are deemed to be part of a fracture. A database of fracture images from trauma patients was collected from the emergency department at the Flinders Medical Centre. From this complete set of images, a development set and test set were created. Experiments on the test set show that diaphysis segmentation and fracture detection are both performed with an accuracy of 83%. Therefore these tools can consistently identify the boundaries between the bone segments, and then accurately highlight midshaft long-bone fractures within the marked diaphysis. Two of the algorithms---the non-linear smoothing and Hough Transform---are relatively slow to compute. Methods of decreasing the diagnosis time were investigated, and a set of parallelised algorithms were designed. These algorithms significantly reduced the total calculation time, making use of the algorithm much more feasible. The thesis concludes with an outline of future research and proposed techniques that---along with the methods and results presented---will improve CAD systems for fracture detection, resulting in more accurate diagnosis of fractures, and a reduction of the fracture miss rate.
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BERVANAKIS, GEORGE, and gberva@hotmail com. "DETECTION AND EXPRESSION OF BIOSYNTHETIC GENES IN ACTINOBACTERIA." Flinders University. School of Medicine, 2009. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20090531.033038.

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Most microbial organic molecules are secondary metabolites which consist of diverse chemical structures and a range of biological activities. Actinobacteria form a large group of Eubacteria that are prolific producers of these metabolites. The recurrence of pathogens resistant to antibiotics and a wider use of these metabolites apart from their use as anti-infectives, has been the impetus for pharmaceutical companies to search for compounds produced by rare and existing actinobacterial cultures. Accessing microbial biosynthetic pathway diversity has been possible through the use of sensitive and innovative molecular detection methodologies. The present study evaluated the use of molecular based screening as a rational approach to detect secondary metabolite biosynthetic genes (SMBG) in uncharacterised natural Actinobacterial populations. A polymerase chain reaction (PCR) approach was selected for ease of application and high sample processivity. Rational designed screening approaches using PCR in the discovery of SMBG, involved identifying common functions in secondary metabolite biosynthetic pathways, such as condensation reactions in polyketide synthesis, genes encoding these functions, and using conserved regions of these genes as templates for the design of primers to detect similar sequences in uncharacterised actinobacteria. Design of primers involved rigorous in silico analysis followed by experimentation and validation. PCR screening was applied to 22 uncharacterised environmental isolates, eight of these displayed the presence of the ketosynthase (KS) gene belonging to the type I polyketide synthases and eight contained the ketosynthase (KSĄ) gene belonging to the type II polyketide synthases, six of the isolates contained the presence of a presumptive dTDP-glucose synthase (strD) gene which is involved in the formation of deoxysugar components of aminoglycoside antibiotics and one isolate contained the presence of a presumptive isopenicillin N synthase (pcbC) gene involved in beta-lactam synthesis. Alignments of partially sequenced PCR products from isolates A1488 and A3023 obtained using type II PKS primers showed close similarities with KSĄ genes from antibiotic producing actinobacteria. Similarly, alignments of sequences from isolates A1113 and A0350 showed regions of similarities to KS genes from antibiotic producing actinobacteria. Fermentation techniques were used for inducing expression of secondary metabolites from the uncharacterised actinobacteria isolates. By using antimicrobial guided screening it was determined that most of the isolates possessed the capacity to produce antimicrobial metabolites. Dominant antagonistic activity was detected against Gram positive bacteria and to a minor extent against fungi. Optimal fermentation liquid media were identified for certain isolates for the production of antimicrobial metabolites. Two alternative fermentation methods; solid-state and liquid-oil fermentations were evaluated to improve secondary metabolite production in the uncharacterised isolates. Solid-substrate fermentation showed that it could induce a complex metabolite pattern by TLC analysis, however this pattern varied according to the substrate being used. Liquid media supplemented with refined oils, showed a positive response indicated by higher antibacterial activities detected. Evaluation of semi-purified organic extracts identified two isolates A1113 and A0350 producing similar antimicrobial metabolites as detected by HPLC/UV/MS, a literature database search of similar compounds containing the same molecular weight identified the compound as belonging to the actinomycin group of compounds. A complex metabolic pattern was identified for isolate A2381, database searching identified some of the compounds as having similar molecular weights to actinopyrones, trichostatins, antibiotics PI 220, WP 3688-5 and YL 01869P. Drug discovery screening can serve to benefit from PCR detection of biochemical genotypes in initial screens, providing a rapid approach in identifying secondary metabolite producing capabilities of microorganisms prior to the commencement of costly and time consuming fermentation studies. Additionally the identification of biochemical genotypes allows a directed approach in using fermentation media designed to induce biosynthetic pathways of specific classes of compounds.
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Trauchessec, Vincent. "Local magnetic detection and stimulation of neuronal activity." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS301/document.

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L’activité cérébrale se traduit par des courants ioniques circulant dans le réseau neuronal.La compréhension des mécanismes cérébraux implique de sonder ces courants, via des mesures électriques ou magnétiques, couvrant différentes échelles spatiales. A l’échelle cellulaire, les techniques d’électrophysiologie sont maitrisées depuis plusieurs décennies, mais il n’existe pas actuellement d’outils de mesure locale des champs magnétiques engendrés par les courants ioniques au sein du réseau neuronal. La magnéto-encéphalographie(MEG) utilise des SQUIDs(Superconducting QUantum Interference Devices)fonctionnant à très basse température, placés en surface du crâne, qui fournissent une cartographie des champs magnétiques mais dont la résolution spatiale est limitée du fait de la distance séparant les capteurs des cellules actives. Le travail présenté dans cette thèse propose de développer des capteurs magnétiques à la fois suffisamment sensibles pour être capable de détecter le champ magnétique extrêmement faible générés par les courants neuronaux (de l’ordre de 10⁻⁹ T), et dont la géométrie est adaptable aux dimensions des cellules, tout en fonctionnant à température ambiante. Ces capteurs,basés sur l’effet quantique de magnétorésistance géante (GMR, sont suffisamment miniaturisables pour être déposés à l’extrémité de sondes d’une finesse de l’ordre de 100 μm. L’utilisation de capteurs GMR pour la mesure de signaux biomagnétiques fut d’abord testée lors d’expériences in-vitro, réalisées sur le muscle soléaire de souris. Ce système biologique a été choisi pour sa simplicité,rendant la modélisation accessible, ainsi que pour sa robustesse, permettant d’avoir des résultats fiables et reproductibles. Le parfait accord entre les prédictions théoriques et les signaux magnétiques mesurés valide cette technologie. Enfin, des expériences in vivo dans le cortex visuel du chat ont permis de réaliser la toute première mesure de la signature magnétique de potentiels d’action générés par des neurones corticaux, ouvrant la voie à la magnétophysiologie
Information transmission in the brain occurs through ionic currents flowing inside the neuronal network. Understanding how the brain operates requires probing this electrical activity by measuring the associated electric or magnetic field. At the cellular scale, electrophysiology techniques are well mastered, but there is no tool to perform magnetophysiology. Mapping brain activity through the magnetic field generated by neuronal communication is done via magnetoencephalography (MEG). This technique is based on SQUIDs (Superconducting Quantum Interference Devices) that operate at liquid Helium temperature. This parameter implies to avoid any contact with living tissue and a shielding system that increases the distance between the neurons and the sensors, limiting spatial resolution. This thesis work aims at providing a new tool to performmagnetic recordings at the neuronal scale. The sensors developed during this thesis are based on the Giant Magneto-Resistance (GMR) effect. Operating at room temperature, they can be miniaturize and shaped according to the experiment, while exhibiting a sensitivity that allows to measure amplitude of 10⁻⁹ T. Before targeting neurons, the use of GMR-based sensors for magnetic recordings of biological activity has been validated through invitro experiments on the mouse soleus muscle. This biological system has been chosen because of its simple organization, allowing for a realistic modelling, and for its robustness, in order to get reliable and replicable results. The perfect agreement between the measurements and the theoretical predictions represents a consistent validation of the GMR technology for biological applications. Then a specially adapted needle-shaped probe carrying micron-sized GMR sensors has been developed for in-vivo experiment in cat visual cortex. The very first magnetic signature of action potentials inside the neuropil has been measured, paving the way towards magnetophysiology
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Gill, Rupinder S. "Intrusion detection techniques in wireless local area networks." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/29351/1/Rupinder_Gill_Thesis.pdf.

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This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection techniques to address these monitoring requirements and also studies correlation of the generated alarms across wireless intrusion detection system (WIDS) sensors and the detection techniques themselves for greater reliability and robustness. The specific outcomes of this research are: A comprehensive review of the outstanding vulnerabilities and attacks in IEEE 802.11i RSNs. A comprehensive review of the wireless intrusion detection techniques currently available for detecting attacks on RSNs. Identification of the drawbacks and limitations of the currently available wireless intrusion detection techniques in detecting attacks on RSNs. Development of three novel wireless intrusion detection techniques for detecting RSN attacks and security policy violations in RSNs. Development of algorithms for each novel intrusion detection technique to correlate alarms across distributed sensors of a WIDS. Development of an algorithm for automatic attack scenario detection using cross detection technique correlation. Development of an algorithm to automatically assign priority to the detected attack scenario using cross detection technique correlation.
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Gill, Rupinder S. "Intrusion detection techniques in wireless local area networks." Queensland University of Technology, 2009. http://eprints.qut.edu.au/29351/.

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This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection techniques to address these monitoring requirements and also studies correlation of the generated alarms across wireless intrusion detection system (WIDS) sensors and the detection techniques themselves for greater reliability and robustness. The specific outcomes of this research are: A comprehensive review of the outstanding vulnerabilities and attacks in IEEE 802.11i RSNs. A comprehensive review of the wireless intrusion detection techniques currently available for detecting attacks on RSNs. Identification of the drawbacks and limitations of the currently available wireless intrusion detection techniques in detecting attacks on RSNs. Development of three novel wireless intrusion detection techniques for detecting RSN attacks and security policy violations in RSNs. Development of algorithms for each novel intrusion detection technique to correlate alarms across distributed sensors of a WIDS. Development of an algorithm for automatic attack scenario detection using cross detection technique correlation. Development of an algorithm to automatically assign priority to the detected attack scenario using cross detection technique correlation.
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Books on the topic "Local detection"

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Morik, Katharina, Jean-François Boulicaut, and Arno Siebes, eds. Local Pattern Detection. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b137601.

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Great Britain. Office of Fair Trading. Cartels: Detection and remedies : a guide for local authorities. UK: H.M.S.O., 1991.

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Badami, Vinay S. A link failure detection scheme at level 2. Bangalore: Dept. of Electrical Communication Engineering, Indian Institute of Science, 1993.

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Hawrelluk, Heather. Detection of dementia: Are local physicians satisfied with current screening tools? Sudbury, Ont: Huntington University College, 2006.

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IEEE Computer Society. Technical Committee on Computer Communications., ed. Carrier sense multiple access with collision detection (CSMA/CD) access method and physical layer specifications: IEEE standards for local area networks. New York, NY, USA: Institute of Electrical and Electronics Engineers, 1985.

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Katharina, Morik, Boulicaut Jean-François, and Siebes Arno 1958-, eds. Local pattern detection: International seminar : Dagstuhl Castle, Germany, April 12-16, 2004 : revised selected papers. Berlin: Springer, 2005.

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Institute, American National Standards. IEEE standards for local and metropolitan area networks: Supplement to carrier sense multiple access with collision detection (CSMA/CD) access method and physical layer specifications. New York: Institute of Electrical and Electronics Engineers, 1992.

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IEEE Computer Society. Technical Committee on Computer Communications. and American National Standards Institute, eds. Supplements to Carrier sense multiple access with collision detection (CSMA/CD) access method and physical layer specifications: ANSI/IEEE 802.3a-1988, medium attachment unit and baseband medium specifications, type 10BASE2 (section 10) ... : IEEE standards for local area networks. New York, NY, USA: Institute of Electrical and Electronics Engineers, 1987.

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Institute, American National Standards. Supplements to Carrier sense multiple access with collision detection (CSMA/CD) access method and physical layer specifications: ANSI/IEEE 802.3a-1988, medium attachment unit and baseband medium specifications, type 10BASE2 (section 10) ... : IEEEstandards for local area networks. New York, NY, USA: Institute of Electrical and Electronics Engineers, 1987.

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Institute, American National Standards. IEEE standards for local and metropolitan area networks: Supplement to carrier sense multiple access with collision detection (CSMA/CD) access method and physical layer specifications : layer management for 10 Mb/s baseband repeaters (section 19). New York, NY: Institute of Electrical and Electronics Engineers, 1992.

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Book chapters on the topic "Local detection"

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Wrachtrup, J., C. Borczyskowski, M. Vogel, A. Gruber, J. Bernard, R. Brown, and M. Orrit. "Detection of a single electron spin." In Photons and Local Probes, 313–18. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0423-4_28.

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Gessner, Manuel, Heinz-Peter Breuer, and Andreas Buchleitner. "The Local Detection Method: Dynamical Detection of Quantum Discord with Local Operations." In Quantum Science and Technology, 275–307. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53412-1_14.

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Mittelbach, Arno, Lasse Lehmann, Christoph Rensing, and Ralf Steinmetz. "Automatic Detection of Local Reuse." In Sustaining TEL: From Innovation to Learning and Practice, 229–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16020-2_16.

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Cravino, Nuno, and Álvaro Figueira. "Community Detection by Local Influence." In Advances in Intelligent Systems and Computing, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36981-0_18.

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Dang, Xuan Hong, Barbora Micenková, Ira Assent, and Raymond T. Ng. "Local Outlier Detection with Interpretation." In Advanced Information Systems Engineering, 304–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40994-3_20.

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Studer, Ahren, and Chenxi Wang. "Adaptive Detection of Local Scanners." In RoboCup 2005: Robot Soccer World Cup IX, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11767480_1.

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Gama, João, and Gladys Castillo. "Learning with Local Drift Detection." In Advanced Data Mining and Applications, 42–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_4.

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Agrawal, Ankur. "Local Subspace Based Outlier Detection." In Communications in Computer and Information Science, 149–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03547-0_15.

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Pandya, Urja, Vidhi Mistry, Anjana Rathwa, Himani Kachroo, and Anjali Jivani. "2DBSCAN with Local Outlier Detection." In Advances in Intelligent Systems and Computing, 255–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1518-7_21.

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Höppner, Frank. "Local Pattern Detection and Clustering." In Lecture Notes in Computer Science, 53–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11504245_4.

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Conference papers on the topic "Local 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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Seo, Jangwon, and W. Bruce Croft. "Local text reuse detection." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390432.

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Du, Haizhou, Shengjie zhao, and Daqiang zhang. "Robust Local Outlier Detection." In 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2015. http://dx.doi.org/10.1109/icdmw.2015.114.

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Cidon, I., and J. M. Jaffe. "Local distributed deadlock detection by knot detection." In the ACM SIGCOMM conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/18172.18214.

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Greening, C. M. "Handwriting identification using global and local features for forensic purposes." In European Convention on Security and Detection. IEE, 1995. http://dx.doi.org/10.1049/cp:19950511.

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Lepisto, Leena, Aki Launiainen, and Iivari Kunttu. "Red eye detection using color and shape." In 2009 International Workshop on Local and Non-Local Approximation in Image Processing (LNLA 2009). IEEE, 2009. http://dx.doi.org/10.1109/lnla.2009.5278391.

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Tianqiang Yuan and Xiaoou Tang. "Efficient local reflectional symmetries detection." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530608.

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Glumov, N. I., and A. V. Kuznetsov. "Local Artificial Image's Changes Detection." In ACIT - Information and Communication Technology. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.691-024.

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Mi, Hongjuan, and Jikui Wang. "CBLOS: Improving local outlier detection." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5881753.

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Lloyd, K., P. L. Rosin, A. D. Marshall, and S. C. Moore. "Violent behaviour detection using local trajectory response." In 7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016). Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/ic.2016.0082.

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Reports on the topic "Local detection"

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Gok, M. Rengin, Farra Al-Jerri, Douglas Dodge, Abdullah Al-Enezi, Terri Hauk, and R. Mellors. Detection of Local/Regional Events in Kuwait Using Next-Generation Detection Algorithms. Office of Scientific and Technical Information (OSTI), December 2014. http://dx.doi.org/10.2172/1252605.

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Shand, Lyndsay, Kelsie Larson, Andrea Staid, Erika Roesler, Donald Lyons, Katherine Simonson, Lekha Patel, James Hickey, and Skyler Gray. Local limits of detection for anthropogenic aerosol-cloud interactions. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1855009.

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Paragiri, S., V. Govindan, and M. Mudigonda. Bidirectional Forwarding Detection (BFD) for Virtual eXtensible Local Area Network (VXLAN). Edited by S. Pallagatti and G. Mirsky. RFC Editor, December 2020. http://dx.doi.org/10.17487/rfc8971.

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Carmichael, Joshua Daniel, Christina Carr, and Erin C. Pettit. Fully Autonomous Multiplet Event Detection: Application to Local-Distance Monitoring of Blood Falls Seismicity. Office of Scientific and Technical Information (OSTI), June 2015. http://dx.doi.org/10.2172/1186035.

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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.

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This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.
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Stevens, Alan. Local shielding requirements for the STAR detector. Office of Scientific and Technical Information (OSTI), June 1992. http://dx.doi.org/10.2172/1118844.

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Bercovier, Herve, and Ronald P. Hedrick. Diagnostic, eco-epidemiology and control of KHV, a new viral pathogen of koi and common carp. United States Department of Agriculture, December 2007. http://dx.doi.org/10.32747/2007.7695593.bard.

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Original objectives and revisions-The proposed research included these original objectives: field validation of diagnostic tests (PCR), the development and evaluation of new sensitive tools (LC-PCR/TaqManPCR, antibody detection by ELISA) including their use to study the ecology and the epidemiology of KHV (virus distribution in the environment and native cyprinids) and the carrier status of fish exposed experimentally or naturally to KHV (sites of virus replication and potential persistence or latency). In the course of the study we completed the genome sequence of KHV and developed a DNA array to study the expression of KHV genes in different conditions. Background to the topics-Mass mortality of koi or common carp has been observed in Israel, USA, Europe and Asia. These outbreaks have reduced exports of koi from Israel and have created fear about production, import, and movements of koi and have raised concerns about potential impacts on native cyprinid populations in the U.S.A. Major conclusions-A suite of new diagnostic tools was developed that included 3 PCR assays for detection of KHV DNA in cell culture and fish tissues and an ELISA assay capable of detecting anti-KHV antibodies in the serum of koi and common carp. The TKPCR assay developed during the grant has become an internationally accepted gold standard for detection of viral DNA. Additionally, the ELISA developed for detecting serum anti-KHV antibodies is now in wide use as a major nonlethal screening tool for evaluating virus status of koi and common carp populations. Real time PCR assays have been able to detect viral DNA in the internal organs of survivors of natural and wild type vaccine exposures at 1 and 10³ genome equivalents at 7 months after exposure. In addition, vaccinated fish were able to transmit the virus to naive fish. Potential control utilizing hybrids of goldfish and common carp for production demonstrated they were considerably more resistant than pure common carp or koi to both KHV (CyHV-3). There was no evidence that goldfish or other tested endemic cyprinids species were susceptible to KHV. The complete genomic sequencing of 3 strains from Japan, the USA, and Israel revealed a 295 kbp genome containing a 22 kbp terminal direct repeat encoding clear gene homologs to other fish herpesviruses in the family Herpesviridae. The genome encodes156 unique protein-coding genes, eight of which are duplicated in the terminal repeat. Four to seven genes are fragmented and the loss of these genes may be associated with the high virulence of the virus. Viral gene expression was studies by a newly developed chip which has allowed verification of transcription of most all hypothetical genes (ORFs) as well as their kinetics. Implications, both scientific and agricultural- The results from this study have immediate application for the control and management of KHV. The proposal provides elements key to disease management with improved diagnostic tools. Studies on the ecology of the virus also provide insights into management of the virus at the farms that farmers will be able to apply immediately to reduce risks of infections. Lastly, critical issues that surround present procedures used to create “resistant fish” must be be resolved (e.g. carriers, risks, etc.). Currently stamping out may be effective in eradicating the disease. The emerging disease caused by KHV continues to spread. With the economic importance of koi and carp and the vast international movements of koi for the hobby, this disease has the potential for even further spread. The results from our studies form a critical component of a comprehensive program to curtail this emerging pathogen at the local, regional and international levels.
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Greinert, Jens. Mine Monitoring in the German Baltic Sea 2020; Dumped munition monitoring AL548, 03rd – 16th November 2020, Kiel (Germany) – Kiel (Germany) „MineMoni-II 2020“. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2021. http://dx.doi.org/10.3289/cr_al548.

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ALKOR cruise AL548 took place as part of the EMFF (European Maritime and Fisheries Fund)-funded project BASTA (Boost Applied munition detection through Smart data inTegration and AI workflows; https://www.basta-munition.eu) and as continuation of the munition monitoring started within the BMBF-funded project UDEMM (Environmental Monitoring for the Delaboration of Munition in the Sea; https://udemm.geomar.de/). In October 2018, a first cruise (POS530 MineMoni2018) was conducted, to gather data for a broad baseline study in the German Baltic Sea. Results show a moderate contamination level on regional and coastal scale, but indicate higher levels for specific local areas. Within UDEMM, expertise was developed to detect, exactly locate and monitor munition (e.g. torpedoes, sea mines, ground mines) on the seafloor using optical and hydroacoustic means. In addition, chemical analyses of dissolved contaminants in the water and sediments was performed. Data acquired during this cruise are used in BASTA, which aims for enhanced munition detection via AUV-based artificial intelligence applied on multi-sensor datasets. At the same time, the project ExPloTect (Ex-situ, near-real-time exPlosive compound deTection in seawater) (also EMFF-funded) addresses the need for an innovative approach to detect explosive compounds in seawater. A prototype system was used and successfully tested for the first time during this cruise. The main focus was placed onto the two already known dumpsites Kolberger Heide and Lübeck Bight. Additionally, new areas Falshöft (Schleswig-Holstein) and Cadet Channel, Trollegrund and Großklützhöved (Mecklenburg-Vorpommern) were explored. In each area high-resolution multibeam mapping was performed and contact lists, indicating potential munition objects were produced on board. AUV surveys were conducted to ground-truth possible contacts via detailed photograph and magnetometer mapping. This was complemented with towed video (TV)-CTD profiles. The transits to and between those sites were planned along former constraint routes during WWII. These routes were main targets of the British Air Force and mines and bombs can be expected along these ways. During transits water samples were taken with on a CTD- (conductivity, temperature, depth) rosette-mounted Niskin bottles in regular distances, in order to obtain a comprehensive understanding munition compounds (inter alia trinitrotoluene (TNT)) measurements across the German Baltic Sea.
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Albrecht, Jochen, Andreas Petutschnig, Laxmi Ramasubramanian, Bernd Resch, and Aleisha Wright. Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns. Mineta Transportation Institute, May 2021. http://dx.doi.org/10.31979/mti.2021.2037.

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Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.
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Doo, Johnny. Unsettled Issues Concerning eVTOL for Rapid-response, On-demand Firefighting. SAE International, August 2021. http://dx.doi.org/10.4271/epr2021017.

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Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL technology could offer timely, on-demand, and potentially cost-effective aerial mobility capabilities to counter these challenges. Early detection and suppression of wildfires could prevent many fires from becoming large-scale disasters. eVTOL aircraft may not have the capacity of larger aerial assets for firefighting, but targeted suppression, potentially in swarm operations, could be valuable. Most importantly, on-demand aerial extraction of firefighters can be a crucial benefit during wildfire control operations. Aerial firefighter dispatch from local fire stations or vertiports can result in more effective operations, and targeted aerial fire suppression and civilian extraction from high-rise buildings could enhance capabilities significantly. There are some challenges that need to be addressed before the identified capabilities and benefits are realized at scale, including the development of firefighting-specific eVTOL vehicles; sense and avoid capabilities in complex, smoke-inhibited environments; autonomous and remote operating capabilities; charging system compatibility and availability; operator and controller training; dynamic airspace management; and vehicle/fleet logistics and support. Acceptance from both the first-responder community and the general public is also critical for the successful implementation of these new capabilities. The purpose of this report is to identify the benefits and challenges of implementation, as well as some of the potential solutions. Based on the rapid development progress of eVTOL aircraft and infrastructures with proactive community engagement, it is envisioned that these challenges can be addressed soon. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
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