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Origins of Diamond Surface Noise Probed by Correlating Single-Spin measurements with Surface Spectroscopy

Physical Review X 2019年第3期9卷 031052-031052页

作者： Sorawis Sangtawesin Bo L. Dwyer Srikanth Srinivasan James J. Allred Lila V. H. Rodgers Kristiaan De Greve Alastair Stacey Nikolai Dontschuk Kane M. O’Donnell Di Hu D. Andrew Evans Cherno Jaye Daniel A. Fischer Matthew L. Markham Daniel J. Twitchen Hongkun Park Mikhail D. Lukin Nathalie P. de Leon Department of Electrical Engineering Princeton University Princeton New Jersey 08544 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA Centre for Quantum Computation and Communication Technology School of Physics University of Melbourne Parkville Victoria 3010 Australia Department of Physics Astronomy and Medical Radiation Science Curtin University Bentley Western Australia 6102 Australia Department of Physics Aberystwyth University SY23 3BZ Aberystwyth United Kingdom Material Measurement Laboratory National Institute of Standards and Technology Gaithersburg Maryland 20899 USA Element Six Harwell OX11 0QR United Kingdom Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA

The nitrogen-vacancy (NV) center in diamond exhibits spin-dependent fluorescence and long spin coherence times under ambient conditions, enabling applications in quantum information processing and sensing. NV centers near the surface can have strong interactions with external materials and spins, enabling new forms of nanoscale spectroscopy. However, NV spin coherence degrades within 100 nm of the surface, suggesting that diamond surfaces are plagued with ubiquitous defects. Prior work on characterizing near-surface noise has primarily relied on using NV centers themselves as probes; while this has the advantage of exquisite sensitivity, it provides only indirect information about the origin of the noise. Here we demonstrate that surface spectroscopy methods and single-spin measurements can be used as complementary diagnostics to understand sources of noise. We find that surface morphology is crucial for realizing reproducible chemical termination, and use this insight to achieve a highly ordered, oxygen-terminated surface with suppressed noise. We observe NV centers within 10 nm of the surface with coherence times extended by an order of magnitude.

关键词： NV centers Quantum sensing Surface & interfacial phenomena

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Research On Image Segmentation Based On Support Vector Machine

Research On Image Segmentation Based On Support Vector Machi...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chong Tan Ying Sun Gong-Fa Li Guo-Zhang Jiang Jian-Yi Kong Bo Tao Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Research Center of Biologic Manipulator and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China 3D Printing and Intelligent Manufacturing Engineering Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781538652152

Image segmentation technology is one of the important topics in the field of digital image processing. However, the existing image segmentation technology does not have a uniform standard, and the traditional image segmentation technology is only suitable for some fixed situations. Therefore, the image segmentation technology on new theories and new methods deserves further research and development. The SVM algorithm for image segmentation, a variety of image features can be used to get a better segmentation results. So, this paper based on the theory of support vector machines, introduces the basic idea of SVM in detail, and the current state of image segmentation and the development trend of image segmentation are described in detail. Finally, the necessity of introducing statistical learning into image segmentation and the possibility of introducing SVM into image segmentation are studied and analyzed in depth. The results show that support vector machine can well segment the image target.

关键词： Image segmentation Support vector machines Statistical learning Classification algorithms Training Kernel

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Investigating Correlation of Rough Surface Diffuse Scattering in Frequency Domain

Investigating Correlation of Rough Surface Diffuse Scatterin...

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European Conference on Antennas and Propagation, EuCAP

作者： Yang MIAO Mingming Gan Claude Oestges Katsuyuki Haneda Ke Guan Andrés Alayón Glazunov Wei Wang Yi Gong Dept. of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Universite catholique de Louvain Louvain-la-Neuve BE Institute of Information and Communication Technologies Electronics and Applied Mathematics (ICTM) Universit Catholique de Louvain Louvain-la-Neuve Belgium Dept. of Electronics and Nanoengineering Aalto University Espoo Finland State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Dept. of Electrical Engineering University of Twente Enschede Netherlands School of Information Engineering Chang’an University Xi’an China

This paper investigates numerically the correlation function in the frequency domain due to diffuse scattering originated from rough surfaces. The scattered fields are computed by means of physical optics methods. The irregularities of the rough surface are modeled as a correlated Gaussian process. The correlation of the channel transfer function comprising only diffuse scattering components is analyzed. In the analysis, radio propagation channels comprising orthogonally oriented dipoles at the link ends are considered while assuming different Gaussian roughness profiles. This study offers a useful characterization of the channel behavior in a multipath-rich environment due to diffuse scattering. The presented results are of particular interest to wireless systems based on orthogonal frequency-division multiplexing.

关键词： Rough surfaces Surface roughness Correlation Optical surface waves Scattering Surface impedance Frequency-domain analysis

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Research On Image Compression technology Based On Bp Neural Network

Research On Image Compression Technology Based On Bp Neural ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHENG-CHENG LI GONG-FA LI YING SUN GUO-ZHANG JIANG JIAN-YI KONG SHUANG XU Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Research Center of Biologic Manipulator and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China 3D Printing and Intelligent Manufacturing Engineering Institute Wuhan University of Science and Technology Wuhan China

The traditional image compression technique uses the redundancy of the image data to compress the image without lossless coding by the corresponding coding techniques. Different compression coding techniques are redundant for different image data. Under the background of many new applications, the traditional image compression technology can no longer meet the requirement of further improving the quality of compressed images. In recent years, with the continuous development of artificial neural network technology, Some good properties of neural network, such as nonlinear, fault-tolerant, self-organizing and adaptive, has been widely applied in image processing technology, which greatly simplifies the complexity of image processing. This paper mainly introduces the basic principles of BP neural network, studies the application of neural network in image compression, carries out simulation experiments through MATLAB, and analyzes the feasibility and advantages of BP neural network applied to image compression.

关键词： Image coding Training Biological neural networks Neurons Image reconstruction Transforms

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Pretreatment of sEMG Using Wavelet Threshold Method

Pretreatment of sEMG Using Wavelet Threshold Method

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Du Jiang Gong-Fa Li Ying Sun Guo-Zhang Jiang Jian-Yi Kong Shuang Xu Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Research Center of Biologic Manipulator and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China 3D Printing and Intelligent Manufacturing Engineering Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781538652152

Surface electromyography (sEMG) is derived from human skeletal muscle and can be employed as an accurate signal to reflect the body's muscle state. Because of its non-invasiveness and convenience, it is commonly used in many fields such as pattern recognition-based medical rehabilitation and intelligent humanoid robots. In view of the fact that sEMG signals are weak and easy to be interference by various noise, this paper proposes a method of sEMG preprocessing based on wavelet threshold method to eliminate the noise. At the same time, the RMS value, the waveform length feature, and the nonlinear feature sample entropy are extracted to form the feature vector for subsequent pattern recognition. Finally, sEMG signals of 9 common hand movements were used to establish the gesture recognition model based on SVM. The recognition rate reached over 99%, which verified the effectiveness of the proposed method.

关键词： Noise reduction Wavelet transforms Electromyography Electrodes Signal to noise ratio Thumb Muscles

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Pressure-tunable large anomalous Hall effect of the ferromagnetic kagome-lattice Weyl semimetal Co3Sn2S2

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Physical Review B 2019年第16期100卷 165145-165145页

作者： Xuliang Chen Maoyuan Wang Chuanchuan Gu Shuyang Wang Yonghui Zhou Chao An Ying Zhou Bowen Zhang Chunhua Chen Yifang Yuan Mengyao Qi Lili Zhang Haidong Zhou Jianhui Zhou Yugui Yao Zhaorong Yang Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory Chinese Academy of Sciences Hefei 230031 China Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing 100081 China Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Physical Sciences University of Science and Technology of China Hefei 230026 China Institutes of Physical Science and Information Technology Anhui University Hefei 230601 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201204 China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201204 China Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996-1200 USA Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education) School of Physics and Astronomy Shanghai JiaoTong University Shanghai 200240 China Collaborative Innovation Center of Advanced Microstructures Nanjing 210093 China

We report a systematic high-pressure study of magnetic topological semimetal Co3Sn2S2 through measurements of synchrotron x-ray diffraction (XRD), magnetization, electrical, and Hall transports combined with first-principle theoretical calculations. No evident trace of structural phase transition is detected through synchrotron x-ray diffraction over the measured pressure range of 0.2–50.9 GPa. We find that the ferromagnetism and the anomalous Hall resistivity are monotonically suppressed as increasing pressure and almost vanish around 22 GPa. The anomalous Hall conductivity varies nonmonotonically against pressure at low temperatures, involving competition between original and emergent Weyl nodes. Combined with first-principle calculations, we reveal that the intrinsic mechanism due to the Berry curvature dominates the anomalous Hall effect under high pressure.

关键词： Anomalous Hall effect Pressure effects Transport phenomena Topological materials First-principles calculations X-ray powder diffraction

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Indoor massive multiple-input multiple-output channel characterization and performance evaluation

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Frontiers of Information technology & Electronic engineering 2017年第6期18卷 773-787页

作者： Jian-zhi LI Bo AI Rui-si HE Qi WANG Mi YANG Bei ZHANG Ke GUAN Dan-ping HE Zhang-dui ZHONG Ting ZHOU Nan LI State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China Engineering University of Armed Police Force Telecommunication Engineering DepartmentXi'an 710086China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China ZTE Corporation Beijing 100191China

We present a measurement campaign to characterize an indoor massive multiple-input multiple-output (MIMO) channel system, using a 64-element virtual linear array, a 64-element virtual planar array, and a 128-element virtual planar array. The array topologies are generated using a 3D mechanical turntable. The measurements are conducted at 2, 4, 6, 11, 15, and 22 GHz, with a large bandwidth of 200 MHz. Both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios are considered. The typical channel parameters are extracted, including path loss,shadow fading, power delay profile, and root mean square (RMS) delay spread. The frequency dependence of these channel parameters is analyzed. The correlation between shadow fading and RMS delay spread is discussed. In addition, the performance of the standard linear precoder—the matched filter, which can be used for intersymbol interference (ISI) mitigation by shortening the RMS delay spread, is investigated. Other performance measures,such as entropy capacity, Demmel condition number, and channel ellipticity, are analyzed. The measured channels,which are in a rich-scattering indoor environment, are found to achieve a performance close to that in independent and identically distributed Rayleigh channels even in an LOS scenario.

关键词： Massive MIMO Channel modeling 5G Shadow fading Delay spread Matched filter Entropy capacity Condition number Channel ellipticity

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Dynamic characteristics of the annular rotor components in a rotor-bearing system under high frequency stress conditions

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Journal of Physics: Conference Series 2019年第1期1183卷

作者： Chenglin Wang Huating Chen Weichen Sun H J Wang Beijing Wuzi University China. Beijing Institute of Technology China. School of Mechanical and Electrical Engineering Beijing Information Science and Technology University China. Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology UniversityChina.

The dynamic characteristics of rotor components can directly affect the stability of a rotor-bearing system. Under certain conditions, such as high frequency stress, the dynamic performance of the rotor components is of particular importance. In this paper, a typical annular rotor is studied, and a testing system presented based on the base excitation resonance mass (BERM) method in order to measure key parameters, such as the radial and axial stiffness coefficients, damping coefficient, and loss factor. Based on this system, the dynamic characteristics of typical rotor components under radial and axial sine excitations below 2000 Hz were tested and analyzed. A predictive model of the characteristic parameters was then built based on the grey prediction method, and coupling between the radial and axial dimensions was evaluated. Further, the coupling mechanisms and relationships were analyzed, and the mechanism of interaction between microstructure features under high frequency stress was determined and combined with the changes to the internal microstructure of the rotor components to build a complete picture. Based on the experimental testing and theoretical results, our work suggests a strong correlation between the rotor stiffness coefficient and frequency of use, and thus provides empirical evidence and a reference for the future optimization of the dynamic characteristics of rotor components, in particular for the design of parameters for rotor components the experience high frequency stress conditions on the bearings.

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Conventional Object Detection Techniques in Robotics: A Review

Conventional Object Detection Techniques in Robotics: A Revi...

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International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)

作者： Vipin Vibhute Yogesh Angal B. Suryakanth Department of Instrumentation & Control D.Y. Patil College of Engineering Akurdi Pune Maharashtra India Department of Electronics & Telecommunication JSPM’s Bhivarabai Sawant Institute of Technology and Research Wagholi Pune Maharashtra India Department of Electronics & Communication Bheemanna Khandre Institute of Technology Bhalki Bidar Karnataka India

ISBN: (纸本)9781538677100

Robots have become an integral part in industry as well as domestic use. The future of mankind cannot be seen without robots. Artificial Intelligence is core of any autonomous robot, and it should be capable to recognize an object or human by the vision provided to it. Representations of an object or human are complex functions and various algorithms are being implemented to analyze these complex functions. This review paper is a survey of different conventional techniques used to detect an object in an image. The paper commences with basics of image processing algorithms for object detection and summarizes the findings based on the research and experimental results of different researchers.

关键词： Object detection Service robots Image color analysis Feature extraction Correlation Histograms

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Search for continuous gravitational waves from 20 accreting millisecond x-ray pulsars in O3 LIGO data

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Physical Review D 2022年第2期105卷 022002-022002页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati G. Cagnoli C. Cahillane J. Calderón Bustillo J. D. Callaghan T. A. Callister E. Ca LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière F-69622 Villeurbanne France Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Un

Results are presented of searches for continuous gravitational waves from 20 accreting millisecond x-ray pulsars with accurately measured spin frequencies and orbital parameters, using data from the third observing run of the Advanced LIGO and Advanced Virgo detectors. The search algorithm uses a hidden Markov model, where the transition probabilities allow the frequency to wander according to an unbiased random walk, while the J-statistic maximum-likelihood matched filter tracks the binary orbital phase. Three narrow subbands are searched for each target, centered on harmonics of the measured spin frequency. The search yields 16 candidates, consistent with a false alarm probability of 30% per subband and target searched. These candidates, along with one candidate from an additional target-of-opportunity search done for SAX J1808.4−3658, which was in outburst during one month of the observing run, cannot be confidently associated with a known noise source. Additional follow-up does not provide convincing evidence that any are a true astrophysical signal. When all candidates are assumed nonastrophysical, upper limits are set on the maximum wave strain detectable at 95% confidence, h095%. The strictest constraint is h095%=4.7×10−26 from IGR J17062−6143. Constraints on the detectable wave strain from each target lead to constraints on neutron star ellipticity and r-mode amplitude, the strictest of which are ε95%=3.1×10−7 and α95%=1.8×10−5 respectively. This analysis is the most comprehensive and sensitive search of continuous gravitational waves from accreting millisecond x-ray pulsars to date.

关键词： Gravitational waves Binary stars Neutron stars & pulsars

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