Literatura académica sobre el tema "Automatic identification sensor"
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Artículos de revistas sobre el tema "Automatic identification sensor"
Álvarez-Bazo, Fernando, Santos Sánchez-Cambronero, David Vallejo, Carlos Glez-Morcillo, Ana Rivas y Inmaculada Gallego. "A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis". Sensors 20, n.º 19 (29 de septiembre de 2020): 5589. http://dx.doi.org/10.3390/s20195589.
Texto completoKushwaha, Ruchi, Rohit Shambharkar, Suyash Gupta y Monika Malik. "Integration of Block chain Model for Energy Efficient WSN for IOT Application". International Journal for Research in Applied Science and Engineering Technology 11, n.º 2 (28 de febrero de 2023): 34–37. http://dx.doi.org/10.22214/ijraset.2023.48942.
Texto completoGiurgiutiu, Victor y Andrei N. Zagrai. "Embedded Self-Sensing Piezoelectric Active Sensors for On-Line Structural Identification". Journal of Vibration and Acoustics 124, n.º 1 (1 de julio de 2001): 116–25. http://dx.doi.org/10.1115/1.1421056.
Texto completoLiu, Li Min. "Internet of Things and RFID Technology". Applied Mechanics and Materials 336-338 (julio de 2013): 2512–15. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2512.
Texto completoZhou, Guang-Dong, Mei-Xi Xie, Ting-Hua Yi y Hong-Nan Li. "Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm". Advances in Structural Engineering 22, n.º 4 (4 de octubre de 2018): 907–18. http://dx.doi.org/10.1177/1369433218797074.
Texto completoBeligni, Alessio, Claudio Sbarufatti, Andrea Gilioli, Francesco Cadini y Marco Giglio. "Robust Identification of Strain Waves due to Low-Velocity Impact with Different Impactor Stiffness". Sensors 19, n.º 6 (14 de marzo de 2019): 1283. http://dx.doi.org/10.3390/s19061283.
Texto completoZheng, Jun Hui y Bing Li. "Fire Seat Intelligent Identification System". Applied Mechanics and Materials 536-537 (abril de 2014): 421–25. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.421.
Texto completoZheng, Fu. "Design of Auto Route Identified Vehicle Model Based on MC9S12XS128". Applied Mechanics and Materials 187 (junio de 2012): 146–50. http://dx.doi.org/10.4028/www.scientific.net/amm.187.146.
Texto completoBeiderman, Yevgeny, Mark Kunin, Eli Kolberg, Ilan Halachmi, Binyamin Abramov, Rafael Amsalem y Zeev Zalevsky. "Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle". Journal of Agricultural Engineering 45, n.º 4 (21 de diciembre de 2014): 153. http://dx.doi.org/10.4081/jae.2014.418.
Texto completoLi, Dongya, Wei Wang y De Zhao. "A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors". Sustainability 14, n.º 15 (2 de agosto de 2022): 9474. http://dx.doi.org/10.3390/su14159474.
Texto completoTesis sobre el tema "Automatic identification sensor"
Ammineni, Chandini Muniratnam. "Design of Lignin Sensor for Identification of Paper Grades for an Automatic Waste Paper SortingSystem". NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010907-181312.
Texto completoAMMINENI, CHANDINI MUNIRATNAM. Design of Lignin Sensor forIdentification of Paper Grades for an Automatic Waste Paper SortingSystem. (Under the direction of Dr. M. K. Ramasubramanian.)The purpose of this research has been to design a lignin sensor fornon-destructive, real-time identification of waste paper grades, toaid in automating a waste paper sorting process. The sensor iscapable of identifying about 500 papers in one second. It is based onthe principle that fluorescence light emitted from paper followingabsorption of visible light has a wavelength distribution determinedby the chemical composition of the paper. The sensor is the most critical part in waste paper sorting, whichhas hitherto not been automated due to the inability to design asensor that distinguishes paper grades. This sensor is vastlysuperior to all other sensors previously designed for this purposebecause, it does not use the conventional reflective type opticalproperties of paper, and this is the only sensor that can identifyall grades unlike the previous sensors that could identify only whiteledger papers.
Narby, Erik. "Modeling and Estimation of Dynamic Tire Properties". Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6153.
Texto completoInformation about dynamic tire properties has always been important for drivers of wheel driven vehicles. With the increasing amount of systems in modern vehicles designed to measure and control the behavior of the vehicle information regarding dynamic tire properties has grown even more important.
In this thesis a number of methods for modeling and estimating dynamic tire properties have been implemented and evaluated. The more general issue of estimating model parameters in linear and non-linear vehicle models is also addressed.
We conclude that the slope of the tire slip curve seems to dependent on the stiffness of the road surface and introduce the term combined stiffness. We also show that it is possible to estimate both longitudinal and lateral combined stiffness using only standard vehicle sensors.
Souza, Vinicius Mourão Alves de. "Classificação de fluxo de dados não estacionários com aplicação em sensores identificadores de insetos". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13122016-113648/.
Texto completoMany applications are able to generate data continuously over t ime in an ordered and uninterrupted way in a dynamic environment , called data streams. Among possible tasks that can be performed with these data, classification is one of the most prominent . Due to non-stationarity of the environment that generates the data, the features that describe the concepts of the classes can change over time. Thus, the classifiers that deal with data streams require constants updates in their classification models to maintain a stable accuracy over time. In the update phase, most of the approaches assume that after the classification of each example from the stream, their actual class label is available without any t ime delay (zero latency). Given the high label costs, it is more reasonable to consider that this delay could vary for the most portion of the data. In the more challenging case, there are applications with extreme latency, where in after the classification of the examples, heir actual class labels are never available to the algorithm. In this scenario, it is not possible to use traditional approaches. Thus, there is the need of new methods that are able to maintain a classification model updated in the absence of labeled data. In this thesis, besides to discuss the problem of latency to obtain actual labels in data stream classification problems, neglected by most of the works, we also propose two new algorithms to deal with extreme latency, called SCARGC and MClassification. Both algorithms are based on the use of clustering approaches to adapt to changes in an unsupervised way. The proposed algorithms are intuitive, simpleand showed superior or equivalent results in terms of accuracy and computation time compared to other approaches from literature in an evaluation on synthetic and real data. In addition to the advance in the state-of-the-art in the stream learning area, this thesis also presents contributions to an important technological application with social and public health impacts. Specifically, it was studied an optical sensor to automatically identify insect species by the means of the analysis of information coming from wing beat of insects. To describe the data, we conclude that the Mel-cepst ral coefficients guide to the best results among different evaluated digital signal processing techniques. This sensor is a concrete example of an applicat ion that generates a data st ream for which it is necessary to perform real-time classification. During the classification phase, this sensor must adapt their classification model to possible variat ions in environmental conditions, responsible for changing the behavior of insects. To address this problem, we propose a System with Multiple Classifiers that dynamically selects the most adequate classifier according to characteristics of each test example. In evaluations with minor changes in the environmental conditions, we achieved a classification accuracy close to 90% in a scenario with multiple classes and 95% when identifying Aedes aegypti species considering the training phase with only the positive class. In the scenario with considerable changes in the environmental conditions, we achieved 91% of accuracy considering 5 species and 96% to classify vector mosquitoes of important diseases as dengue and zika virus.
Skoglar, Per. "Modelling and control of IR/EO-gimbal for UAV surveillance applications". Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1281.
Texto completoThis thesis is a part of the SIREOS project at Swedish Defence Research Agency which aims at developing a sensor system consisting of infrared and video sensors and an integrated navigation system. The sensor system is placed in a camera gimbal and will be used on moving platforms, e.g. UAVs, for surveillance and reconnaissance. The gimbal is a device that makes it possible for the sensors to point in a desired direction.
In this thesis the sensor pointing problem is studied. The problem is analyzed and a system design is proposed. The major blocks in the system design are gimbal trajectory planning and gimbal motion control. In order to develop these blocks, kinematic and dynamic models are derived using techniques from robotics. The trajectory planner is based on the kinematic model and can handle problems with mechanical constraints, kinematic singularity, sensor placement offset and reference signal transformation.
The gimbal motion controller is tested with two different control strategies, PID and LQ. The challenge is to perform control that responds quickly, but do not excite the damping flexibility too much. The LQ-controller uses a linearization of the dynamic model to fulfil these requirements.
Abdul, Nour Charles. "Identification de paramètres optiques de structures tissulaires : instrumentation prototype associée : application à la dosimétrie de la thérapie photo-dynamique". Vandoeuvre-les-Nancy, INPL, 1994. http://www.theses.fr/1994INPL006N.
Texto completoHarichandran, Aparna. "Sensor Placement, Operation Identification and Fault Detection for Automated Construction Monitoring". Thesis, Curtin University, 2022. http://hdl.handle.net/20.500.11937/87927.
Texto completoCURRERI, Francesco. "Soft Sensor Design, Transferability and Causality through Machine Learning Techniques". Doctoral thesis, Università degli Studi di Palermo, 2023. https://hdl.handle.net/10447/582112.
Texto completoDann, Aaron. "Identification and simulation of an automated guided vechile for minimal sensor applications". Thesis, University of Canterbury. Mechanical Engineering, 1996. http://hdl.handle.net/10092/6410.
Texto completoŠíbl, Josef. "Studie řízení plynulých materiálových toků s využitím značení produktů". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2009. http://www.nusl.cz/ntk/nusl-222051.
Texto completoBayram, Alican. "Identification Of Kinematic Parameters Using Pose Measurements And Building A Flexible Interface". Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614819/index.pdf.
Texto completoLibros sobre el tema "Automatic identification sensor"
Piramuthu, Selwyn. RFID & sensor network automation in the food industry: Ensuring quality and safety through supply chain visibility. Hoboken: John Wiley & Sons Inc., 2015.
Buscar texto completoPiramuthu, Selwyn y Weibiao Zhou. RFID and Sensor Network Automation in the Food Industry: Ensuring Quality and Safety Through Supply Chain Visibility. Wiley & Sons, Limited, John, 2016.
Buscar texto completoPiramuthu, Selwyn y Weibiao Zhou. RFID and Sensor Network Automation in the Food Industry: Ensuring Quality and Safety Through Supply Chain Visibility. Wiley & Sons, Incorporated, John, 2016.
Buscar texto completoPiramuthu, Selwyn y Weibiao Zhou. RFID and Sensor Network Automation in the Food Industry: Ensuring Quality and Safety Through Supply Chain Visibility. Wiley & Sons, Incorporated, John, 2016.
Buscar texto completoCapítulos de libros sobre el tema "Automatic identification sensor"
Lambrecht, S. y J. L. Pons. "Automatic Identification of Sensor Localization on the Upper Extremity". En IFMBE Proceedings, 1497–500. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_370.
Texto completoNtalampiras, Stavros y Georgios Giannopoulos. "Automatic Fault Identification in Sensor Networks Based on Probabilistic Modeling". En Critical Information Infrastructures Security, 344–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31664-2_35.
Texto completoBradley, Elizabeth y Matthew Easley. "Reasoning about sensor data for automated system identification". En Advances in Intelligent Data Analysis Reasoning about Data, 561–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052871.
Texto completoCatucci, Antonella, Alessia Tricomi, Laura De Vendictis, Savvas Rogotis y Nikolaos Marianos. "Farm Weather Insurance Assessment". En Big Data in Bioeconomy, 247–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_19.
Texto completoJavidi, Bahram, Timothy O’Connor, Arun Anand, Inkyu Moon, Adrian Stern y Manuel Martinez-Corral. "Compact and Field Portable Biophotonic Sensors for Automated Cell Identification (Plenary Address)". En Springer Proceedings in Physics, 15–18. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9259-1_4.
Texto completoPea, Roy D., Paulina Biernacki, Maxwell Bigman, Kelly Boles, Raquel Coelho, Victoria Docherty, Jorge Garcia et al. "Four Surveillance Technologies Creating Challenges for Education". En AI in Learning: Designing the Future, 317–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09687-7_19.
Texto completoMalhotra, Baljeet, Hoyoung Jeung, Thomas Kister, Stéphane Bressan y Kian-Lee Tan. "Maritime Data Management and Analytics: A Survey of Solutions Based on Automatic Identification System". En Building Sensor Networks, 249–70. CRC Press, 2017. http://dx.doi.org/10.1201/b15479-11.
Texto completoTONGCO, E. C. y D. R. MELDRUM. "OPTIMAL SENSOR PLACEMENT FOR IDENTIFICATION OF LARGE FLEXIBLE SPACE STRUCTURES". En Automatic Control in Aerospace 1994 (Aerospace Control '94), 249–54. Elsevier, 1995. http://dx.doi.org/10.1016/b978-0-08-042238-1.50042-2.
Texto completoChowdhury, Dhrubajit, Alexander Melin y Kris Villez. "Method for automatic correction of offset drift in online sensors". En Celebrating passion for Water, Science and Technology, 17–42. IWA Publishing, 2022. http://dx.doi.org/10.2166/9781789063370_0017.
Texto completoJoshi, Deepak y Michael E. Hahn. "Electromyogram and Inertial Sensor Signal Processing in Locomotion and Transition Classification". En Computational Tools and Techniques for Biomedical Signal Processing, 195–211. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0660-7.ch009.
Texto completoActas de conferencias sobre el tema "Automatic identification sensor"
Iwamoto, Takashi. "Practical Identification of Specific Emitters Used in the Automatic Identification System". En 2015 Sensor Signal Processing for Defence (SSPD). IEEE, 2015. http://dx.doi.org/10.1109/sspd.2015.7288518.
Texto completoLi, Hongyu, Hairong Wang, Luyang Liu y Marco Gruteser. "Automatic Unusual Driving Event Identification for Dependable Self-Driving". En SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274783.3274838.
Texto completoWang, Yicheng y Murat Uney. "Fast Trajectory Forecasting With Automatic Identification System Broadcasts". En 2022 Sensor Signal Processing for Defence Conference (SSPD). IEEE, 2022. http://dx.doi.org/10.1109/sspd54131.2022.9896218.
Texto completoGafurov, Davrondzhon, Einar Snekkenes y Patrick Bours. "Gait Authentication and Identification Using Wearable Accelerometer Sensor". En 2007 IEEE Workshop on Automatic Identification Advanced Technologies. IEEE, 2007. http://dx.doi.org/10.1109/autoid.2007.380623.
Texto completoWisanmongkol, J., T. Sanpechuda y U. Ketprom. "Automatic vehicle identification with sensor-integrated RFID system". En 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2008. http://dx.doi.org/10.1109/ecticon.2008.4600541.
Texto completoLin, Chung-Yen, Wenjie Chen y Masayoshi Tomizuka. "Automatic Sensor Frame Identification in Industrial Robots With Joint Elasticity". En ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3836.
Texto completoHeydary, Mohammadreza Hajy, Pritesh Pimpale y Anand Panangadan. "Automatic Identification of Use of Public Transportation from Mobile Sensor Data". En 2018 IEEE Green Technologies Conference (GreenTech). IEEE, 2018. http://dx.doi.org/10.1109/greentech.2018.00042.
Texto completoKuzume, Koichi, Yoshitugu Watanabe, Haruko Masuda y Tomonari Masuzaki. "Inference System for Automatic Identification of Braille Blocks Using a Pressure Sensor Array". En 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2022. http://dx.doi.org/10.1109/percomworkshops53856.2022.9767257.
Texto completoLiu, Zhongdi, Xiang'ao Meng, Jiajia Cui, Zhipei Huang y Jiankang Wu. "Automatic Identification of Abnormalities in 12-Lead ECGs Using Expert Features and Convolutional Neural Networks". En 2018 International Conference on Sensor Networks and Signal Processing (SNSP). IEEE, 2018. http://dx.doi.org/10.1109/snsp.2018.00038.
Texto completoKuzume, Koichi, Haruko Masuda y Yudai Murakami. "Automatic Identification of Braille Blocks by Neural Network Using Multi-Channel Pressure Sensor Array". En CIIS 2020: 2020 The 3rd International Conference on Computational Intelligence and Intelligent Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3440840.3440858.
Texto completoInformes sobre el tema "Automatic identification sensor"
Burks, Thomas F., Victor Alchanatis y Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, octubre de 2009. http://dx.doi.org/10.32747/2009.7591739.bard.
Texto completoSeginer, Ido, Louis D. Albright y Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, febrero de 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
Texto completoEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Texto completoGalili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs y Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, octubre de 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
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