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arXiv 2021年

作者： Wu, Yingying Francisco, Brian Wang, Wei Zhang, Yu Wan, Caihua Han, Xiufen Chi, Hang Hou, Yasen Lodesani, Alessandro Cui, Yong-Tao Wang, Kang L. Moodera, Jagadeesh S. Francis Bitter Magnet Laboratory Plasma Science and Fusion Center Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Key Laboratory of Flexible Electronics & Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing211816 China Institute of Physics Chinese Academy of Sciences Beijing100190 China U.S. Army CCDC Army Research Laboratory AdelphiMD20783 United States Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States Physics Department Massachusetts Institute of Technology CambridgeMA02139 United States

Multiple magnetic skyrmion phases add an additional degree of freedom for skyrmion based ultrahigh-density spin memory devices. Extending the field to two-dimensional van der Waals magnets is a rewarding challenge, where the realizable degree of freedoms (e.g. thickness, twisting angle and electrical gating) and high skyrmion density result in intriguing new properties and enhanced functionality. We report a van der Waals interface, formed by two 2D ferromagnets Cr2Ge2Te6 and Fe3GeTe2 with a Curie temperature of ∼ 65 K and ∼ 205 K, respectively, hosting two groups of magnetic skyrmions. Two sets of topological Hall effect are observed below 60 K when Cr2Ge2Te6 is magnetically ordered. These two groups of skyrmions are directly imaged using magnetic force microscopy. Interestingly, the magnetic skyrmions persist in the heterostructure in the remanent state with zero applied magnetic field. Our results are promising for the realization of skyrmionic devices based on van der Waals heterostructures hosting multiple skyrmion phases. © 2021, CC BY.

关键词： Van der Waals forces

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Solder joint image adaptive block compressive sensing with convex optimisation and Gini index

Solder joint image adaptive block compressive sensing with c...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Zhao, Huihuang Wang, Yaonan Zheng, Jinhua Qiao, Zhijun Zhang, Yun Department of Computer Science and Technology College of Computer Science and Technology Hengyang Normal University Hengyang Hunan China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China Department of Electrical and Engineering College of Electrical and Information Engineering Hunan University Changsha China School of Mathematical and Statistical Sciences University of Texas Rio Grande ValleyTX United States Institute of Radio and TV Technology Communication University of Zhejiang Hangzhou310018 China

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness. © 2019 Institution of engineering and technology. All rights reserved.

关键词： Convex optimization

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Opacity of Nondeterministic Transition Systems: A (Bi)Simulation Relation Approach

Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

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作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

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Observation of Nonlinear Standing Waves Excited by plasma-Series-Resonance-Enhanced Harmonics in Capacitive Discharges

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Physical Review Letters 2019年第18期122卷 185002-185002页

作者： Kai Zhao De-Qi Wen Yong-Xin Liu Michael A. Lieberman Demetre J. Economou You-Nian Wang Key Laboratory of Materials Modification by Laser Ion and Electron Beams (Ministry of Education) School of Physics Dalian University of Technology Dalian 116024 China Plasma Processing Laboratory Department of Chemical and Biomolecular Engineering University of Houston Houston Texas 77204-4004 USA Department of Electrical Engineering University of California Berkeley California 94720 USA

We report the first experimental observation of nonlinear standing waves excited by plasma-series-resonance-enhanced harmonics in low pressure, very high frequency, parallel plate, capacitively coupled plasmas. Spatial structures of the harmonics of the magnetic field, measured by a magnetic probe, are in very good agreement with simulations based on a nonlinear electromagnetics model. At relatively low pressure, the nonlinear sheath motion generates high-order harmonics that can be strongly enhanced near the series resonance frequencies. Satisfying certain conditions, such nonlinear harmonics induce radial standing waves, with voltage and current maxima on axis, resulting in center-high plasma density. Excitation of higher harmonics is suppressed at higher pressures.

关键词： High-frequency & RF discharges in plasmas Magnetic field generation & plasma dynamo plasma discharges in vacuum plasma sources Wave-wave, wave-particle interactions

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Information Transmission Through Solids Using Ultrasound Invited Paper

Information Transmission Through Solids Using Ultrasound Inv...

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2018 IEEE International Ultrasonics Symposium, IUS 2018

作者： Saniie, Jafar Wang, Boyang Huang, Xin Department of Electrical and Computer Engineering Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory ChicagoIL United States

ISBN: (纸本)9781538634257

Ultrasonic signals can be utilized as a viable communication method to transmit information through gas, liquid, and solid channels or a mixed media consisting of a solid interfaced with liquid and/or gas. Underwater communication channels using sound waves cause many challenges, often unforeseen, due to absorption, scattering, refractions, reverberations, multipaths, doppler shifts, temperature, salinity, and acoustic scintillation. Ultrasonic communication through the air is less prone to the environmental challenges that are often encountered in the underwater communications. Ultrasonic communication in solid channels is also adversely affected by absorption, scattering, refractions, reverberations, beam skewing, dispersion, mode conversation, multipaths, and above all these challenges are compounded by the geometrical structure of solids and type of ultrasonic waves. With solid structures many different ultrasonic wave types can be generated including Longitudinal (Compressional), Shear (Transverse), Surface-Rayleigh, Plate-Lamb (Symmetrical or Extensional Mode and Asymmetric or Flexural Mode), Plate-Love, and Stoneley (Leaky Rayleigh). The type of waves is governed by the frequency and angle of the incident waves on a solid structure where the quality of the signal for communication is limited by the composition and geometrical shape of the solids. For this study we have developed a testbed platform using the ZYNQ SoC (System-on-Chip) FPGA by Xilinx which offers reconfigurability and high-performance computational capability, high speed signal converters, power amplifiers for excitation of transducers, low noise receiving amplifiers and high frequency ultrasonic transducers for transmitting and receiving information. Theoretically, ASK (amplitude shift keying) or any form of digital modulation can be realized with the system using the concept of the software-defined ultrasonic communications (SDUC). In practice, the received signal is very complex, primarily ca

关键词： Underwater acoustics

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Automatic flower and visitor detection system 26

Automatic flower and visitor detection system

引用

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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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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Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification

arXiv

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arXiv 2020年

作者： Qu, Ying Baghbaderani, Razieh Kaviani Li, Wei Gao, Lianru Zhang, Yuxiang Qi, Hairong The Advanced Imaging and Collaborative Information Processing Group Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and non-negligible computational resources are required to retrain the whole network. In this paper, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied on the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically-constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state-of-the-art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier

arXiv

引用

arXiv 2019年

作者： Zhang, Zhao Jiang, Weiming Qin, Jie Zhang, Li Li, Fanzhang Zhang, Min Yan, Shuicheng School of Computer Science and Technology Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Computer Vision Laboratory ETH Zürich Zürich8092 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In this paper, we propose an analysis mechanism based structured Analysis Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates the analysis discriminative dictionary learning, analysis representation and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learnt dictionaries, representations and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the sub-dictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm constraint applied in most existing DL criteria makes the training phase time consuming. The codes-extraction projection that bridges data with the sparse codes by extracting special features from the given samples is calculated via minimizing a sparse codes approximation term. Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers. Thus, the classification approach of our model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms. Simulations on real image databases demonstrate that our ADDL model can obtain superior performance over other state-of-the-arts. Copyright © 2019, The Authors. All rights reserved.

关键词： Codes (symbols)

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