Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theo...Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.展开更多
Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems,which requires analog updates of their artificial synaptic strengths for the b...Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems,which requires analog updates of their artificial synaptic strengths for the best recognition performance and low energy consumption.Here,we report synaptic devices made from highly insulating ferroelectric LiNbO_(3)(LNO)thin films bonded to SiO_(2)/Si wafers.Through the creation/annihilation of periodically arrayed antiparallel domains within LNO nanocells,which are stimulated using positive/negative voltage pulses(synaptic plasticity),we can modulate the synaptic conductance linearly by controlling the number of the conducting domain walls.The multilevel conductance is nonvolatile and reproducible with negligible dispersion over 100 switching cycles,representing much better performance than that of random defect-based nonlinear memristors,which generally exhibit large-scale resistance dispersion.The simulation of a neuromorphic network using these LNO artificial synapses achieves 95.6%recognition accuracy for faces,thus approaching the theoretical yield of ideal neuromorphic computing devices.展开更多
A low-phase-noise E-A fractional-N frequency synthesizer for GSM/PCS/DCS/WCDMA transceivers is presented. The voltage controlled oscillator is designed with a modified digital controlled capacitor array to extend the ...A low-phase-noise E-A fractional-N frequency synthesizer for GSM/PCS/DCS/WCDMA transceivers is presented. The voltage controlled oscillator is designed with a modified digital controlled capacitor array to extend the tuning range and minimize phase noise. A high-resolution adaptive frequency calibration technique is introduced to automatically choose frequency bands and increase phase-noise immunity. A prototype is implemented in 0.13 #m CMOS technology. The experimental results show that the designed 1.2 V wideband frequency synthesizer is locked from 3.05 to 5.17 GHz within 30 μs, which covers all five required frequency bands. The measured in-band phase noise are -89, -95.5 and -101 dBc/Hz for 3.8 GHz, 2 GHz and 948 MHz carriers, respectively, and accordingly the out-of-band phase noise are -121, -123 and -132 dBc/Hz at 1 MHz offset, which meet the phase-noise-mask requirements of the above-mentioned standards.展开更多
基金This work is supported in part by the Major Fundamental Research of Natural Science Foundation of Shandong Province under Grant ZR2019ZD05Joint Fund for Smart Computing of Shandong Natural Science Foundation under Grant ZR2020LZH013+1 种基金the Scientific Research Platform and Projects of Department of Education of Guangdong Province under Grant 2019GKQNCX121the Intelligent Perception and Computing Innovation Platform of the Shenzhen Institute of Information Technology under Grant PT2019E001.
文摘Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.
基金This work was supported by the National Key R&D Program of China(No.2019YFA0308500)the National Natural Science Foundation of China(No.61904034)We acknowledge the use of the Yale Face Database.We thank David MacDonald,MSc,from Liwen Bianji,Edanz Editing China(www.liwenbianji.cn/ac),for editing the English text of a draft of this manuscript.
文摘Brain-inspired neuromorphic computing can overcome the energy and throughput limitations of traditional von Neumann-type computing systems,which requires analog updates of their artificial synaptic strengths for the best recognition performance and low energy consumption.Here,we report synaptic devices made from highly insulating ferroelectric LiNbO_(3)(LNO)thin films bonded to SiO_(2)/Si wafers.Through the creation/annihilation of periodically arrayed antiparallel domains within LNO nanocells,which are stimulated using positive/negative voltage pulses(synaptic plasticity),we can modulate the synaptic conductance linearly by controlling the number of the conducting domain walls.The multilevel conductance is nonvolatile and reproducible with negligible dispersion over 100 switching cycles,representing much better performance than that of random defect-based nonlinear memristors,which generally exhibit large-scale resistance dispersion.The simulation of a neuromorphic network using these LNO artificial synapses achieves 95.6%recognition accuracy for faces,thus approaching the theoretical yield of ideal neuromorphic computing devices.
基金Project supported by the National High Technology Research and Development Program of China(No.2009AA011605)
文摘A low-phase-noise E-A fractional-N frequency synthesizer for GSM/PCS/DCS/WCDMA transceivers is presented. The voltage controlled oscillator is designed with a modified digital controlled capacitor array to extend the tuning range and minimize phase noise. A high-resolution adaptive frequency calibration technique is introduced to automatically choose frequency bands and increase phase-noise immunity. A prototype is implemented in 0.13 #m CMOS technology. The experimental results show that the designed 1.2 V wideband frequency synthesizer is locked from 3.05 to 5.17 GHz within 30 μs, which covers all five required frequency bands. The measured in-band phase noise are -89, -95.5 and -101 dBc/Hz for 3.8 GHz, 2 GHz and 948 MHz carriers, respectively, and accordingly the out-of-band phase noise are -121, -123 and -132 dBc/Hz at 1 MHz offset, which meet the phase-noise-mask requirements of the above-mentioned standards.
