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Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge

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International Journal of Agricultural and Biological engineering 2024年第2期17卷 200-210页

作者： Yuanqiao Wang Wenbo Gou Chuanyu Wang Jiangchuan Fan Weiliang Wen Xianju Lu Xinyu Guo Chunjiang Zhao College of information engineering Northwest A&F UniversityYangling 712100ShaanxiChina china national engineering research center for information technology in agriculture(NERCITA) Beijing 100097China beijing Key Laboratory of Digital Plant Beijing Research Center for Information Technology in AgricultureBeijing 100097China beijing PAIDE Science and technology Development Co. Ltd.Beijing 100097China

In order to address the challenge of non-destructive detection of tomato fruit ripeness in controlled environments,this study proposed a real-time instance segmentation method based on the edge *** method combined the principles of phenotype robots and machine vision based on deep learning.A compact and remotely controllable phenotype detection robot was employed to acquire precise data on tomato *** video data were then processed by using an efficient backbone and the FeatFlowNet structure for feature extraction and analysis of key-frame to non-key-frame mapping from video *** enhance the diversity of training datasets and the generalization of the model,an innovative approach was chosen by using random enhancement ***,the PolyLoss optimization technique was applied to further improve the accuracy of the ripeness multi-class detection *** validation,the method of this study achieved real-time processing speeds of 90.1 fps(RTX 3070Ti)and 65.5 fps(RTX 2060 S),with an average detection accuracy of 97%compared to manually measured *** is more accurate and efficient than other instance segmentation models according to actual testing in a ***,the results of this research can be deployed in edge devices and provide technical support for unmanned greenhouse monitoring devices or fruit-picking robots in facility environments.

关键词： instance segmentation phenotype robot tomato greenhouse-based plant phenotyping ripeness detection

Advances in neuromorphic computing:Expanding horizons for AI development through novel artificial neurons and in-sensor computing

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Chinese Physics B 2024年第3期33卷 1-23页

作者：杨玉波赵吉哲刘胤洁华夏扬王天睿郑纪元郝智彪熊兵孙长征韩彦军王健李洪涛汪莱罗毅 Department of Electronic engineering Tsinghua UniversityBeijing 100084China beijing national research center for information Science and technology Tsinghua UniversityBeijing 100084China

AI development has brought great success to upgrading the information *** the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing *** the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing *** address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy *** by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or *** it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration *** vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive *** paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.

关键词： neuromorphic computing spiking neural network(SNN) in-sensor computing artificial intelligence

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Super-resolution diffractive neural network for alloptical direction of arrival estimation beyond diffraction limits

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Light(Science & Applications) 2024年第8期13卷 1602-1614页

作者： Sheng Gao Hang Chen Yichen Wang Zhengyang Duan Haiou Zhang Zhi Sun Yuan Shen Xing Lin Department of Electronic engineering Tsinghua UniversityBeijing 100084China beijing national research center for information Science and technology Tsinghua UniversityBeijing 100084China

Wireless sensing of the wave propagation direction from radio sources lays the foundation for communication,radar,navigation,***,the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process,which places the fundamental limit on its sensing speed and energy ***,we propose the super-resolution diffractive neural networks(S-DNN)to process electromagnetic(EM)waves directly for the DOA estimation at the speed of *** multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction *** spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of *** S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in *** results achieve the angular resolution over an order of magnitude,experimentally demonstrated with four times,higher than diffraction-limited *** also apply S-DNN’s edge computing capability,assisted by reconfigurable intelligent surfaces,for extremely low-latency integrated sensing and communication with low power *** work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.

关键词： beyond optical network

Resource Allocation for Channel Estimation in Reconfigurable Intelligent Surface-Aided Multi-Cell Networks

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Journal of Communications and information Networks 2024年第1期9卷 64-79页

作者： Yining Xu Sheng Zhou Department of Electronic engineering Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China

Reconfigurable intelligent surface(RIS)is a promising solution to deal with the blockage-sensitivity of millimeter wave band and reduce the high energy consumption caused by network densification. However, deploying large scale RISs may not bring expected performance gain due to significant channel estimation overhead and non-negligible reflected *** this paper,we derive the analytical expressions of the coverage probability, area spectrum efficiency(ASE)and energy efficiency (EE)of a downlink RIS-aided multi-cell *** order to optimize the network performance, we investigate the conditions for the optimal number of training symbols of each antenna-to-antenna and antenna-to-element path (referred to as the optimal unit training overhead) in channel *** study shows that:1)RIS deployment is not“the more, the better”, only when blockage objects are dense should one deploy more RISs;2) the coverage probability is maximized when the unit training overhead is designed as large as possible;3)however,the ASE-and-EE-optimal unit training overhead exists. It is a monotonically increasing function of the frame length and a monotonically decreasing function of the average signal-to-noise-ratio (in the high signal-to-noise-ratio region). Additionally,the optimal unit training overhead is smaller when communication nodes deploy particularly few or many antennas.

关键词： reconfigurable intelligent surface multi-cell network directional transmissions channel estimation resource allocation stochastic geometry

Simulation and forecasting of fishery weather based on statistical machine learning

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information Processing in agriculture 2024年第1期11卷 127-142页

作者： Xueqian Fu Chunyu Zhang Fuhao Chang Lingling Han Xiaolong Zhao Zhengjie Wang Qiaoyu Ma national Innovation center for Digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina beijing engineering and technology research center for Internet of Things in agriculture BeijingChina College of information and Electrical engineering China Agricultural UniversityBeijingChina

As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery *** fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery *** objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery ***,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather *** also illustrate the main technical means and principles of SML ***,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.

关键词： Fishery weather Statistical machine learning Weather generation Weather forecasting Extreme weather warning

Crop pest image recognition based on the improved ViT method Author links open overlay panel

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information Processing in agriculture 2024年第2期11卷 249-259页

作者： Xueqian Fu Qiaoyu Ma Feifei Yang Chunyu Zhang Xiaolong Zhao Fuhao Chang Lingling Han College of information and Electrical engineering China Agricultural UniversityBeijingChina national Innovation center for Digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina beijing engineering and technology research center for Internet of Things in agriculture BeijingChina

The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable ***,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low *** recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict *** study introduced the improved Vision Transformer(ViT)method for crop pest image *** them,the region with the most obvious features can be effectively selected by block *** self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion *** the experiment,data with 7 classes of examples are used for *** can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.

关键词： Crop diseases recognition Fruit tree leaf disease Vision Transformer Deep learning Computer Vision

Privacy-Preserving Federated Mobility Prediction with Compound Data and Model Perturbation Mechanism

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china Communications 2024年第3期21卷 160-173页

作者： Long Qingyue Wang Huandong Chen Huiming Jin Depeng Zhu Lin Yu Li Li Yong beijing national research center for information Science and technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China china Mobile research Beijing 100032China

Human mobility prediction is important for many ***,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important *** rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the ***,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy *** this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction *** experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.

关键词： federated learning mobility prediction privacy

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Near-field wideband beam training for ELAA with uniform circular array

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Science china(information Sciences) 2024年第6期67卷 370-383页

作者： Yuhao CHEN Linglong DAI Department of Electronic engineering Tsinghua UniversityBeijing National Research Center for Information Science and Technology (BNRist)

Extremely large-scale antenna array(ELAA) at millimeter wave(mmWave) and Terahertz(THz)band has been considered a key technology for combating high attenuation in high-frequency bands in future6G communications. Uniform circular arrays(UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing(FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDFbased beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead.

关键词： extremely large-scale antenna array (ELAA) uniform circular array (UCA) near-field wideband beam training

Mega-Constellations Based TT&C Resource Sharing: Keep Reliable Aeronautical Communication in an Emergency

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china Communications 2024年第2期21卷 1-16页

作者： Haoran Xie Yafeng Zhan Jianhua Lu Department of Electronic engineering Tsinghua UniversityBeijing 100084China beijing national research center for information Science and technology Tsinghua UniversityBeijing 100084China

With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control center(OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking,and Command(TT&C) architecture named Collaborative TT&C(CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium(NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.

关键词： aeronautical emergency communication mega-constellation networked TT&C resource allocation stackelberg game

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