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arXiv 2021年

作者： Bessadok, Alaa Nebli, Ahmed Mahjoub, Mohamed Ali Li, Gang Lin, Weili Shen, Dinggang Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse 4011 Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Mannouba Tunisia Department of Radiology and BRIC University of North Carolina Chapel HillNC United States School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development United Imaging Intelligence Co. Ltd. Shanghai China

Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of works has focused on predicting baby brain evolution trajectory from a neonatal brain connectome derived from a single modality. Although promising, large training datasets are essential to boost model learning and to generalize to a multi-trajectory prediction from different modalities (i.e., functional and morphological connectomes). Here, we unprecedentedly explore the question: "Can we design a few-shot learning-based framework for predicting brain graph trajectories across different modalities?" To this aim, we propose a Graph Multi-Trajectory Evolution Network (GmTE-Net), which adopts a teacher-student paradigm where the teacher network learns on pure neonatal brain graphs and the student network learns on simulated brain graphs given a set of different timepoints. To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs). To boost the performance of the student network, we introduce a local topology-aware distillation loss that forces the predicted graph topology of the student network to be consistent with the teacher network. Experimental results demonstrate substantial performance gains over benchmark methods. Hence, our GmTE-Net can be leveraged to predict atypical brain connectivity trajectory evolution across various modalities. Our code is available at https://***/basiralab/GmTE-Net. © 2021, CC BY-NC-ND.

关键词： Trajectories

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Quantum Metamaterial for Broadband Detection of Single Microwave Photons

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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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Artificial intelligence performance evaluation for URLLC of industrial IoT applications: A review, open challenges and future directions

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Physical Communication 2025年

作者： Abdinasir Hirsi Lukman Audah Adeb Salh Mohammed Alhartomi Zhili Sun Ahmed Hammoodi Salman Ahmed Advanced Telecommunication Research Center (ATRC) Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Malaysia Faculty of Engineering Jamhuriya University of Science and Technology Mogadishu Somalia Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Faculty of Information and Communication Technology University Tunku Abdul Rahman (UTAR) Kampar 31900 Malaysia Department of Electrical Engineering University of Tabuk Tabuk 71491 Saudi Arabia Institute of Communication Systems (ICS) School of Computer Science and Electronic Engineering Faculty of Physical Science and Engineering University of Surrey UK Renewable Energy Research Centre Department University of Anbar Ramadi Iraq VLSI and Embedded Technology (VEST) Focus Group Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Malaysia

The integration of fifth-generation/sixth-generation (5G/6G) ultra-reliable low-latency communication (URLLC) with industrial Internet of Things (IIoT) applications is revolutionizing Industry 4.0, and enhancing IIoT performance through artificial intelligence (AI) simulations. The critical need for low latency and high reliability in IIoT devices can be effectively addressed by leveraging AI techniques, optimizing data processing, and making decisions in real time. Traditional methods achieve some level of efficiency and safety, but AI offers significant improvements in decision-making, safety, quality prediction, and employee adoption. Integrating AI into IIoT applications enhances industrial workflows, while presenting opportunities and challenges. Machine learning (ML) and deep learning (DL) algorithms enable industrial applications to operate efficiently and intelligently. This paper outlines the requirements for reliable and low-latency communication links between IIoT devices and primary research areas where AI algorithms can be employed, such as fault diagnosis, intelligent anomaly detection, edge computing, network performance, and intrusion detection systems in IIoT applications. Special attention is paid to the role of AI techniques in enhancing IIoT system performance and efficiency, highlighting its advantages, applications, and challenges. The current state-of-the-art challenges and future directions of AI in IIoTs are discussed, providing insights for further research. Potential areas for further research include developing new techniques, integrating 5G/6G technologies, autonomous decision-making, self-optimization, addressing mission-critical applications, and shifting AI processing to the edge. This comprehensive review will benefit academics, researchers, professionals in AI and IIoT, and industries seeking to leverage AI technologies to enhance IIoT performance and efficiency.

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Clifford wavelets for fetal ECG extraction

arXiv

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arXiv 2021年

作者： Jallouli, Malika Arfaoui, Sabrine Mabrouk, Anouar Ben Cattani, Carlo Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Laboratory of Algebra Number Theory and Nonlinear Analysis LR18ES15 Department of Mathematics Faculty of Sciences Monastir5019 Tunisia Department of Mathematics Faculty of Sciences University of Tabuk Saudi Arabia Department of Mathematics Higher Institute of Applied Mathematics and Computer Science University of Kairouan Street of Assad Ibn Al-Fourat Kairouan3100 Tunisia Tuscia University Viterbo Italy

Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being reliable and non-invasive technique. For this aim, we propose in this paper a wavelet/multiwavelet method allowing to extract perfectly the feta ECG parameters from the abdominal mother ECG. The method is essentially due to the exploitation of Clifford wavelets as recent variants in the field. We prove that these wavelets are more efficient and performing against classical ones. The experimental results are therefore due to two basic classes of wavelets and multi-wavelets. A first class is the classical Haar-Schauder, and a second one is due to Clifford valued wavelets and multi-wavelets. These results showed that wavelets/multiwavelets are already good bases for the FECG processing, provided that Clifford ones are the *** Codes 42C40, 92C55 © 2021, CC0.

关键词： Electrocardiography

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Batch-fabrication of diamagnetically levitated microrobots

Batch-fabrication of diamagnetically levitated microrobots

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2018 Solid-State Sensors, Actuators and Microsystems Workshop, Hilton Head 2018

作者： Velez, Camilo Pelrine, Ronald E. Wong-Foy, Annjoe Arnold, David P. Department of Electrical and Computer Engineering University of Florida United States Advanced Technology and Systems Division SRI International Menlo Park United States

ISBN: (纸本)9781940470030

This paper reports the batch fabrication of magnetically patterned bases for millimeter-scale, diamagnetically levitated microrobots. A selective-magnetization process is used to imprint checkerboard magnetic pole patterns in a single NdFeB substrate. Further machining of the substrate produces nine microrobots per batch (limited by the surface area of the magnetic substrate). This process represents a leap forward compared to the current fabrication approach, which relies on hand-assembly and gluing arrays of discrete magnets. The diamagnetic repelling forces of bases with two thickness (400 μm and 280 μm) are directly measured (227 μN and 64 μN), showing that the thicker base produced higher force. Furthermore, the microrobots are capable of assisted levitation (external magnetic field bias) and operation in sliding mode (lateral movement) using microfactory drive platform. © 2016 TRF.

关键词： Fabrication

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Free-electron Brewster radiation

arXiv

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arXiv 2023年

作者： Chen, Ruoxi Chen, Jialin Gong, Zheng Zhang, Xinyan Zhu, Xingjian Yang, Yi Kaminer, Ido Chen, Hongsheng Zhang, Baile Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel School of Physics Zhejiang University Hangzhou310027 China Department of Physics University of Hong Kong 999077 Hong Kong Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

Free-electron radiation offers an enticing route to create light emission at arbitrary spectral regime. However, this type of light emission is generally weak, which is intrinsically limited by the weak particle-matter interaction and unavoidably impedes the development of many promising applications, such as the miniaturization of free-electron radiation sources and high-energy particle detectors. Here we reveal a mechanism to enhance the particle-matter interaction by exploiting the pseudo-Brewster effect of gain materials – presenting an enhancement of at least four orders of magnitude for the light emission. This mechanism is enabled by the emergence of an unprecedented phase diagram that maps all phenomena of free-electron radiation into three distinct phases in a gain-thickness parameter space, namely the conventional, intermediate, and Brewster phases, when an electron penetrates a dielectric slab with a modest gain and a finite thickness. Essentially, our revealed mechanism corresponds to the free-electron radiation in the Brewster phase, which also uniquely features ultrahigh directionality, always at the Brewster angle, regardless of the electron velocity. Counterintuitively, we find that the intensity of this free-electron Brewster radiation is insensitive to the Fabry-Pérot resonance condition and thus the variation of slab thickness, and moreover, a weaker gain could lead to a stronger enhancement for the light emission. The scheme of free-electron Brewster radiation, especially along with its compatibility with low-energy electrons, may enable the development of high-directionality high-intensity light sources at any frequency. © 2023, CC BY.

关键词： Electrons

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Exploring Fault-Tolerant Erasure Codes for Scalable All-Flash Array Clusters

arXiv

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arXiv 2019年

作者： Koh, Sungjoon Zhang, Jie Kwon, Miryeong Yoon, Jungyeon Donofrio, David Kim, Nam Sung Jung, Myoungsoo Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology School of Integrated Technology Yonsei Institute Convergence Technology Yonsei University Lawrence Berkeley National Laboratory Georgia Institute of Technology University of Illinois Urbana-Champaign

—Large-scale systems with all-flash arrays have become increasingly common in many computing segments. To make such systems resilient, we can adopt erasure coding such as Reed-Solomon (RS) code as an alternative to replication because erasure coding incurs a significantly lower storage overhead than replication. To understand the impact of using erasure coding on the system performance and other system aspects such as CPU utilization and network traffic, we build a storage cluster that consists of approximately 100 processor cores with more than 50 high-performance solid-state drives (SSDs), and evaluate the cluster with a popular open-source distributed parallel file system, called Ceph. Specifically, we analyze the behaviors of a system adopting erasure coding from the following five viewpoints, and compare with those of another system using replication: (1) storage system I/O performance;(2) computing and software overheads;(3) I/O amplification;(4) network traffic among storage nodes, and (5) impact of physical data layout on performance of RS-coded SSD arrays. For all these analyses, we examine two representative RS configurations, used by Google file systems, and compare them with triple replication employed by a typical parallel file system as a default fault tolerance mechanism. Lastly, we collect 96 block-level traces from the cluster and release them to the public domain for the use of other researchers. Copyright © 2019, The Authors. All rights reserved.

关键词： File organization

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Recent progress of integrated circuits and optoelectronic chips

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Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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A novel and efficient tumor detection framework for pancreatic cancer via ct images

arXiv

引用

arXiv 2020年

作者： Zhang, Zhengdong Li, Shuai Wang, Ziyang Lu, Yun State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery Affiliated Hospital of Qingdao University Qingdao266003 China

As Deep Convolutional Neural Networks (DCNNs) have shown robust performance and results in medical image analysis, a number of deep-learning-based tumor detection methods were developed in recent years. Nowadays, the automatic detection of pancreatic tumors using contrast-enhanced Computed Tomography (CT) is widely applied for the diagnosis and staging of pancreatic cancer. Traditional hand-crafted methods only extract low-level features. Normal convolutional neural networks, however, fail to make full use of effective context information, which causes inferior detection results. In this paper, a novel and efficient pancreatic tumor detection framework aiming at fully exploiting the context information at multiple scales is designed. More specifically, the contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation (DC) Module. A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly. Then, the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions. Finally, the DC Module is specifically designed to capture the interaction information between proposals and surrounding tissues. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Cooperative bi-path metric for few-shot learning

arXiv

引用

arXiv 2020年

作者： Wang, Zeyuan Zhao, Yifan Li, Jia Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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