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NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

national science Review 2024年第5期11卷 202-216页

作者： Weijie Zheng Huawei Mu Zhiyi Chen Jiajun Liu Debin Xia Yuxiao Cheng Qi Jing Pak-Ming Lau Jin Tang Guo-Qiang Bi Feng Wu Hao Wang AHU-IAI AI Joint Laboratory Anhui University Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science Center National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of China Division of Life Sciences and Medicine University of Science and Technology of China Interdisciplinary Center for Brain Information Brain Cognition and Brain Disease InstituteShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep learning–based high-efficiency,high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

关键词： whole-brain imagingneuronal activity trace mapping deep learning quantitative analysis

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Tailoring a Back-Contact Barrier for a Self-Powered Broadband Kesterite Photodetector With Ultralow Dark Current Enabling Ultra-Weak-Light Detection

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Carbon Energy 2025年第5期7卷 39-48页

作者： Qianfeng Wu Chuanhao Li Shuo Chen Zhenghua Su Muhammad Abbas Chao Chen Qianqian Lin Jingting Luo Liming Ding Guangxing Liang Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Wuhan National Laboratory for Optoelectronics (WNLO) and School of Optical and Electronic Information (SOEI) Huazhong University of Science and Technology Key Lab of Artificial Micro-and Nano-Structures of Ministry of Education of China Hubei Luojia Laboratory School of Physics and Technology Wuhan University School of Chemical Engineering and Light Industry Guangdong University of Technology

Visible and near-infrared photodetectors are widely used in intelligent driving, health monitoring, and other fields. However, the application of photodetectors in the near-infrared region is significantly impacted by high dark current, which can greatly reduce their performance and sensitivity, thereby limiting their effectiveness in certain applications. In this work, the introduction of a C60back interface layer successfully mitigated back interface reactions to decrease the thickness of the Mo（S,Se）2layer, tailoring the back-contact barrier and preventing reverse charge injection, resulting in a kesterite photodetector with an ultralow dark current density of 5.2 × 10-9m A/cm2and ultra-weak-light detection at levels as low as 25 p W/cm2. Besides, under a self-powered operation, it demonstrates outstanding performance, achieving a peak responsivity of 0.68 A/W, a wide response range spanning from 300 to 1600 nm, and an impressive detectivity of 5.27 × 1014Jones. In addition, it offers exceptionally rapid response times,with rise and decay times of 70 and 650 ns, respectively. This research offers important insights for developing high-performance self-powered near-infrared photodetectors that have high responsivity, rapid response times, and ultralow dark current.

关键词： detectivity kesterite photodetector thin film weak light detection

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Development of a high-resolution near-surface meteorological forcing dataset for the Third Pole region

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science China Earth sciences 2025年第4期68卷 1274-1290页

作者： Yaozhi JIANG Wenjun TANG Kun YANG Jie HE Changkun SHAO Xu ZHOU Hui LU Yingying CHEN Xin LI Jiancheng SHI School of Resources and Environment University of Electronic Science and Technology of ChinaChengdu611731China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing100084China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing100101China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing100084China National Space Science Center Chinese Academy of SciencesBeijing100190China

Recently,the Third Pole(TP)region has experienced rapid environmental *** data are essential for hydrometeorological and ecological applications but still have large uncertainties on the TP owing to the heterogeneous land surface,complex terrain,and sparse weather *** this study,a long-term(1979–2020)high-resolution(1/30°)meteorological forcing dataset for the TP(TPMFD)was developed,as a sister to the widely used China Meteorological Forcing Dataset(CMFD).The TPMFD comprises seven components necessary for driving land surface *** have previously contributed precipitation and downward shortwave radiation data for the TPMFD,and this study presents the development of five other components and focuses on validations for all ***,2-meter air temperature,2-meter specific humidity,10-meter wind speed,and surface air pressure were generated by combining the fifth-generation atmospheric reanalysis for European Center for Medium-Range Weather Forecasts(ERA5),a short-term high-resolution atmospheric simulation,and in situ observations,and the downward longwave radiation was calculated using semi-physical *** cross-validation and independent-validation demonstrated that most variables in the developed dataset outperformed those in widely used reanalysis datasets,including ERA5,ERA5-Land,and the Global Land Data Assimilation system(GLDAS).This dataset is expected to be beneficial for climate analyses and modeling applications of land-surface processes on the TP.

关键词： Meteorological data Third Pole High-resolution Data downscaling and merging

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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In situ calibrated angle between the quantization axis and the propagating direction of the light field for trapping neutral atoms

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Chinese Physics B 2024年第2期33卷 318-323页

作者：郭瑞军何晓东盛诚王坤鹏许鹏刘敏王谨孙晓红曾勇詹明生 Henan Key Laboratory of Laser and Optoelectronic Information National Center for International Joint Research of Electronic Materials and SystemsSchool of Electrical and Information EngineeringZhengzhou UniversityZhengzhou 450001China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430071China

The recently developed magic-intensity trapping technique of neutral atoms efficiently mitigates the detrimental effect of light shifts on atomic qubits and substantially enhances the coherence time. This technique relies on applying a bias magnetic field precisely parallel to the wave vector of a circularly polarized trapping laser field. However, due to the presence of the vector light shift experienced by the trapped atoms, it is challenging to precisely define a parallel magnetic field, especially at a low bias magnetic field strength, for the magic-intensity trapping of85Rb qubits. In this work, we present a method to calibrate the angle between the bias magnetic field and the trapping laser field with the compensating magnetic fields in the other two directions orthogonal to the bias magnetic field direction. Experimentally, with a constantdepth trap and a fixed bias magnetic field, we measure the respective resonant frequencies of the atomic qubits in a linearly polarized trap and a circularly polarized one via the conventional microwave Rabi spectra with different compensating magnetic fields and obtain the corresponding total magnetic fields via the respective resonant frequencies using the Breit–Rabi formula. With known total magnetic fields, the angle is a function of the other two compensating magnetic ***, the projection value of the angle on either of the directions orthogonal to the bias magnetic field direction can be reduced to 0(4)° by applying specific compensating magnetic fields. The measurement error is mainly attributed to the fluctuation of atomic temperature. Moreover, it also demonstrates that, even for a small angle, the effect is strong enough to cause large decoherence of Rabi oscillation in a magic-intensity trap. Although the compensation method demonstrated here is explored for the magic-intensity trapping technique, it can be applied to a variety of similar precision measurements with trapped neutral atoms.

关键词： quantization axis trapping laser angle compensating magnetic fields

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Superior triethylamine-sensing properties based on SnO2 hollow nanospheres synthesized via one-step process

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Progress in Natural science:Materials International 2024年第4期34卷 682-689页

作者： Mingyue Xu Shu Yan Zia Ur Rehman Jianhua Hou Liangyun Zhai Shengjie Zhu Chuanbao Cao Wanfeng Xie Junyu Zhong Jun Yang State Key Laboratory of Bio-fibers and Eco-textiles School of Electronics and Information College of Materials Science and Engineering College of Chemistry and Chemical Engineering Qingdao University College of Environmental Science and Engineering Yangzhou University State Key Laboratory of Chemical Safety SINOPEC Research Institute of Safety Engineering Co. Ltd. National Registration Center for Chemicals Ministry of Emergency Management Research Center of Materials Science Beijing Institute of Technology State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences

Due to serious harm of triethylamine（TEA） to environmental safety and human health, it is significant to synthesize gas-sensitive materials with high performance for TEA detection. However, it is still a challenge to achieve high-sensitivity detection of TEA at low temperature for a sensor synthesized through an economical and efficient method. In this work, hollow-structured SnO2（HS-SnO2） nanospheres have been fabricated by a facile, low-cost hydrothermal method in one step, which exhibit superior TEA-sensing properties, including not only ultrahigh response（127.75） for 100 ppm TEA, good selectivity, but also fast response and recovery time（17/28 s）, low detection threshold（1 ppm） and robust stability at a relatively low optimum operational temperature of 225°*** excellent gas-sensitizing performances are ascribed to porous hollow structures with rich oxygen vacancies that provide abundant active sites for raising O2adsorption and reaction of TEA and oxygen species. This work offers an effective and economical strategy for fabricating high-performance TEA sensors for industrial applications.

关键词： Hollow-structured SnO_2 nanospheres TEA High response Gas sensors

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An approach for formation design and flight performance prediction based on aerodynamic formation unit:Energy-saving considerations

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Chinese Journal of Aeronautics 2024年第3期37卷 77-91页

作者： Nanxuan QIAO Tielin MA Xiangsheng WANG Jie WANG Jingcheng FU Pu XUE School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Institute of Unmanned System Beihang UniversityBeijing 100191China Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System Ministry of Industry and Information TechnologyBeijing 100191China School of Transportation Science and Engineering Beihang UniversityBeijing 102206China Beijing Institute of Space Long March Vehicle Beijing 100076China

The performance improvement of swarm drones through aerodynamic shape optimization may be challenging due to folded size constraints imposed by the specific launch ***,fixed-wing aircraft swarms can benefit from formation flight in terms of energy *** study introduces the concept of the"aerodynamic formation unit",which consists of two or three aircraft that form an inseparable unit of the *** the Unmanned Aerial Vehicle(UAV)distribution and wingtip vortex interference in the formation,two typical aerodynamic formation units are optimized by the variable-fidelity aerodynamic optimization method based on space *** aerodynamic characteristics of the formation UAVs that affect flight performance,such as lift-to-drag ratio(L/D ratio)and static stability are analyzed by Computational Fluid Dynamics(CFD)*** L/D ratio(cruising condition)of the following aircraft can be increased by 22.8%and 57.5%in the optimal units that involve two and three aircraft ***,this study conducts several CFD simulations for multi-aircraft formations formed by the units,which show that the average L/D ratio of the formation can be improved by more than 19%.These results verify the feasibility and effectiveness of the"aerodynamic formation unit"concept and the optimization framework for formation parameters.

关键词： Formation flight CFD Aerodynamic formation unit Wingtip vortices Foldable tandem-wing aircraft

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High-Precision DOA Estimation Method Based on Synthetic Aperture Technique

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Journal of Beijing Institute of technology 2024年第2期33卷 111-118页

作者： Yongjia Dou Guangcai Sun Yuqi Wang Mengdao Xing National Key Laboratory of Radar Signal Processing Xidian UniversityXi’an 710071China School of Communication and Information Engineering Xi’an University of Science and TechnologyXi’an 710054China

The existing direction-of-arrival(DOA)estimation methods only utilize the current received signals,which are susceptible to *** this paper,a method for DOA estimation based on a motion platform is proposed to achieve high-precision DOA estimation by utilizing past and present *** concept of synthetic aperture is introduced to construct a linear DOA estima-tion model.A DOA fine-tuning method based on the linear model is proposed to eliminate the lin-ear DOA variation,achieving a non-coherent accumulation of DOA ***,the baseband modulation and the phase modulation caused by the range history are compensated to achieve the coherent accumulation of all the DOA *** results show that the proposed method can significantly improve the DOA estimated accuracy at low signal-to-noise ratios(SNR).

关键词： synthetic aperture direction-of-arrival(DOA)estimation coherent accumulation

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