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Quantum Metamaterial for Broadband Detection of Single Microwave Photons

Physical Review Applied 2021年第3期15卷 034074-034074页

作者： Arne L. Grimsmo Baptiste Royer John Mark Kreikebaum Yufeng Ye Kevin O’Brien Irfan Siddiqi Alexandre Blais Institut quantique and Départment de Physique Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney Australia Department of Physics Yale University New Haven Connecticut 06520 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Quantum Nanoelectronics Laboratory Department of Physics University of California Berkeley California 94720 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Canadian Institute for Advanced Research Toronto Canada

Detecting traveling photons is an essential primitive for many quantum-information processing tasks. We introduce a single-photon detector design operating in the microwave domain, based on a weakly nonlinear metamaterial where the nonlinearity is provided by a large number of Josephson junctions. The combination of weak nonlinearity and large spatial extent circumvents well-known obstacles limiting approaches based on a localized Kerr medium. Using numerical many-body simulations we show that the single-photon detection fidelity increases with the length of the metamaterial to approach one at experimentally realistic lengths. A remarkable feature of the detector is that the metamaterial approach allows for a large detection bandwidth. The detector is nondestructive and the photon population wavepacket is minimally disturbed by the detection. This detector design offers promising possibilities for quantum information processing, quantum optics and metrology in the microwave frequency domain.

关键词： Josephson effect Quantum information architectures & platforms Quantum information with solid state qubits Quantum sensing Metamaterials Optical sources & detectors Superconducting devices Microwave techniques

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Middle East and North African Health Informatics Association (MENAHIA): Technological initiatives for 'One Health'

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Yearbook of Medical Informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health Informatics Association) is the International Medical Informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health Informatics Health Information Technologies International Medical Informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

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Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

arXiv

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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2021年

作者： Wu, Yicheng Ge, Zongyuan Zhang, Donghao Xu, Minfeng Zhang, Lei Xia, Yong Cai, Jianfei Department of Data Science & AI Faculty of Information Technology Monash University MelbourneVIC3800 Australia Monash-Airdoc Research Monash University MelbourneVIC3800 Australia Monash Medical AI Monash eResearch Centre MelbourneVIC3800 Australia DAMO Academy Alibaba Group Hangzhou311121 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g., adhesive edges or thin branches) for medical image segmentation. Leveraging these challenging samples can make the semi-supervised segmentation model training more effective. Therefore, our proposed MC-Net+ model consists of two new designs. First, the model contains one shared encoder and multiple slightly different decoders (i.e., using different up-sampling strategies). The statistical discrepancy of multiple decoders' outputs is computed to denote the model's uncertainty, which indicates the unlabeled hard regions. Second, we apply a novel mutual consistency constraint between one decoder's probability output and other decoders' soft pseudo labels. In this way, we minimize the discrepancy of multiple outputs (i.e., the model uncertainty) during training and force the model to generate invariant results in such challenging regions, aiming at regularizing the model training. We compared the segmentation results of our MC-Net+ model with five state-of-the-art semi-supervised approaches on three public medical datasets. Extension experiments with two standard semi-supervised settings demonstrate the superior performance of our model over other methods, which sets a new state of the art for semi-supervised medical image segmentation. Our code is released publicly at https://***/ycwu1997/MC-Net. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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Hidden Markov model tracking of continuous gravitational waves from a binary neutron star with wandering spin. III. Rotational phase tracking

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Physical Review D 2021年第4期104卷 042003-042003页

作者： A. Melatos P. Clearwater S. Suvorova L. Sun W. Moran R. J. Evans School of Physics University of Melbourne Parkville Victoria 3010 Australia Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav) University of Melbourne Parkville Victoria 3010 Australia Data61 Commonwealth Scientific and Industrial Research Organisation Corner Vimiera & Pembroke Roads Marsfield NSW 2122 Australia Department of Electrical and Electronic Engineering University of Melbourne Parkville Victoria 3010 Australia School of Electrical and Computer Engineering RMIT University Melbourne Victoria 3000 Australia LIGO Laboratory California Institute of Technology Pasadena California 91125 USA OzGrav-ANU Centre for Gravitational Astrophysics College of Science Australian National University Australian Capital Territory 2601 Australia

A hidden Markov model (HMM) solved recursively by the Viterbi algorithm can be configured to search for persistent, quasimonochromatic gravitational radiation from an isolated or accreting neutron star, whose rotational frequency is unknown and wanders stochastically. Here an existing HMM analysis pipeline is generalized to track rotational phase and frequency simultaneously, by modeling the intrastep rotational evolution according to a phase-wrapped Ornstein-Uhlenbeck process, and by calculating the emission probability using a phase-sensitive version of the Bayesian matched filter known as the B-statistic, which is more sensitive than its predecessors. The generalized algorithm tracks signals from isolated and binary sources with characteristic wave strain h0≥1.3×10−26 in Gaussian noise with amplitude spectral density 4×10−24 Hz−1/2, for a simulated observation composed of NT=37 data segments, each Tdrift=10 days long, the typical duration of a search for the low-mass x-ray binary (LMXB) Sco X−1 with the Laser Interferometer Gravitational Wave Observatory (LIGO). It is equally sensitive to isolated and binary sources and ≈1.5 times more sensitive than the previous pipeline, which achieves h0≥2.0×10−26 for a comparable search. Receiver operating characteristic curves (to demonstrate a recipe for setting detection thresholds) and errors in the recovered parameters are presented for a range of practical h0 and NT values. The generalized algorithm successfully detects every available synthetic signal in Stage I of the Sco X−1 Mock Data Challenge convened by the LIGO Scientific Collaboration, recovering the frequency and orbital semimajor axis with accuracies of better than 9.5×10−7 Hz (one part in ∼108) and 1.6×10−3 lt s (one part in ∼103) respectively. The Viterbi solver runs in ≈2×103 CPU-hr for an isolated source and ∼105 CPU-hr for a LMXB source in a typical, broadband (0.5-kHz) search, i.e., ≲10 times slower than the previous pipeline.

关键词： Gravitational wave detection Neutron stars & pulsars

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Dissociation and synthesis of target components during laser ablation of LiNbO3 6

Dissociation and synthesis of target components during laser...

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6th International School and Conference on Optoelectronics, Photonics, engineering and Nanostructures, SPbOPEN 2019

作者： Vakulov, Z. Zamburg, E.G. Klimin, V.S. Miakonkikh, A.V. Clemente, I.E. Rudenko, K.V. Ageev, O.A. Research and Education Centre Nanotechnology Southern Federal University Taganrog347922 Russia Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore Valiev Institute of Physics and Technology RAS Moscow117218 Russia

This paper shows the results of thermodynamic analysis of interfacial interaction in the lithium-niobium-oxygen system. The temperature dependences of the change in the Gibbs free energy calculated, taking into account the non-linear temperature dependences of the thermophysical properties of materials. Additionally, estimated the possibility of dissociation reactions of lithium and niobium oxides, as well as the reactions of lithium and niobium with oxygen. The results obtained can be used in the design and manufacture of integrated acousto-optic and piezoelectric devices, as well as for sensitive elements of sensors using various effects of surface acoustic waves. © Published under licence by IOP Publishing Ltd.

关键词： Oxygen

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Drone-vs-Bird Detection Challenge at IEEE AVSS2021

Drone-vs-Bird Detection Challenge at IEEE AVSS2021

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Angelo Coluccia Alessio Fascista Arne Schumann Lars Sommer Anastasios Dimou Dimitrios Zarpalas Fatih Cagatay Akyon Ogulcan Eryuksel Kamil Anil Ozfuttu Sinan Onur Altinuc Fardad Dadboud Vaibhav Patel Varun Mehta Miodrag Bolic Iraj Mantegh University of Salento via Monteroni Lecce Italy Fraunhofer IOSB Fraunhoferstrasse 1 Karlsruhe Germany Vision and Fusion Lab Karlsruhe Institute of Technology KIT Adenauerring 4 Karlsruhe Germany Centre for Research and Technology Hellas (CERTH) Information Technologies Institute 6th km Harilaou - Thermi Thessaloniki Greece OBSS AI OBSS Technology Universiteler Mah. Ankara Computational Analysis and Acceleration Research Group (CARG) School of Electrical Engineering and Computer Science (SEECS) University of Ottawa 800 King Edward Ottawa On. Canada National Research Council Canada (NRC) Montreal Qc Canada

ISBN: (纸本)9781665433976

This paper presents the 4-th edition of the “drone-vs-bird” detection challenge, launched in conjunction with the the 17-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The objective of the challenge is to tackle the problem of detecting the presence of one or more drones in video scenes where birds may suddenly appear, taking into account some important effects such as the background and foreground motion. The proposed solutions should identify and localize drones in the scene only when they are actually present, without being confused by the presence of birds and the dynamic nature of the captured scenes. The paper illustrates the results of the challenge on the 2021 dataset, which has been further extended compared to the previous edition run in 2020.

关键词： Surveillance Conferences Dynamics Object detection Birds Convolutional neural networks Drones

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Effect of Annealing on the Structural, Optical Properties of Barium Titanate Nanoparticles and its Impact on Second Harmonic Generation

SSRN

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SSRN 2023年

作者： Mayandi, Jeyanthinath Mahalakshmi, Selvaraj Venkatesan, Ragavendran Asokan, Vijay Shankar Alam, Mohammed Mujahid Pearce, Joshua M. Venkatachalapathy, Vishnukanthan Department of Physics R.D. Govt. Arts College Tamilnadu Sivaganga630561 India Department of Material Science School of Chemistry Madurai Kamaraj University Tamilnadu Madurai625 021 India Chalmers University of Technology Sweden King Khalid University Abha61413 Saudi Arabia Department of Chemistry College of Science King Khalid University Abha61413 Saudi Arabia Department of Electrical and Computer Engineering Western University LondonON Canada Department of Physics Centre for Materials Science and Nanotechnology University of Oslo P.O Box 1048 Blindern OsloNO-0316 Norway Department of Materials Science National Research Nuclear University "MEPhI" 31 Kashirskoe sh Moscow Russia

In this report, barium titanate (BaTiO3) nanoparticles were synthesized at 200°C by sol-gel hydrothermal process and annealed at five different temperatures from 100-500°C. The sample synthesized at 200°C exhibited distorted cubic structure with a mishmash of tetragonal and orthorhombic phase which was analyzed from XRD studies. To further empathize its characteristic features, the samples were annealed for various temperatures. The grain size and lattice parameters of the annealed samples were calculated from the XRD spectrum. When the annealing temperature increases a non-uniformity in the grain size and lattice parameters was observed in the BaTiO3 nanoparticles. XRD also reveals that there was phase transition from distorted cubic to tetragonal phase and this phase was stable for all the annealed samples. The morphology of annealed barium titanate nanoparticles was analyzed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) which specifies the presence of spheres and rods. Optical studies were carried out by UV-Vis, Photoluminescence and Raman Spectroscopy. The change in non-linear optical (NLO) activity of BaTiO3 nanoparticles were studied using second harmonic generation (SHG). Samples that possess phase distortion produced SHG as it lacks center of symmetry. Remarkably, the annealing process created an effective atmosphere for the formation of single crystalline tetragonal phase within 300-500°C, which is normally possible only above 800°C. © 2023, The Authors. All rights reserved.

关键词： Raman spectroscopy

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Automatic fish weight estimation and 3D surface reconstruction with a lightweight instance segmentation model

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Expert Systems with Applications 2025年 288卷

作者： Guangxu Wang Jiaxuan Yu Sitao Liu Wenkai Xu Xin Li Yinfeng Hao Daoliang Li National Innovation Center for Digital Fishery China Agricultural University Beijing 100083 China Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock Ministry of Agriculture and Rural Affairs Beijing 100083 China Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture China Agriculture University Beijing 100083 China College of Information and Electrical Engineering China Agricultural University Beijing 100083 China School of Computer and Information Engineering Henan University Kaifeng 475004 Henan China

Automatic methods for fish weight estimation are crucial for biomass assessment, health monitoring, and precision feeding in aquaculture. However, variations in posture and rapid motion of fish present substantial challenges to the accurate estimation of the weight in free-swimming fish. This study presents an innovative surface area-based weight estimation system for free-swimming fish, using a binocular stereo vision system integrated with advanced instance segmentation techniques. Our proposed lightweight instance segmentation model employs an efficient vision transformer as the backbone network, integrated with a cross-scale feature fusion module to enhance feature perception and aggregation, and a segmentation head optimized through an attention mechanism. This approach effectively captures salient features of fish, enabling accurate segmentation and pose recognition. Furthermore, our methodology enables precise three-dimensional (3D) reconstruction of fish surfaces by leveraging stereo matching, depth estimation, 3D point cloud generation, and mesh reconstruction. The experimental results indicate that our model considerably surpasses current approaches, achieving a mean average precision (mAP 50 ) of 94.0 % in segmentation accuracy and exhibiting robust performance in real-world scenarios. Addressing the challenges posed by posture variation and rapid motion in free-swimming fish, our study demonstrates a strong correlation (R = 0.967) between surface area and weight of fish, highlighting the potential of integrating stereo vision and instance segmentation for accurate biomass estimation in aquaculture.

关键词： Stereo vision

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The computer Vision-based Tolerancing Callout Detection Model

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Procedia CIRP 2020年 92卷 134-139页

作者： Xin Deng Tukun Li Yuanping Xu Yanlong Cao Chao Kong Errui Zhang School of Software Engineering Chengdu University of Information Technology Chengdu China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH UK Shandong Industrial Technology Research Institute of Zhejiang University Shandong 277500 China College of Engineering and Computer Science Australia National University Canberra ACT 2600 Australia

Tolerancing symbols play an important role in mechanical product drawings, and they directly determine the functions, mating properties, interchangeability and working life of geometrical products. A symbolic tolerancing callout containing a set of symbols represents a set of pre-ordered operations with attributes. It includes an amount of knowledge from the drawing and standard documents, which is often reconstructed manually by engineers. Thus, at the same time, a symbolic tolerancing callout makes it difficult for the end-user to understand and interpret these callouts manually. To this end, this study puts forward a tolerancing callout detection model via the use of off-the-shelf costumer-grade cameras on current mobile devices for extracting and recognizing tolerancing callout blocks and symbols in them intelligently. This model has four core components: image preprocessing, callout location and extraction, symbol and character segmentation, and deep learning-based symbol recognition. The image preprocessing component is developed to remove the interferences on the target technical drawings through the corresponding morphological methods. This study proposes a novel solution on callout block locations and extractions in callout intensive scenarios since the callout locations and extractions can directly affect the accuracy of symbol and character recognitions. Then, Huff Transform and improved projection methods have been devised to symbol and character segmentations. Finally, this study constructed a convolutional neural network (CNN) to train a symbol recognition model. The experimental results show that the proposed model gains applicability on intelligent callout extractions and the corresponding symbol recognitions.

关键词： Tolerancing symbols Callout location extraction Symbol character segmentation CNN

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