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Статті в журналах з теми "SCALP framework":
Pflieger, Mark E. "Inferring contextual field interactions from scalp EEG." Behavioral and Brain Sciences 26, no. 1 (February 2003): 99–100. http://dx.doi.org/10.1017/s0140525x03390028.
Faes, Luca, Daniele Marinazzo, Giandomenico Nollo, and Alberto Porta. "An Information-Theoretic Framework to Map the Spatiotemporal Dynamics of the Scalp Electroencephalogram." IEEE Transactions on Biomedical Engineering 63, no. 12 (December 2016): 2488–96. http://dx.doi.org/10.1109/tbme.2016.2569823.
Kotiuchyi, Ivan, Riccardo Pernice, Anton Popov, Luca Faes, and Volodymyr Kharytonov. "A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks." Brain Sciences 10, no. 9 (September 22, 2020): 657. http://dx.doi.org/10.3390/brainsci10090657.
Kilicarslan, Atilla, Robert G. Grossman, and Jose Luis Contreras-Vidal. "A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements." Journal of Neural Engineering 13, no. 2 (February 10, 2016): 026013. http://dx.doi.org/10.1088/1741-2560/13/2/026013.
Carvalhaes, Claudio G., and Patrick Suppes. "A Spline Framework for Estimating the EEG Surface Laplacian Using the Euclidean Metric." Neural Computation 23, no. 11 (November 2011): 2974–3000. http://dx.doi.org/10.1162/neco_a_00192.
Wei, Boxuan, Xiaohui Zhao, Lijuan Shi, Lu Xu, Tao Liu, and Jicong Zhang. "A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram." Journal of Neural Engineering 18, no. 4 (July 21, 2021): 0460b3. http://dx.doi.org/10.1088/1741-2552/ac0d60.
Tuckute, Greta, Sofie Therese Hansen, Nicolai Pedersen, Dea Steenstrup, and Lars Kai Hansen. "Single-Trial Decoding of Scalp EEG under Natural Conditions." Computational Intelligence and Neuroscience 2019 (April 17, 2019): 1–11. http://dx.doi.org/10.1155/2019/9210785.
Sharma, Shanu, Ashwani Kumar Dubey, and Priya Ranjan. "Affective Video Tagging Framework using Human Attention Modelling through EEG Signals." International Journal of Intelligent Information Technologies 18, no. 1 (January 1, 2022): 1–18. http://dx.doi.org/10.4018/ijiit.306968.
Gorina-Careta, Natàlia, Teresa Ribas-Prats, Sonia Arenillas-Alcón, Marta Puertollano, M. Dolores Gómez-Roig, and Carles Escera. "Neonatal Frequency-Following Responses: A Methodological Framework for Clinical Applications." Seminars in Hearing 43, no. 03 (August 2022): 162–76. http://dx.doi.org/10.1055/s-0042-1756162.
Bhagubai, Miguel, Kaat Vandecasteele, Lauren Swinnen, Jaiver Macea, Christos Chatzichristos, Maarten De Vos, and Wim Van Paesschen. "The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG." Bioengineering 10, no. 4 (April 20, 2023): 491. http://dx.doi.org/10.3390/bioengineering10040491.
Дисертації з теми "SCALP framework":
Mao, Tingting. "Interoperable internet-scale security framework for RFID networks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47741.
Includes bibliographical references (leaves 124-129).
It is estimated that over 3 billion Radio Frequency Identification (RFID) tags have been deployed through 2007. Most tags are used in supply chains where the Electronic Product Code (EPC) and associated business event data are transmitted through RFID networks. Security and privacy issues are critically important in RFID networks because EPC data and their associated business events are valuable assets. Companies need to share these data with restricted business partners and, under some conditions, such as product recall, more widely with regulators and non business partners. At present, no security or privacy framework has been chosen as an EPCglobal standard(industry-driven standards for EPC) due to the difficulty of sharing information between parties who have no direct business relationships and hence no business rules for sharing these data. To date, no security schemes have been deployed that can support data exchange with multiple identity techniques and interchangeable complex business rules, as required by RFID networks. In this thesis, an Interoperable Internet-Scale Security (IISS) framework for RFID networks is proposed. The IISS framework performs authentication and authorization based on an aggregation of business rules, enterprise information, and RFID tag information. IISS provides a protocol for several authentication schemes and identity techniques. It also provides an engine for reasoning over business rules from different domains. Moreover, the IISS framework is able to resolve provenance information of RFID tags, which can identify the history of a particular piece of EPC data through the supply chain.
(cont.) The IISS framework and the IISS ontologies to model the information in RFID networks are also described, and how the IISS framework can be developed for access control in RFID enabled supply chains is discussed. Finally, the IISS framework's efficiency is tested using a supply chain EPC simulator as the testing platform, which allows optimization of the IISS protocol's performance.
by Tingting Mao.
Ph.D.
Kolmistr, Tomáš. "Frameworky pro jednotkové testování v jazyce Scala." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-203973.
Donepudi, Harinivesh. "An Apache Hadoop Framework for Large-Scale Peptide Identification." TopSCHOLAR®, 2015. http://digitalcommons.wku.edu/theses/1527.
Park, Dong-Jun. "Video event detection framework on large-scale video data." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2754.
Kervazo, Christophe. "Optimization framework for large-scale sparse blind source separation." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS354/document.
During the last decades, Blind Source Separation (BSS) has become a key analysis tool to study multi-valued data. The objective of this thesis is however to focus on large-scale settings, for which most classical algorithms fail. More specifically, it is subdivided into four sub-problems taking their roots around the large-scale sparse BSS issue: i) introduce a mathematically sound robust sparse BSS algorithm which does not require any relaunch (despite a difficult hyper-parameter choice); ii) introduce a method being able to maintain high quality separations even when a large-number of sources needs to be estimated; iii) make a classical sparse BSS algorithm scalable to large-scale datasets; and iv) an extension to the non-linear sparse BSS problem. The methods we propose are extensively tested on both simulated and realistic experiments to demonstrate their quality. In-depth interpretations of the results are proposed
Schuchmann, Roberta. "A framework for unlocking large-scale urban regeneration projects." Thesis, Curtin University, 2018. http://hdl.handle.net/20.500.11937/75246.
Chacin, Martínez Pablo Jesus. "A Middleware framework for self-adaptive large scale distributed services." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/80538.
Las Aplicaciones Orientadas a Servicios modernas demandan la capacidad de adaptarse a condiciones variables y situaciones inesperadas mientras mantienen un cierto nivel de servio esperado (QoS). Los enfoques de auto-adaptación existentes parecen no ser adacuados debido a sus supuestos no se cumplen en infrastructuras compartidas de gran escala. La principal motivación de nuestra investigación es inerir un conjunto de principios para guiar el desarrollo de servicios auto-adaptativos de gran escala. Nuesto objetivo es proveer abstraciones de modelaje apropiadas, basadas en un marco conceptual claro, y su implemetnacion en un middleware que soporte el desarrollo de estos servicios. Tomando como inspiración conceptos económicos de mercados decentralizados, hemos propuesto una solución basada en tres principios: auto-organización emergente, comportamiento guiado por la utilidad y adaptación sin modelos. Basados en estos principios diseñamos Collectives, un middleware que proveer una solución exhaustiva para los diversos aspectos de adaptación que surgen en el desarrollo de sistemas distribuidos. La adecuación y completitud de Collectives ha sido provada por medio de la implementación de eUDON, un middleware para servicios auto-adaptativos, el ha sido evaluado de manera exhaustiva por medio de un modelo de simulación, analizando sus propiedades de adaptación en diversos escenarios de uso. Hemos encontrado que eUDON exhibe las propiedades esperadas: se adapta a diversas condiciones como picos en la carga de trabajo o fallos masivos, mateniendo su calidad de servicio y haciendo un uso eficiente de los recusos disponibles. Es altamente escalable y robusto; puedeoo ser implementado en servicios existentes de manera no intrusiva; y no requiere la obtención de un modelo de desempeño para los servicios. Podemos concluir que nuestro trabajo nos ha permitido desarrollar una solucion que aborda los requerimientos de auto-adaptacion en escenarios de uso exigentes sin introducir complejidad adicional. En este sentido, consideramos que nuestra propuesta hace una contribución significativa hacia el desarrollo de la futura generación de aplicaciones orientadas a servicios.
Mohamed, Ibrahim Daoud Ahmed. "Automatic history matching in Bayesian framework for field-scale applications." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3170.
Zafari, Afshin. "Adapting a Radial Basis Functions Framework for Large-Scale Computing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-182859.
Orgerie, Anne-Cécile. "An Energy-Efficient Reservation Framework for Large-Scale Distributed Systems." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2011. http://tel.archives-ouvertes.fr/tel-00672130.
Книги з теми "SCALP framework":
Block, Sascha. Large-Scale Agile Frameworks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9.
Block, Sascha. Large-Scale Agile Frameworks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67782-7.
Bunjongjit, Naruemol. Small-scale industries and institutional framework in Thailand. Paris: Organisation for Economic Co-operation and Development, 1992.
Dewey, Allen M. Principles of VLSI system planning: A framework for conceptual design. Boston: Kluwer Academic Publishers, 1990.
Cruickshank, John. Healthcare without walls: A framework for delivering telehealth at scale. London: 2020health.org, 2010.
Chen-Becker, Derek. The definitive guide to Lift: A scala-based web framework. Berkeley, CA: Apress, 2009.
Ikiara, Gerrishon K. Macro policy framework for small-scale industries (SSI) in Kenya. Addis Ababa: International Labour Organization, Eastern Africa Multidisciplinary Advisory Team, 1996.
North, Brian. The development of a common framework scale of language proficiency. New York: P. Lang, 2000.
Bhatty, I. Z. Policies for promoting small scale industries: A framework for analysis. Bangkok, Thailand: ILO, ARTEP, 1985.
Pomeroy, R. S., and Neil Andrew. Small-scale fisheries management: Frameworks and approaches for the developing world. Wallingford, Oxfordshire, UK: CAB International, 2011.
Частини книг з теми "SCALP framework":
Carrión, Salvador, Álvaro López-Chilet, Javier Martínez-Bernia, Joan Coll-Alonso, Daniel Chorro-Juan, and Jon Ander Gómez. "Automatic Detection of Epileptic Seizures with Recurrent and Convolutional Neural Networks." In Lecture Notes in Computer Science, 522–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_46.
Karunakaran, Prem Kumar. "Play Views and Templating with Scala." In Introducing Play Framework, 65–74. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5645-9_4.
Cho, Chung Yik, Rong Kun Jason Tan, John A. Leong, and Amandeep S. Sidhu. "Query Framework." In Large Scale Data Analytics, 27–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03892-2_4.
Block, Sascha. "Large-Scale Agile Frameworks." In Large-Scale Agile Frameworks, 49–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_3.
Block, Sascha. "Agiles Priorisierungsmodell für Software-Hersteller." In Large-Scale Agile Frameworks, 179–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_5.
Block, Sascha. "Datenqualität – Lebenselixier der Digitalisierung." In Large-Scale Agile Frameworks, 311–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_8.
Block, Sascha. "Digitale Transformation & Agile Priorisierung." In Large-Scale Agile Frameworks, 9–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_2.
Block, Sascha. "Agile Tools: Werkzeugkasten für Produkt Owner & Agile Teams." In Large-Scale Agile Frameworks, 235–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_7.
Block, Sascha. "Einleitung." In Large-Scale Agile Frameworks, 3–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_1.
Block, Sascha. "Herausforderungen bei der Etablierung eines Large-Scale Agile Frameworks im Unternehmen." In Large-Scale Agile Frameworks, 209–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_6.
Тези доповідей конференцій з теми "SCALP framework":
Anandakumar, Mithunjha, Jathurshan Pradeepkumar, Simon L. Kappel, Chamira U. S. Edussooriya, and Anjula C. de Silva. "A Knowledge Distillation Framework for Enhancing Ear-EEG Based Sleep Staging with Scalp-EEG Data." In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023. http://dx.doi.org/10.1109/smc53992.2023.10394011.
Gambarotta, Luigi, Roberta Massabo, and Renata Morbiducci. "Mechanical Characterization of Human Skin From In-Vivo Tests and Simulation of Reconstructive Surgery." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-43335.
Georgiev, Dobromir, Emanouil Atanassov, and Vassil Alexandrov. "A Framework for Parallel Genetic Algorithms for Distributed Memory Architectures." In 2014 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA). IEEE, 2014. http://dx.doi.org/10.1109/scala.2014.13.
Knutz, Eva. "Scaling experiments in urban space – an exploratory framework." In Nordes 2021: Matters of Scale. Nordes, 2021. http://dx.doi.org/10.21606/nordes.2021.51.
Rizzardini, Rocael Hernández, and Hector R. Amado-Salvatierra. "Full Engagement Educational Framework." In L@S 2017: Fourth (2017) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3051457.3053969.
ENOS, RYAN, DIANYUN ZHANG, and QINGXUAN WEI. "INTEGRATED OPTIMIZATION FRAMEWORK FOR COMPOSITE MANUFACTURING: MINIMIZING SPRING-IN EFFECTS AND IMPROVING CURE CYCLES." In Proceedings for the American Society for Composites-Thirty Eighth Technical Conference. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/asc38/36621.
Taylor, Ian, Brian Adamson, Ian Downard, and Joe Macker. "AgentJ: Enabling Java NS-2 Simulations for Large Scale Distributed Multimedia Applications." In The 2nd International Conference on Distributed Frameworks for Multimedia Applications. IEEE, 2006. http://dx.doi.org/10.1109/dfma.2006.296911.
Nystrom, Nathaniel. "A Scala framework for supercompilation." In SPLASH '17: Conference on Systems, Programming, Languages, and Applications: Software for Humanity. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3136000.3136011.
Pestovs, Pavels, Ilze Saleniece, and Dace Namsone. "SCIENCE LARGE-SCALE ASSESSMENT ALIGNMENT TO THE REVISED SCIENCE CURRICULUM." In 3rd International Baltic Symposium on Science and Technology Education (BalticSTE2019). Scientia Socialis Ltd., 2019. http://dx.doi.org/10.33225/balticste/2019.162.
Dodson, Samuel, Ido Roll, Matthew Fong, Dongwook Yoon, Negar M. Harandi, and Sidney Fels. "An active viewing framework for video-based learning." In L@S '18: Fifth (2018) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3231644.3231682.
Звіти організацій з теми "SCALP framework":
Smith, Jeremy. Multi-Scale Modeling Framework for Mercury Biogeochemistry. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1671770.
Palsson, Bernhard O., Ali Ebrahim, and Steve Federowicz. The OME Framework for genome-scale systems biology. Office of Scientific and Technical Information (OSTI), December 2014. http://dx.doi.org/10.2172/1169326.
Rovinelli, A., A. Venkataraman, and M. Messner. Initial framework for engineering-scale statistical creep-fatigue modeling. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1825886.
Shen, Han-Wei. An Information-Theoretic Framework for Enabling Extreme-scale Science Discovery. Office of Scientific and Technical Information (OSTI), November 2014. http://dx.doi.org/10.2172/1164477.
Peterman, Z. Hydrogeologic Framework Model for the Saturated-Zone Site-Scale Flow. Office of Scientific and Technical Information (OSTI), March 2003. http://dx.doi.org/10.2172/836529.
Eardley, P., A. Morton, M. Bagnulo, T. Burbridge, P. Aitken, and A. Akhter. A Framework for Large-Scale Measurement of Broadband Performance (LMAP). RFC Editor, September 2015. http://dx.doi.org/10.17487/rfc7594.
Kwasinski, Alexis, Joseph Trainor, Brian Wolshon, and Francis M. Lavelle. A Conceptual Framework for Assessing Resilience at the Community Scale. National Institute of Standards and Technology, January 2016. http://dx.doi.org/10.6028/nist.gcr.16-001.
Rose, Amy, Sam Koebrich, David Palchak, Ilya Chernyakhovskiy, and Claire Wagner. A Framework for Readiness Assessments of Utility-Scale Energy Storage. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1735627.
Ackerman, Thomas P. Evaluating and Improving Cloud Processes in the Multi-Scale Modeling Framework. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1171707.
Muliana, Anastasia H. A Multi-Scale Framework for Multi-Field Analyses of Smart Composites. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada612732.