Literatura científica selecionada sobre o tema "CO₂ detection"
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Artigos de revistas sobre o assunto "CO₂ detection"
Langenfeld-Heyser, R., Bruno Schella, Kirsten Buschmann e Frieder Speck. "Microautoradiographic detection of CO". Trees 10, n.º 4 (1996): 255. http://dx.doi.org/10.1007/s004680050031.
Texto completo da fonteFu, Huazhu, Xiaochun Cao e Zhuowen Tu. "Cluster-Based Co-Saliency Detection". IEEE Transactions on Image Processing 22, n.º 10 (outubro de 2013): 3766–78. http://dx.doi.org/10.1109/tip.2013.2260166.
Texto completo da fontePardo Pedraza, Diana. "Sensory Co-laboring". Environmental Humanities 15, n.º 3 (1 de novembro de 2023): 30–51. http://dx.doi.org/10.1215/22011919-10745968.
Texto completo da fonteLu, Xiaofei, Jingjing Jia, Zonghua Wang e Wenjing Wang. "MXene/Carbon Dots Nanozyme Composites for Glutathione Detection and Tumor Therapy". Nanomaterials 14, n.º 13 (25 de junho de 2024): 1090. http://dx.doi.org/10.3390/nano14131090.
Texto completo da fonteYANG Ming-yu, 杨名宇. "Detecting of photoelectric peeping devices based on active laser detection". Chinese Optics 8, n.º 2 (2015): 255–62. http://dx.doi.org/10.3788/co.20150802.0255.
Texto completo da fonteDananché, Cédric, Gláucia Paranhos-Baccalà, Mélina Messaoudi, Mariam Sylla, Shally Awasthi, Ashish Bavdekar, Jean-William Pape et al. "Nasopharyngeal Viral and Bacterial Co-Detection among Children from Low- and Middle-Income Countries with and without Pneumonia". American Journal of Tropical Medicine and Hygiene 106, n.º 4 (6 de abril de 2022): 1086–93. http://dx.doi.org/10.4269/ajtmh.21-0980.
Texto completo da fonteYe, Linwei, Zhi Liu, Junhao Li, Wan-Lei Zhao e Liquan Shen. "Co-Saliency Detection via Co-Salient Object Discovery and Recovery". IEEE Signal Processing Letters 22, n.º 11 (novembro de 2015): 2073–77. http://dx.doi.org/10.1109/lsp.2015.2458434.
Texto completo da fonteProbst, Varvara, Bhinnata Piya, Laura Stewart, Susan Gerber, Brian Rha, Joana Yu, Suman Das, Angela P. Campbell, John V. Williams e Natasha B. Halasa. "741. Impact of Adenovirus Co-detections on Illness Severity". Open Forum Infectious Diseases 5, suppl_1 (novembro de 2018): S266. http://dx.doi.org/10.1093/ofid/ofy210.748.
Texto completo da fonteEncrenaz, Th, E. Lellouch, P. Drossart, H. Feuchtgruber, G. S. Orton e S. K. Atreya. "First detection of CO in Uranus". Astronomy & Astrophysics 413, n.º 2 (18 de dezembro de 2003): L5—L9. http://dx.doi.org/10.1051/0004-6361:20034637.
Texto completo da fonteBeheshtian, Javad, Zargham Bagheri, Mohammad Kamfiroozi e Ali Ahmadi. "Toxic CO detection by B12N12 nanocluster". Microelectronics Journal 42, n.º 12 (dezembro de 2011): 1400–1403. http://dx.doi.org/10.1016/j.mejo.2011.10.010.
Texto completo da fonteTeses / dissertações sobre o assunto "CO₂ detection"
Li, Yuan. "Multiuser Detection for Co-channel Interference Cancellation". 京都大学 (Kyoto University), 2000. http://hdl.handle.net/2433/180902.
Texto completo da fonteHogan, Justin Allan. "Multi-spectral imaging of vegetation for COâ‚‚ leak detection". Thesis, Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/hogan/HoganJ0511.pdf.
Texto completo da fonteZavala, Martin. "Autonomous detection and characterization of nuclear materials using co-robots". Thesis, Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55052.
Texto completo da fonteChen, Yi-Ching. "Co-design of Fault-Tolerant Systems with Imperfect Fault Detection". Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104942.
Texto completo da fonteSahin, Mustafa. "Baseband receiver algorithms for 4G co-channel femtocells". [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003283.
Texto completo da fonteRosso, Kevin M. "Detection limits of CO₂in fluid inclusions using microthermometry and Raman spectroscopy and the spectroscopic characterization of CO₂". Thesis, Virginia Tech, 1994. http://hdl.handle.net/10919/40534.
Texto completo da fonteMaster of Science
Rosso, Kevin Michael. "Detection limits of CO₂ in fluid inclusions using microthermometry and Raman spectroscopy and the spectroscopic characterization of CO₂ /". This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-01052009-091123/.
Texto completo da fonteRaisi, Elaheh. "Weakly Supervised Machine Learning for Cyberbullying Detection". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/89100.
Texto completo da fonteDoctor of Philosophy
Social media has become an inevitable part of individuals social and business lives. Its benefits, however, come with various negative consequences such as online harassment, cyberbullying, hate speech, and online trolling especially among the younger population. According to the American Academy of Child and Adolescent Psychiatry,1 victims of bullying can suffer interference to social and emotional development and even be drawn to extreme behavior such as attempted suicide. Any widespread bullying enabled by technology represents a serious social health threat. In this research, we develop automated, data-driven methods for harassment-based cyberbullying detection. The availability of tools such as these can enable technologies that reduce the harm and toxicity created by these detrimental behaviors. Our general framework is based on consistency of two detectors that co-train one another. One learner identifies bullying incidents by examining the language content in the message; another learner considers social structure to discover bullying. When designing the general framework, we address three tasks: First, we use machine learning with weak supervision, which significantly alleviates the need for human experts to perform tedious data annotation. Second, we incorporate the efficacy of distributed representations of words and nodes such as deep, nonlinear models in the framework to improve the predictive power of models. Finally, we decrease the sensitivity of the framework to language describing particular social groups including race, gender, religion, and sexual orientation. This research represents important steps toward improving technological capability for automatic cyberbullying detection.
Haddad, Lema. "New approaches to co-segregation studies and mutation detection in familial hypercholesterolaemia". Thesis, University College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286402.
Texto completo da fonteDoblas, Jiménez David. "Exploration and detection of ultra-traces of explosives by chip calorimetry". Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAE017/document.
Texto completo da fonteBeing able to sense the minuscule amounts of energetic materials is crucial in the context of the fight against terrorism. Apart from the methods of detection of EM, which are specific to the chemical structure, one could use the enthalpy variations of the EM decomposition process for their detection by means of thermal analysis. However, the sensitivity of classical methods would be still insufficient to sense particles in the nanogram range. By contrast, the recently developed technique of chip calorimetry is perfectly suited for characterizing small amounts of samples and is therefore fully adequate for this task.In order to explore the possibilities of detection and identification of solid micro-particles of EM with thermal analysis, we discuss on the protocols optimized for the detection and identification of nanogram-size particles of EM and its mixtures with the chip calorimeter accessory. The results obtained on pure EM and its mixtures show that the detection threshold can be put at approximately several hundred picograms. The experiments were completed by the in-situ structural analysis using a combination with nanofocus synchrotron XRD
Livros sobre o assunto "CO₂ detection"
Mulligan, Carol Heather. The detection and toxicological evaluation of polluting inputs to the lower river Faughan Co. Londonderry. [s.l: The Author], 1993.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. Comparison of 2 [micron]m Ho and 10 [micron]m CO lidar for atmospheric backscatter and Doppler windshear detection: Progress report. Tampa, Fla: Dept. of Physics, University of South Florida, 1991.
Encontre o texto completo da fonteOctavia, Camps, e United States. National Aeronautics and Space Administration., eds. Detection of obstacles in monocular image sequences: Final technical report for NASA co-operative research agreement number NCC 2-916, "A vision-based obstacle detection system for aircraft navigation," period of grant--August 1, 1995 to July 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.
Encontre o texto completo da fonteGreat Britain. Department of the Environment, Transport and the Regions., ed. A low-cost on-line steam leak detection sysytem: Holroyd Instruments Ltd, University of Sunderland, Allen Consultant Engineering Co Ltd, National Power plc, Cleveland Potash Ltd. Garston: BRECSU, 1999.
Encontre o texto completo da fonteResearch Co-ordination Meeting on the Use of Novel DNA Fingerprinting Techniques for the Detection and Characterization of Genetic Variation in Vegetatively Propagated Crops (3rd 1997 Mumbai, India). Use of novel DNA fingerprinting techniques for the detection and characterization of genetic variation in vegetatively propagated crops: Proceedings of a final Research Co-ordination Meeting organized by the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture and held in Mumbai, India, 24-28 February 1997. Vienna, Austria: International Atomic Energy Agency, 1998.
Encontre o texto completo da fonteSpenneberg, Ralf. Intrusion Detection und Prevention mit Snort 2 & Co. Addison Wesley Verlag, 2004.
Encontre o texto completo da fonteHaroon, Muhammad. Co-morbidities. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0015.
Texto completo da fonteNon Co-Operative Detection of LPI/LPD Signals Via Cyclic Spectral Analysis. Storming Media, 1999.
Encontre o texto completo da fonteDetection of obstacles in monocular image sequences: Final technical report for NASA co-operative research agreement number NCC 2-916, "A vision-based obstacle detection system for aircraft navigation," period of grant--August 1, 1995 to July 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.
Encontre o texto completo da fonteProbability of Symbol Error for Coherent and Non-Coherent Detection of M-ary Frequency-Shift Keyed (MFSK) Signals Affected by Co-Channel Interference and Additive White Gaussian Noise (AWGN) in a. Storming Media, 2000.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "CO₂ detection"
Bao, Sid Yingze, Yu Xiang e Silvio Savarese. "Object Co-detection". In Computer Vision – ECCV 2012, 86–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33718-5_7.
Texto completo da fonteZhang, Zhao, Wenda Jin, Jun Xu e Ming-Ming Cheng. "Gradient-Induced Co-Saliency Detection". In Computer Vision – ECCV 2020, 455–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58610-2_27.
Texto completo da fonteİnci, Mehmet Sinan, Berk Gulmezoglu, Thomas Eisenbarth e Berk Sunar. "Co-location Detection on the Cloud". In Constructive Side-Channel Analysis and Secure Design, 19–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43283-0_2.
Texto completo da fontePapalexakis, Evangelos E., Alex Beutel e Peter Steenkiste. "Network Anomaly Detection Using Co-clustering". In Encyclopedia of Social Network Analysis and Mining, 1–17. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_354-1.
Texto completo da fontePapalexakis, Evangelos E., Alex Beutel e Peter Steenkiste. "Network Anomaly Detection Using Co-clustering". In Encyclopedia of Social Network Analysis and Mining, 1054–68. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_354.
Texto completo da fontePapalexakis, Evangelos E., Alex Beutel e Peter Steenkiste. "Network Anomaly Detection Using Co-clustering". In Encyclopedia of Social Network Analysis and Mining, 1501–16. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_354.
Texto completo da fonteLi, Bo, Zhengxing Sun, Jiagao Hu e Junfeng Xu. "Co-saliency Detection via Sparse Reconstruction and Co-salient Object Discovery". In Advances in Multimedia Information Processing – PCM 2017, 222–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77383-4_22.
Texto completo da fonteGhidoni, Stefano, Arrigo Guizzo e Emanuele Menegatti. "Crowd Detection Based on Co-occurrence Matrix". In Biologically Inspired Cognitive Architectures 2012, 145–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34274-5_28.
Texto completo da fonteLei, Zhengchao, Weiyan Chai, Sanyuan Zhao, Hongmei Song e Fengxia Li. "Co-saliency Detection Based on Siamese Network". In Communications in Computer and Information Science, 99–109. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8890-2_8.
Texto completo da fonteMetin, Senem Kumova. "Standard Co-training in Multiword Expression Detection". In Intelligent Human Computer Interaction, 178–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72038-8_14.
Texto completo da fonteTrabalhos de conferências sobre o assunto "CO₂ detection"
Ding, Chuang, Yang Wu, Huihui Song, Kaihua Zhang, Xu Zhang e Zhenhua Guo. "Language-Guided Semantic Alignment for Co-saliency Detection". In 2024 IEEE International Conference on Multimedia and Expo (ICME), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687964.
Texto completo da fonteLebedeva, E. D., A. M. Buryakov, P. Yu Avdeev e A. V. Gorbatova. "INVESTIGATION OF THZ RADIATION PARAMETERS IN CO/WSE2 AND CO/IRMN3 STRUCTURES". In Terahertz and Microwave Radiation: Generation, Detection and Applications (ТЕRА-2023). Moscow: Our Style, 2023. http://dx.doi.org/10.59043/9785604953914_106_1.
Texto completo da fonteGuo, Xin, Dong Liu, Brendan Jou, Mojun Zhu, Anni Cai e Shih-Fu Chang. "Robust Object Co-detection". In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.412.
Texto completo da fonteChen, Hwann-Tzong. "Preattentive co-saliency detection". In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5650014.
Texto completo da fonteXie, Yufeng, Linwei Ye, Zhi Liu e Xuemei Zou. "Video co-saliency detection". In Eighth International Conference on Digital Image Processing (ICDIP 2016), editado por Charles M. Falco e Xudong Jiang. SPIE, 2016. http://dx.doi.org/10.1117/12.2245113.
Texto completo da fonteGai, Tianyang, Tong Qu, Xiaojing Su, Shuhan Wang, Lisong Dong, Libin Zhang, Rui Chen, Yajuan Su, Yayi Wei e Tianchun Ye. "Multi-level layout hotspot detection based on multi-classification with deep learning". In Design-Technology Co-optimization XV, editado por Chi-Min Yuan e Ryoung-Han Kim. SPIE, 2021. http://dx.doi.org/10.1117/12.2583726.
Texto completo da fonteYang, Haoyu, Piyush Pathak, Frank E. Gennari, Ya-Chieh Lai e Bei Yu. "Hotspot detection using squish-net". In Design-Process-Technology Co-optimization for Manufacturability XIII, editado por Jason P. Cain e Chi-Min Yuan. SPIE, 2019. http://dx.doi.org/10.1117/12.2515172.
Texto completo da fonteHsu, Kuang-Jui, Yen-Yu Lin e Yung-Yu Chuang. "DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection". In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00905.
Texto completo da fonteYu, Heng, Ying Lu, Cong Yu, Hongya Zhao e Lei Wang. "Co-Occurrence Morphological Edge Detection". In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2019. http://dx.doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00086.
Texto completo da fonteWei, Lina, Shanshan Zhao, Omar El Farouk Bourahla, Xi Li e Fei Wu. "Group-wise Deep Co-saliency Detection". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/424.
Texto completo da fonteRelatórios de organizações sobre o assunto "CO₂ detection"
Wigley, T. M. L., e P. D. Jones. Detection of CO sub 2 -Induced climatic change. Office of Scientific and Technical Information (OSTI), agosto de 1989. http://dx.doi.org/10.2172/5721338.
Texto completo da fonteWigley, T., e P. Jones. Detection of CO sub 2 -induced climatic change. Office of Scientific and Technical Information (OSTI), julho de 1990. http://dx.doi.org/10.2172/6371968.
Texto completo da fonteAlonso, Jesus. Intrinsic Fiber Optic Chemical Sensors for Subsurface Detection of CO2. Office of Scientific and Technical Information (OSTI), janeiro de 2016. http://dx.doi.org/10.2172/1245137.
Texto completo da fonteHanson, Ronald K. Novel Extended-Wavelength Diode Lasers to Enable Sensitive Detection of CO and NOx. Fort Belvoir, VA: Defense Technical Information Center, abril de 2000. http://dx.doi.org/10.21236/ada383138.
Texto completo da fonteSinclair, Michael B., Jeb Hunter Flemming, Raymond Blair e Kent Bryant Pfeifer. Detection of carbon monoxide (CO) as a furnace byproduct using a rotating mask spectrometer. Office of Scientific and Technical Information (OSTI), fevereiro de 2006. http://dx.doi.org/10.2172/903153.
Texto completo da fonteSun, Alexander. Pressure-Based Inversion and Data Assimilation System (PIDAS) for CO2 Leakage Detection. Office of Scientific and Technical Information (OSTI), dezembro de 2018. http://dx.doi.org/10.2172/1494374.
Texto completo da fonteYang, X., T. A. Buscheck, K. Mansoor e S. A. Carroll. Likelihood of Brine and CO2 Leak Detection using Magnetotellurics and Electrical Resistivity Tomography Methods. Office of Scientific and Technical Information (OSTI), setembro de 2017. http://dx.doi.org/10.2172/1393348.
Texto completo da fonteHaider, Huma. Malaria, HIV and TB in Nigeria: Epidemiology and Disease Control Challenges. Institute of Development Studies (IDS), dezembro de 2021. http://dx.doi.org/10.19088/k4d.2022.040.
Texto completo da fonteDeschamps, Henschel e Robert. PR-420-123712-R01 Lateral Ground Movement Detection Capabilities Derived from Synthetic Aperture Radar. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), novembro de 2014. http://dx.doi.org/10.55274/r0010831.
Texto completo da fonteKamrath, Matthew, Vladimir Ostashev, D. Wilson, Michael White, Carl Hart e Anthony Finn. Vertical and slanted sound propagation in the near-ground atmosphere : amplitude and phase fluctuations. Engineer Research and Development Center (U.S.), maio de 2021. http://dx.doi.org/10.21079/11681/40680.
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