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26th European signal Processing Conference, EUSIPCO 2018

作者： Tran, Dat Thanh Høye, Toke Thomas Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Finland Department of Bioscience Aarhus University Aarhus Denmark Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

ISBN: (纸本)9789082797015

The visit patterns of insects to specific flowers at specific times during the diurnal cycle and across the season play important roles in pollination biology. Thus, the ability to automatically detect flowers and visitors occurring in video sequences greatly reduces the manual human efforts needed to collect such data. Data-dependent approaches, such as supervised machine learning algorithms, have become the core component in several automation systems. In this paper, we describe a flower and visitor detection system using deep Convolutional Neural Networks (CNN). Experiments conducted in image sequences collected during field work in Greenland during June-July 2017 indicate that the system is robust to different shading and illumination conditions, inherent in the images collected in the outdoor environments. © EURASIP 2018.

关键词： Deep neural networks

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Time Series Forecasting of New Cases and New Deaths Rate for COVID-19 using Deep Learning Methods

arXiv

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arXiv 2021年

作者： Ayoobi, Nooshin Sharifrazi, Danial Alizadehsani, Roohallah Shoeibi, Afshin Gorriz, Juan M. Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Chofreh, Abdoulmohammad Gholamzadeh Goni, Feybi Ariani Klemeš, Jiří Jaromír Mosavi, Amir Department of Mathematics Savitribai Phule Pune University Pune411007 India Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Mathematics Faculty of Science University of Bojnord Iran Sustainable Process Integration Laboratory - SPIL NETME Centre Faculty of Mechanical Engineering Brno University of Technology - VUT Brno Technická 2896/2 Brno616 69 Czech Republic Department of Management Faculty of Business and Management Brno University of Technology VUT Brno Kolejní 2906/4 Brno612 00 Czech Republic Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences 1430 Norway

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. Several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organizations working against COVID-19 and determining their long-term plans. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Deep CNN for Extraction of Sidelobes from SAR Imagery in Spectrally Restricted Environment

Deep CNN for Extraction of Sidelobes from SAR Imagery in Spe...

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Asilomar Conference on signals, Systems & computers

作者： Lam H. Nguyen Trac D. Tran RF Signal Processing and Modeling Branch CCDC Army Research Laboratory Adelphi MD USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

This paper explores the critical problem of spectral estimation and recovery via deep convolutional neural networks (CNNs). This problem often arises in ultra-wideband (UWB) synthetic aperture radar (SAR) applications where the wide available bandwidth dictates the final SAR image resolution and quality. Our simple robust solution here also complements well the recently developed stepped-frequency radars built by the U.S. Army Research laboratory, allowing the system to bypass prohibited frequency bands specified by local frequency management agencies and congested frequency bands that are deemed to contain too much interference. Extensive experimental results confirm the validity as well as the promising prospect of the proposed approach - our CNN-based generative network can still robustly recover SAR images when up to 90% of the operating spectrum is missing.

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Correction to: A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility

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Neuroinformatics 2022年第1期20卷 37-38页

作者： Mathew Birdsall Abrams Jan G Bjaalie Samir Das Gary F Egan Satrajit S Ghosh Wojtek J Goscinski Jeffrey S Grethe Jeanette Hellgren Kotaleski Eric Tatt Wei Ho David N Kennedy Linda J Lanyon Trygve B Leergaard Helen S Mayberg Luciano Milanesi Roman Mouček J B Poline Prasun K Roy Stephen C Strother Tong Boon Tang Paul Tiesinga Thomas Wachtler Daniel K Wójcik Maryann E Martone INCF Secretariat Karolinska Institutet Stockholm Sweden. mathew@***. Institute of Basic Medical Sciences University of Oslo Oslo Norway. McGill Centre for Integrative Neuroscience McGill University Montreal QC Canada. Monash Biomedical Imaging Monash University Clayton VIC Australia. McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA USA. Department of Otolaryngology - Head and Neck Surgery Harvard Medical School Boston Boston MA USA. Monash eResearch Centre Monash University Melbourne VIC Australia. Department of Neuroscience School of Medicine University of California San Diego La Jolla CA USA. KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden. Centre for Intelligent Signal and Imaging Research Institute of Health and Analytics Universiti Teknologi PETRONAS Perak Malaysia. Department of Psychiatry University of Massachusetts Medical School Worcester MA USA. Serendipitea.World Hasselby Sweden. Nash Family Center for Advanced Circuit Therapeutics Icahn School of Medicine New York NY USA. Institute of Biomedical Technologies National Research Council (CNR) Milan Italy. Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic. Montreal Neurological Institute Faculty of Medicine and Health Sciences McGill University Montreal Canada. Computational Neuroscience & Neuroimaging Laboratory School of Bio-Medical Engineering Indian Institute of Technology (BHU) Varanasi UP India. Rotman Research Institute Baycrest Centre Department of Medical Biophysics University of Toronto Toronto ON Canada. Centre for Intelligent Signal and Imaging Research Institute of Health and Analytics Universiti Teknologi PETRONAS Bandar Seri Iskandar Malaysia. Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Nijmegen Netherlands. Department of Biology II Ludwig-Maximilians-Univers

A Correction to this paper has been published: https://***/10.1007/s12021-021-09522-x

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AIM 2020 Challenge on Image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Block Sparse Bayesian Learning for Bursty Impulsive Noise Detection in Broadband PLC 2

Block Sparse Bayesian Learning for Bursty Impulsive Noise De...

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2nd International Conference on Imaging, signal Processing and Communication, ICISPC 2018

作者： Mariyam, Prita Dewi Liu, Jing Juwono, Filbert H. Gunawan, Dadang Universitas Indonesia Department of Electrical Engineering Depok West Java16424 Indonesia National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China Curtin University Malaysia Department of Electrical and Computer Engineering Miri Sarawak98009 Malaysia

ISBN: (纸本)9781538669822

Broadband power-line communications (PLC) is considered as an enabling technology for the Internet-of-Things (IoT) systems. However, PLC faces some transmission issues as a power line was not designed to transmit communication data. Impulsive noise is one of the major issues in broadband PLC systems. In practice, impulsive noise may occur in bursts. The performance of the PLC systems degrades significantly under bursty impulsive noise environment. We propose to use a compressive sensing algorithm to detect and mitigate the bursty impulsive noise. In this paper, block sparse Bayesian learning (BSBL) algorithms are used to detect and estimate the bursty impulsive noise. The estimated impulsive noise is then subtracted from the contaminated signal. BSBL has relatively better performance than other block compressive sensing algorithms. We examine the performance of two types of BSBL algorithms, i.e. BSBL-bound optimization (BSBL-BO) and BSBL-expectation-maximization (BSBL-EM) under various bursty impulsive noise conditions. The results show that both algorithms have comparable performance in terms of bit error rate (BER). However, BSBL-EM gives better minimum square error (MSE) but much slower in the CPU processing time than BSBL-BO. © 2018 IEEE.

关键词： Impulse noise

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette 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 Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems 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 Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal 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 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 Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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A Parallel Cell Impedance Measurement System for Real-time Cell Growth Detection

A Parallel Cell Impedance Measurement System for Real-time C...

引用

International Conference on Information Systems and computer Aided Education (ICISCAE)

作者： Yibo Han Kin Fong Lei Sio Hang Pun Peng Un Mak Mang I VAI Department of Electrical and Computer Engineering University of Macau Macao SAR China Graduate Institute of Medical Mechatronics Chang Gung University Taoyuan City Taiwan State Key Laboratory of Analog and Mixed-Signal VLSI AMSV Faculty of Science and Technology University of Macau Macao SAR China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau on leave to Department of Chemical Engineering and Biotechnology University of Cambridge United Kingdom

ISBN: (数字)9781728130668

ISBN: (纸本)9781728130675

This paper presents a new automatic cell bio-impedance measurement system for multiple cell condition detections. To make sure this system can work in an easy and efficient way, an effective impedance detection method based on on-board cell number-impedance theory was put forward. The method aimed to measure cell impedance by detecting the changes of interfaces between cells and electrodes. Cell impedance can be detected, recorded and processed by an embedded electronic circuit system. Some carefully designed tests proved that this new system remained high sensitivity, high efficiency and good precision in real-time cell impedance monitoring. It has the potential for further application in other cell sensing fields.

关键词： Impedance Impedance measurement Electrodes Current measurement Voltage measurement Resistors Mathematical model

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Analysis and experimental demonstration of orthant-symmetric four-dimensional 7 bit/4D-sym modulation for optical fiber communication

arXiv

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arXiv 2020年

作者： Chen, Bin Alvarado, Alex van der Heide, Sjoerd van den Hout, Menno Hafermann, Hartmut Okonkwo, Chigo The School of Computer Science and Information Engineering Hefei University of Technology Hefei China Eindhoven University of Technology Eindhoven5600 MB Netherlands The Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands The High Capacity Optical Transmission Laboratory Eindhoven University of Technology Eindhoven5600 MB Netherlands The Optical Communication Technology Lab Paris Research Center Huawei Technologies France SASU Boulogne-Billancourt92100 France

We propose a new four-dimensional orthant-symmetric 128-ary modulation format (4D-OS128) with a spectral efficiency of 7 bit/4D-sym. The proposed format fills the gap between polarization-multiplexed 8- and 16-ary quadrature-amplitude modulation (PM-8QAM and PM-16QAM). Numerical simulations show that 4D-OS128 outperforms two well-studied 4D geometrically-shaped modulation formats: 128SP-16QAM and 7b4D-2A8PSK by up to 0.65 dB for bit-interleaved coded modulation at the same spectral efficiency. These gains are experimentally demonstrated in a 11×233 Gbit/s wavelength division multiplexing (WDM) transmission system operating at 5.95 bit/4D-sym over 6000 km and 9000 km for both EDFA-only and hybrid amplification scenarios, respectively. A reach increase of 15% is achieved with respect to 128-ary set-partitioning 16QAM. Furthermore, the proposed 4D-OS128 is also compared to D4 lattice-based constellation, 16QAM and probabilistically-shaped 16QAM with finite blocklength via simulation. © 2020, CC BY.

关键词： Quadrature amplitude modulation

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