Literatura académica sobre el tema "Synthetic sensor"
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Artículos de revistas sobre el tema "Synthetic sensor"
Veeramuthu, Loganathan, Manikandan Venkatesan, Fang-Cheng Liang, Jean-Sebastien Benas, Chia-Jung Cho, Chin-Wen Chen, Ye Zhou, Rong-Ho Lee y Chi-Ching Kuo. "Conjugated Copolymers through Electrospinning Synthetic Strategies and Their Versatile Applications in Sensing Environmental Toxicants, pH, Temperature, and Humidity". Polymers 12, n.º 3 (5 de marzo de 2020): 587. http://dx.doi.org/10.3390/polym12030587.
Texto completoLeja, Laura, Vitālijs Purlans, Rihards Novickis, Andrejs Cvetkovs y Kaspars Ozols. "Mathematical Model and Synthetic Data Generation for Infra-Red Sensors". Sensors 22, n.º 23 (3 de diciembre de 2022): 9458. http://dx.doi.org/10.3390/s22239458.
Texto completoJhuo, Yan-Ru, Chi-Yu Chen, Yu-Hsuan Yang, Hsing-Chuan Hsieh y Yuh-Jye Lee. "Continuous Monitoring of the Ambient Factors via ε-Smooth Support Vector Regression". Proceedings 31, n.º 1 (21 de noviembre de 2019): 63. http://dx.doi.org/10.3390/proceedings2019031063.
Texto completoHoa, Nguyen Duc, Nguyen Van Duy, Sherif A. El-Safty y Nguyen Van Hieu. "Meso-/Nanoporous Semiconducting Metal Oxides for Gas Sensor Applications". Journal of Nanomaterials 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/972025.
Texto completoGrabka, Michał, Przemysław Kula, Mateusz Szala, Krzysztof Jasek y Michał Czerwiński. "Fluorophenol-Containing Hydrogen-Bond Acidic Polysiloxane for Gas Sensing-Synthesis and Characterization". Polymers 14, n.º 6 (13 de marzo de 2022): 1147. http://dx.doi.org/10.3390/polym14061147.
Texto completoTORMO GARCIA, FRANCISCO JAVIER, Juan Ivorra Martínez, Teodomiro Boronat y NESTOR MONTAÑES MUÑOZ. "LOW-COST FABRICATION AND CHARACTERISATION OF A FLEXIBLE GRAPHITE-BASED TOUCH SENSOR". DYNA DYNA-ACELERADO (26 de octubre de 2022): [ 5 pp.]. http://dx.doi.org/10.6036/10577.
Texto completoXu, Chen-Yan, Kang-Ping Ning, Zheng Wang, Yao Yao, Qin Xu y Xiao-Ya Hu. "Flexible Electrochemical Platform Coupled with In Situ Prepared Synthetic Receptors for Sensitive Detection of Bisphenol A". Biosensors 12, n.º 12 (25 de noviembre de 2022): 1076. http://dx.doi.org/10.3390/bios12121076.
Texto completoCavalli, Rosa Maria. "The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice". Sensors 23, n.º 1 (1 de enero de 2023): 454. http://dx.doi.org/10.3390/s23010454.
Texto completoSherman, Christopher, Robert Mellors, Joseph Morris y Frederick Ryerson. "Geomechanical modeling of distributed fiber-optic sensor measurements". Interpretation 7, n.º 1 (1 de febrero de 2019): SA21—SA27. http://dx.doi.org/10.1190/int-2018-0063.1.
Texto completoMin, Jun-Ki. "CMOS: Efficient Clustered Data Monitoring in Sensor Networks". Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/704957.
Texto completoTesis sobre el tema "Synthetic sensor"
Kallin, Niklas. "Sensor simulation Is - AGXUnity a viable platform for adding synthetic sensors". Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-158017.
Texto completoClare, Anthony Joseph. "Real-time modelling and sensor fusion for a synthetic vision system". Thesis, University of Sheffield, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434515.
Texto completoRosander, Regina. "Sensor fusion between a Synthetic Attitude and Heading Reference System and GPS". Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1766.
Texto completoSensor fusion deals with the merging of several signals into one, extracting a better and more reliable result. Traditionally the Kalmanfilter is used for this purpose and the aircraft navigation has benefited tremendously from its use. This thesis considers the merge of two navigation systems, the GPS positioning system and the Saab developed Synthetic Attitude and Heading Reference System (SAHRS). The purpose is to find a model for such a fusion and to investigate whether the fusion will improve the overall navigation performance. The non-linear nature of the navigation equations will lead to the use of the extended Kalman filter and the model is evaluated against both simulated and real data. The results show that this strategy indeed works but problems will arise when the GPS signal falls away.
Meng, Rui Daniel. "Design and implementation of sensor fusion for the towed synthetic aperture sonar". Thesis, University of Canterbury. Electrical and Computer Engineering, 2007. http://hdl.handle.net/10092/1199.
Texto completoHolder, Martin Friedrich [Verfasser], Hermann [Akademischer Betreuer] Winner y Erwin [Akademischer Betreuer] Biebl. "Synthetic Generation of Radar Sensor Data for Virtual Validation of Autonomous Driving / Martin Friedrich Holder ; Hermann Winner, Erwin Biebl". Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1233429426/34.
Texto completoRiedel, Jan Verfasser], Reinhard [Akademischer Betreuer] Köster y Dagmar [Akademischer Betreuer] [Wirth. "Development of a synthetic sensor system for the detection of infectious and inflammatory signals / Jan Riedel ; Reinhard Köster, Dagmar Wirth". Braunschweig : Technische Universität Braunschweig, 2019. http://d-nb.info/1194158609/34.
Texto completoPrendes, Jorge. "New statistical modeling of multi-sensor images with application to change detection". Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLC006/document.
Texto completoRemote sensing images are images of the Earth surface acquired from satellites or air-borne equipment. These images are becoming widely available nowadays and its sensor technology is evolving fast. Classical sensors are improving in terms of resolution and noise level, while new kinds of sensors are proving to be useful. Multispectral image sensors are standard nowadays and synthetic aperture radar (SAR) images are very popular.The availability of different kind of sensors is very advantageous since it allows us to capture a wide variety of properties of the objects contained in a scene. These properties can be exploited to extract richer information about these objects. One of the main applications of remote sensing images is the detection of changes in multitemporal datasets (images of the same area acquired at different times). Change detection for images acquired by homogeneous sensors has been of interest for a long time. However the wide range of different sensors found in remote sensing makes the detection of changes in images acquired by heterogeneous sensors an interesting challenge.Accurate change detectors adapted to heterogeneous sensors are needed for the management of natural disasters. Databases of optical images are readily available for an extensive catalog of locations, but, good climate conditions and daylight are required to capture them. On the other hand, SAR images can be quickly captured, regardless of the weather conditions or the daytime. For these reasons, optical and SAR images are of specific interest for tracking natural disasters, by detecting the changes before and after the event.The main interest of this thesis is to study statistical approaches to detect changes in images acquired by heterogeneous sensors. Chapter 1 presents an introduction to remote sensing images. It also briefly reviews the different change detection methods proposed in the literature. Additionally, this chapter presents the motivation to detect changes between heterogeneous sensors and its difficulties.Chapter 2 studies the statistical properties of co-registered images in the absence of change, in particular for optical and SAR images. In this chapter a finite mixture model is proposed to describe the statistics of these images. The performance of classical statistical change detection methods is also studied by taking into account the proposed statistical model. In several situations it is found that these classical methods fail for change detection.Chapter 3 studies the properties of the parameters associated with the proposed statistical mixture model. We assume that the model parameters belong to a manifold in the absence of change, which is then used to construct a new similarity measure overcoming the limitations of classic statistical approaches. Furthermore, an approach to estimate the proposed similarity measure is described. Finally, the proposed change detection strategy is validated on synthetic images and compared with previous strategies.Chapter 4 studies Bayesian non parametric algorithm to improve the estimation of the proposed similarity measure. This algorithm is based on a Chinese restaurant process and a Markov random field taking advantage of the spatial correlations between adjacent pixels of the image. This chapter also defines a new Jeffreys prior for the concentration parameter of this Chinese restaurant process. The estimation of the different model parameters is conducted using a collapsed Gibbs sampler. The proposed strategy is validated on synthetic images and compared with the previously proposed strategy. Finally, Chapter 5 is dedicated to the validation of the proposed change detection framework on real datasets, where encouraging results are obtained in all cases. Including the Bayesian non parametric model into the change detection strategy improves change detection performance at the expenses of an increased computational cost
Ton, Xuan-Anh. "Fiber optic chemical sensors based on molecularly imprinted polymers for the detection of mycotoxins". Phd thesis, Université de Technologie de Compiègne, 2013. http://tel.archives-ouvertes.fr/tel-01002117.
Texto completoNord, Sofia. "Multivariate Time Series Data Generation using Generative Adversarial Networks : Generating Realistic Sensor Time Series Data of Vehicles with an Abnormal Behaviour using TimeGAN". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302644.
Texto completoNär man applicerar en modell för att utföra en maskininlärningsuppgift, till exempel att förutsäga utfall eller upptäcka avvikelser, är det viktigt med stora dataset för att uppnå hög prestanda, noggrannhet och generalisering. Det är dock inte ovanligt att dataset är små eller obalanserade eftersom insamling av data kan vara svårt, tidskrävande och dyrt. När man vill samla tidsserier från sensorer på fordon är dessa problem närvarande och de kan hindra bilindustrin i dess utveckling. Generering av syntetisk data har blivit ett växande intresse bland forskare inom flera områden som ett sätt att hantera problemen med datainsamling. Bland de metoder som undersökts för att generera data har generative adversarial networks (GANs) blivit ett populärt tillvägagångssätt i forskningsvärlden på grund av dess breda applikationsdomän och dess framgångsrika resultat. Denna avhandling fokuserar på att generera flerdimensionell tidsseriedata som liknar fordonssensoravläsningar av lufttryck i bromssystemet av fordon med onormalt beteende, vilket innebär att det finns ett läckage i systemet. En ny GAN modell kallad TimeGAN tränades för att genera sådan data och utvärderades sedan både kvalitativt och kvantitativt. Två versioner av denna modell testades och jämfördes. De erhållna resultaten visade att båda modellerna lärde sig distributionen och den underliggande informationen inom de olika signalerna i den verkliga datan. Målet med denna avhandling uppnåddes och kan lägga grunden för framtida arbete inom detta område.
Costa, Jorge Alberto Lopes da. "Avaliação de dados de radar do sensor SAR-R99B no mapeamento do uso e cobertura da terra na Amazônia Central, município de Manaus, AM". Universidade Federal do Amazonas, 2011. http://tede.ufam.edu.br/handle/tede/4514.
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In recent decades the areas of rainforest in the Amazon region has been heavily impacted by a rapid process of conversion of vegetation cover in other types of use due to human action. In the context of global change, the use of mapping and monitoring land cover and provide information for the analysis and evaluation of environmental impacts due to accelerated changes in the landscape. Therefore, this study evaluated the potential of data from synthetic aperture radar for discriminating use and land cover in the region of Manaus, Amazonas state. We used a multipolarized image from sensor airborne SAR-R99B (L band), with 3 m spatial resolution. Were evaluated the MAXVER-ICM and SVM (Support Vector Machine) classifiers, where in all cases we used the images individually multipolarized amplitude (HH, HV and VV), in pairs (HH and HV), (HV and VV) and (HH and VV) and together (HH, HV and VV). The results were compared using as parameter the Kappa coefficient. The SVM classifier had higher accuracy compared to MAXVER-ICM classifier. The best classifications were obtained for the dual polarization (HH and VV) with MARVER-ICM classifier and (HH, HV and VV) with the SVM classifier both using the images with the filter. The accuracy was highest with SVM for classification and filter images (kappa = 0.7736). Were analyzed the influence of using GAMMA filter performance on the classifiers where it showed that filtered images have provided an increase in the results, on average, about 8%. Thus there was the analysis of the classification results, which found that the best result was provided by the dataset multipolarized (HH, HV and VV) classified by the SVM method. Thus, we concluded that the use of radar imagery in mapping thematic classes use and land cover in tropical regions, can be considered as a viable proposal.
Nas últimas décadas as áreas de floresta tropical na região Amazônica têm sido fortemente impactada por um rápido processo de conversão da cobertura vegetal em outros tipos de uso devido à ação antrópica. No contexto das mudanças globais, os mapeamentos e monitoramentos de uso e cobertura da terra fornecem subsídios para as análises e avaliações dos impactos ambientas em virtude de acelerados processos de mudança na paisagem. Neste contexto, este estudo avaliou o potencial dos dados de radar de abertura sintética para discriminação de uso e cobertura da terra na região de Manaus, estado do Amazonas. Foi utilizada uma imagem multipolarizada do sensor aerotransportado SAR-R99B (banda L), com 3 metros de resolução espacial. Realizaram-se classificações na imagem radar sem filtro e com filtro Gamma 3x3. Avaliou-se o classificador pontual MAXVER-ICM e o SVM (Support Vector Machine), onde em todos os casos utilizou-se das imagens multipolarizadas em amplitude individualmente (HH, HV e VV), aos pares (HH e HV), (HV e VV) e (HH e VV) e em conjunto (HH, HV e VV). Os resultados obtidos foram comparados utilizando-se como parâmetro o coeficiente de concordância Kappa. O classificador SVM apresentou acurácia superior em relação ao classificador MAXVER-ICM. As melhores classificações foram obtidas para a polarização dual HH e VV com o classificador MAXVER-ICM e (HH, HV e VV) com o classificador SVM ambos utilizando as imagens com filtro. A acurácia mais elevada foi para a classificação com SVM e imagens com filtro (kappa = 0,7736). Analisou-se a influência do uso de filtro GAMMA no desempenho dos classificadores onde se contatou que as imagens filtradas proporcionaram um incremento nos resultados, em média, na ordem de 8%. Deste modo realizou-se a análise dos resultados das classificações, onde se constatou que o melhor resultado foi proporcionado pelo conjunto de dados multipolarizados (HH, HV e VV)classificados através do método SVM. Assim, concluiu-se que o uso de imagens de radar no mapeamento de classes temáticas de uso e cobertura da terra, em regiões tropicais, pode ser considerado como uma proposta viável.
Libros sobre el tema "Synthetic sensor"
Jan Roelof van der Meer. Bacterial sensors: Synthetic design and application principles. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Buscar texto completoNadovich, Chris. Synthetic instruments: Concepts and applications. Burlington, MA: Elsevier/Newnes, 2005.
Buscar texto completoMorea, G. Synthesis, surface characterization and sensor behaviour of conducting polymers. Manchester: UMIST, 1993.
Buscar texto completoLind, Rick. Choosing sensor configuration for a flexible structure using full control synthesis. [Washington, DC: National Aeronautics and Space Administration, 1997.
Buscar texto completoJohnston, A. G. The design and synthesis of molecular receptors as chemical sensors for small molecules. Manchester: UMIST, 1996.
Buscar texto completoK, Dhar Nibir, Dutta Achyut K, Islam M. Saiful 1970- y Society of Photo-optical Instrumentation Engineers., eds. Nanomaterial synthesis and integration for sensors, electronics, photonics, and electro-optics: 1-4 October, 2006, Boston, Massachusetts, USA. Bellingham, Wash: SPIE, 2006.
Buscar texto completoMetal oxide nanostructures as gas sensing devices. Boca Raton: Taylor & Francis, 2011.
Buscar texto completoSymposium on the Application of Sensors and Modeling to Materials Processing (1997 Orlando, Fla.). Sensors and modeling in materials processing: Techniques and applications : proceedings of a Symposium on the Application of Sensors and Modeling to Materials Processing, spononsored by the EPD/MDMD Synthesis, Control, and Analysis in Materials Processing Committee and the EPD Process Fundamentals Committee, held at the 126th annual meeting of the Minerals, Metals, and Materials Society, Orlando, February 9-13, 1997. Warrendale, Pa: The Society, 1997.
Buscar texto completoNational Aeronautics and Space Administration (NASA) Staff. Evaluation of Alternate Concepts for Synthetic Vision Flight Displays with Weather-Penetrating Sensor Image Inserts During Simulated Landing Approaches. Independently Published, 2018.
Buscar texto completoWich, Serge A. y Lian Pin Koh. Sensors. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198787617.003.0003.
Texto completoCapítulos de libros sobre el tema "Synthetic sensor"
Allbeck, Jan M. y Norman I. Badler. "Simulating Human Activities for Synthetic Inputs to Sensor Systems". En Distributed Video Sensor Networks, 193–205. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-127-1_13.
Texto completoHagn, Korbinian y Oliver Grau. "Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation". En Deep Neural Networks and Data for Automated Driving, 127–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_4.
Texto completoOliva, G., A. S. Fiorillo y S. A. Pullano. "Development of VOCs Sensor Based on Synthetic Zeolite Layers". En Proceedings of SIE 2022, 128–33. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26066-7_20.
Texto completoIvanchenko, L. A., V. S. Sulyma y N. D. Pinchuk. "The Researches of Properties of Biomaterials Based on Biological Hydroxyapatite in Synthetic and Natural Physiological Mediums". En Nanostructured Materials and Coatings for Biomedical and Sensor Applications, 77–82. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0157-1_8.
Texto completoHummel, Georg, Levente Kovács, Peter Stütz y Tamás Szirányi. "Data Simulation and Testing of Visual Algorithms in Synthetic Environments for Security Sensor Networks". En Communications in Computer and Information Science, 212–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33161-9_31.
Texto completoSterling, Gerald, Elizabeth Chang y Tharam Dillon. "Semantics of a Multimedia Database for Support within Synthetic Environments for Multiple Sensor Systems". En Database Semantics, 413–34. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35561-0_23.
Texto completoAnil Patel, Bhavik. "Diamond Sensors for Neurochemistry". En Synthetic Diamond Films, 511–50. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118062364.ch20.
Texto completoDavis, A. P. "Molecular Recognition: Synthetic Receptors by Rational Design and Targeted Synthesis". En Molecular Electronics: Bio-sensors and Bio-computers, 427–55. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0141-0_21.
Texto completoKubik, Stefan. "Cyclopeptide Derived Synthetic Receptors". En Artificial Receptors for Chemical Sensors, 135–67. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2010. http://dx.doi.org/10.1002/9783527632480.ch5.
Texto completoWerner, Matthias. "CVD-Diamond Sensors for Temperature and Pressure". En Low-Pressure Synthetic Diamond, 243–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-71992-9_13.
Texto completoActas de conferencias sobre el tema "Synthetic sensor"
Hill, Jonathan, Hilary Bart-Smith, C. Barbier y J. A. C. Humphrey. "Investigation of a Bioinspired Whisker-Like Fluid Motion Sensor". En ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14508.
Texto completoIbadah, Nisrine, Khalid Minaoui, Mohammed Rziza y Mohammed Oumsis. "Experimental Synthesis of Routing Protocols and Synthetic Mobility Modeling for MANET". En 6th International Conference on Sensor Networks. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006203601680173.
Texto completoKozlov, Vitali, Eran Rebenshtok y Pavel Ginzburg. "Synthetic software defined radar (Conference Presentation)". En Radar Sensor Technology XXIV, editado por Ann M. Raynal y Kenneth I. Ranney. SPIE, 2020. http://dx.doi.org/10.1117/12.2556221.
Texto completoPAVEL, M., J. LARIMER y A. AHUMADA. "Sensor fusion for synthetic vision". En 8th Computing in Aerospace Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-3730.
Texto completoBlundell, V., T. Clarke y D. Williams. "Synthetic signals for signal processing". En Sensor Signal Processing for Defence (SSPD 2010). IET, 2010. http://dx.doi.org/10.1049/ic.2010.0229.
Texto completoDoerry, Armin W. "Comments on rendering synthetic aperture radar (SAR) images". En Radar Sensor Technology XXV, editado por Ann M. Raynal y Kenneth I. Ranney. SPIE, 2021. http://dx.doi.org/10.1117/12.2585845.
Texto completoDoerry, Armin W. y Douglas L. Bickel. "Motion measurement impact on synthetic aperture radar (SAR) geolocation". En Radar Sensor Technology XXV, editado por Ann M. Raynal y Kenneth I. Ranney. SPIE, 2021. http://dx.doi.org/10.1117/12.2585846.
Texto completoPaulson, Christopher R., Adam R. Nolan, Lori Westerkamp y Edmund Zelnio. "Multi-sensor synthetic data generation for performance characterization". En Algorithms for Synthetic Aperture Radar Imagery XXVI, editado por Edmund Zelnio y Frederick D. Garber. SPIE, 2019. http://dx.doi.org/10.1117/12.2523579.
Texto completoPenn, Joseph A. y Gertrude H. Kornfeld. "Various FLIR sensor effects applied to synthetic thermal imagery". En Recent Advances in Sensors, Radiometric Calibration, and Processing of Remotely Sensed Data. SPIE, 1993. http://dx.doi.org/10.1117/12.161560.
Texto completoAksu, Ridvan, Mohammad M. Rahman y Sevgi Z. Gurbuz. "3D scene reconstruction from multi-sensor EO-SAR data". En Algorithms for Synthetic Aperture Radar Imagery XXVII, editado por Edmund Zelnio y Frederick D. Garber. SPIE, 2020. http://dx.doi.org/10.1117/12.2558350.
Texto completoInformes sobre el tema "Synthetic sensor"
Pinkus, Alan R., David W. Dommett, H. L. Task, Sheldon E. Unger y David W. Sivert. Synthetic Observer Approach to Multispectral Sensor Resolution Assessment. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2010. http://dx.doi.org/10.21236/ada528908.
Texto completoLundquist, J., B. Kosovic y R. Belles. Synthetic Event Reconstruction Experiments for Defining Sensor Network Characteristics. Office of Scientific and Technical Information (OSTI), diciembre de 2005. http://dx.doi.org/10.2172/894010.
Texto completoVelten, Vincent J. Geometric Invariance for Synthetic Aperture Radar (SAR) Sensors. Fort Belvoir, VA: Defense Technical Information Center, abril de 2004. http://dx.doi.org/10.21236/ada426700.
Texto completoSteven Buckley, Reza Gharavi y Marco Leon. Multiplexed Sensor for Synthesis Gas Compsition and Temperature. Office of Scientific and Technical Information (OSTI), octubre de 2007. http://dx.doi.org/10.2172/951064.
Texto completoKim, Hajin J., Michael C. Cornell y Charles B. Naumann. AMRDEC's HWIL Synthetic Environment Development Efforts for LADAR Sensors. Fort Belvoir, VA: Defense Technical Information Center, enero de 2004. http://dx.doi.org/10.21236/ada461374.
Texto completoPérez, Pablo A. López, Ricardo Aguilar-López, Omar S. Castillo-Baltazar, Emmanuel Vallejo Castañeda y Vicente Peña Caballero. Virtual Sensors for Biofuels Production: a Brief Mathematical Description for Synthesis of Algorithms. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, octubre de 2019. http://dx.doi.org/10.7546/crabs.2019.10.11.
Texto completoJenkins, C. W., R. B. King y I. Bresinska. Synthesis of Polystyrene-Supported Dithizone Analogues for Use as Chemical Sensors for Heavy Metals. Office of Scientific and Technical Information (OSTI), julio de 1998. http://dx.doi.org/10.2172/656434.
Texto completoJunhang Dong, Hai Xiao, Xiling Tang, Hongmin Jiang, Kurtis Remmel y Amardeep Kaur. DEVELOPMENT OF NOVEL CERAMIC NANOFILM-FIBER INTEGRATED OPTICAL SENSORS FOR RAPID DETECTION OF COAL DERIVED SYNTHESIS GAS. Office of Scientific and Technical Information (OSTI), septiembre de 2012. http://dx.doi.org/10.2172/1060495.
Texto completoDeng, Zhiqun, John A. Serkowski, Tao Fu, Thomas J. Carlson y Marshall C. Richmond. Synthesis of Sensor Fish Data for Assessment of Fish Passage Conditions at Turbines, Spillways, and Bypass Facilities ? Phase 1: The Dalles Dam Spillway Case Study. Office of Scientific and Technical Information (OSTI), diciembre de 2007. http://dx.doi.org/10.2172/926962.
Texto completoSoenen, Karen, Dana Gerlach, Christina Haskins, Taylor Heyl, Danie Kinkade, Sawyer Newman, Shannon Rauch et al. How can BCO-DMO help with your oceanographic data? How can BCO-DMO help with your oceanographic data?, diciembre de 2021. http://dx.doi.org/10.1575/1912/27803.
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