Добірка наукової літератури з теми "User activity detection"
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Статті в журналах з теми "User activity detection"
Zhu, Hao, and Georgios B. Giannakis. "Exploiting Sparse User Activity in Multiuser Detection." IEEE Transactions on Communications 59, no. 2 (February 2011): 454–65. http://dx.doi.org/10.1109/tcomm.2011.121410.090570.
Повний текст джерелаMitra, U., and H. V. Poor. "Activity detection in a multi-user environment." Wireless Personal Communications 3, no. 1-2 (1996): 149–74. http://dx.doi.org/10.1007/bf00333928.
Повний текст джерелаLee, Junho, and Seung-Hwan Lee. "Low dimensional multiuser detection exploiting low user activity." Journal of Communications and Networks 15, no. 3 (June 2013): 283–91. http://dx.doi.org/10.1109/jcn.2013.000051.
Повний текст джерелаZou, Shihong, Huizhong Sun, Guosheng Xu, and Ruijie Quan. "Ensemble Strategy for Insider Threat Detection from User Activity Logs." Computers, Materials & Continua 65, no. 2 (2020): 1321–34. http://dx.doi.org/10.32604/cmc.2020.09649.
Повний текст джерелаWang, Shuwen, Xingquan Zhu, Weiping Ding, and Amir Alipour Yengejeh. "Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View." IEEE/CAA Journal of Automatica Sinica 9, no. 8 (August 2022): 1384–405. http://dx.doi.org/10.1109/jas.2022.105740.
Повний текст джерелаPark, Hansol, Kookjin Kim, Dongil Shin, and Dongkyoo Shin. "BGP Dataset-Based Malicious User Activity Detection Using Machine Learning." Information 14, no. 9 (September 13, 2023): 501. http://dx.doi.org/10.3390/info14090501.
Повний текст джерелаParwez, Md Salik, Danda B. Rawat, and Moses Garuba. "Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network." IEEE Transactions on Industrial Informatics 13, no. 4 (August 2017): 2058–65. http://dx.doi.org/10.1109/tii.2017.2650206.
Повний текст джерелаPathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet Kondoz. "CNN for User Activity Detection Using Encrypted In-App Mobile Data." Future Internet 14, no. 2 (February 21, 2022): 67. http://dx.doi.org/10.3390/fi14020067.
Повний текст джерелаBashir, Sulaimon Adebayo, Andrei Petrovski, and Daniel Doolan. "A framework for unsupervised change detection in activity recognition." International Journal of Pervasive Computing and Communications 13, no. 2 (June 5, 2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.
Повний текст джерелаKim, Park, Kim, Cho, and Kang. "Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms." Applied Sciences 9, no. 19 (September 25, 2019): 4018. http://dx.doi.org/10.3390/app9194018.
Повний текст джерелаДисертації з теми "User activity detection"
Amanzi, Richard. "A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/6204.
Повний текст джерелаSagheer, Fakher. "Bayesian statistical methods for joint user activity detection, channel estimation, and data decoding in dynamic wireless networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. https://theses.hal.science/tel-04874844.
Повний текст джерелаGrant-free non-orthogonal multiple access (GF-NOMA) is gradually becoming an integral part of the physical layer of future radio access systems. By allowing access to a base station without explicit allocation of time/frequency/code resources, GF-NOMA not only improves spectral efficiency, but also enables ultra-reliable low latency communications (URLLC) . Such requirements will make it possible to meet the specific challenges of wireless applications such as the Internet of Things, virtual reality, online video games, communications between machines, vehicles, etc.However, GF-NOMA introduces a new challenge that does not exist in conventional communication systems, namely user activity detection: in addition to channel estimation, detection and decoding of interfering users, the base station receiver must be able to classify them into two categories: those who are active and transmitting and those who are not. The massiveness of the system, the absence of power control on transmission and/or orthogonality of user pilot sequences are all characteristics which complicate processing at the receiver.The general subject of this thesis is the study of new statistical methods based on message passing algorithms on appropriate factor graphs in order to jointly handle all these tasks at the receiver level.Are studied more precisely:- a method (1) of hybrid Bayesian inference based on the belief propagation algorithm (BP) and the expectation propagation algorithm (EP) to solve the problem of joint activity detection, channel estimation, and multi-user detection in a synchronous GF-NOMA system with no transmit power control, orthogonal pilot sequences and multiple receiver antennas. By introducing an approximation criterion to express message passing as Gaussian laws, channel estimation and multi-user detection can be efficiently processed by the EP algorithm. This proving impossible in this form for detecting user activity, message passing in BP form is used for this purpose. The proposed method includes a step of estimating the hyperparameters of the model, which are the energy of the received signals and the spatial correlation between the receiving antennas. A reduced complexity variant ignoring the spatial correlation between receiving antennas is also proposed;- a method (2) of Bayesian inference based on the EP algorithm exploiting complex analysis methods (Wirtinger derivatives) making it possible to process user activity detection also in the form of a Gaussian message passing algorithm;- a method (3) preceding method (2) with a Bayesian compressed acquisition method responsible for the initial estimation of the channel and user activity in the more complex context of massive access with non-orthogonal pilot sequences for the users.The evaluation by simulations of these different methods is carried out in the particular case of a synchronous GF-NOMA system by coding, interleaving and OFDM modulation (GF-OFDM-IDMA). The performance obtained (measured in terms of residual bit error rate for detection and decoding, root mean square error for channel estimation, and false alarm and missed-detection probabilities for activity detection) compare favorably with those obtained with traditional methods published in the literature. Keywords: NOMA, grant-free, massive access, OFDM, factor graphs, message passing algorithms, belief propagation, expectation propagation
Dias, Pereira dos Santos Augusto. "Using Motion Sensor and Machine Learning to Support the Assessment of Rhythmic Skills in Social Partner Dance: Bridging Teacher, Student and Machine Contexts." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21302.
Повний текст джерелаOdehnal, Jiří. "Řízení a měření sportovních drilů hlasem/zvuky." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399705.
Повний текст джерелаMcEachern, Matthew. "Neural Voice Activity Detection and its practical use." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119733.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 87-90).
The task of producing a Voice Activity Detector (VAD) that is robust in the presence of non-stationary background noise has been an active area of research for several decades. Historically, many of the proposed VAD models have been highly heuristic in nature. More recently, however, statistical models, including Deep Neural Networks (DNNs) have been explored. In this thesis, I explore the use of a lightweight, deep, recurrent neural architecture for VAD. I also explore a variant that is fully end-to-end, learning features directly from raw waveform data. In obtaining data for these models, I introduce a data augmentation methodology that allows for the artificial generation of large amounts of noisy speech data from a clean speech source. I describe how these neural models, once trained, can be deployed in a live environment with a real-time audio stream. I find that while these models perform well in their closed-domain testing environment, the live deployment scenario presents challenges related to generalizability.
by Matthew McEachern.
M. Eng.
Tholen, Andrea. "The use of animal activity data and milk components as indicators of clinical mastitis." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76826.
Повний текст джерелаMaster of Science
Azhar, Faisal. "Marker-less human body part detection, labelling and tracking for human activity recognition." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/69575/.
Повний текст джерелаMyles, Kimberly. "Activity-Based Target Acquisition Methods for Use in Urban Environments." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28422.
Повний текст джерелаPh. D.
Henriksson, Mikael. "Implementation of a Hardware Coordinate Wise Descend Algorithm with Maximum Likelihood Estimator for Use in mMTC Activity Detection." Thesis, Linköpings universitet, Datorteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171071.
Повний текст джерелаGoodlich, Benjamin I. "Machine learning algorithms for the automatic detection and classification of physical activity in children with cerebral palsy who use mobility aids for ambulation." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/389853.
Повний текст джерелаThesis (Masters)
Master of Medical Research (MMedRes)
School of Medical Science
Griffith Health
Full Text
Книги з теми "User activity detection"
Nazarov, Dmitriy, and Anton Kopnin. Information technologies in professional activity: data mining and business analytics. ru: INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2110964.
Повний текст джерелаMeijer, Ewout H., and Bruno Verschuere. Detection Deception Using Psychophysiological and Neural Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190612016.003.0010.
Повний текст джерелаShaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.
Повний текст джерелаOurada, Jason D., and Kenneth L. Appelbaum. Intoxication and drugs in facilities. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199360574.003.0024.
Повний текст джерелаWalczak, Jean-Sébastien. Understanding the responsiveness of C-fibres. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0006.
Повний текст джерелаChinoy, Hector, and Robert G. Cooper. Polymyositis and dermatomyositis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0124.
Повний текст джерелаHarper, Lorraine, and David Jayne. The patient with vasculitis. Edited by Giuseppe Remuzzi. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0160.
Повний текст джерелаUfimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Повний текст джерелаЧастини книг з теми "User activity detection"
Baek, Jonghun, Geehyuk Lee, Wonbae Park, and Byoung-Ju Yun. "Accelerometer Signal Processing for User Activity Detection." In Lecture Notes in Computer Science, 610–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30134-9_82.
Повний текст джерелаJahn, Andreas, Marek Bachmann, Philipp Wenzel, and Klaus David. "Focus on the User: A User Relative Coordinate System for Activity Detection." In Modeling and Using Context, 582–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57837-8_47.
Повний текст джерелаScardino, Giuseppe, Ignazio Infantino, and Filippo Vella. "Recognition of Human Identity by Detection of User Activity." In Lecture Notes in Computer Science, 49–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39345-7_6.
Повний текст джерелаMiu, TungNgai, Chenxu Wang, Daniel Xiapu Luo, and Jinhe Wang. "Modeling User Browsing Activity for Application Layer DDoS Attack Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 747–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59608-2_42.
Повний текст джерелаGrushka-Cohen, Hagit, Ofer Biller, Oded Sofer, Lior Rokach, and Bracha Shapira. "Simulating User Activity for Assessing Effect of Sampling on DB Activity Monitoring Anomaly Detection." In Policy-Based Autonomic Data Governance, 82–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17277-0_5.
Повний текст джерелаMärker, Marcus, Sebastian Wolf, Oliver Scharf, Daniel Plorin, and Tobias Teich. "KNX-Based Sensor Monitoring for User Activity Detection in AAL-environments." In Ambient Assisted Living and Daily Activities, 18–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_4.
Повний текст джерелаTokhtabayev, Arnur, Anton Kopeikin, Nurlan Tashatov, and Dina Satybaldina. "Malware Analysis and Detection via Activity Trees in User-Dependent Environment." In Lecture Notes in Computer Science, 211–22. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65127-9_17.
Повний текст джерелаLukashin, Aleksey, Mikhail Popov, Dmitrii Timofeev, and Igor Mikhalev. "Employee Performance Analytics Approach Based on Anomaly Detection in User Activity." In Proceedings of International Scientific Conference on Telecommunications, Computing and Control, 321–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6632-9_28.
Повний текст джерелаBurghouwt, Pieter, Marcel Spruit, and Henk Sips. "Towards Detection of Botnet Communication through Social Media by Monitoring User Activity." In Information Systems Security, 131–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25560-1_9.
Повний текст джерелаOrtega, Jose Luis Gomez, Liangxiu Han, and Nicholas Bowring. "Modelling and Detection of User Activity Patterns for Energy Saving in Buildings." In Emerging Trends and Advanced Technologies for Computational Intelligence, 165–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33353-3_9.
Повний текст джерелаТези доповідей конференцій з теми "User activity detection"
Derya, Sertac, Shih-Chen Yu, Hsu-Wen Young, Eduard A. Jorswieck, Pin-Hsun Lin, and Shih-Chun Lin. "Joint Delay And User Activity Detection in Asynchronous Massive Access." In 2024 33rd Wireless and Optical Communications Conference (WOCC), 175–79. IEEE, 2024. https://doi.org/10.1109/wocc61718.2024.10786075.
Повний текст джерелаSun, Guangyue, Zhaoji Zhang, and Ying Li. "Hybrid Model-Data-Driven User-Activity Detection Network for Massive Random Access." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683656.
Повний текст джерелаZhang, Yuanyuan, Yongtian Luo, Jin Xu, Weihua Liu, Lixun Huang, and Zhe Zhang. "Structured Compressive Sensing Based Joint User Activity and Data Detection for Grant-Free mMTC." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), 206–10. IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743465.
Повний текст джерелаKhan, Muhammad Usman, Enrico Testi, Marco Chiani, and Enrico Paolini. "Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks." In 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), 432–36. IEEE, 2024. http://dx.doi.org/10.1109/rtsi61910.2024.10761492.
Повний текст джерелаForsch, Christian, Alexander Karataev, and Laura Cottatellucci. "Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO." In 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 531–35. IEEE, 2024. http://dx.doi.org/10.1109/spawc60668.2024.10694527.
Повний текст джерелаWang, Yixin, Yichen Wang, Tao Wang, and Julian Cheng. "Joint Channel Estimation and User Activity Detection for mmWave Grant-Free Massive MTC Networks Under Pilot Contamination Attack." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683251.
Повний текст джерелаZhong, Sheng, Li Zhen, Shuchang Li, Ping Dong, and Hao Qin. "Joint User Activity Detection and Channel Estimation for Grant-Free Temporal-Correlated Random Access in LEO Satellite Based IoT." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT), 738–43. IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10671064.
Повний текст джерелаHasara Pathmaperuma, Madushi, Yogachandran Rahulamathavan, Safak Dogan, and Ahmet M. Kondoz. "User Mobile App Encrypted Activity Detection." In ESCC '21: The 2nd European Symposium on Computer and Communications. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3478301.3478303.
Повний текст джерелаSwarnalaxmi, S., I. Elakkiya, M. Thilagavathi, Anil Thomas, and Gunasekaran Raja. "User Activity Analysis Driven Anomaly Detection in Cellular Network." In 2018 Tenth International Conference on Advanced Computing (ICoAC). IEEE, 2018. http://dx.doi.org/10.1109/icoac44903.2018.8939064.
Повний текст джерелаHu, Qiaona, Baoming Tang, and Derek Lin. "Anomalous User Activity Detection in Enterprise Multi-source Logs." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.110.
Повний текст джерелаЗвіти організацій з теми "User activity detection"
Malik, Arun, Andrea López, and Paul E. Carrillo. Pollution or Crime: The Effect of Driving Restrictions on Criminal Activity. Inter-American Development Bank, July 2016. http://dx.doi.org/10.18235/0011747.
Повний текст джерелаMichaels, Trevor. Red-tailed boa (Boa constrictor) surveys at Salt River Bay National Park, St. Croix U.S. Virgin Islands: 2023 report of activities. National Park Service, 2024. http://dx.doi.org/10.36967/2303799.
Повний текст джерелаBarefoot, Susan F., Bonita A. Glatz, Nathan Gollop, and Thomas A. Hughes. Bacteriocin Markers for Propionibacteria Gene Transfer Systems. United States Department of Agriculture, June 2000. http://dx.doi.org/10.32747/2000.7573993.bard.
Повний текст джерелаCytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598174.bard.
Повний текст джерелаHanna, Benjamin, Tom Bubenik, and Barbara Padgett. PR186-203813-R01 Literature Review Pipeline Mid-wall Defect Detection and FFS Assessment. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2021. http://dx.doi.org/10.55274/r0012076.
Повний текст джерелаWongpakdeea, Thinnapong, Karin Crenshaw, Hery Figueroa Wong, Duangjai Nacapricha, and Bruce McCord. Advancements in Analytical Techniques for Rapid Identification of Gunshot Residue and Low Explosives through Electrochemical Detection and Surface-Enhanced Raman Spectroscopy. Florida International University, 2024. https://doi.org/10.25148/gfjcsr.2024.7.
Повний текст джерелаChen, Yona, Jeffrey Buyer, and Yitzhak Hadar. Microbial Activity in the Rhizosphere in Relation to the Iron Nutrition of Plants. United States Department of Agriculture, October 1993. http://dx.doi.org/10.32747/1993.7613020.bard.
Повний текст джерелаBurns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
Повний текст джерелаGraham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.
Повний текст джерелаZárate-Solano, Héctor M., and Norberto Rodríguez-Niño. Consumer Prices Trends in Colombia: Detecting Breaks and Forecasting Inflation. Banco de la República, December 2024. https://doi.org/10.32468/be.1289.
Повний текст джерела