The rapid development of internet of things(loT)urgently needs edge miniaturized computing devices with high efficiency and low-power consumption.In-sensor computing has emerged as a promising technology to enable in-...The rapid development of internet of things(loT)urgently needs edge miniaturized computing devices with high efficiency and low-power consumption.In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array.Here,we report an optoelectronic array for in-sensor computing by integrating photodiodes(PDs)with resistive random-access memories(RRAMs).The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states.Furthermore,a 3×3 PD-RRAM array is fabricated to demonstrate optical image recognition,achieving a universal architecture with ultralow latency and low power consumption.This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-effcient in-sensor computing primitive for future IoT applications.展开更多
With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to rele...With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to release unnecessary data transmission.In edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically.ECDs involved in one task are required to cooperate to process the task data.The privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and tamperresistance.In crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced.As mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality assessment.Therefore,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this paper.DPoR(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in BFEC.Iteratively,the final result is calculated without revealing the privacy of participants.Experiments on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.展开更多
To enable efficient and low-cost automated apple harvesting,this study presented a multi-class instance segmentation model,SCAL(Star-CAA-LADH),which utilizes a single RGB sensor for image acquisition.The model achieve...To enable efficient and low-cost automated apple harvesting,this study presented a multi-class instance segmentation model,SCAL(Star-CAA-LADH),which utilizes a single RGB sensor for image acquisition.The model achieves accurate segmentation of fruits,fruit-bearing branches,and main branches using only a single RGB image,providing comprehensive visual inputs for robotic harvesting.A Star-CAA module was proposed by integrating Star operation with a Context-Anchored Attention mechanism(CAA),enhancing directional sensitivity and multi-scale feature perception.The Backbone and Neck networks were equipped with hierarchically structured SCA-T/F modules to improve the fusion of highand low-level features,resulting in more continuous masks and sharper boundaries.In the Head network,a Segment_LADH module was employed to optimize classification,bounding box regression,and mask generation,thereby improving segmentation accuracy for small and adherent targets.To enhance robustness in adverse weather conditions,a Chain-of-Thought Prompted Adaptive Enhancer(CPA)module was integrated,thereby increasing model resilience in degraded environments.Experimental results demonstrate that SCAL achieves 94.9%AP_M and 95.1%mAP_M,outperforming YOLOv11s by 6.6%and 4.6%,respectively.Under multi-weather testing conditions,the CPA-SCAL variant consistently outperforms other comparison models in accuracy.After INT8 quantization,the model size was reduced to 14.5 MB,with an inference speed of 47.2 frames per second(fps)on the NVIDIA Jetson AGX Xavier.Experiments conducted in simulated orchard environments validate the effectiveness and generalization capabilities of the SCAL model,demonstrating its suitability as an efficient and comprehensive visual solution for intelligent harvesting in complex agricultural settings.展开更多
基金the National Key Research and Development Program(2021YFA0716400)the National Natural Science Foundation of China(62225405,62350002,61991443,62127814,62235005,and 61927811)the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving Electronics。
文摘The rapid development of internet of things(loT)urgently needs edge miniaturized computing devices with high efficiency and low-power consumption.In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array.Here,we report an optoelectronic array for in-sensor computing by integrating photodiodes(PDs)with resistive random-access memories(RRAMs).The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states.Furthermore,a 3×3 PD-RRAM array is fabricated to demonstrate optical image recognition,achieving a universal architecture with ultralow latency and low power consumption.This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-effcient in-sensor computing primitive for future IoT applications.
基金supported by the Key Science and Technology Project of Henan Province(201300210400)National Key Research and Development Project(2018YFB1800304)+1 种基金National Natural Science Foundation of China(61762058),Fundamental Research Funds for the Central Universities(xzy012020112)Natural Science Foundation of Gansu Province(21JR7RA282).
文摘With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to release unnecessary data transmission.In edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically.ECDs involved in one task are required to cooperate to process the task data.The privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and tamperresistance.In crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced.As mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality assessment.Therefore,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this paper.DPoR(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in BFEC.Iteratively,the final result is calculated without revealing the privacy of participants.Experiments on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.
基金supported by the Qinchuangyuan Project of Shaanxi Province(Grant No.2023KXJ-016).
文摘To enable efficient and low-cost automated apple harvesting,this study presented a multi-class instance segmentation model,SCAL(Star-CAA-LADH),which utilizes a single RGB sensor for image acquisition.The model achieves accurate segmentation of fruits,fruit-bearing branches,and main branches using only a single RGB image,providing comprehensive visual inputs for robotic harvesting.A Star-CAA module was proposed by integrating Star operation with a Context-Anchored Attention mechanism(CAA),enhancing directional sensitivity and multi-scale feature perception.The Backbone and Neck networks were equipped with hierarchically structured SCA-T/F modules to improve the fusion of highand low-level features,resulting in more continuous masks and sharper boundaries.In the Head network,a Segment_LADH module was employed to optimize classification,bounding box regression,and mask generation,thereby improving segmentation accuracy for small and adherent targets.To enhance robustness in adverse weather conditions,a Chain-of-Thought Prompted Adaptive Enhancer(CPA)module was integrated,thereby increasing model resilience in degraded environments.Experimental results demonstrate that SCAL achieves 94.9%AP_M and 95.1%mAP_M,outperforming YOLOv11s by 6.6%and 4.6%,respectively.Under multi-weather testing conditions,the CPA-SCAL variant consistently outperforms other comparison models in accuracy.After INT8 quantization,the model size was reduced to 14.5 MB,with an inference speed of 47.2 frames per second(fps)on the NVIDIA Jetson AGX Xavier.Experiments conducted in simulated orchard environments validate the effectiveness and generalization capabilities of the SCAL model,demonstrating its suitability as an efficient and comprehensive visual solution for intelligent harvesting in complex agricultural settings.
