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The European R&D-Project MORPH: Marine robotic systems of self-organizing, logically linked physical nodes

IFAC Proceedings Volumes 2012年第5期45卷 349-354页

作者： J. Kalwa A. Pascoal P. Ridao A. Birk M. Eichhorn L. Brignone M. Caccia J. Alvez R.S. Santos ATLAS ELEKTRONIK GmbH Bremen Germany Institute of Robotics of the Instituto Superior Tècnico Lisbon Portugal Universiat de Girona Girona Spain Jacobs Universität Bremen Bremen Germany Ilmenau University of Technology Faculty of Computer Science and Automation Ilmenau Germany Institut français de recherche pour l'exploitation de la mer La Seyne sur Mer France Consiglio Nazionale delle Ricerche - Istituto di studi sui sistemi intelligenti per l'automazion Genova Italy NATO Undersea Research Centre La Spezia Italy Instituto do Mar Horta Portugal

The MORPH project (FP 7, 2012-2016) advances the novel concept of an underwater robotic system composed of a number of spatially separated mobile robot-modules, carrying complementary resources. Instead of being physically coupled, the modules are connected via communication links that rely on the flow of information among them, i. e. inter-module interactions are enabled by underwater communication networks at distant and close ranges and supported by visual perception at very close range. Without rigid links, the MSV can reconfigure itself and adapt in response to the shape of the terrain, including walls with negative slope. The MORPH concept requires qualitatively new behaviors such as adaptive sensor placement for perception and navigation, as well as environmental modeling in complex environments. On site view planning will lead to a solution well beyond the operational state of the art for underwater cliff surveys and other similar missions. A final demonstration on a vertical cliff, unfeasible automatically with today's technology, will validate the efficacy of the methods developed at the end of the project.

关键词： complementary navigation control architecture adaptive behaviour robotic vehicle

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Time Delay Estimation with Fixed-time SMC Scheme for Nonlinear Dynamics

Time Delay Estimation with Fixed-time SMC Scheme for Nonline...

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International Conference on Control, automation and Diagnosis (ICCAD)

作者： Saim Ahmed Ahmad Taher Azar Arezki Fekik Haoping Wang Chakib Ben Njima College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira Algeria CNRS LS2N UMR 6004 Nantes Université École Centrale Nantes Nantes France School of Automation Nanjing University of Science and Technology China University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics.

关键词： Uncertainty automation Delay effects Estimation 2-DOF Stability analysis Nonlinear dynamical systems

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Study of tau neutrinos and non-unitary neutrino mixing with the first six detection units of KM3NeT/ORCA

arXiv

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arXiv 2025年

作者： Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coyle, P. Creusot, A. Wasseige, G. De Decoene, V. Rosso, I. Del Mauro, L.S. Di Palma, I. Di Díaz, A.F. Diego-Tortosa, D. Distefano, C. Donzaud, C. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Eberl, T. Eckerová, E. Eddymaoui, A. Eff, M. Bojaddaini, I. El Hedri, S. El Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gagliardini, S. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczynski, P. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Lamoureux, M. Larosa, G. Lastoria, C. Lazar, J. Lazo, A. Le Stum, S. Lehaut, G. Lemaître, V. Leonora, E. Lessing, N. Levi, G. Clark, M. Lindsey Magnani, F. Majumdar, J. Malerba, L. Mamedov, F. Manfreda, A. Manousakis, A. Marconi, M. Margiotta, A. Marinelli, A. Markou, C. Martin, L. Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Valencia Paterna46980 Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Università di Genova Via Dodecaneso 33 Genova16146 Italy INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia INFN Sezione di Bologna v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università di Bologna Dipartimento di Fisica e Astronomia v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università degli Studi della Campania"Luigi Vanvitelli" Dipartimento di Matematica e Fisica viale Lincoln 5 Caserta81100 Italy LPC Campus des Cézeaux 24 av

Oscillations of atmospheric muon and electron neutrinos produce tau neutrinos with energies in the GeV range, which can be observed by the ORCA detector of the KM3NeT neutrino telescope in the Mediterranean Sea. First measurements with ORCA6, an early subarray corresponding to about 5% of the final detector, are presented. A sample of 5828 neutrino candidates has been selected from the analysed exposure of 433 kton-years. The ντ normalisation, defined as the ratio between the number of observed and expected tau neutrino events, is measured to be Sτ = 0.48+0−0.335 . This translates into a ντ charged-current cross section measurement of στmeas = (2.5+2−1.68) × 10−38 cm2 nucleon−1 at the median ντ energy of 20.3 GeV. The result is consistent with the measurements of other experiments. In addition, the current limit on the non-unitarity parameter affecting the τ-row of the neutrino mixing matrix was improved, with α33 > 0.95 at the 95% confidence level. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

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Probing invisible neutrino decay with the first six detection units of KM3NeT/ORCA

arXiv

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arXiv 2025年

作者： Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Aublin, J. Badaracco, F. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Bendahman, M. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coleiro, A. Condorelli, A. Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. Dallier, R. De Benedittis, A. De Wasseige, G. Decoene, V. Deguire, P. Del Rosso, I. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. El Mentawi, S. Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gagliardini, S. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goswami, P. Gozzini, S.R. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Lamoureux, M. Larosa, G. Lastoria, C. Lazar, J. Lazo, A. Le Stum, S. Lehaut, G. Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Università di Genova Via Dodecaneso 33 Genova16146 Italy INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia INFN Sezione di Bologna v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università di Bologna Dipartimento di Fisica e Astronomia v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università degli Studi della Campania "Luigi Vanvitelli" Dipartimento di Matematica e Fisica viale Lincoln 5 Caserta81100 Italy LPC Campus des Cézeaux 24 a

In the era of precision measurements of neutrino oscillation parameters, it is necessary for experiments to disentangle discrepancies that may indicate physics beyond the Standard Model in the neutrino sector. KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study the oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on the initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photosensors. A high-purity neutrino sample was extracted during 2020 and 2021, corresponding to an exposure of 433 kton-years. This sample is analysed following a binned log-likelihood approach to search for invisible neutrino decay, in a three-flavour neutrino oscillation scenario, where the third neutrino mass state ν3 decays into an invisible state, e.g. a sterile neutrino. The resulting best fit of the invisible neutrino decay parameter is (formula presented), corresponding to a scenario with θ23 in the second octant and normal neutrino mass ordering. The results are consistent with the Standard Model, within a 2.1 σ interval. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic ray detectors

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Corrigendum to “An Optimized RBF Neural Network Based on Beetle Antennae Search Algorithm for Modeling the Static Friction in a Robotic Manipulator Joint”

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Mathematical Problems in Engineering 2020年第1期2020卷

作者： Yuxiang Wang Zhangwei Chen Hongfei Zu Xiang Zhang State Key Laboratory of Fluid Power and Mechatronics Systems Zhejiang University Hangzhou 310000 *** Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou 310000 *** Institute of Computer Science Hangzhou Dianzi University Hangzhou 310000 ***

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2018年 733卷 v-vi页

作者： Peng, Sheng-Lung Wang, Shiuh-Jeng Balas, Valentina Emilia Zhao, Ming Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Information Management Central Police University Taoyuan Taiwan Department of Automation and Applied Informatics Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania School of Computer Technology Jingzhou China

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Preface

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Advances in Intelligent Systems and Computing 2017年 617卷 v-vi页

作者： Vittorini, Pierpaolo Gennari, Rosella Di Masciow, Tania Rodríguez, Sara De la Prieta, Fernando Ramos, Carlos Silveira, Ricardo Azambuja Department of Life Health and Environmental Sciences University of L’Aquila L’Aquila Italy Computer Science Faculty Free University of Bozen-Bolzano Bolzano Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Department of Computer Science and Automation Control University of Salamanca Salamanca Spain Politécnico do Porto GECAD Instituto Superior de Engenharia do Porto Porto Portugal Department of Computer Science and Statistics Federal University of Santa Catarina FlorianópolisSC Brazil

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Developing an AI literacy test for junior secondary students: The first stage

Developing an AI literacy test for junior secondary students...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： King Woon Yau Ching Sing Chai Thomas K. F. Chiu Helen Meng Irwin King Savio Wai-Ho Wong Chandni Saxena Yeung Yam Faculty of Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Curriculum and Instruction The Chinese University of Hong Kong Shatin Hong Kong China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Educational Psychology The Chinese University of Hong Kong Shatin Hong Kong China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China

Artificial Intelligence (AI) education for K-12 is increasingly important as the emerging AI technologies are pervasively infused into human daily lives. Eleven countries have endorsed and implemented the AI curricula at K-12 levels [1]. The "Five Big Ideas" about AI, as proposed by the Association for the Advancement of Artificial Intelligence (AAAI) and the computer science Teacher Association (CSTA) have emerged to form the core components of K-12 AI education [2], [3]. However, the assessment of students’ learning in AI education for K-12 is still in its initial stage. Surveys and interviews are the major assessing tools based on students’ self- perceptions. Standardized tests on students’ AI literacy are still rare. This paper focuses on the first stage of an AI literacy test based on the Five Big Ideas for Grade 7 to Grade 9 students. The test aims to measure the changes of students’ AI literacy before and after attending a one-year AI course. The preliminary findings show the students have increased in their AI literacy after attending the course. The test can provide insights for understanding students’ competencies and needs in AI literacy and improving the curriculum guidelines for students’ effective learning.

关键词：

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines Brain Accuracy Stroke (medical condition) Feature extraction Motors Brain-computer interfaces Neurorehabilitation Decoding Paralysis

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A Pilot Study on the Development and Validation of AI Literacy Test Items for Grade 7 to Grade 9 Students

A Pilot Study on the Development and Validation of AI Litera...

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International Symposium on Educational Technology (ISET)

作者： Yifan Chen King Woon Yau Ching Sing Chai Thomas K. F. Chiu Helen Meng Irwin King Savio Wai-Ho Wong Yeung Yam Faculty of Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong China Department of Curriculum and Instruction The Chinese University of Hong Kong Shatin Hong Kong China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Educational Psychology The Chinese University of Hong Kong Shatin Hong Kong China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China

ISBN: (数字)9798350361414

ISBN: (纸本)9798350361421

To provide K-12 students with an understanding of AI’s potential opportunities and challenges, AI education has been incorporated into the technology curriculum. Most existing methods for assessing students’ AI literacy rely on self-assessment questionnaires, which may not offer an objective measure of their understanding. This study, therefore, sought to develop and validate a 25 -item multiple-choice test tailored for Hong Kong secondary school students to gauge their AI literacy. A total of 144 students from six secondary schools participated in a pilot test. Item Response Theory (IRT) incorporating a Markov Chain Monte Carlo (MCMC) algorithm, was developed to estimate the difficulty and discrimination indices of test items. The 3-Parameter Logistic (3-PL) model was selected for its effectiveness in providing a more nuanced understanding of item characteristics. Preliminary analysis suggested that the test warrants further revision and enhancement, as evidenced by a Kuder-Richardson Formula 20 (KR-20) reliability coefficient of 0.68. Nonetheless, all test items exhibited satisfactory discrimination capabilities, indicating their proficiency in differentiating among varying levels of respondent aptitude. Thus, this study can provide a foundation for developing a standardized AI literacy test in the future.

关键词： Monte Carlo methods Atmospheric measurements Instruments Educational technology Particle measurements Reliability Iterative methods

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