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A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang Zhu Wensheng Zhang Xuebing Yang Bob Zhang School of Computer Science and Engineering Central South UniversityChangshaChina Hunan Engineering Research Center of Machine vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of Automation Chinese Academy of SciencesBeijingChina Department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

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

Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

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China Communications 2024年第1期21卷 88-101页

作者： Shanzhi Chen Xinxin He Rui Zhao Jinling Hu Xin Zhang China Information and Communication Technology Group Co. Ltd.(CICT)the State Key Laboratory of Wireless Mobile CommunicationsBeijing 100083China beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing University of Posts and TelecommunicationsBeijing 100876China CICT Connected and Intelligent Technologies Co. LtdChina Academy of Telecommunication TechnologyBeijing 100191China CICT Mobile Communications Technology Co.Ltd.

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and *** millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary *** this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage *** vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

关键词： C-V2X enabling technologies mmWave NR-V2X

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用于重建物理和虚拟抓取的可重构数据手套

Engineering 2024年第1期32卷 202-216页

作者： Hangxin Liu Zeyu Zhang Ziyuan Jiao Zhenliang Zhang Minchen Li Chenfanfu Jiang Yixin Zhu Song-Chun Zhu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence(BIGAI)Beijing 100080China Center for vision CognitionLearning and AutonomyUniversity of CaliforniaLos AngelesCA 90095USA Multi-Physics Lagrangian-Eulerian Simulations Laboratory Department of MathematicsUniversity of CaliforniaLos AngelesCA 90095USA Institute for Artificial Intelligence Peking UniversityBeijing 100871China Department of Automation Tsinghua UniversityBeijing 100084China

In this work,we present a reconfigurable data glove design to capture different modes of human hand-object interactions,which are critical in training embodied artificial intelligence(AI)agents for fine manipulation *** achieve various downstream tasks with distinct features,our reconfigurable data glove operates in three modes sharing a unified backbone design that reconstructs hand gestures in real *** the tactile-sensing mode,the glove system aggregates manipulation force via customized force sensors made from a soft and thin piezoresistive material;this design minimizes interference during complex hand *** virtual reality(VR)mode enables real-time interaction in a physically plausible fashion:A caging-based approach is devised to determine stable grasps by detecting collision *** a state-of-the-art finite element method,the simulation mode collects data on fine-grained four-dimensionalmanipulation events comprising hand and object motions in three-dimensional space and how the object's physical properties(e.g.,stress and energy)change in accordance with manipulation over ***,the glove system presented here is the first to use high-fidelity simulation to investigate the unobservable physical and causal factors behind manipulation *** a series of experiments,we characterize our data glove in terms of individual sensors and the overall *** specifically,we evaluate the system's three modes by①recording hand gestures and associated forces,②improving manipulation fluency in VR,and③producing realistic simulation effects of various tool uses,*** on these three modes,our reconfigurable data glove collects and reconstructs fine-grained human grasp data in both physical and virtual environments,thereby opening up new avenues for the learning of manipulation skills for embodied AI agents.

关键词： Data glove Tactile sensing Virtual reality Physics-based simulation

HWD-YOLO:A New vision-Based Helmet Wearing Detection Method

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Computers, Materials & Continua 2024年第9期80卷 4543-4560页

作者： Licheng Sun Heping Li Liang Wang College of Information Science and Technology Beijing University of TechnologyBeijing100124China Chinese Institute of Coal Science Beijing100013China State Key Laboratory for Intelligent Coal Mining and Strata Control Beijing100013China Engineering Research Center of Digital Community of Ministry of Education Beijing100124China

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine *** existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective ***,a new safety helmet-wearing detection method based on deep learning is ***,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural ***,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction *** block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small ***,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance *** experiments on open dataset validate the proposed *** reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art *** be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version *** generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

关键词： Object detection deep learning safety helmet wearing detection feature extraction attention mechanism

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

Structural Dependence Learning Based on Self-attention for Face Alignment

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Machine Intelligence Research 2024年第3期21卷 514-525页

作者： Biying Li Zhiwei Liu Wei Zhou Haiyun Guo Xin Wen Min Huang Jinqiao Wang Foundation Model Research Center Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100083China Alpha(beijing)Private Equity Beijing100083China School of Computer Science National University of Defense TechnologyChangsha410073China

Self-attention aggregates similar feature information to enhance the features. However, the attention covers nonface areas in face alignment, which may be disturbed in challenging cases, such as occlusions, and fails to predict landmarks. In addition, the learned feature similarity variance is not large enough in the experiment. To this end, we propose structural dependence learning based on self-attention for face alignment (SSFA). It limits the self-attention learning to the facial range and adaptively builds the significant landmark structure dependency. Compared with other state-of-the-art methods, SSFA effectively improves the performance on several standard facial landmark detection benchmarks and adapts more in challenging cases.

关键词： Computer vision face alignment self-attention facial structure contextual information

The influence of corneal ablation patterns on prediction error after cataract surgery in post-myopic-LASIK eyes

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Eye and vision 2024年第4期10卷 23-31页

作者： Yunqian Yao jing Zhao Jifeng Yu Wenwen He Ling Wei Xingtao Zhou Yi Lu Xiangjia Zhu Eye Institute and Department of Ophthalmology Eye&ENT HospitalFudan University83 Fenyang RoadShanghai 200031China National Health Center Key Laboratory of Myopia(Fudan University) Key Laboratory of MyopiaChinese Academy of Medical SciencesShanghaiChina Shanghai Key Laboratory of Visual Impairment and Restoration ShanghaiChina Shanghai Research Center of Ophthalmology and optometry ShanghaiChina Department of Ophthalmology Beijing Children's HospitalNational Center for Children's HealthCapital Medical UniversityBeijingChina State Key Laboratory of Medical Neurobiology Fudan UniversityShanghaiChina

Purpose:To evaluate the influence of corneal ablation patterns on the prediction error after cataract surgery in postmyopic-LASIK ***:Eighty-three post-myopic-LASIK eyes of 83 patients that underwent uneventful cataract surgery were retrospectively *** postoperative spherical equivalence(SE)was calculated for the implanted lens using the Haigis-L and Barrett True-K *** error at one month postsurgery was calculated as actual SE minus predicted *** each eye,area and decentration of the ablation zone was measured using the tangential curvature *** associations between prediction errors and corneal ablation patterns were ***:The mean prediction error was-0.83±1.00 D with the Haigis-L formula and-1.00±0.99 D with the Barrett True-K *** error was positively correlated with keratometry(K)value and negatively correlated with ablation zone area using either formula,and negatively correlated with decentration of the ablation zone using the Barrett True-K formula(all P<0.05).In the K<37.08 D group,prediction error was negatively correlated with decentration of the ablation zone with both formulas(all P<0.05).Multivariate analysis showed that with the Haigis-L formula,prediction error was associated with axial length(AL),K value and decentration,and with the Barrett True-K formula,prediction error was associated with AL and decentration(all P<0.05).Conclusion:A flatter cornea,larger corneal ablation zone and greater decentration will lead to more myopic prediction error after cataract surgery in post-myopic-LASIK eyes.

关键词： Cataract surgery Laser in situ keratomileusis Intraocular lens power Keratometry Ablation zone decentration Ablation zone size

A star-like photodetector for angle-based light sensing in 3D space

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Nano Research 2024年第8期17卷 7567-7573页

作者： Qi Pan Sisi Chen Hongfei Xie Qiling Xu Meng Su Yanlin Song College of Chemistry Zhengzhou UniversityZhengzhou 450051China Key Laboratory of Green Printing CAS Research/Education Center for Excellence in Molecular SciencesInstitute of ChemistryChinese Academy of Sciences(ICCAS)Beijing Engineering Research Center of Nanomaterials for Green Printing TechnologyBeijing National Laboratory for Molecular Sciences(BNLMS)Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China

The development of three-dimensional(3D)space light angle detection is vital in optical technology for applications such as 3D imaging,computer vision,and augmented *** methods involve advanced sensors and algorithms,including time-offlight cameras,which need multiple cameras and light sources to improve ***,it is a great challenge to integrate these complex components into compact *** semiconductor structures offer optical resonance characteristics,enabling precise light–matter interaction regulation.A 3D star-like photodetector,fabricated using a template assistant printing strategy,demonstrates optical resonances of the subwavelength facade and the shielding effect of spatial *** achieves light angle detection with the resolution of 10°in vertical space and the resolution of 36°in horizontal space,making it a promising prototype for various applications.

关键词： photodetector light angle detection printing subwavelength structure shielding effect