Change search
Refine search result
22232425262728 1201 - 1250 of 124264
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1201.
    Abbaszadeh Shahri, Abbas
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering. Islamic Azad University.
    An Optimized Artificial Neural Network Structure to Predict Clay Sensitivity in a High Landslide Prone Area Using Piezocone Penetration Test (CPTu) Data: A Case Study in Southwest of Sweden2016In: Geotechnical and Geological Engineering, ISSN 0960-3182, E-ISSN 1573-1529, p. 1-14Article in journal (Refereed)
    Abstract [en]

    Application of artificial neural networks (ANN) in various aspects of geotechnical engineering problems such as site characterization due to have difficulty to solve or interrupt through conventional approaches has demonstrated some degree of success. In the current paper a developed and optimized five layer feed-forward back-propagation neural network with 4-4-4-3-1 topology, network error of 0.00201 and R2 = 0.941 under the conjugate gradient descent ANN training algorithm was introduce to predict the clay sensitivity parameter in a specified area in southwest of Sweden. The close relation of this parameter to occurred landslides in Sweden was the main reason why this study is focused on. For this purpose, the information of 70 piezocone penetration test (CPTu) points was used to model the variations of clay sensitivity and the influences of direct or indirect related parameters to CPTu has been taken into account and discussed in detail. Applied operation process to find the optimized ANN model using various training algorithms as well as different activation functions was the main advantage of this paper. The performance and feasibility of proposed optimized model has been examined and evaluated using various statistical and analytical criteria as well as regression analyses and then compared to in situ field tests and laboratory investigation results. The sensitivity analysis of this study showed that the depth and pore pressure are the two most and cone tip resistance is the least effective factor on prediction of clay sensitivity.

  • 1202.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics. College of Civil Engineering, Roudehen branch, Islamic Azad University, Tehran, Iran.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    CPT-SPT correlations using artificial neural network approach: A Case Study in Sweden2015In: The Electronic journal of geotechnical engineering, ISSN 1089-3032, E-ISSN 1089-3032, Vol. 20, no 28, p. 13439-13460Article in journal (Refereed)
    Abstract [en]

    The correlation between Standard and Cone Penetration Tests (SPT and CPT) as two of the most used in-situ geotechnical tests is of practical interest in engineering designs. In this paper, new SPT-CPT correlations for southwest of Sweden are proposed and developed using an artificial neural networks (ANNs) approach. The influences of soil type, depth, cone tip resistance, sleeve friction, friction ratio and porewater pressure on obtained correlations has been taken into account in optimized ANN models to represent more comprehensive and accurate correlation functions. Moreover, the effect of particle mean grain size and fine content were investigated and discussed using graph analyses. The validation of ANN based correlations were tested using several statistical criteria and then compared to existing correlations in literature to quantify the uncertainty of the correlations. Using the sensitivity analyses, the most and least effective factors on CPT-SPT predictions were recognized and discussed. The results indicate the ability of ANN as an attractive alternative method regarding to conventional statistical analyses to develop CPT-SPT relations.

  • 1203.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Updated relations for the uniaxial compressive strength of marlstones based on P-wave velocity and point load index test2016In: INNOVATIVE INFRASTRUCTURE SOLUTIONS, ISSN 2364-4176, Vol. 1, no 1, article id UNSP 17Article in journal (Refereed)
    Abstract [en]

    Although there are many proposed relations for different rock types to predict the uniaxial compressive strength (UCS) as a function of P-wave velocity (V-P) and point load index (Is), only a few of them are focused on marlstones. However, these studies have limitations in applicability since they are mainly based on local studies. In this paper, an attempt is therefore made to present updated relations for two previous proposed correlations for marlstones in Iran. The modification process is executed through multivariate regression analysis techniques using a provided comprehensive database for marlstones in Iran, including UCS, V-P and Is from publications and validated relevant sources comprising 119 datasets. The accuracy, appropriateness and applicability of the obtained modifications were tested by means of different statistical criteria and graph analyses. The conducted comparison between updated and previous proposed relations highlighted better applicability in the prediction of UCS using the updated correlations introduced in this study. However, the derived updated predictive models are dependent on rock types and test conditions, as they are in this study.

  • 1204.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Naderi, Shima
    Modified correlations to predict the shear wave velocity using piezocone penetration test data and geotechnical parameters: a case study in the southwest of Sweden2016In: INNOVATIVE INFRASTRUCTURE SOLUTIONS, ISSN 2364-4176, Vol. 1, no 1, article id UNSP 13Article in journal (Refereed)
    Abstract [en]

    Shear wave velocity (VS) is an important geotechnical characteristic for determining dynamic soil properties. When no direct measurements are available, V-S can be estimated based on correlations with common in situ tests, such as the piezocone penetration test (CPTu). In the current paper, three modified equations to predict the V-S of soft clays based on a comprehensive provided CPTu database and related geotechnical parameters for southwest of Sweden were presented. The performance of the obtained relations were examined and investigated by several statistical criteria as well as graph analyses. The best performance was observed by implementing of corrected cone tip resistance (q(t)) and pore pressure ratio (B-q) which directly can be found from CPTu data. The introduced modifications were developed and validated for available soft clays of the studied area in southwest of Sweden, and thus, their applicability for proper prediction in other areas with different characteristics should be controlled. However, the used method as a suitable tool can be employed to investigate.

  • 1205.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Spross, Johan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Kartering av skredbenägenhet medartificiell intelligens2018In: Bygg & teknik, ISSN 0281-658X, no 1Article in journal (Other academic)
  • 1206.
    Abbaszadeh Shahri, Abbas
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Spross, Johan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Johansson, Fredrik
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Larsson, Stefan
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics.
    Storskalig kartering av skredbenägenhet i västra Götaland med artificiell intelligens2018Conference paper (Other academic)
  • 1207. Abbeloos, W.
    et al.
    Ataer-Cansizoglu, E.
    Caccamo, Sergio
    KTH.
    Taguchi, Y.
    Domae, Y.
    3D object discovery and modeling using single RGB-D images containing multiple object instances2018In: Proceedings - 2017 International Conference on 3D Vision, 3DV 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 431-439Conference paper (Refereed)
    Abstract [en]

    Unsupervised object modeling is important in robotics, especially for handling a large set of objects. We present a method for unsupervised 3D object discovery, reconstruction, and localization that exploits multiple instances of an identical object contained in a single RGB-D image. The proposed method does not rely on segmentation, scene knowledge, or user input, and thus is easily scalable. Our method aims to find recurrent patterns in a single RGB-D image by utilizing appearance and geometry of the salient regions. We extract keypoints and match them in pairs based on their descriptors. We then generate triplets of the keypoints matching with each other using several geometric criteria to minimize false matches. The relative poses of the matched triplets are computed and clustered to discover sets of triplet pairs with similar relative poses. Triplets belonging to the same set are likely to belong to the same object and are used to construct an initial object model. Detection of remaining instances with the initial object model using RANSAC allows to further expand and refine the model. The automatically generated object models are both compact and descriptive. We show quantitative and qualitative results on RGB-D images with various objects including some from the Amazon Picking Challenge. We also demonstrate the use of our method in an object picking scenario with a robotic arm.

  • 1208. Abbeloos, W.
    et al.
    Caccamo, Sergio
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Ataer-Cansizoglu, E.
    Taguchi, Y.
    Feng, C.
    Lee, T. -Y
    Detecting and Grouping Identical Objects for Region Proposal and Classification2017In: 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE Computer Society, 2017, Vol. 2017, p. 501-502, article id 8014810Conference paper (Refereed)
    Abstract [en]

    Often multiple instances of an object occur in the same scene, for example in a warehouse. Unsupervised multi-instance object discovery algorithms are able to detect and identify such objects. We use such an algorithm to provide object proposals to a convolutional neural network (CNN) based classifier. This results in fewer regions to evaluate, compared to traditional region proposal algorithms. Additionally, it enables using the joint probability of multiple instances of an object, resulting in improved classification accuracy. The proposed technique can also split a single class into multiple sub-classes corresponding to the different object types, enabling hierarchical classification.

  • 1209.
    Abbes, Yacine
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology.
    Educational studies in heat and power technology: how students learn with multimedia tools and problem-based learning2005Licentiate thesis, monograph (Other scientific)
    Abstract [en]

    Higher education is undergoing continuous changes and new learning tools and methods are implemented. Researchers in education do not always agree upon the effectiveness of some of the methods introduced into engineering education. The present thesis consists of two case studies on educational methods introduced at the Department of Energy Technology, at Royal Institute of Technology (KTH), Sweden. The qualitative research methodology has been used in case one and a combination of qualitative and quantitative methodology has been used in the second case. The sources of evidences consisted of: unstructured interviews, analysis of video recording, questionnaires, and analysis of a variety of documents. In the first case, an educational program in heat and power technology was analysed. The second case consists in an in-depth study of group dynamics in a Problem –Based Learning course. These studies showed that the learning approach adopted by students depends strongly on the way they view the particular learning tool or method. The first case study revealed the existence of two types of learners. Surfacelearners follow the structure suggested by the designers of the multimedia program. This category of learners focuses only on the material available in the program. Deep-learners go beyond the information and the structure suggested in the program and combine different learning tools in their learning. These students do not follow the structure of the tutorials’ of the multimedia program. This study showed that students who had a strong view how to learn with a multimedia program or a learning method benefited less from the learning tools available. Students with weak views on how to learn from educational program or leaning tool benefit less from the presentation and engage in more surface learning. Self-motivated learners use the multimedia presentation in novel ways and crosscheck the information given with other material. The second study showed that students have unclear and weak views on how to learn with student-directed Problem- Based Learning model. Four types of learners were identified in Problem-Based Learning project: Leaders, Key Actors, Common Students and Social Loafers. Leaders and Key Actors are self-motivated individuals and participate most in the projects. Students who viewed themselves or were viewed as leaders were held responsible to take most of the decisions and students expected them to work more than the average student. Students who viewed themselves as common team members expected a lower workload than leaders’. Key Actors are self-motivated students who do not view themselves as separate from other group members but who participate more than others. Leaders learned more group and social processes, that they did not fully take part in, while common students learned more from the project management aspects that they did not take part in. The study also found that Problem-Based Learning groups can become very cohesive, and can develop distorted views on how to learn with Problem-Based Learning, and un-common group dynamics phenomena such as groupthink can occur in Problem-Based Learning setting.

  • 1210. Abbondanno, U
    et al.
    Aerts, G
    Alvarez, F
    Alvarez, H
    Andriamonje, S
    Andrzejewski, J
    Badurek, G
    Baumann, P
    Becvar, F
    Benlliure, J
    Berthomieux, E
    Betev, B
    Calvino, F
    Cano-Ott, D
    Capote, R
    Cennini, P
    Chepel, V
    Chiaveri, E
    Colonna, N
    Cortes, G
    Cortina, D
    Couture, A
    Cox, J
    Dababneh, S
    David, S
    Dolfini, R
    Domingo-Pardo, C
    Duran, I
    Embid-Segura, M
    Ferrant, L
    Ferrari, A
    Ferreira-Marques, R
    Frais-Koelbl, H
    Furman, W
    Goncalves, I
    Gonzalez-Romero, E
    Goverdovski, A
    Gramegna, F
    Griesmayer, E
    Gunsing, F
    Haas, B
    Haight, R
    Heil, M
    Herrera-Martinez, A
    Isaev, S
    Jericha, E
    Kadi, Y
    Kappeler, F
    Kerveno, M
    Ketlerov, V
    Koehler, P E
    Konovalov, V
    Krticka, M
    Leeb, H
    Lindote, A
    Lopes, M I
    Lozano, M
    Lukic, S
    Marganiec, J
    Marrone, S
    Martinez-Val, J
    Mastinu, P
    Mengoni, A
    Milazzo, P M
    Molina-Coballes, A
    Moreau, C
    Mosconi, M
    Neves, F
    Oberhummer, H
    O'Brien, S
    Pancin, J
    Papaevangelou, T
    Paradela, C
    Pavlik, A
    Pavlopoulos, P
    Perlado, J M
    Perrot, L
    Peskov, Vladmir
    KTH, School of Engineering Sciences (SCI), Physics.
    Plag, R
    Plompen, A
    Plukis, A
    Poch, A
    Policarpo, A
    Pretel, C
    Quesada, J M
    Rapp, W
    Rauscher, T
    Reifarth, R
    Rosetti, M
    Rubbia, C
    Rudolf, G
    Rullhusen, P
    Salgado, J
    Schafer, E
    Soares, J C
    Stephan, C
    Tagliente, G
    Tain, J L
    Tassan-Got, L
    Tavora, L M N
    Terlizzi, R
    Vannini, G
    Vaz, P
    Ventura, A
    Villamarin-Fernandez, D
    Vincente-Vincente, M
    Vlachoudis, V
    Voss, F
    Wendler, H
    Wiescher, M
    Wisshak, K
    The data acquisition system of the neutron time-of-flight facility n_TOF at CERN2005In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 538, no 1-3, p. 692-702Article in journal (Refereed)
    Abstract [en]

    The n_TOF facility at CERN has been designed for the measurement of neutron capture, fission and (n, xn) cross-sections with high accuracy. This requires a flexible and-due to the high instantaneous neutron flux-almost dead time free data acquisition system. A scalable and versatile data solution has been designed based on 8-bit flash-ADCs with sampling rates up to 2 GHz and 8 Mbyte memory buffer. The software is written in C and C++ and is running on PCs equipped with RedHat Linux.

  • 1211. Abbondanno, U
    et al.
    Carlson, Per
    KTH, Superseded Departments, Physics.
    Peskov, Vladimir
    KTH, Superseded Departments, Physics.
    Zanini, L
    et al.,
    New experimental validation of the pulse height weighting technique for capture cross-section measurements2004In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 521, no 2-3, p. 454-467Article in journal (Refereed)
    Abstract [en]

    The accuracy of the pulse height weighting technique for the determination of neutron capture cross-sections is investigated. The technique is applied to measurements performed with C6D6 liquid scintillation detectors of two different types using capture samples of various dimensions. The data for well-known (n, gamma) resonances are analyzed using weighting functions obtained from Monte Carlo simulations of the experimental set-up. Several causes of systematic deviation are identified and their effect is quantified. In all the cases measured the reaction yield agrees with the standard value within 2%.

  • 1212. Abbott, Andrew
    et al.
    Addicoat, Matthew
    Aldous, Leigh
    Bhuin, Radha Gobinda
    Borisenko, Natalia
    Lopes, Jose Nuno Canongia
    Clark, Ryan
    Coles, Samuel
    Gomes, Margarida Costa
    Cross, Benjamin
    Everts, Jeffrey
    Firestone, Millicent
    Gardas, Ramesh
    Gras, Matthieu
    Halstead, Simon
    Hardacre, Christopher
    Holbrey, John
    Itoh, Toshiyuki
    Ivanistsev, Vladislav
    Jacquemin, Johan
    Jessop, Philip
    Jones, Robert
    Kirchner, Barbara
    Li, Sichao
    KTH.
    Lynden-Bell, Ruth
    MacFarlane, Doug
    Maier, Florian
    Mezger, Markus
    Padua, Agilio
    Pavel, Octavian D.
    Perkin, Susan
    Purcell, Simon
    Rutland, Mark
    Slattery, John M.
    Suzer, Sefik
    Tamura, Kazuhisa
    Thomas, Morgan L.
    Tiwari, Shraeddha
    Tsuzuki, Seiji
    Uralcan, Betul
    Wallace, William
    Watanabe, Masayoshi
    KTH.
    Wishart, James
    Ionic liquids at interfaces: general discussion2018In: Faraday discussions (Online), ISSN 1359-6640, E-ISSN 1364-5498, Vol. 206, p. 549-586Article in journal (Refereed)
  • 1213. Abbott, B
    et al.
    Morley, Anthony
    KTH, School of Engineering Sciences (SCI), Physics.
    Zwalinski, L.
    Production and integration of the ATLAS Insertable B-Layer2018In: Journal of Instrumentation, ISSN 1748-0221, E-ISSN 1748-0221, Vol. 13, no 5, article id T05008Article in journal (Refereed)
    Abstract [en]

    During the shutdown of the CERN Large Hadron Collider in 2013-2014, an additional pixel layer was installed between the existing Pixel detector of the ATLAS experiment and a new, smaller radius beam pipe. The motivation for this new pixel layer, the Insertable B-Layer (IBL), was to maintain or improve the robustness and performance of the ATLAS tracking system, given the higher instantaneous and integrated luminosities realised following the shutdown. Because of the extreme radiation and collision rate environment, several new radiation-tolerant sensor and electronic technologies were utilised for this layer. This paper reports on the IBL construction and integration prior to its operation in the ATLAS detector.

  • 1214. Abbott, BP
    et al.
    Axelsson, Magnus
    KTH.
    Larsson, S
    KTH, School of Engineering Sciences (SCI), Physics.
    Li, Liang
    KTH, School of Engineering Sciences (SCI), Physics.
    Zweizig, John G.
    et al.,
    SUPPLEMENT: "LOCALIZATION AND BROADBAND FOLLOW-UP OF THE GRAVITATIONAL-WAVE TRANSIENT GW150914" (2016, ApJL, 826, L13)2016In: Astrophysical Journal Supplement Series, ISSN 0067-0049, E-ISSN 1538-4365, Vol. 225, no 1, article id 8Article in journal (Refereed)
  • 1215. Abd El Ghany, M. A.
    et al.
    El-Moursy, M. A.
    Ismail, Mohammed
    KTH, School of Information and Communication Technology (ICT), Electronic Systems.
    High throughput architecture for CLICHÉ network on chip2009In: Proceedings - IEEE International SOC Conference, SOCC 2009, 2009, p. 155-158Conference paper (Refereed)
    Abstract [en]

    High Throughput Chip-Level Integration of Communicating Heterogeneous Elements (CLICHÉ) architecture to achieve high performance Networks on Chip (NoC) is proposed. The architecture increases the throughput of the network by 40% while preserving the average latency. The area of High Throughput CLICHÉ switch is decreased by 18% as compared to CLICHÉ switch. The total metal resources required to implement High Throughput CLICHÉ design is increased by 7% as compared to the total metal resources required to implement CLICHÉ design. The extra power consumption required to achieve the proposed architecture is 8% of the total power consumption of the CLICHÉ architecture.

  • 1216. Abd El Ghany, M. A.
    et al.
    El-Moursy, M. A.
    Ismail, Mohammed
    KTH, School of Information and Communication Technology (ICT), Electronic Systems. Ohio State University, Columbus, United States .
    High throughput architecture for high performance NoC2009In: ISCAS: 2009 IEEE International Symposium on Circuits and Systems, IEEE , 2009, p. 2241-2244Conference paper (Refereed)
    Abstract [en]

    High Throughput Butterfly Fat Tree (HTBFT) architecture to achieve high performance Networks on Chip (NoC) is proposed. The architecture increases the throughput of the network by 38% while preserving the average latency. The area of HTBFT switch is decreased by 18% as compared to Butterfly Fat Tree switch. The total metal resources required to implement HTBFT design is increased by 5% as compared to the total metal resources required to implement BFT design. The extra power consumption required to achieve the proposed architecture is 3% of the total power consumption of the BFT architecture.

  • 1217. Abd El Ghany, M. A.
    et al.
    El-Moursy, M. A.
    Korzec, D.
    Ismail, Mohammed
    KTH, School of Information and Communication Technology (ICT), Integrated Devices and Circuits. Ohio State University, Columbus, OH, United States .
    Asynchronous BFT for low power networks on chip2010In: ISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, IEEE , 2010, p. 3240-3243Conference paper (Refereed)
    Abstract [en]

    Asynchronous Butterfly Fat Tree (BFT) architecture is proposed to achieve low power Network on Chip (NoC). Asynchronous design could reduce the power dissipation of the network if the activity factor of the data transfer between two switches (αdata satisfies a certain condition. The area of Asynchronous BFT switch is increased by 25% as compared to Synchronous switch. However, the power dissipation of the Asynchronous architecture could be decreased by up to 33% as compared to the power dissipation of the conventional Synchronous architecture when the αdata equals 0.2 and the activity factor of the control signals is equal to 1/64 of the αdata. The total metal resources required to implement Asynchronous design is decreased by 12%.

  • 1218. Abd El Ghany, M. A.
    et al.
    El-Moursy, M. A.
    Korzec, D.
    Ismail, Mohammed
    KTH, School of Information and Communication Technology (ICT), Integrated Devices and Circuits. Ohio State University, Columbus, OH, United States .
    Power characteristics of networks on chip2010In: ISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, IEEE , 2010, p. 3721-3724Conference paper (Refereed)
    Abstract [en]

    Power characteristics of different Network on Chip (NoC) topologies are developed. Among different NoC topologies, the Butterfly Fat Tree (BFT) dissipates the minimum power. With the advance in technology, the relative power consumption of the interconnects and the associate repeaters of the BFT decreases as compared to the power consumption of the network switches. The power dissipation of interswitch links and repeaters for BFT represents only 1% of the total power dissipation of the network. In addition of providing high throughput, the BFT is a power efficient topology for NoCs.

  • 1219. Abd El Ghany, M. A.
    et al.
    El-Moursy, M. A.
    Korzec, D.
    Ismail, Mohammed
    KTH, School of Information and Communication Technology (ICT), Integrated Devices and Circuits.
    Power efficient networks on chip2009In: 2009 16th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2009, 2009, p. 105-108Conference paper (Refereed)
    Abstract [en]

    a low power switch design is proposed to achieve power-efficient Network on Chip (NoC). The proposed NoC switch reduce. The power consumption oy the Butterfly Fat Tree (BFT) architecture by 28 % as compared to the conventional BFT switch. Moreover. The power reduction technique is applied to different NoC architectures. The technique reduce. The power consumption oy the network by up to 41%. Whe. The power consumption oy the whole network includin. The interswich links and repeaters is taken into account. The overall power consumption is decreased by up to 33% at the maximum operating frequency oy the switch. The BFT architecture consume. The minimum power as compared to other NoC architectures.

  • 1220. Abd Elghany, M. A.
    et al.
    El-Moursy, M. A.
    Korzec, D.
    Ismail, Mohammed
    KTH, School of Information and Communication Technology (ICT), Integrated Devices and Circuits. Ohio State University, United States .
    High throughput architecture for OCTAGON network on chip2009In: 2009 16th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2009, IEEE , 2009, p. 101-104Conference paper (Refereed)
    Abstract [en]

    High Throughput Octagon architecture to achieve high performance Networks on Chip (NoC) is proposed. The architecture increase. The throughput oy the network by 17% while preservin. The average latency. The area of High Throughput OCTAGON switch is decreased by 18% as compared to OCTAGON switch. The total metal resources required to implement High Throughput OCTAGON design is increased by 8% as compared to the total metal resources required to implement OCTAGON design. The extra power consumption required to achiev. The proposed architecture is 2% oy the total power consumption oy the OCTAGON architecture.

  • 1221.
    Abdali, Yasser
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Building Technology and Design.
    Holm, Lovisa
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Building Technology and Design.
    En jämförelse mellan Tekla Structures och Civil 3D i krökta betongkonstruktioner2017Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Within the construction industry, there is a lot of discussion around BIM (Building InformationModelling). There is a need to move away from customized 2D-drawings and instead focus oninformative models which can be used throughout the whole cycle of construction. This developmenthas been ongoing within house construction, but this approach is harder to implement within thefacility sector.The difficulty in implementing a more BIM-oriented approach within construction is partly due tosoftware currently not being fully developed to create informative and reliable models of constructionswith complex geometries, such as bridges.Since Trafikverket, the largest construction developer within Sweden, has started to place a largerdemand on 3D there is a need for both technology and human resource to develop.The aim of this dissertation is to present a case study investigating the suitability of the software TeklaStructure and AutoCAD Civil 3D to perform a 3D-model of a double-curved bridge. The two pieces ofsoftware will be contrasted in a final comparison whereby both pros and cons will be presented.The study has made it clear that BIM is not just about creating an informative model, but it is alsoabout an approach which has the ability to completely change the construction sector to work morecollaboratively and effectively.The comparison between the two relevant software in this study shows that they both have strengthsand weaknesses and are suitable for our purpose. However, they differ in how detailed models theycan create and how well they have the ability to work with BIM to a greater extent.

  • 1222.
    Abdalla, Ahmed
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Satellite Positioning.
    Determination of a gravimetric geoid model of Sudan using the KTH method2009Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The main objective of this study is to compute a new gravimetric geoid model of Sudan

    using the KTH method based on modification of Stokes’ formula for geoid determination.

    The modified Stokes’ formula combines regional terrestrial gravity with long-wavelength

    gravity information provided by the global gravitational model (GGM). The collected

    datasets for this study contained the terrestrial gravity measurements, digital elevation

    model (DEM), GPS/levelling data and four global gravitational Models (GGMs), (EGM96,

    EIGEN-GRACE02S, EIGEN-GL04C and GGM03S).

    The gravity data underwent cross validation technique for outliers detection, three gridding

    algorithms (Kriging, Inverse Distance Weighting and Nearest Neighbor) have been tested,

    thereafter the best interpolation approach has been chosen for gridding the refined gravity

    data. The GGMs contributions were evaluated with GPS/levelling data to choose the best

    one to be used in the combined formula.

    In this study three stochastic modification methods of Stokes’ formula (Optimum, Unbiased

    and Biased) were performed, hence an approximate geoid height was computed. Thereafter,

    some additive corrections (Topographic, Downward Continuation, Atmospheric and Ellipsoidal)

    were added to the approximated geoid height to get corrected geoid height.

    The new gravimetric geoid model (KTH-SDG08) has been determined over the whole

    country of Sudan at 5′ x 5′ grid for area ( 4 ). The optimum method

    provides the best agreement with GPS/levelling estimated to 29 cm while the agreement for

    the relative geoid heights to 0.493 ppm. A comparison has also been made between the new

    geoid model and a previous model, determined in 1991 and shows better accuracy.

    􀁄 ≤φ ≤ 23􀁄 , 22􀁄 ≤ λ ≤ 38􀁄

    Keywords: geoid model, KTH method, stochastic modification methods, modified Stokes’ formula,

    additive corrections.

  • 1223. Abd-Alla, E. S. M .
    et al.
    Moriyoshi, A.
    Partl, Manfred
    EMPA, Swiss Federal Laboratories for Materials Testing and Research.
    Takahashi, K.
    Kondo, T.
    Tomoto, T.
    New wheel tracking test to analyze movements of aggregates in multi-layered asphalt specimens2006In: Journal of the Japan Petroleum Institute, ISSN 1346-8804, E-ISSN 1349-273X, Vol. 49, no 5, p. 274-279Article in journal (Refereed)
    Abstract [en]

    This paper describes a new wheel tracking test for analyzing movements of aggregates in mixtures. The test device is conducted using as examples four-layered specimens taken from two Swiss national motorways, where severe rutting (G section) and longitudinal cracking (H section) were observed. This test method was developed by Moriyoshi. Tests can be carried out under temperature distributions similar to field situation. Two-dimensional movements and strains between aggregates for four-layered specimens due to the moving wheel loads were analyzed by right angle for direction of wheel pass. For this purpose, the cross section of the slabs with a width of 30 cm was divided optically into 5 vertical subsections. The transverse permanent surface deformations, the area changes in the transversal subsections as well as the maximum deformation of the surface and layer-interface through the centerline of the applied wheel load were determined. Strain distributions between aggregates in mixtures at high temperature (45 degrees C) under 600 passes were also measured by photo analysis. Test results show consolidation of the asphalt mixtures and material flow on the surface near the wheel load. The results also demonstrate that the aggregates (size of aggregate: 2 mm or larger) in each mixture move mainly in vertical direction. Large strains (40% or larger) between aggregates at summer condition were measured in the surface mixture near wheel track after 600 passes.

  • 1224. Abdalla, H.
    et al.
    Abramowski, A.
    Aharonian, F.
    Benkhali, F. Ait
    Akhperjanian, A. G.
    Andersson, T.
    Anguener, E. O.
    Arrieta, M.
    Aubert, P.
    Backes, M.
    Balzer, A.
    Barnard, M.
    Becherini, Y.
    Tjus, J. Becker
    Berge, D.
    Bernhard, S.
    Bernloehr, K.
    Blackwell, R.
    Bottcher, M.
    Boisson, C.
    Bolmont, J.
    Bordas, P.
    Brun, F.
    Brun, P.
    Bryan, M.
    Bulik, T.
    Capasso, M.
    Carr, J.
    Casanova, S.
    Cerruti, M.
    Chakraborty, N.
    Chalme-Calvet, R.
    Chaves, R. C. G.
    Chen, A.
    Chevalier, J.
    Chretien, M.
    Colafrancesco, S.
    Cologna, G.
    Condon, B.
    Conrad, J.
    Couturier, C.
    Cui, Y.
    Davids, I. D.
    Degrange, B.
    Deil, C.
    Devin, J.
    dewilt, P.
    Dirson, L.
    Djannati-Atai, A.
    Domainko, W.
    Donath, A.
    Drury, L. O 'C.
    Dubus, G.
    Dutson, K.
    Dyks, J.
    Edwards, T.
    Egberts, K.
    Eger, P.
    Ernenwein, J. -P
    Eschbach, S.
    Farnier, C.
    Fegan, S.
    Fernandes, M. V.
    Fiasson, A.
    Fontaine, G.
    Forster, A.
    Funk, S.
    Fuessling, M.
    Gabici, S.
    Gajdus, M.
    Gallant, Y. A.
    Garrigoux, T.
    Giavitto, G.
    Giebels, B.
    Glicenstein, J. F.
    Gottschall, D.
    Goyal, A.
    Grondin, M. -H
    Hadasch, D.
    Hahn, J.
    Haupt, M.
    Hawkes, J.
    Heinzelmann, G.
    Henri, G.
    Hermann, G.
    Hervet, O.
    Hillert, A.
    Hinton, J. A.
    Hofmann, W.
    Hoischen, C.
    Holler, M.
    Horns, D.
    Ivascenko, A.
    Jacholkowska, A.
    Jamrozy, M.
    Janiak, M.
    Jankowsky, D.
    Jankowsky, F.
    Jingo, M.
    Jogler, T.
    Jouvin, L.
    Jung-Richardt, I.
    Kastendieck, M. A.
    Katarzynski, K.
    Katz, U.
    Kerszberg, D.
    Khelifi, B.
    Kieffer, M.
    King, J.
    Klepser, S.
    Klochkov, D.
    Kluzniak, W.
    Kolitzus, D.
    Komin, Nu.
    Kosack, K.
    Krakau, S.
    Kraus, M.
    Krayzel, F.
    Kruger, P. P.
    Laffon, H.
    Lamanna, G.
    Lau, J.
    Lees, J. -P
    Lefaucheur, J.
    Lefranc, V.
    Lemiere, A.
    Lemoine-Goumard, M.
    Lenain, J. -P
    Leser, E.
    Lohse, T.
    Lorentz, M.
    Liu, R.
    Lopez-Coto, R.
    Lypova, I.
    Marandon, V.
    Marcowith, A.
    Mariaud, C.
    Marx, R.
    Maurin, G.
    Maxted, N.
    Mayer, M.
    Meintjes, P. J.
    Meyer, M.
    Mitchell, A. M. W.
    Moderski, R.
    Mohamed, M.
    Mohrmann, L.
    Mora, K.
    Moulin, E.
    Murach, T.
    de Naurois, M.
    Niederwanger, F.
    Niemiec, J.
    Oakes, L.
    O'Brien, P.
    Odaka, H.
    Oul, S.
    Ohm, S.
    Ostrowski, M.
    Oya, I.
    Padovani, M.
    Panter, M.
    Parsons, R. D.
    Arribas, M. Paz
    Pekeur, N. W.
    Pelletier, G.
    Perennes, C.
    Petrucci, P. -O
    Peyaud, B.
    Pita, S.
    Poon, H.
    Prokhorov, D.
    Prokoph, H.
    Puehlhofer, G.
    Punch, M.
    Quirrenbach, A.
    Raab, S.
    Reimer, A.
    Reimer, O.
    Renaud, M.
    de los Reyes, R.
    Rieger, F.
    Romoli, C.
    Rosier-Lees, S.
    Rowell, G.
    Rudak, B.
    Rulten, C. B.
    Sahakian, V.
    Salek, D.
    Sanchez, D. A.
    Santangelo, A.
    Sasaki, M.
    Schlickeiser, R.
    Schussler, F.
    Schulz, A.
    Schwanke, U.
    Schwemmer, S.
    Settimo, M.
    Seyffert, A. S.
    Shafi, N.
    Shilon, I.
    Simoni, R.
    Sol, H.
    Spanier, F.
    Spengler, G.
    Spies, F.
    Stawarz, L.
    Steenkamp, R.
    Stegmann, C.
    Stinzing, F.
    Stycz, K.
    Sushch, I.
    Tavernet, J. -P
    Tavernier, T.
    Taylor, A. M.
    Terrier, R.
    Tibaldo, L.
    Tiziani, D.
    Tluczykont, M.
    Trichard, C.
    Tuffs, R.
    Uchiyama, Y.
    van der Walt, D. J.
    van Edik, C.
    van Soelen, B.
    Vasileiadis, G.
    Veh, J.
    Venter, C.
    Viana, A.
    Vincent, P.
    Vink, J.
    Voisin, F.
    Voelk, H. J.
    Vuillaume, T.
    Wadiasingh, Z.
    Wagner, S. J.
    Wagner, P.
    Wagner, R. M.
    White, R.
    Wierzcholska, A.
    Willmann, P.
    Woernlein, A.
    Wouters, D.
    Yang, R.
    Zabalza, V.
    Zaborov, D.
    Zacharias, M.
    Zdziarski, A. A.
    Zech, A.
    Zefi, F.
    Ziegler, A.
    Zywucka, N.
    Ackermann, M.
    Ajello, M.
    Baldini, L.
    Barbiellini, G.
    Bellazzini, R.
    Blandford, R. D.
    Bonino, R.
    Bregeon, J.
    Bruel, P.
    Buehler, R.
    Caliandro, G. A.
    Cameron, R. A.
    Caragiulo, M.
    Caraveo, P. A.
    Cavazzuti, E.
    Cecchi, C.
    Chiang, J.
    Chiaro, G.
    Ciprini, S.
    Cohen-Tanugi, J.
    Costanza, F.
    Cutini, S.
    D'Ammando, F.
    de Palma, F.
    Desiante, R.
    Di Lalla, N.
    Di Mauro, M.
    Di Venere, L.
    Donaggio, B.
    Favuzzi, C.
    Focke, W. B.
    Fusco, P.
    Gargano, F.
    Gasparrini, D.
    Giglietto, N.
    Giordano, F.
    Giroletti, M.
    Guillemot, L.
    Guiriec, S.
    Horan, D.
    Johannesson, G.
    Kamae, T.
    Kensei, S.
    Kocevski, D.
    Larsson, Stefan
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Li, J.
    Longo, F.
    Loparco, F.
    Lovellette, M. N.
    Lubrano, P.
    Maldera, S.
    Manfreda, A.
    Mazziotta, M. N.
    Michelson, P. F.
    Mizuno, T.
    Monzani, M. E.
    Morselli, A.
    Negro, M.
    Nuss, E.
    Orienti, M.
    Orlando, E.
    Paneque, D.
    Perkins, J. S.
    Pesce-Rollins, M.
    Piron, F.
    Pivato, G.
    Porter, T. A.
    Principe, G.
    Raino, S.
    Razzano, M.
    Simone, D.
    Siskind, E. J.
    Spada, F.
    Spinelli, P.
    Thayer, J. B.
    Torres, D. F.
    Torresi, E.
    Troja, E.
    Vianello, G.
    Wood, K. S.
    Gamma-ray blazar spectra with HESS II mono analysis: The case of PKS2155-304 and PG1553+1132017In: Astronomy and Astrophysics, ISSN 0004-6361, E-ISSN 1432-0746, Vol. 600, article id A89Article in journal (Refereed)
    Abstract [en]

    Context. The addition of a 28 m Cherenkov telescope (CT5) to the H.E.S.S. array extended the experiment's sensitivity to lower energies. The lowest energy threshold is obtained using monoscopic analysis of data taken with CT5, providing access to gamma-ray energies below 100 GeV for small zenith angle observations. Such an extension of the instrument's energy range is particularly beneficial for studies of active galactic nuclei with soft spectra, as expected for those at a redshift >= 0.5. The high-frequency peaked BL Lac objects PKS 2155-304 (z = 0.116) and PG 1553 + 113 (0.43 < z < 0.58) are among the brightest objects in the gamma-ray sky, both showing clear signatures of gamma-ray absorption at E > 100 GeV interpreted as being due to interactions with the extragalactic background light (EBL). Aims. The aims of this work are twofold: to demonstrate the monoscopic analysis of CT5 data with a low energy threshold, and to obtain accurate measurements of the spectral energy distributions (SED) of PKS 2155-304 and PG 1553 + 113 near their SED peaks at energies approximate to 100 GeV. Methods. Multiple observational campaigns of PKS 2155 304 and PG 1553 + 113 were conducted during 2013 and 2014 using the full H.E.S.S. II instrument (CT1-5). A monoscopic analysis of the data taken with the new CT5 telescope was developed along with an investigation into the systematic uncertainties on the spectral parameters which are derived from this analysis. Results. Using the data from CT5, the energy spectra of PKS 2155 304 and PG 1553 + 113 were reconstructed down to conservative threshold energies of 80 GeV for PKS 2155 304, which transits near zenith, and 110 GeV for the more northern PG 1553 + 113. The measured spectra, well fitted in both cases by a log-parabola spectral model ( with a 5.0 similar to statistical preference for non-zero curvature for PKS 2155 304 and 4.5 sigma for PG 1553+113), were found consistent with spectra derived from contemporaneous Fermi-LAT data, indicating a sharp break in the observed spectra of both sources at E approximate to 100 GeV. When corrected for EBL absorption, the intrinsic H.E.S.S. II mono and Fermi-LAT spectrum of PKS 2155 304 was found to show significant curvature. For PG 1553+113, however, no significant detection of curvature in the intrinsic spectrum could be found within statistical and systematic uncertainties.

  • 1225. Abdalla, M. A.
    et al.
    Frojdh, C.
    Petersson, C. Sture
    KTH, Superseded Departments, Microelectronics and Information Technology, IMIT.
    A CMOS APS for dental X-ray imaging using scintillating sensors2001In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 460, no 1, p. 197-203Article in journal (Refereed)
    Abstract [en]

    In this paper we present an integrating CMOS Active Pixel Sensor (APS) circuit to be used with scintillator type X-ray sensors for intra oral dental X-ray imaging systems. Different pixel architectures were constructed to explore their performance characteristics and to study the feasibility of the development of such systems using the CMOS technology. A prototype 64 x 80 pixel array has been implemented in a CMOS 0.8 mum double poly n-well process with a pixel pitch of 50 mum. A spectral sensitivity measurement for the different pixels topologies, as well as measured X-ray direct absorption in the different APSs are presented. A measurement of the output signal showed a good linearity over a wide dynamic range. This chip showed that the very low sensitivity of the CMOS APSs to direct X-ray exposure adds a great advantage to the various CMOS advantages over CCD-based imaging systems,

  • 1226. Abdalla, M. A.
    et al.
    Frojdh, C.
    Petersson, C. Sture
    KTH, Superseded Departments, Microelectronics and Information Technology, IMIT.
    An integrating CMOS APS for X-ray imaging with an in-pixel preamplifier2001In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 466, no 1, p. 232-236Article in journal (Refereed)
    Abstract [en]

    We present in this paper an integrating CMOS Active Pixel Sensor (APS) circuit coated with scintillator type sensors for intra-oral dental X-ray imaging systems. The photosensing element in the pixel is formed by the p-diffusion on the n-well diode. The advantage of this photosensor is its very low direct absorption of X-rays compared to the other available photosensing elements in the CMOS pixel. The pixel features an integrating capacitor in the feedback loop of a preamplifier of a finite gain in order to increase the optical sensitivity. To verify the effectiveness of this in-pixel preamplification, a prototype 32 x 80 element CMOS active pixel array was implemented in a 0.8 mum CMOS double poly, n-well process with a pixel pitch of 50 mum. Measured results confirmed the improved optical sensitivity performance of the APS. Various measurements on device performance are presented.

  • 1227.
    Abdalla, Munir
    KTH, Superseded Departments, Microelectronics and Information Technology, IMIT.
    Pixel Detectors and Electronics for High Energy Radiation Imaging2001Doctoral thesis, comprehensive summary (Other scientific)
  • 1228.
    Abdallah, Magdy
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Entrepreneurship and innovation.
    Indirect Marketing through Influencers on Social Media: Comparing Faceebok paid advertisement services to advertisement by influencers on social media2015Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Social media platforms are an increasingly popular advertising medium, because ofthe opportunities for targeted advertising they provide, but there are also opportunitiesto pay prominent content generators, known as influencers, to publicize brands.This thesis focuses on a case study with Truecaller, a Swedish mobile applicationcompany, advertising in Egypt through a sarcasm page on Facebook. Sarcasm isa very common trait in the everyday life in Egypt and Truecaller is an establishedbrand in the market. The results show that it is more cost effective to pay the sarcasmpage to joke with the company on Facebook, than paying Facebook to promotethe company’s page. The cost per impression was 27% lower, while the cost per userengagement was 31% lower and the cost per user reached was 5% lower. Overall thecampaign increased the number of average application downloads per day by 30%.

  • 1229.
    ABDALMAHMOODABADI, MAHBOOBEH
    KTH, School of Information and Communication Technology (ICT).
    The value of downstream information sharing in two-level supply chain: AN APPROACH TO AGENT-BASED MODELING2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Many supply chain firms have taken initiatives to facilitate demand information sharing between downstream and upstream entities. Information sharing is a key factor of collaboration in supply chain management (SCM). In the last decades, many efforts have been made to model supply chain mathematically. Mathematical models are incapable of capturing the dynamic nature of the system. It is necessary to study multidimensional supply chain model in which not only there is communication between supplier and retailer but also communication among retailers must be considered. Mathematical models can be seen as a simple decision making optimization between two entities in which the effect of cooperation of other entities is completely ignored. These models are far from real world systems. The purpose of this thesis is to create an agent-based model, as a substitute to mathematical modeling, to appraise the importance of sharing information on supplier side when there is relation among retailers by means of stock sharing. The conceptual model of two-echelon supply chain is designed and implemented in Java using Repast suit. The model includes four types of agents namely supply chain, supplier, retailer and mediator agents that interact with each other in a discrete event based simulation. Multi level factorial design is used to evaluate performance of supply chain, in terms of total cost saving, under different demand patterns. The significant difference between experimental settings is tested statistically using ANOVA, Pairwise, and Univariate tests. Data analysis indicates that the significance of information sharing can be rather high, in particular when end customers' demands are considerably correlated. Such cost saving that is achieved by sharing information is due to reducing stock level and at the expense of increasing the amount of backorder.

  • 1230.
    Abdalmoaty, Mohamed
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH Royal Institute of Technology.
    Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. During the last decade, several numerical methods have been developed to approximately solve the maximum likelihood problem. A class of algorithms that attracted considerable attention is based on sequential Monte Carlo algorithms (also known as particle filters/smoothers) and particle Markov chain Monte Carlo algorithms. These algorithms were able to obtain impressive results on several challenging benchmark problems; however, their application is so far limited to cases where fundamental limitations, such as the sample impoverishment and path degeneracy problems, can be avoided.

    This thesis introduces relatively simple alternative parameter estimation methods that may be used for fairly general stochastic nonlinear dynamical models. They are based on one-step-ahead predictors that are linear in the observed outputs and do not require the computations of the likelihood function. Therefore, the resulting estimators are relatively easy to compute and may be highly competitive in this regard: they are in fact defined by analytically tractable objective functions in several relevant cases. In cases where the predictors are analytically intractable due to the complexity of the model, it is possible to resort to {plain} Monte Carlo approximations. Under certain assumptions on the data and some conditions on the model, the convergence and consistency of the estimators can be established. Several numerical simulation examples and a recent real-data benchmark problem demonstrate a good performance of the proposed method, in several cases that are considered challenging, with a considerable reduction in computational time in comparison with state-of-the-art sequential Monte Carlo implementations of the ML estimator.

    Moreover, we provide some insight into the asymptotic properties of the proposed methods. We show that the accuracy of the estimators depends on the model parameterization and the shape of the unknown distribution of the outputs (via the third and fourth moments). In particular, it is shown that when the model is non-Gaussian, a prediction error method based on the Gaussian assumption is not necessarily more accurate than one based on an optimally weighted parameter-independent quadratic norm. Therefore, it is generally not obvious which method should be used. This result comes in contrast to a current belief in some of the literature on the subject. 

    Furthermore, we introduce the estimating functions approach, which was mainly developed in the statistics literature, as a generalization of the maximum likelihood and prediction error methods. We show how it may be used to systematically define optimal estimators, within a predefined class, using only a partial specification of the probabilistic model. Unless the model is Gaussian, this leads to estimators that are asymptotically uniformly more accurate than linear prediction error methods when quadratic criteria are used. Convergence and consistency are established under standard regularity and identifiability assumptions akin to those of prediction error methods.

    Finally, we consider the problem of closed-loop identification when the system is stochastic and nonlinear. A couple of scenarios given by the assumptions on the disturbances, the measurement noise and the knowledge of the feedback mechanism are considered. They include a challenging case where the feedback mechanism is completely unknown to the user. Our methods can be regarded as generalizations of some classical closed-loop identification approaches for the linear time-invariant case. We provide an asymptotic analysis of the methods, and demonstrate their properties in a simulation example.

  • 1231.
    Abdalmoaty, Mohamed
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors2017Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.

    The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.

    In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.

  • 1232.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem2018In: 18th IFAC Symposium on System Identification, 2018Conference paper (Refereed)
    Abstract [en]

    The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presentedand discussed.

  • 1233.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 784-789Article in journal (Refereed)
    Abstract [en]

    The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presented and discussed.

  • 1234.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors2018Conference paper (Refereed)
  • 1235.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Linear Prediction Error Methods for Stochastic Nonlinear Models2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 49-63Article in journal (Refereed)
    Abstract [en]

    The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem.

    The full text will be freely available from 2021-04-01 16:05
  • 1236.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Identication of a Class of Nonlinear Dynamical Networks2018Conference paper (Refereed)
    Abstract [en]

    Identifcation of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

  • 1237.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Identification of a Class of Nonlinear Dynamical Networks⁎2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 868-873Article in journal (Refereed)
    Abstract [en]

    Identification of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

  • 1238.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Henrion, D.
    Rodrigues, L.
    Measures and LMIs for optimal control of piecewise-affine systems2013In: 2013 European Control Conference, ECC 2013, IEEE, 2013, p. 3173-3178, article id 6669627Conference paper (Refereed)
    Abstract [en]

    This paper considers the class of deterministic continuous-time optimal control problems (OCPs) with piecewise-affine (PWA) vector field, polynomial Lagrangian and semialgebraic input and state constraints. The OCP is first relaxed as an infinite-dimensional linear program (LP) over a space of occupation measures. This LP is then approached by an asymptotically converging hierarchy of linear matrix inequality (LMI) relaxations. The relaxed dual of the original LP returns a polynomial approximation of the value function that solves the Hamilton-Jacobi-Bellman (HJB) equation of the OCP. Based on this polynomial approximation, a suboptimal policy is developed to construct a state feedback in a sample-and-hold manner. The results show that the suboptimal policy succeeds in providing a suboptimal state feedback law that drives the system relatively close to the optimal trajectories and respects the given constraints.

  • 1239.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems2016In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3060-3065, article id 7798727Conference paper (Refereed)
    Abstract [en]

    This paper introduces a simulation-based method for maximum likelihood estimation of stochastic Wienersystems. It is well known that the likelihood function ofthe observed outputs for the general class of stochasticWiener systems is analytically intractable. However, when the distributions of the process disturbance and the measurement noise are available, the likelihood can be approximated byrunning a Monte-Carlo simulation on the model. We suggest the use of Laplace importance sampling techniques for the likelihood approximation. The algorithm is tested on a simple first order linear example which is excited only by the process disturbance. Further, we demonstrate the algorithm on an FIR system with cubic nonlinearity. The performance of the algorithm is compared to the maximum likelihood method and other recent techniques.

  • 1240.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control.
    On Re-Weighting, Regularization Selection, and Transient in Nuclear Norm Based Identification2015Conference paper (Refereed)
    Abstract [en]

    In this contribution, we consider the classical problem of estimating an Output Error model given a set of input-output measurements. First, we develop a regularization method based on the re-weighted nuclear norm heuristic. We show that the re-weighting improves the estimate in terms of better fit. Second, we suggest an implementation method that helps in eliminating the regularization parameters from the problem by introducing a constant based on a validation criterion. Finally, we develop a method for considering the effect of the transient when the initial conditions are unknown. A simple numerical example is used to demonstrate the proposed method in comparison to classical and another recent method based on the nuclear norm heuristic.

  • 1241.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models2017In: The 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 14058-14063Conference paper (Refereed)
    Abstract [en]

    Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.

  • 1242.
    Abdel Alim, Richard
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Formation of Soft Particles in Drop-in Fuels2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    As the mission to the decrease global warming and phase out highly pollutingenvironmental practices globally, regulations including Euro 6 and policies generated by theUnited Nations Framework Convention on Climate Change (UNFCCC) are pushing companiesto be more innovative when it comes to their energy sources. These regulations involve manyfactors related to the cleanliness of the fuel and produced emissions, for example, propertiesof the fuels such as sulfur content, ash content, water content, and resulting emission valuesof Carbon dioxide (CO2) and Nitrogen Oxides (NOx). Furthermore, Sweden has set achallenging target of a fossil-fuel-independent vehicle fleet by 2030 and no net greenhousegasemissions by 2050.One way to cut down on the polluting properties in the fuel, as well as weakening thedependence on fossil fuel based fuel includes utilizing higher blending ratios of biofuels in thetransport sector. This transition to biofuels comes with many challenges to the transportindustry due to higher concentrations of these new fuels leads to clogging of the filters in theengine, as well as, internal diesel injector deposits (IDIDs) that produce injector fouling. Thisclogging of the filters leads to lower performance by the engines which leads to higher repairtimes (uptime) and less time on the road to transport goods. The formation of these softparticles at the root of the clogging issue is a pivotal issue because the precise mechanismsbehind their formation are highly unknown. Scania, a leader in the Swedish automotiveindustry, is very interested in figuring out what mechanisms are the most influential in theformation of these particles in the engine. Understanding the key mechanisms would allowScania to make appropriate adjustments to the fuel or the engines to ensure more time onthe road and less maintenance.There are many conditions known to be possible causes of the formation of softparticles in engines such as water content, ash content, and temperature. After generatingsoft particles using a modified accelerated method, particles were analyzed using infraredtechnology (RTX-FTIR) and a Scanning Electric Microscope (SEM-EDX). Many differentexperiments were performed to be able to make a conclusion as to which mechanisms weremost influential including temperature, time, water, air, and oil. The combination of agingbiofuels (B100, B10, HVO) with metals, and water produced the largest amount of particlesfollowed by aging the biofuels with aged oil, metals, and water. Aging the fuels with aged oilincreased particles, meanwhile the addition of water prevented particle production possiblydue to additives. B100 produced the highest amount of particles when aged with Copper, B10with Brass, and HVO with Iron.

  • 1243.
    Abdel Hussein, Mustafa
    KTH, School of Electrical Engineering (EES).
    Modeling and Control of Unmanned Air Vehicles2015Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
  • 1244.
    Abdel Rehim, Abobakr
    KTH, School of Biotechnology (BIO).
    Elucidating CD3-gamma-epsilon and T-cell receptor-beta ectodomain interaction2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
  • 1245.
    Abdel-Karim, R.
    et al.
    Department of Metallurgy, Faculty of Engineering, Cairo University.
    Reda, Y.
    Department of Metallurgy, Faculty of Engineering, Cairo University.
    Muhammed, Mamoun A.
    KTH, School of Information and Communication Technology (ICT), Material Physics, Functional Materials, FNM.
    El-Raghy, S.
    Department of Metallurgy, Faculty of Engineering, Cairo University.
    Shoeib, M.
    Metals Technology Department Central Metallurgical Research and Development Institute.
    Ahmed, H.
    Department of Metallurgy, Faculty of Engineering, Cairo University.
    Electrodeposition and Characterization of Nanocrystalline Ni-Fe Alloys2011In: Journal of Nanomaterials, ISSN 1687-4110, E-ISSN 1687-4129, p. 519274-Article in journal (Refereed)
    Abstract [en]

    Nanocrystalline Ni-Fe deposits with different composition and grain sizes were fabricated by electrodeposition. Deposits with iron contents in the range from 7 to 31% were obtained by changing the Ni(2+)/Fe(2+) mass ratio in the electrolyte. The deposits were found to be nanocrystalline with average grain size in the range 20-30 nm. The surface morphology was found to be dependent on Ni(2+)/Fe(2+) mass ratio as well as electroplating time. The grains size decreased with increasing the iron content, especially in case of short time electroplating. Increasing the electroplating time had no significant effect on grain size. The microhardness of the materials followed the regular Hall-Petch relationship with amaximum value (762 Hv) when applying Ni(2+)/Fe(2+) mass ratio equal to 9.8.

  • 1246. Abdel-Khalek, Ahmed A.
    et al.
    Ali, M. M.
    Ashour, Radwa
    Abdel-Magied, A. F.
    Chemical Studies on Uranium Extraction from Concentrated Phosphoric Acid by Using PC88A and DBBP Mixture.2011In: Journal of Radioanalytical and Nuclear Chemistry, ISSN 0236-5731, E-ISSN 1588-2780, Vol. 290, p. 353-359Article in journal (Refereed)
    Abstract [en]

    Liquid–liquid extraction of U (VI) from concentrated phosphoric acid by using (2-ethyl hexyl) phosphonic acid, mono (2-ethyl hexyl) ester (PC88A) and di-butyl butyl phosphonate (DBBP) has been investigated. The effect of different factors affecting the extraction process (PC88A concentration, DBBP concentration, shaking time, aqueous/organic phase ratio, phosphoric acid concentration and effect of diluents) have been investigated. The obtained data of temperature on the extraction showed that the enthalpy change is −17.15 kJ mol−1. Uranium was extracted from the strip liquor by using di (2-ethylhexyl) phosphoric acid and tri-octyl phosphine oxide mixture and finally converted to a high purity UO3 product using precipitation with hydrogen peroxide and heat treatment at 365 °C.

  • 1247. Abdellah, Mohamed
    et al.
    Zhang, Shihuai
    Wang, Mei
    Hammarstrom, Leif
    Competitive Hole Transfer from CdSe Quantum Dots to Thiol Ligands in CdSe-Cobaloxime Sensitized NiO Films Used as Photocathodes for H-2 Evolution2017In: ACS ENERGY LETTERS, ISSN 2380-8195, Vol. 2, no 11, p. 2576-2580Article in journal (Refereed)
    Abstract [en]

    Quantum dot (QD) sensitized NiO photocathodes rely on efficient photoinduced hole injection into the NiO valence band. A system of a mesoporous NiO film co-sensitized with CdSe QDs and a molecular proton reduction catalyst was studied. While successful electron transfer from the excited QDs to the catalyst is observed, most of the photogenerated holes are instead quenched very rapidly (ps) by hole trapping at the surface thiols of the capping agent used as linker molecules. We confirmed our conclusion by first using a thiol free capping agent and second varying the thiol concentration on the QD's surface. The later resulted in faster hole trapping as the thiol concentration increased. We suggest that this hole trapping by the linker limits the H-2 yield for this photocathode in a device.

  • 1248. Abdellaoui, G.
    et al.
    Abe, S.
    Acheli, A.
    Adams, J. H. J. H.
    Ahmad, S.
    Ahriche, A.
    Albert, J. -N
    Allard, D.
    Alonso, G.
    Anchordoqui, L.
    Andreev, V.
    Anzalone, A.
    Aouimeure, W.
    Arai, Y.
    Arsene, N.
    Asano, K.
    Attallah, R.
    Attoui, H.
    Pemas, M. Ave
    Bacholle, S.
    Bakiri, M.
    Baragatti, P.
    Barrillon, P.
    Bartocci, S.
    Batsch, T.
    Bayer, J.
    Bechini, R.
    Belenguer, T.
    Bellotti, R.
    Belov, A.
    Belov, K.
    Benadda, B.
    Benmessai, K.
    Berlind, A. A.
    Bertaina, M.
    Biermann, P. L.
    Biktemerova, S.
    Bisconti, F.
    Blanc, N.
    Blecki, J.
    Blin-Bondil, S.
    Bobik, P.
    Bogomilov, M.
    Bonamente, M.
    Boudaoud, R.
    Bozzo, E.
    Briggs, M. S.
    Bruno, A.
    Caballero, K. S.
    Cafagna, F.
    Campana, D.
    Capdevielle, J. -N
    Capel, Francesca
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Caramete, A.
    Caramete, L.
    Carlson, Per
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Caruso, R.
    Casolino, M.
    Cassardo, C.
    Castellina, A.
    Castellini, G.
    Catalano, C.
    Catalano, O.
    Cellino, A.
    Chikawa, M.
    Chiritoi, G.
    Christl, M. J.
    Connaughton, V.
    Conti, L.
    Cordero, G.
    Crawford, H. J.
    Cremonini, R.
    Csorna, S.
    Dagoret-Campagne, S.
    De Donato, C.
    de la Taille, C.
    De Santis, C.
    del Peral, L.
    Di Martino, M.
    Djemil, T.
    Djenas, S. A.
    Dulucq, F.
    Dupieux, M.
    Dutan, I.
    Ebersoldt, A.
    Ebisuzaki, T.
    Engel, R.
    Eser, J.
    Fang, K.
    Fenu, F.
    Fernandez-Gonzalez, S.
    Fernandez-Soriano, J.
    Ferrarese, S.
    Finco, D.
    Flamini, M.
    Fornaro, C.
    Fouka, M.
    Franceschi, A.
    Franchini, S.
    Fuglesang, Christer
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Fujimoto, J.
    Fukushima, M.
    Galeotti, P.
    Garcia-Ortega, E.
    Garipov, G.
    Gascon, E.
    Geary, J.
    Gelmini, G.
    Genci, J.
    Giraudo, G.
    Gonchar, M.
    Alvarado, C. Gonzalez
    Gorodetzky, P.
    Guarino, F.
    Guehaz, R.
    Guzman, A.
    Hachisu, Y.
    Haiduc, M.
    Harlov, B.
    Haungs, A.
    Carretero, J. Hernandez
    Hidber, W.
    Higashide, K.
    Ikeda, D.
    Ikeda, H.
    Inoue, N.
    Inoue, S.
    Isgro, F.
    Itow, Y.
    Jammer, T.
    Joven, E.
    Judd, E. G.
    Jung, A.
    Jochum, J.
    Kajino, F.
    Kajino, T.
    Kalli, S.
    Kaneko, I.
    Kang, D.
    Kanouni, F.
    Karadzhov, Y.
    Karczmarczyk, J.
    Karus, M.
    Katahira, K.
    Kawai, K.
    Kawasaki, Y.
    Kedadra, A.
    Khales, H.
    Khrenov, B. A.
    Kim, Jeong-Sook
    Kim, Soon-Wook
    Kim, Sug-Whan
    Kleifges, M.
    Klimov, P. A.
    Kolev, D.
    Kreykenbohm, I.
    Kudela, K.
    Kurihara, Y.
    Kusenko, A.
    Kuznetsov, E.
    Lacombe, M.
    Lachaud, C.
    Lahmar, H.
    Lakhdari, F.
    Larsson, Oscar
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Lee, J.
    Licandro, J.
    Lim, H.
    Campano, L. Lopez
    Maccarone, M. C.
    Mackovjak, S.
    Mandi, M.
    Maravilla, D.
    Marcelli, L.
    Marcos, J. L.
    Marini, A.
    Martens, K.
    Martin, Y.
    Martinez, O.
    Masciantonio, G.
    Mase, K.
    Matev, R.
    Matthews, J. N.
    Mebarki, N.
    Medina-Tanco, G.
    Mehrad, L.
    Mendoza, M. A.
    Merino, A.
    Memik, T.
    Meseguer, J.
    Messaoud, S.
    Micu, O.
    Mimouni, J.
    Miyamoto, H.
    Miyazaki, Y.
    Mizumoto, Y.
    Modestino, G.
    Monaco, A.
    Monnier-Ragaigne, D.
    de los Rios, J. A. Morales
    Moretto, C.
    Morozenko, V. S.
    Mot, B.
    Murakami, T.
    Nadji, B.
    Nagano, M.
    Nagata, M.
    Nagataki, S.
    Nakamura, T.
    Napolitano, T.
    Nardellis, A.
    Naumov, D.
    Nava, R.
    Neronov, A.
    Nomoto, K.
    Nonaka, T.
    Ogawa, T.
    Ogio, S.
    Ohmori, H.
    Olinto, A. V.
    Orleariski, P.
    Osteria, G.
    Painter, W.
    Panasyuk, M. I.
    Panico, B.
    Parizot, E.
    Park, I. H.
    Park, H. W.
    Pastircak, B.
    Patzak, T.
    Paul, T.
    Pennypacker, C.
    Perdichizzi, M.
    Perez-Grande, I.
    Perfetto, F.
    Peter, T.
    Picozza, P.
    Pierog, T.
    Pindado, S.
    Piotrowski, L. W.
    Pirainou, S.
    Placidis, L.
    Plebaniak, Z.
    Pliego, S.
    Pollini, A.
    Popescu, E. M.
    Prat, P.
    Prevot, G.
    Prieto, H.
    Putis, M.
    Rabanal, J.
    Radu, A. A.
    Rahmani, M.
    Reardon, P.
    Reyes, M.
    Rezazadeh, M.
    Ricci, M.
    Frias, M. D. Rodriguez
    Ronga, F.
    Roth, M.
    Rothkaehl, H.
    Roudil, G.
    Rusinov, I.
    Rybczynski, M.
    Sabau, M. D.
    Cano, G. Saez
    Sagawa, H.
    Sahnoune, Z.
    Saito, A.
    Sakaki, N.
    Sakata, M.
    Salazar, H.
    Sanchez, J. C.
    Sanchez, J. L.
    Santangelo, A.
    Cruz, L. Santiago
    Sanz-Andres, A.
    Palomino, M. Sanz
    Saprykin, O.
    Sarazin, F.
    Sato, H.
    Sato, M.
    Schanz, T.
    Schieler, H.
    Scotti, V.
    Segreto, A.
    Selmane, S.
    Semikoz, D.
    Serra, M.
    Sharakin, S.
    Shibata, T.
    Shimizu, H. M.
    Shinozaki, K.
    Shirahama, T.
    Siemieniec-Ozieblo, G.
    Sledd, J.
    Slomiriska, K.
    Sobey, A.
    Stan, I.
    Sugiyama, T.
    Supanitsky, D.
    Suzuki, M.
    Szabelska, B.
    Szabelski, J.
    Tahi, H.
    Tajima, F.
    Tajima, N.
    Tajima, T.
    Takahashi, Y.
    Takami, H.
    Takeda, M.
    Takizawa, Y.
    Talai, M. C.
    Tenzer, C.
    Tibolla, O.
    Tkachev, L.
    Tokuno, H.
    Tomida, T.
    Tone, N.
    Toscano, S.
    Traiche, M.
    Tsenov, R.
    Tsunesada, Y.
    Tsuno, K.
    Tymieniecka, T.
    Uchihori, Y.
    Unger, M.
    Vaduvescu, O.
    Valdes-Galicia, J. F.
    Vallania, P.
    Vankova, G.
    Vigorito, C.
    Villasenor, L.
    Vicek, B.
    von Ballmoos, P.
    Vrabel, M.
    Wada, S.
    Watanabe, J.
    Watanabe, S.
    Watts, J., Jr.
    Weber, M.
    Munoz, R. Weigand
    Weindl, A.
    Weiler, T. J.
    Wibig, T.
    Wiencke, L.
    Wille, M.
    Wilms, J.
    Wlodarczyk, Z.
    Yamamoto, T.
    Yamamoto, Y.
    Yang, J.
    Yano, H.
    Yashin, I. V.
    Yonetoku, D.
    Yoshida, S.
    Young, R.
    Zgura, I. S.
    Zotov, M. Yu.
    Marchi, A. Zuccaro
    Meteor studies in the framework of the JEM-EUSO program2017In: Planetary and Space Science, ISSN 0032-0633, E-ISSN 1873-5088, Vol. 143, p. 245-255Article in journal (Refereed)
    Abstract [en]

    We summarize the state of the art of a program of UV observations from space of meteor phenomena, a secondary objective of the JEM-EUSO international collaboration. Our preliminary analysis indicates that JEM-EUSO, taking advantage of its large FOV and good sensitivity, should be able to detect meteors down to absolute magnitude close to 7. This means that JEM-EUSO should be able to record a statistically significant flux of meteors, including both sporadic ones, and events produced by different meteor streams. Being unaffected by adverse weather conditions, JEM-EUSO can also be a very important facility for the detection of bright meteors and fireballs, as these events can be detected even in conditions of very high sky background. In the case of bright events, moreover, exhibiting some persistence of the meteor train, preliminary simulations show that it should be possible to exploit the motion of the ISS itself and derive at least a rough 3D reconstruction of the meteor trajectory. Moreover, the observing strategy developed to detect meteors may also be applied to the detection of nuclearites, exotic particles whose existence has been suggested by some theoretical investigations. Nuclearites are expected to move at higher velocities than meteoroids, and to exhibit a wider range of possible trajectories, including particles moving upward after crossing the Earth. Some pilot studies, including the approved Mini-EUSO mission, a precursor of JEM-EUSO, are currently operational or in preparation. We are doing simulations to assess the performance of Mini-EUSO for meteor studies, while a few meteor events have been already detected using the ground-based facility EUSO-TA.

  • 1249.
    Abdellaoui, G.
    et al.
    Univ Abou Bekr Belkaid Tlemcen, Fac Technol, Telecom Lab, Tilimsen, Algeria..
    Abe, S.
    Nihon Univ Chiyoda, Tokyo, Japan..
    Adams, J. H., Jr.
    Univ Alabama, Huntsville, AL 35899 USA..
    Ahriche, A.
    Univ Jijel, Lab Theoret Phys LPT, Jijel, Algeria..
    Allard, D.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Allen, L.
    Univ Chicago, Chicago, IL 60637 USA..
    Alonso, G.
    UPM, Madrid, Spain..
    Anchordoqui, L.
    CUNY, Lehman Coll, Bronx, NY USA..
    Anzalone, A.
    Ist Nazl Fis Nucl, Sez Catania, Catania, Italy.;INAF Ist Astrofis Spaziale & Fis Cosm Palermo, Palermo, Italy..
    Arai, Y.
    High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki, Japan..
    Asano, K.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Attallah, R.
    Univ Badji Mokhtar, Fac Sci, Dept Phys, LPR, Annaba, Algeria..
    Attoui, H.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Ave Pernas, M.
    Univ Alcala UAH, Madrid, Spain..
    Bacholle, S.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Bakiri, M.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Baragatti, P.
    UTIU, Dipartimento Ingn, Rome, Italy..
    Barrillon, P.
    Univ Paris 11, CNRS IN2P3, LAL, Orsay, France..
    Bartocci, S.
    UTIU, Dipartimento Ingn, Rome, Italy..
    Bayer, J.
    Univ Tubingen, Kepler Ctr, Inst Astron & Astrophys, Tubingen, Germany..
    Beldjilali, B.
    Univ Abou Bekr Belkaid Tlemcen, Fac Technol, Telecom Lab, Tilimsen, Algeria..
    Belenguer, T.
    INTA, Madrid, Spain..
    Belkhalfa, N.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Bellotti, R.
    Ist Nazl Fis Nucl, Sez Bari, Bari, Italy.;Univ Bari Aldo Moro, Bari, Italy.;INFN, Sez Bari, Bari, Italy..
    Belov, A.
    Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia..
    Belov, K.
    NASA, Jet Prop Lab, Pasadena, CA USA..
    Benmessai, K.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Bertainaek, M.
    Univ Turin, Dipartimento Fis, Turin, Italy..
    Biermann, P. L.
    KIT, Karlsruhe, Germany..
    Biktemerova, S.
    Joint Inst Nucl Res, Dubna, Russia..
    Bisconti, F.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy..
    Blanc, N.
    Swiss Ctr Elect & Microtechnol CSEM, Neuchatel, Switzerland..
    Blecki, J.
    Polish Acad Sci CBK, Space Res Ctr, Warsaw, Poland..
    Blin-Bondil, S.
    Ecole Polytech, CNRS IN2P3, Omega, Palaiseau, France..
    Bobik, P.
    Inst Expt Phys, Kosice, Slovakia..
    Bogomilov, M.
    St Kliment Ohridski Univ Sofia, Sofia, Bulgaria..
    Bozzo, E.
    ISDC Data Ctr Astrophys, Versoix, Switzerland..
    Bruno, A.
    Univ Bari Aldo Moro, Bari, Italy.;INFN, Sez Bari, Bari, Italy..
    Caballero, K. S.
    Univ Autonoma Chiapas UNACH, Chiapas, Mexico..
    Cafagna, F.
    Ist Nazl Fis Nucl, Sez Bari, Bari, Italy..
    Campana, D.
    Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy..
    Capdevielle, J-N
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Capel, Francesca
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Caramete, A.
    ISS, Magurele, Romania..
    Caramete, L.
    ISS, Magurele, Romania..
    Carlson, Per
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Caruso, R.
    Univ Catania, Dipartimento Fis & Astron, Catania, Italy.;Ist Nazl Fis Nucl, Sez Catania, Catania, Italy..
    Casolino, M.
    Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy.;RIKEN, Wako, Saitama, Japan..
    Cassardo, C.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Univ Turin, Dipartimento Fis, Turin, Italy..
    Castellina, A.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Ist Nazl Astrofis, Osservatorio Astrofis Torino, Turin, Italy..
    Catalano, C.
    Univ Toulouse, CNRS, IRAP, Toulouse, France..
    Catalano, O.
    Ist Nazl Fis Nucl, Sez Catania, Catania, Italy.;INAF Ist Astrofis Spaziale & Fis Cosm Palermo, Palermo, Italy..
    Cellino, A.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Ist Nazl Astrofis, Osservatorio Astrofis Torino, Turin, Italy..
    Chikawa, M.
    Kinki Univ, Higashi Osaka, Japan..
    Chiritoi, G.
    ISS, Magurele, Romania..
    Christl, M. J.
    NASA, Marshall Space Flight Ctr, Washington, DC 20546 USA..
    Connaughton, V
    Univ Alabama, Huntsville, AL 35899 USA..
    Conti, L.
    UTIU, Dipartimento Ingn, Rome, Italy..
    Cordero, G.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Cotto, G.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Univ Turin, Dipartimento Fis, Turin, Italy..
    Crawford, H. J.
    Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA..
    Cremonini, R.
    Univ Turin, Dipartimento Fis, Turin, Italy..
    Csorna, S.
    Vanderbilt Univ, Nashville, TN 37235 USA..
    Cummings, A.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Dagoret-Campagne, S.
    Univ Paris 11, CNRS IN2P3, LAL, Orsay, France..
    De Donato, C.
    Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy..
    de la Taille, C.
    Ecole Polytech, CNRS IN2P3, Omega, Palaiseau, France..
    De Santis, C.
    Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy..
    del Peral, L.
    Univ Alcala UAH, Madrid, Spain..
    Di Martino, M.
    Ist Nazl Astrofis, Osservatorio Astrofis Torino, Turin, Italy..
    Damian, A. Diaz
    Univ Toulouse, CNRS, IRAP, Toulouse, France..
    Djemil, T.
    Univ Badji Mokhtar, Fac Sci, Dept Phys, LPR, Annaba, Algeria..
    Dutan, I
    ISS, Magurele, Romania..
    Ebersoldt, A.
    KIT, Karlsruhe, Germany..
    Ebisuzaki, T.
    RIKEN, Wako, Saitama, Japan..
    Engel, R.
    KIT, Karlsruhe, Germany..
    Eser, J.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Fenuek, F.
    Univ Turin, Dipartimento Fis, Turin, Italy..
    Fernandez-Gonzalez, S.
    Univ Leon ULE, Leon, Spain..
    Fernandez-Soriano, J.
    Univ Alcala UAH, Madrid, Spain..
    Ferrarese, S.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Univ Turin, Dipartimento Fis, Turin, Italy..
    Flamini, M.
    UTIU, Dipartimento Ingn, Rome, Italy..
    Fornaro, C.
    UTIU, Dipartimento Ingn, Rome, Italy..
    Fouka, M.
    CRAAG, Dept Astron, Algiers, Algeria..
    Franceschi, A.
    Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy..
    Franchini, S.
    UPM, Madrid, Spain..
    Fuglesang, Christer
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Fujii, T.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Fujimoto, J.
    High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki, Japan..
    Fukushima, M.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Galeotti, P.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Univ Turin, Dipartimento Fis, Turin, Italy..
    Garcia-Ortega, E.
    Univ Leon ULE, Leon, Spain..
    Garipov, G.
    Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia..
    Gascon, E.
    Univ Leon ULE, Leon, Spain..
    Genci, J.
    Tech Univ Kosice TUKE, Kosice, Slovakia..
    Giraudo, G.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy..
    Gonzalez Alvarado, C.
    INTA, Madrid, Spain..
    Gorodetzky, P.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Greg, R.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Guarino, F.
    Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy.;Univ Napoli Federico II, Dipartimento Fis, Naples, Italy..
    Guzman, A.
    Univ Tubingen, Kepler Ctr, Inst Astron & Astrophys, Tubingen, Germany..
    Hachisu, Y.
    RIKEN, Wako, Saitama, Japan..
    Haiduc, M.
    ISS, Magurele, Romania..
    Harlov, B.
    TsNIIMash, Cent Res Inst Machine Bldg, Korolev, Russia..
    Haungs, A.
    KIT, Karlsruhe, Germany..
    Hernandez Carretero, J.
    Univ Alcala UAH, Madrid, Spain..
    Hidber Cruz, W.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Ikeda, D.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Inoue, N.
    Saitama Univ, Saitama, Japan..
    Inoue, S.
    RIKEN, Wako, Saitama, Japan..
    Isgro, F.
    Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy.;Univ Napoli Federico II, DIETI, Naples, Italy..
    Itow, Y.
    Nagoya Univ, Inst Space Earth Environm Res, Nagoya, Aichi, Japan..
    Jammer, T.
    Univ Tubingen, Kepler Ctr, Expt Phys Inst, Tubingen, Germany..
    Jeong, S.
    Sungkyunkwan Univ, Seoul, South Korea..
    Joven, E.
    IAC, Tenerife, Spain..
    Judd, E. G.
    Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA..
    Jung, A.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Jochum, J.
    Univ Tubingen, Kepler Ctr, Expt Phys Inst, Tubingen, Germany..
    Kajino, F.
    Konan Univ, Kobe, Hyogo, Japan..
    Kajino, T.
    Natl Astron Observ, Mitaka, Tokyo, Japan..
    Kalli, S.
    Univ Msila, Fac Sci, Dept Phys, Msila, Algeria..
    Kaneko, I
    RIKEN, Wako, Saitama, Japan..
    Karadzhov, Y.
    St Kliment Ohridski Univ Sofia, Sofia, Bulgaria..
    Karczmarczyk, J.
    Natl Ctr Nucl Res, Lodz, Poland..
    Katahira, K.
    RIKEN, Wako, Saitama, Japan..
    Kawai, K.
    RIKEN, Wako, Saitama, Japan..
    Kawasaki, Y.
    RIKEN, Wako, Saitama, Japan..
    Kedadra, A.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Khales, H.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Khrenov, B. A.
    Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia..
    Kim, Jeong-Sook
    Korea Astron & Space Sci Inst KASI, Daejeon, South Korea..
    Kim, Soon-Wook
    Korea Astron & Space Sci Inst KASI, Daejeon, South Korea..
    Kleifges, M.
    KIT, Karlsruhe, Germany..
    Klimov, P. A.
    Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia..
    Kolev, D.
    St Kliment Ohridski Univ Sofia, Sofia, Bulgaria..
    Krantz, H.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Kreykenbohm, I
    Univ Erlangen Nurnberg, ECAP, Erlangen, Germany..
    Kudela, K.
    Inst Expt Phys, Kosice, Slovakia..
    Kurihara, Y.
    High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki, Japan..
    Kusenko, A.
    Univ Tokyo, Tokyo, Japan.;NASA, Jet Prop Lab, Pasadena, CA USA..
    Kuznetsov, E.
    Univ Alabama, Huntsville, AL 35899 USA..
    La Barbera, A.
    Ist Nazl Fis Nucl, Sez Catania, Catania, Italy.;INAF Ist Astrofis Spaziale & Fis Cosm Palermo, Palermo, Italy..
    Lachaud, C.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Lahmar, H.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Lakhdari, F.
    UROP CDTA, Res Unit Opt & Photon, Setif, Algeria..
    Larson, R.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Larsson, O.
    KTH.
    Lee, J.
    Sungkyunkwan Univ, Seoul, South Korea..
    Licandro, J.
    IAC, Tenerife, Spain..
    Lopez Campano, L.
    Univ Leon ULE, Leon, Spain..
    Maccarone, M. C.
    Ist Nazl Fis Nucl, Sez Catania, Catania, Italy.;INAF Ist Astrofis Spaziale & Fis Cosm Palermo, Palermo, Italy..
    Mackovjak, S.
    ISDC Data Ctr Astrophys, Versoix, Switzerland..
    Mahdi, M.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Maravilla, D.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Marcelli, L.
    Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy..
    Marcos, J. L.
    Univ Leon ULE, Leon, Spain..
    Marini, A.
    Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy..
    Marszal, W.
    Natl Ctr Nucl Res, Lodz, Poland..
    Martens, K.
    Univ Tokyo, Tokyo, Japan..
    Martin, Y.
    IAC, Tenerife, Spain..
    Martinez, O.
    BUAP, Puebla, Mexico..
    Martucci, M.
    Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy..
    Masciantonio, G.
    Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy..
    Mase, K.
    Chiba Univ, Chiba, Japan..
    Mastafa, M.
    Univ Alabama, Huntsville, AL 35899 USA..
    Matev, R.
    St Kliment Ohridski Univ Sofia, Sofia, Bulgaria..
    Matthews, J. N.
    Univ Utah, Salt Lake City, UT USA..
    Mebarki, N.
    Univ Constantine I, Lab Math & Subatom Phys LPMPS, Constantine, Algeria..
    Medina-Tanco, G.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Mendoza, M. A.
    CDA IPN, Mexico City, DF, Mexico..
    Menshikov, A.
    KIT, Karlsruhe, Germany..
    Merino, A.
    Univ Leon ULE, Leon, Spain..
    Meseguer, J.
    UPM, Madrid, Spain..
    Meyer, S. S.
    Univ Chicago, Chicago, IL 60637 USA..
    Mimouni, J.
    Univ Constantine I, Lab Math & Subatom Phys LPMPS, Constantine, Algeria..
    Miyamoto, H.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Univ Turin, Dipartimento Fis, Turin, Italy..
    Mizumoto, Y.
    Natl Astron Observ, Mitaka, Tokyo, Japan..
    Monaco, A.
    Ist Nazl Fis Nucl, Sez Bari, Bari, Italy.;Univ Bari Aldo Moro, Bari, Italy.;INFN, Sez Bari, Bari, Italy..
    Morales de los Rios, J. A.
    Univ Alcala UAH, Madrid, Spain..
    Moretto, C.
    Univ Paris 11, CNRS IN2P3, LAL, Orsay, France..
    Nagataki, S.
    RIKEN, Wako, Saitama, Japan..
    Naitamor, S.
    CRAAG, Dept Astron, Algiers, Algeria..
    Napolitano, T.
    Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy..
    Naslund, W.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Nava, R.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Neronov, A.
    ISDC Data Ctr Astrophys, Versoix, Switzerland..
    Nomoto, K.
    Univ Tokyo, Tokyo, Japan..
    Nonaka, T.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Ogawa, T.
    RIKEN, Wako, Saitama, Japan..
    Ogio, S.
    Osaka City Univ, Grad Sch Sci, Osaka, Japan..
    Ohmori, H.
    RIKEN, Wako, Saitama, Japan..
    Olinto, A. , V
    Orleanski, P.
    Polish Acad Sci CBK, Space Res Ctr, Warsaw, Poland..
    Osteria, G.
    Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy..
    Pagliaro, A.
    Ist Nazl Fis Nucl, Sez Catania, Catania, Italy.;INAF Ist Astrofis Spaziale & Fis Cosm Palermo, Palermo, Italy..
    Painter, W.
    KIT, Karlsruhe, Germany..
    Panasyuk, M. , I
    Panico, B.
    Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy..
    Pasqualino, G.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Parizot, E.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Park, I. H.
    Sungkyunkwan Univ, Seoul, South Korea..
    Pastircak, B.
    Inst Expt Phys, Kosice, Slovakia..
    Patzak, T.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Paul, T.
    CUNY, Lehman Coll, Bronx, NY USA..
    Perez-Grande, I
    UPM, Madrid, Spain..
    Perfetto, F.
    Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy..
    Peter, T.
    ETH, Inst Atmospher & Climate Sci, Zurich, Switzerland..
    Picozza, P.
    Ist Nazl Fis Nucl, Sez Roma Tor Vergata, Rome, Italy.;Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy.;RIKEN, Wako, Saitama, Japan..
    Pindado, S.
    UPM, Madrid, Spain..
    Piotrowski, L. W.
    RIKEN, Wako, Saitama, Japan..
    Piraino, S.
    Univ Tubingen, Kepler Ctr, Inst Astron & Astrophys, Tubingen, Germany..
    Placidi, L.
    UTIU, Dipartimento Ingn, Rome, Italy..
    Plebaniak, Z.
    Natl Ctr Nucl Res, Lodz, Poland..
    Pliego, S.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Pollini, A.
    Swiss Ctr Elect & Microtechnol CSEM, Neuchatel, Switzerland..
    Polonski, Z.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Popescu, E. M.
    ISS, Magurele, Romania..
    Prat, P.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Prevot, G.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Prieto, H.
    Univ Alcala UAH, Madrid, Spain..
    Puehlhofer, G.
    Univ Tubingen, Kepler Ctr, Inst Astron & Astrophys, Tubingen, Germany..
    Putis, M.
    Inst Expt Phys, Kosice, Slovakia..
    Rabanal, J.
    Univ Paris 11, CNRS IN2P3, LAL, Orsay, France..
    Radu, A. A.
    ISS, Magurele, Romania..
    Reyes, M.
    IAC, Tenerife, Spain..
    Rezazadeh, M.
    Univ Chicago, Chicago, IL 60637 USA..
    Ricci, M.
    Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy..
    Rodriguez Frias, M. D.
    Univ Alcala UAH, Madrid, Spain..
    Rodencal, M.
    Univ Alabama, Huntsville, AL 35899 USA..
    Ronga, F.
    Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy..
    Roudil, G.
    Univ Toulouse, CNRS, IRAP, Toulouse, France..
    Rusinov, I
    St Kliment Ohridski Univ Sofia, Sofia, Bulgaria..
    Rybczynski, M.
    Jan Kochanowski Univ Humanities & Sci, Inst Phys, Kielce, Poland..
    Sabau, M. D.
    INTA, Madrid, Spain..
    Saez Cano, G.
    Univ Alcala UAH, Madrid, Spain..
    Sagawa, H.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Sahnoune, Z.
    CRAAG, Dept Astron, Algiers, Algeria..
    Saito, A.
    Kyoto Univ, Kyoto, Japan..
    Sakaki, N.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Salazar, H.
    BUAP, Puebla, Mexico..
    Sanchez Balanzar, J. C.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Sanchez, J. L.
    Univ Leon ULE, Leon, Spain..
    Santangelo, A.
    Univ Tubingen, Kepler Ctr, Inst Astron & Astrophys, Tubingen, Germany..
    Sanz-Andres, A.
    UPM, Madrid, Spain..
    Sanz Palomino, M.
    INTA, Madrid, Spain..
    Saprykin, O.
    TsNIIMash, Cent Res Inst Machine Bldg, Korolev, Russia..
    Sarazin, F.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Sato, M.
    Hokkaido Univ, Sapporo, Hokkaido, Japan..
    Schanz, T.
    Univ Tubingen, Kepler Ctr, Inst Astron & Astrophys, Tubingen, Germany..
    Schieler, H.
    KIT, Karlsruhe, Germany..
    Scotti, V
    Ist Nazl Fis Nucl, Sez Napoli, Naples, Italy..
    Selmane, S.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Semikoz, D.
    Univ Paris Diderot, Sorbonne Paris Cite, Obs Paris, CEA Irfu,CNRS IN2P3,APC, Paris, France..
    Serra, M.
    IAC, Tenerife, Spain..
    Sharakin, S.
    Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia..
    Shimizu, H. M.
    Nagoya Univ, Nagoya, Aichi, Japan..
    Shinozaki, K.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Univ Turin, Dipartimento Fis, Turin, Italy..
    Shirahama, T.
    Saitama Univ, Saitama, Japan..
    Spataro, B.
    Ist Nazl Fis Nucl, Lab Nazl Frascati, Frascati, Italy..
    Stan, I
    ISS, Magurele, Romania..
    Sugiyama, T.
    Nagoya Univ, Nagoya, Aichi, Japan..
    Supanitsky, D.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Suzuki, M.
    JAXA, Inst Space & Astronaut Sci, Sagamihara, Kanagawa, Japan..
    Szabelska, B.
    Natl Ctr Nucl Res, Lodz, Poland..
    Szabelski, J.
    Natl Ctr Nucl Res, Lodz, Poland..
    Tajima, N.
    RIKEN, Wako, Saitama, Japan..
    Tajima, T.
    RIKEN, Wako, Saitama, Japan..
    Takahashi, Y.
    Hokkaido Univ, Sapporo, Hokkaido, Japan..
    Takami, H.
    High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki, Japan..
    Takeda, M.
    Univ Tokyo, Inst Cosm Ray Res, Kashiwa, Chiba, Japan..
    Takizawa, Y.
    RIKEN, Wako, Saitama, Japan..
    Talai, M. C.
    Univ Badji Mokhtar, Fac Sci, Dept Phys, LPR, Annaba, Algeria..
    Tenzer, C.
    Univ Tubingen, Kepler Ctr, Inst Astron & Astrophys, Tubingen, Germany..
    Thomas, S. B.
    Univ Utah, Salt Lake City, UT USA..
    Tibolla, O.
    Ctr Mesoamer Fis Teor MCTP, Chiapas, Mexico..
    Tkachev, L.
    Joint Inst Nucl Res, Dubna, Russia..
    Tokuno, H.
    Tokyo Inst Technol, Interact Res Ctr Sci, Tokyo, Japan..
    Tomida, T.
    Shinshu Univ, Nagano, Japan..
    Tone, N.
    RIKEN, Wako, Saitama, Japan..
    Toscano, S.
    ISDC Data Ctr Astrophys, Versoix, Switzerland..
    Traiche, M.
    Ctr Dev Adv Technologies CDTA, Algiers, Algeria..
    Tsenov, R.
    St Kliment Ohridski Univ Sofia, Sofia, Bulgaria..
    Tsunesada, Y.
    Osaka City Univ, Grad Sch Sci, Osaka, Japan..
    Tsuno, K.
    RIKEN, Wako, Saitama, Japan..
    Tubbs, J.
    Univ Alabama, Huntsville, AL 35899 USA..
    Turriziani, S.
    RIKEN, Wako, Saitama, Japan..
    Uchihori, Y.
    Natl Inst Radiol Sci, Chiba, Japan..
    Vaduvescu, O.
    IAC, Tenerife, Spain..
    Valdes-Galicia, J. F.
    Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico..
    Vallania, P.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Ist Nazl Astrofis, Osservatorio Astrofis Torino, Turin, Italy..
    Vankova, G.
    St Kliment Ohridski Univ Sofia, Sofia, Bulgaria..
    Vigorito, C.
    Ist Nazl Fis Nucl, Sez Torino, Turin, Italy.;Univ Turin, Dipartimento Fis, Turin, Italy..
    Villasenor, L.
    UMSNH, Morelia, Michoacan, Mexico..
    Vlcek, B.
    Univ Alcala UAH, Madrid, Spain..
    Von Ballmoos, P.
    Univ Toulouse, CNRS, IRAP, Toulouse, France..
    Vrabel, M.
    Tech Univ Kosice TUKE, Kosice, Slovakia..
    Wada, S.
    RIKEN, Wako, Saitama, Japan..
    Watanabe, J.
    Natl Astron Observ, Mitaka, Tokyo, Japan..
    Watts, J., Jr.
    Univ Alabama, Huntsville, AL 35899 USA..
    Weber, M.
    KIT, Karlsruhe, Germany..
    Weigand Munoz, R.
    Univ Leon ULE, Leon, Spain..
    Weindl, A.
    KIT, Karlsruhe, Germany..
    Wiencke, L.
    Colorado Sch Mines, Golden, CO 80401 USA..
    Wille, M.
    Univ Erlangen Nurnberg, ECAP, Erlangen, Germany..
    Wilms, J.
    Univ Erlangen Nurnberg, ECAP, Erlangen, Germany..
    Wlodarczyk, Z.
    Jan Kochanowski Univ Humanities & Sci, Inst Phys, Kielce, Poland..
    Yamamoto, T.
    Konan Univ, Kobe, Hyogo, Japan..
    Yang, J.
    Ewha Womans Univ, Seoul, South Korea..
    Yano, H.
    JAXA, Inst Space & Astronaut Sci, Sagamihara, Kanagawa, Japan..
    Yashin, I. , V
    Yonetoku, D.
    Kanazawa Univ, Kanazawa, Ishikawa, Japan..
    Yoshida, S.
    Chiba Univ, Chiba, Japan..
    Young, R.
    NASA, Marshall Space Flight Ctr, Washington, DC 20546 USA..
    Zgura, I. S.
    ISS, Magurele, Romania..
    Zotov, M. Yu
    Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow, Russia..
    Marchi, A. Zuccaro
    RIKEN, Wako, Saitama, Japan..
    First observations of speed of light tracks by a fluorescence detector looking down on the atmosphere2018In: Journal of Instrumentation, ISSN 1748-0221, E-ISSN 1748-0221, Vol. 13, article id P05023Article in journal (Refereed)
    Abstract [en]

    EUSO-Balloon is a pathfinder mission for the Extreme Universe Space Observatory onboard the Japanese Experiment Module (JEM-EUSO). It was launched on the moonless night of the 25(th) of August 2014 from Timmins, Canada. The flight ended successfully after maintaining the target altitude of 38 km for five hours. One part of the mission was a 2.5 hour underflight using a helicopter equipped with three UV light sources (LED, xenon flasher and laser) to perform an inflight calibration and examine the detectors capability to measure tracks moving at the speed of light. We describe the helicopter laser system and details of the underflight as well as how the laser tracks were recorded and found in the data. These are the first recorded laser tracks measured from a fluorescence detector looking down on the atmosphere. Finally, we present a first reconstruction of the direction of the laser tracks relative to the detector.

  • 1250. Abdellaoui, G.
    et al.
    Capel, Francesca
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Carlson, Per
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Fuglesang, Christer
    KTH, School of Engineering Sciences (SCI), Physics, Particle and Astroparticle Physics.
    Larsson, O.
    KTH. RIKEN, Wako, Japan.
    Zuccaro Marchi, A.
    et.al.,
    Cosmic ray oriented performance studies for the JEM-EUSO first level trigger2017In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, p. 150-163Article in journal (Refereed)
    Abstract [en]

    JEM-EUSO is a space mission designed to investigate Ultra-High Energy Cosmic Rays and Neutrinos (E > 5.10(19) eV) from the International Space Station (ISS). Looking down from above its wide angle telescope is able to observe their air showers and collect such data from a very wide area. Highly specific trigger algorithms are needed to drastically reduce the data load in the presence of both atmospheric and human activity related background light, yet retain the rare cosmic ray events recorded in the telescope. We report the performance in offline testing of the first level trigger algorithm on data from JEM-EUSO prototypes and laboratory measurements observing different light sources: data taken during a high altitude balloon flight over Canada, laser pulses observed from the ground traversing the real atmosphere, and model landscapes reproducing realistic aspect ratios and light conditions as would be seen from the ISS itself. The first level trigger logic successfully kept the trigger rate within the permissible bounds when challenged with artificially produced as well as naturally encountered night sky background fluctuations and while retaining events with general air-shower characteristics.

22232425262728 1201 - 1250 of 124264
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf