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Application of fractional calculus for impact oscillator modeling

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Application of fractional calculus for impact oscillator mod...

2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Rekhviashvili, Sergo Pskhu, Arsen Potapov, A. Feng, Tianhua Narozhnov, Viktor Department of Theoretical and Mathematical Physics Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia JNU-IREE RAS Joint Laboratory of Information Techniques and Fractal Signal Processing Jinan University Guangzhou China Department of Fractional Calculus Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia Institute of Radioengineering and Electronics RAS Moscow Russia Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China Laboratory of Measurement Automation Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia

ISBN: (纸本)9781728161068

A new theoretical model for describing an impact oscillator that is based on the use of a fractional derivative is constructed. The acoustic signals that occur when the probe strikes the surface of metals are experimentally investigated. It is shown that acoustic signals can be used to diagnose elastic properties of materials. © 2019 IEEE.

关键词： Energy dissipation

Precoded OFDM over underwater acoustic channels 13

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Precoded OFDM over underwater acoustic channels

13th ACM International Conference on Underwater Networks and Systems, WUWNet 2018

作者： Tao, Jun An, Liang Yao, Shuai Zhou, Lin Han, Xiao Qin, Zhen Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing210096 China State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing100190 China Acoustic Science and Technology Laboratory Harbin Engineering University Harbin150001 China

ISBN: (纸本)9781450361934

The high peak-to-average power ratio (PAPR) and lack of frequency diversity are two well-known issues with orthogonal frequency-division multiplexing (OFDM) modulations, especially over underwater acoustic (UWA) channels. The precoding (spreading) technique is a viable way to simultaneously address the aforementioned issues, and it has long been researched in the wireless communication field. In the past decade, it has also gained increasing attention in multicarrier UWA communications. However, a comprehensive study on precoded OFDMs over UWA channels is still lacking. In this work, a unified system model accommodating linear precoding schemes is introduced for precoded OFDMs. Based on the model, a linear minimum mean square error (LMMSE) detector is developed for performance evaluation. Several typical precoding schemes are then evaluated and compared, in terms of PAPR and uncoded bit error rate (UBER), over the UWA channels measured in an at-sea experiment. This analysis showed the discrete Fourier transform (DFT) and the inverse DFT (IDFT) precoding schemes achieve the lowest PAPR. In terms of UBER, the DFT, IDFT, and discrete cosine transform (DCT) precodings achieved comparable performance that is significantly better than that of the standard OFDM over highly frequency-selective experimental channels. © 2018 Association for Computing Machinery.

关键词： Peak to average power ratio

Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

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arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) Image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Heralded amplification of single-photon entanglement with polarization feature

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Frontiers of physics 2018年第5期13卷 241-249页

作者： Yu-Yu Jin Sheng-Xian Qin Hao Zu Lan Zhou Wei Zhong Yu-Bo Sheng School of Science Nanjing University of Posts and Telecommunications Nanjing 210003 China New Energy Technology Engineering Laboratory of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing 210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing 210003 China Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing 210003 China

Heralded noiseless amplification is beneficial in overcoming transmission photon loss in a noisy quantum channel. We propose a single-photon-assisted heralded noiseless amplification protocol of the single- photon entanglement （SPE）, where the single-photon qubit has an arbitrary unknown polarization feature. We focus on both the complete and partial photon loss during the transmission process. After the amplification, the parties can recover the pure less-entangled SPE into a maximally entangled SPE and increase its fidelity. Moreover, the polarization feature of the single-photon qubit will be well preserved and not be leaked. Our protocol can be realized under our current experimental condition. Based on the features above, our protocol may be useful in the quantum secure communication schemes that encode information in the polarization degree of freedom of photons.

关键词： quantum communication single-photon entanglement quantum state amplification polarization feature

A message passing approach for multiple maneuvering target tracking

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arXiv 2019年

作者： Lan, Hua Ma, Jirong Wang, Zengfu Pan, Quan Xu, Xiong School of Automation Northwestern Polytechnical University Xi’an Shaanxi710072 China Key Laboratory of Information Fusion Technology Ministry of Education Xi’anShaanxi710072 China School of Electronic Engineering Xidian University Xi’an Shaanxi710071 China National Lab of Radar Signal Processing Xi’anShaanxi710071 China Southwest China Institute of Electronic Technology Chengdu Sichuan610036 China

This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli. Copyright © 2019, The Authors. All rights reserved.

关键词： Belief propagation

NTIRE 2021 Challenge on Image Deblurring

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

Square Kilometre Array science Data Challenge 3a: foreground removal for an EoR experiment

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arXiv 2025年

作者： Bonaldi, A. Hartley, P. Braun, R. Purser, S. Acharya, A. Ahn, K. Aparicio Resco, M. Bait, O. Bianco, M. Chakraborty, A. Chapman, E. Chatterjee, S. Chege, K. Chen, X. Chen, Z. Conaboy, L. Cruz, M. Darriba, L. De Santis, M. Denzel, P. Diao, K. Feron, J. Finlay, C. Gehlot, B. Ghosh, S. Giri, S.K. Grumitt, R. Hong, S.E. Ito, T. Jiang, M. Jordan, C. Kim, S. Kim, M. Kim, J. Krishna, S.P. Kulkarni, A. López-Caniego, M. Labadie-García, I. Lee, H. Lee, D. Lee, N. Line, J. Liu, Y. Mao, Y. Mazumder, A. Mertens, F.G. Munshi, S. Nasirudin, A. Ni, S. Nistane, V. Norregaard, C. Null, D. Offringa, A. Oh, M. Oh, S.-H. Parkinson, D. Pritchard, J. Ruiz-Granda, M. Salvador López, V. Shan, H. Sharma, R. Trott, C. Yoshiura, S. Zhang, L. Zhang, X. Zheng, Q. Zhu, Z. Zuo, S. Akahori, T. Alberto, P. Allys, E. An, T. Anstey, D. Baek, J. Basavraj Brackenhoff, S. Browne, P. Ceccotti, E. Chen, H. Chen, T. Choudhuri, S. Choudhury, M. Coles, J. Cook, J. Cornu, D. Cunnington, S. Das, S. De Lera Acedo, E. Delouis, J.-M. Deng, F. Ding, J. Elahi, K.M.A. Fernandez, P. Fernández, C. Alcázar, A. Fernández Galluzzi, V. Gao, L.-Y. Garain, U. Garrido, J. Gendron-Marsolais, M.-L. Gessey-Jones, T. Ghorbel, H. Gong, Y. Guo, S. Hasegawa, K. Hayashi, T. Herranz, D. Holanda, V. Holloway, A.J. Hothi, I. Höfer, C. Jelić, V. Jiang, Y. Jiang, X. Kang, H. Kim, J.-Y. Koopmans, L.V. Lacroix, R. Lee, E. Leeney, S. Levrier, F. Li, Y. Liu, Y. Ma, Q. Meriot, R. Mesinger, A. Mevius, M. Minoda, T. Miville-Deschenes, M.-A. Moldon, J. Mondal, R. Murmu, C. Murray, S. Nirmala, S.R. Niu, Q. Nunhokee, C. O’Hara, O. Pal, S.K. Pal, S. Park, J. Parra, M. Patra, N.N. Pindor, B. Remazeilles, M. Rey, P. Rubino-Martin, J.A. Saha, S. Selvaraj, A. Semelin, B. Shah, R. Shao, Y. Shaw, A.K. Shi, F. Shimabukuro, H. Singh, G. Sohn, B.W. Stagni, M. Starck, J.-L. Sui, C. Swinbank, J.D. Sánchez, J. SKA Observatory Jodrell Bank Lower Whitington MacclesfieldSK11 9FT United Kingdom Max-Planck Institute for Astrophysics Karl-Schwarzschild-Straße 1 Garching85748 Germany Department of Earth Sciences Chosun University Gwangju61452 Korea Republic of Universidad Europea de Madrid Madrid28670 Spain Observatoire de Sauverny Versoix1290 Switzerland Department of Physics Trottier Space Institute McGill University 3600 rue University MontréalQCH3A 2T8 Canada School of Physics and Astronomy The University of Nottingham University Park NottinghamNG7 2RD United Kingdom Department of Physics and Astronomy University of the Western Cape Cape Town Bellville7535 South Africa Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Astronomy The University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Avda. de los Castros s/n SantanderE-39005 Spain Glorieta de la Astronomía s/n Granada18008 Spain Saint-Imier Switzerland Centre for Artificial Intelligence ZHAW Zurich University of Applied Sciences Technikumstrasse 71 Winterthur8400 Switzerland Department of Astronomy Tsinghua University Beijing100084 China Département de Physique Théorique Center for Astroparticle Physics Université de Geneve 24 quai Ernest Ansermet Geneve 41211 Switzerland Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfv´ens väg 12 StockholmSE-106 91 Sweden Korea Astronomy and Space Science Institute Daejeon34055 Korea Republic of Astronomy Campus University of Science and Technology Daejeon34055 Korea Republic of Kumamoto University International Research Organization for Advanced Science and Technology 2-39-1 Kurokami Chuo-ku Kumamoto860-8555 Japan National Key Laboratory of Radar Signal Processing

We present and analyse the results of the science data challenge 3a (SDC3a, https://***/challenges/ foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is 4.2+−204.2 × 10−4 K2h−3cMpc3 across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically underestimated, sometimes very significantly. The most accurate error bars account for 60 ± 20% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation. © 2025, CC BY.

关键词： Cosmology

Simple and practical method of characterizing the parametric down-conversion source

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Chinese Physics B 2017年第11期26卷 83-88页

作者：王东伍娟赵良圆安雪碧银振强陈巍韩正甫王琴 Key Laboratory of Quantum Information University of Science and Technology of China Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications

Parametric down-conversion（PDC） sources play an important role in quantum information processing, therefore characterizing their properties is necessary. Here we present a statistical model to assess the properties of the PDC source with certain distribution, such as the brightness and photon channel transmissions, we only need to measure the singles and coincidences counts in a few seconds. Furthermore, we validate the model by applying it to a PDC source generating highly non-degenerate photon pairs. The results of the experiment indicate that our method is more simple, efficient, and less time consuming.

关键词： parametric down-conversion brightness photon channel transmission

SpeakerBeam: A new deep learning technology for extracting speech of a target speaker based on the speaker's voice characteristics

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NTT Technical Review 2018年第11期16卷 19-24页

作者： Delcroix, Marc Zmolikova, Katerina Kinoshita, Keisuke Araki, Shoko Ogawa, Atsunori Nakatani, Tomohiro Signal Processing Research Group Media Information Laboratory NTT Communication Science Laboratories Japan Brno University of Technology Czech Republic

In a noisy environment such as a cocktail party, humans can focus on listening to a desired speaker, an ability known as selective hearing. Current approaches developed to realize computational selective hearing require knowing the position of the target speaker, which limits their practical usage. This article introduces SpeakerBeam, a deep learning based approach for computational selective hearing based on the characteristics of the target speaker's voice. SpeakerBeam requires only a small amount of speech data from the target speaker to compute his/her voice characteristics. It can then extract the speech of that speaker regardless of his/her position or the number of speakers talking in the background. © 2018 Nippon Telegraph and Telephone *** right reserved.

关键词： Audition