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Single photodiode-per-polarization receiver for 400G systems

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Single photodiode-per-polarization receiver for 400G systems

Optical Fiber Communication Conference, OFC 2018

作者： Corcoran, Bill Foo, Benjamin Lowery, Arthur J. Electro-Photonics Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia Australia Chalmers University of Technology Gothenburg412 58 Sweden

We present a simplified heterodyne receiver using one single ended photodiode per polarization for polarization multiplexed coherent signals. We demonstrate this receiver for the reception of PM-16QAM over field-insta... 详细信息

ISBN: (纸本)9781943580385

关键词： Polarization

TOI-2458 b: A mini-Neptune consistent with in situ hot Jupiter formation

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arXiv 2024年

作者： Šubjak, Jan Gandolfi, Davide Goffo, Elisa Rapetti, David Jankowski, Dawid Mizuki, Toshiyuki Dai, Fei Serrano, Luisa M. Wilson, Thomas G. Goździewski, Krzysztof Nowak, Grzegorz Jenkins, Jon M. Twicken, Joseph D. Winn, Joshua N. Bieryla, Allyson Ciardi, David R. Cochran, William D. Collins, Karen A. Deeg, Hans J. Garcia, Rafael A. Guenther, Eike W. Hatzes, Artie P. Kabath, Petr Korth, Judith Latham, David W. Livingston, John H. Lund, Michael B. Mathur, Savita Narita, Norio Orell-Miquel, Jaume Palle, Enric Persson, Carina M. Redfield, Seth Schwarz, Richard P. Watanabe, David Ziegler, Carl Astronomical Institute Czech Academy of Sciences Fričova 298 Ondřejov251 65 Czech Republic Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Dipartimento di Fisica Universita degli Studi di Torino via Pietro Giuria 1 TorinoI-10125 Italy Thuringer Landessternwarte Tautenburg Sternwarte 5 Tautenburg07778 Germany NASA Ames Research Center Moffett FieldCA94035 United States Research Institute for Advanced Computer Science Universities Space Research Association WashingtonDC20024 United States Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Toruń87-100 Poland Astrobiology Center of NINS 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-2 Osawa Mitaka Tokyo181-8588 Japan Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom SETI Institute Mountain ViewCA94043 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States NASA Exoplanet Science Institute-Caltech/IPAC PasadenaCA91125 United States McDonald Observatory The University of Texas AustinTX United States Center for Planetary Systems Habitability The University of Texas AustinTX United States C. Via Lactea S/N Tenerife La LagunaE-38205 Spain Dept. de Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Saclay Universite Paris Cite CEA CNRS AIM Gif-sur-Yvette91191 France Lund Observatory Division of Astrophysics Department of Physics Lund University Box 118 Lund22100 Sweden Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Chalmers University of Technology Departmen

We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the mainsequence with a mass of M*= 1.05 ± 0.03Mo, a radius of R*= 1.31 ± 0.03 Ro, an effective temperature of Teff = 6005 ± 50 K, and a metallicity of -0.10 ± 0.05 dex. By combining TESS photometry with high-resolution spectra acquired with the HARPS spectrograph, we found that the transiting planet has an orbital period of ∼3.74 days, a mass of Mp = 13.31±0.99M⊕ and a radius of Rp = 2.83±0.20 R⊕. The host star TOI-2458 shows a short activity cycle of ∼54 days revealed in the HARPS S-index and Hα times series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be i*= 10.6+13.3 -10.6 degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPS spectra has identified the presence of another planet with a period of P = 16.55 ± 0.06 days and a minimum mass of Mp sin i = 10.22 ± 1.90M⊕. Using dynamical stability analysis, we constrained the mass of this planet to the range Mc (10, 25)M⊕. © 2024, CC BY.

关键词： Stars

Industrial Anomaly Detection and One-class Classification using Generative Adversarial Networks

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Industrial Anomaly Detection and One-class Classification us...

IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Y. T. K. Lai J. S. Hu Y. H. Tsai W. Y. Chiu Dept. of Electrical Control Engineering National Chiao Tung University Taiwan Dept. of Computer Science National Chiao Tung University Taiwan Mechanical and Mechatronics Systems Research Labs (MMSL) Industrial Technology Research Institute (ITRI) Taiwan

Industrial image datasets for quality inspection are mostly sparse in defects. It is then hard for both automated optical inspection (AOI) machines and simple neural network classifiers to inspect all defects effectively. In this work, we develop a novel framework for industrial anomaly detection in one-class classification manner, which utilized pre-trained generative adversarial networks (GANs) as the rule of thumb to perform anomaly detection. Our results show that GANs are able to capture arbitrary and structural industrial images and can effectively discern defects when the query images are defective.

关键词： Gallium nitride Generators Generative adversarial networks Training Anomaly detection Optimization Image reconstruction

Identifying Undirected Network Structure via Semidefinite Relaxation

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Identifying Undirected Network Structure via Semidefinite Re...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rasoul Shafipour Santiago Segarra Antonio G. Marques Gonzalo Mateos Dept. of Electrical and Computer Engineering University of Rochester Rochester NY USA Inst. for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain

ISBN: (纸本)9781538646595

We address the problem of inferring an undirected graph from nodal observations, which are modeled as non-stationary graph signals generated by local diffusion dynamics on the unknown network. We propose a two-step approach where we first estimate the unknown diffusion (graph) filter, from which we recover the eigenvectors of the so-called graph-shift operator (a matrix representation of the graph). We then estimate the eigenvalues by imposing desirable properties on the graph to be recovered. To carry out the initial system identification step, we assume that second-order statistics of the inputs are available. While such quadratic filter identification problem boils down to a non-convex fourth order polynomial minimization, we propose a semidefinite relaxation with provable performance guarantees. Finally, numerical tests illustrate the use of the proposed algorithm to unveil urban mobility patterns.

关键词： Covariance matrices Symmetric matrices Eigenvalues and eigenfunctions Network topology Diffusion processes Laplace equations Manganese

Common Limitations of Image Processing Metrics: A Picture Story

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

Kiloparsec-scale imaging of the CO(1-0)-traced cold molecular gas reservoir in a z∼3.4 submillimeter galaxy

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arXiv 2022年

作者： Castillo, Marta Frias Rybak, Matus Hodge, Jacqueline van der Werf, Paul Riechers, Dominik A. Vieira, Daniel Rivera, Gabriela Calistro Martínez-Ramírez, Laura N. Walter, Fabian de Blok, Erwin Narayanan, Desika Wagg, Jeff Leiden Observatory Leiden University Niels Bohrweg 2 Leiden2333 CA Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands I. Physikalisches Institut Universität zu Köln Zülpicher Strasse 77 KölnD-50937 Germany Department of Astronomy Cornell University Space Sciences Building IthacaNY14853 United States Karl-Schwarzschild-Straße 2 Garching bei München 85748 Germany Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Av. Vicuna Mackenna 4860 Macul Santiago782-0436 Chile Escuela de Física Universidad Industrial de Santander Bucaramanga680002 Colombia Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany National Radio Astronomy Observatory Pete V. Domenici Array Science Center P.O. Box O SocorroNM87801 United States Postbus 2 Dwingeloo7990 AA Netherlands Dept. of Astronomy Univ. of Cape Town Private Bag X3 Rondebosch7701 South Africa Kapteyn Astronomical Institute University of Groningen Postbus 800 Groningen9700 AV Netherlands Department of Astronomy University of Florida 211 Bryant Space Sciences Center GainesvilleFL32611 United States University of Florida Informatics Institute 432 Newell Drive CISE Bldg E251 GainesvilleFL32611 United States Cosmic Dawn Center The Niels Bohr Institute University of Copenhagen and DTU-Space Technical University of Denmark Denmark SKA Observatory Lower Withington Maccleseld CheshireSK11 9DL United Kingdom

We present a high-resolution study of the cold molecular gas as traced by CO(1-0) in the unlensed z∼3.4 submillimeter galaxy SMM J13120+4242, using multi-configuration observations with the Karl G. Jansky Very Large Array (JVLA). The gas reservoir, imaged on 0.39" (∼3 kpc) scales, is resolved into two components separated by ∼11 kpc with a total extent of 16 ±3 kpc. Despite the large spatial extent of the reservoir, the observations show a CO(1-0) FWHM linewidth of only 267 ±64 km s−1. We derive a revised line luminosity of L'CO(1-0) = (10 ± 3) × 1010 K km s−1 pc2 and a molecular gas mass of Mgas = (13 ± 3) ×1010 (αCO/1) M☉. Despite the presence of a velocity gradient (consistent with previous resolved CO(6-5) imaging), the CO(1-0) imaging shows evidence for significant turbulent motions which are preventing the gas from fully settling into a disk. The system likely represents a merger in an advanced stage. Although the dynamical mass is highly uncertain, we use it to place an upper limit on the CO-to-H2 mass conversion factor αCO of 1.4. We revisit the SED fitting, finding that this galaxy lies on the very massive end of the main sequence at z=3.4. Based on the low gas fraction, short gas depletion time and evidence for a central AGN, we propose that SMM J13120 is in a rapid transitional phase between a merger-driven starburst and an unobscured quasar. The case of SMM J13120 highlights the how mergers may drive important physical changes in galaxies without pushing them off the main sequence. Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

Real-time faulted line localization and PMU placement in power systems through convolutional neural networks

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arXiv 2018年

作者： Li, Wenting Deka, Deepjyoti Chertkov, Michael Wang, Meng Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Theory Division Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM United States

Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availability. This paper proposes a faulted line localization method based on a Convolutional Neural Network (CNN) classifier using bus voltages. Unlike prior data-driven methods, the proposed classifier is based on features with physical interpretations that improve the robustness of the location performance. The accuracy of our CNN based localization tool is demonstrably superior to other machine learning classifiers in the literature. To further improve the location performance, a joint phasor measurement units (PMU) placement strategy is proposed and validated against other methods. A significant aspect of our methodology is that under very low observability (7% of buses), the algorithm is still able to localize the faulted line to a small neighborhood with high probability. The performance of our scheme is validated through simulations of faults of various types in the IEEE 39-bus and 68-bus power systems under varying uncertain conditions, system observability, and measurement quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Phasor measurement units

pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy

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arXiv 2024年

作者： Iyer, Kartheik G. Yunus, Mikaeel O’Neill, Charles Ye, Christine Hyk, Alina McCormick, Kiera Ciucă, Ioana Wu, John F. Accomazzi, Alberto Astarita, Simone Chakrabarty, Rishabh Cranney, Jesse Field, Anjalie Ghosal, Tirthankar Ginolfi, Michele Huertas-Company, Marc Jablońska, Maja Kruk, Sandor Liu, Huiling Marchidan, Gabriel Mistry, Rohit Naiman, J.P. Peek, J.E.G. Polimera, Mugdha Rodríguez Méndez, Sergio J. Schawinski, Kevin Sharma, Sanjib Smith, Michael J. Ting, Yuan-Sen Walmsley, Mike Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Physics and Astronomy Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States School of Computing TheAustralian National University 108 North Rd ActonACT2601 Australia Stanford University 450 Jane Stanford Way StanfordCA94305 United States School of Electrical Engineering and Computer Science Oregon State University 1500 SW Jefferson Way CorvallisOR97331 United States Department of Engineering Loyola University Maryland 4501 North Charles Street BaltimoreMD21210 United States Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Paris France Astralis-AITC - Stromlo RSAA Australian National University Cotter Road WestonACT2600 Australia Department of Computer Science Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Dipartimento di Fisica e Astronomia Universita di Firenze Via G. Sansone 1 Sesto Fiorentino Firenze50019 Italy INAF - Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 FirenzeI-50125 Italy Instituto de Astrofisica de Canarias C. Via Lactea 1 Tenerife La LagunaE-38205 Spain Universidad de la Laguna dept. Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Cite LERMA - Observatoire de Paris PSL Paris France SCIPP University of

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with key-words. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use key-words or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool’s versatility through case studies, showcasing its application in various research scenarios. The system’s performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature. © 2024, CC BY.

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Developing A Dynamic Queueing Model for The Airport Check-in Process

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Developing A Dynamic Queueing Model for The Airport Check-in...

IEEE International Conference on Electro Information technology

作者： Yanlin Zhou Ryan Anderson Hamid Vakilzadian Dietmar P.F Moeller Andreas Deutschmann Dept. of Electrical and Computer Engineering University of Nebraska-Lincoln USA Dept. of Biological and Systems Engineering University of Nebraska-Lincoln USA Inst. of Applied Stochastic and Operations Research Clausthal University of Technology Germany Inst. of Air Transport and Airport Research (FW) DLR Germany Germany

This paper devises a dynamic queueing model for the airport check-in process. The airport scheduling system is highly dynamic, for it is inherently highly dimensional and takes input from a various of involved parties, i.e., passengers, airport staff, airport infrastructure, airline companies and etc. The proposed model presents a top-down focus to guarantee a reliable, punctual and efficient check-in scheduling scheme and is later generalized from bottom-up to be applicable for other processes. In our work, we design a flow logic and derive the framework analytically with MATLAB. Then the simulation of service time and arrival distribution are provided. In the Experiment, the real data collected from Hamburg International Airport was fed into the model and achieved satisfying results. Both analytically and experimentally, we prove that our model is capable of solving dynamic queueing problems.

关键词： Dynamic Modeling Queueing Scheduling Logistic Management