Dynamic detection is crucial for intelligent vision systems,enabling applications like autonomous vehicles and advanced surveillance.Event-based sensors,which convert illumination variations into sparse event spikes,a...Dynamic detection is crucial for intelligent vision systems,enabling applications like autonomous vehicles and advanced surveillance.Event-based sensors,which convert illumination variations into sparse event spikes,are highly effective for dynamic detection with low data redundancy.However,current event-based vision sensors with simplified photosensitive capacitor structures face limitations,particularly in their spectral response,which hinders effective information acquisition in multispectral scenes.Here,we introduce a twoterminal thin-film event-based vision sensor that innovatively integrates an inorganic oxide p-n junction with the pyro-phototronic effect,synergistically combining the photovoltaic and pyroelectric mechanisms.This innovation enables spiking signals with a tenfold increase in responsivity,a dynamic range of 110 dB,and an extended spectral response from ultraviolet(UV)to near-infrared(NIR).With a thin-film sensor array,these spiking signals accurately extract fingerprint edge features even under low-light conditions,benefiting from high sensitivity to minor luminance variations.Additionally,the sensors'broadband spiking response captures richer information,achieving 99.25%accuracy in multispectral dynamic gesture recognition while reducing data processing by over 65%.This approach effectively eliminates redundant data while minimizing information loss,offering a promising alternative to current dynamic perception technologies.展开更多
The rise of the Internet and identity authentication systems has brought convenience to people's lives but has also introduced the potential risk of privacy leaks.Existing biometric authentication systems based on...The rise of the Internet and identity authentication systems has brought convenience to people's lives but has also introduced the potential risk of privacy leaks.Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data.This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution.The neuromorphic vision sensor only transmits the local pixel-level changes induced by the eye blinks when they occur,which leads to advantageous characteristics such as an ultra-low latency response.We first propose a set of effective biometric features describing the motion,speed,energy and frequency signal of eye blinks based on the microsecond temporal resolution of event densities.We then train the ensemble model and non-ensemble model with our Neuro Biometric dataset for biometrics authentication.The experiments show that our system is able to identify and verify the subjects with the ensemble model at an accuracy of 0.948 and with the non-ensemble model at an accuracy of 0.925.The low false positive rates(about 0.002)and the highly dynamic features are not only hard to reproduce but also avoid recording visible characteristics of a user's appearance.The proposed system sheds light on a new path towards safer authentication using neuromorphic vision sensors.展开更多
As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from bo...As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.展开更多
Remarkable developments in image recognition technology trigger demands for more advanced imaging devices.In recent years,traditional image sensors,as the go-to imaging devices,have made substantial progress in their ...Remarkable developments in image recognition technology trigger demands for more advanced imaging devices.In recent years,traditional image sensors,as the go-to imaging devices,have made substantial progress in their optoelectronic characteristics and functionality.Moreover,a new breed of imaging device with information processing capability,known as neuromorphic vision sensors,is developed by mimicking biological vision.In this review,we delve into the recent progress of imaging devices,specifically image sensors and neuromorphic vision sensors.This review starts by introducing their core components,namely photodetectors and photonic synapses,while placing a strong emphasis on device structures,working mechanisms and key performance parameters.Then it proceeds to summarize the noteworthy achievements in both image sensors and neuromorphic vision sensors,including advancements in large-scale and highresolution imaging,filter-free multispectral recognition,polarization sensitivity,flexibility,hemispherical designs,and self-power supply of image sensors,as well as in neuromorphic imaging and data processing,environmental adaptation,and ultra-low power consumption of neuromorphic vision sensors.Finally,the challenges and prospects that lie ahead in the ongoing development of imaging devices are addressed.展开更多
Conventional frame-based image sensors suffer greatly from high energy consumption and latency.Mimicking neurobiological structures and functionalities of the retina provides a promising way to build a neuromorphic vi...Conventional frame-based image sensors suffer greatly from high energy consumption and latency.Mimicking neurobiological structures and functionalities of the retina provides a promising way to build a neuromorphic vision sensor with highly efficient image processing.In this review article,we will start with a brief introduction to explain the working mechanism and the challenges of conventional frame-based image sensors,and introduce the structure and functions of biological retina.In the main section,we will overview recent developments in neuromorphic vision sensors,including the silicon retina based on conventional Si CMOS digital technologies,and the neuromorphic vision sensors with the implementation of emerging devices.Finally,we will provide a brief outline of the prospects and outlook for the development of this field.展开更多
The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords:smaller,faster,and smarter.(1)Smaller:Devices are becoming more compact by integrating previously separated component...The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords:smaller,faster,and smarter.(1)Smaller:Devices are becoming more compact by integrating previously separated components such as sensors,memory,and processing units.As a prime example,the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits,such as simpler circuitry,lower power consumption,and less data redundancy.(2)Swifter:Owing to the nature of physics,smaller and more integrated devices can detect,process,and react to input more quickly.In addition,the methods for sensing and processing optical information using various materials(such as oxide semiconductors)are evolving.(3)Smarter:Owing to these two main research directions,we can expect advanced applications such as adaptive vision sensors,collision sensors,and nociceptive sensors.This review mainly focuses on the recent progress,working mechanisms,image pre-processing techniques,and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.展开更多
现有的前向碰撞预警系统大多采用多个毫米波雷达叠加或毫米波雷达与视觉传感器融合等方式,存在成本高、算法受限等问题。在比较多种传感器的性能及应用优、缺点后,选择双目视觉传感器作为前向碰撞预警系统传感器。将改进后的碰撞时间(Ti...现有的前向碰撞预警系统大多采用多个毫米波雷达叠加或毫米波雷达与视觉传感器融合等方式,存在成本高、算法受限等问题。在比较多种传感器的性能及应用优、缺点后,选择双目视觉传感器作为前向碰撞预警系统传感器。将改进后的碰撞时间(Time to Collision,TTC)算法与卡尔曼滤波融合,结合双目视觉传感器,比较TTC值与适应性阈值,评估风险等级,确保行车安全,降低事故率。在Matlab环境下,基于改进算法对两个不同行车场景进行仿真分析。结果表明,与传统的TTC算法相比,融合卡尔曼滤波TTC算法的碰撞时间预警响应及时性和可靠性显著提高。展开更多
基金National Natural Science Foundation of China,Grant/Award Number:62274007National Key Research and Development Program of China,Grant/Award Number:2023YFB3611400Young Elite Scientists Sponsorship Program by CAST,Grant/Award Number:2023QNRC001。
文摘Dynamic detection is crucial for intelligent vision systems,enabling applications like autonomous vehicles and advanced surveillance.Event-based sensors,which convert illumination variations into sparse event spikes,are highly effective for dynamic detection with low data redundancy.However,current event-based vision sensors with simplified photosensitive capacitor structures face limitations,particularly in their spectral response,which hinders effective information acquisition in multispectral scenes.Here,we introduce a twoterminal thin-film event-based vision sensor that innovatively integrates an inorganic oxide p-n junction with the pyro-phototronic effect,synergistically combining the photovoltaic and pyroelectric mechanisms.This innovation enables spiking signals with a tenfold increase in responsivity,a dynamic range of 110 dB,and an extended spectral response from ultraviolet(UV)to near-infrared(NIR).With a thin-film sensor array,these spiking signals accurately extract fingerprint edge features even under low-light conditions,benefiting from high sensitivity to minor luminance variations.Additionally,the sensors'broadband spiking response captures richer information,achieving 99.25%accuracy in multispectral dynamic gesture recognition while reducing data processing by over 65%.This approach effectively eliminates redundant data while minimizing information loss,offering a promising alternative to current dynamic perception technologies.
基金supported by the National Natural Science Foundation of China(61906138)the National Science and Technology Major Project of the Ministry of Science and Technology of China(2018AAA0102900)+2 种基金the Shanghai Automotive Industry Sci-Tech Development Program(1838)the European Union’s Horizon 2020 Research and Innovation Program(785907)the Shanghai AI Innovation Development Program 2018。
文摘The rise of the Internet and identity authentication systems has brought convenience to people's lives but has also introduced the potential risk of privacy leaks.Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data.This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution.The neuromorphic vision sensor only transmits the local pixel-level changes induced by the eye blinks when they occur,which leads to advantageous characteristics such as an ultra-low latency response.We first propose a set of effective biometric features describing the motion,speed,energy and frequency signal of eye blinks based on the microsecond temporal resolution of event densities.We then train the ensemble model and non-ensemble model with our Neuro Biometric dataset for biometrics authentication.The experiments show that our system is able to identify and verify the subjects with the ensemble model at an accuracy of 0.948 and with the non-ensemble model at an accuracy of 0.925.The low false positive rates(about 0.002)and the highly dynamic features are not only hard to reproduce but also avoid recording visible characteristics of a user's appearance.The proposed system sheds light on a new path towards safer authentication using neuromorphic vision sensors.
基金National Natural Science Foundation of China(Grant No.62101138)Shandong Natural Science Foundation(Grant No.ZR2021QD148)+1 种基金Guangdong Natural Science Foundation(Grant No.2022A1515012573)Guangzhou Basic and Applied Basic Research Project(Grant No.202102020701)for providing funds for publishing this paper。
文摘As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.
基金financially supported by the National Natural Science Foundation of China(Nos.52202181,52125205,U20A20166,52192614,52372154,52002246 and U22A2077)the National Key R&D Program of China(Nos.2021YFB3200302 and 2021YFB3200304)+3 种基金the Natural Science Foundation of Beijing Municipality(Nos.2180011 and 2222088)China Postdoctoral Science Foundation(No.2022M712166)Shenzhen Science and Technology Program(No.KQTD20170810105439418)the Fundamental Research Funds for the Central Universities。
文摘Remarkable developments in image recognition technology trigger demands for more advanced imaging devices.In recent years,traditional image sensors,as the go-to imaging devices,have made substantial progress in their optoelectronic characteristics and functionality.Moreover,a new breed of imaging device with information processing capability,known as neuromorphic vision sensors,is developed by mimicking biological vision.In this review,we delve into the recent progress of imaging devices,specifically image sensors and neuromorphic vision sensors.This review starts by introducing their core components,namely photodetectors and photonic synapses,while placing a strong emphasis on device structures,working mechanisms and key performance parameters.Then it proceeds to summarize the noteworthy achievements in both image sensors and neuromorphic vision sensors,including advancements in large-scale and highresolution imaging,filter-free multispectral recognition,polarization sensitivity,flexibility,hemispherical designs,and self-power supply of image sensors,as well as in neuromorphic imaging and data processing,environmental adaptation,and ultra-low power consumption of neuromorphic vision sensors.Finally,the challenges and prospects that lie ahead in the ongoing development of imaging devices are addressed.
基金Research Grant Council of Hong Kong(15205619)the Shenzhen Science and Technology Innovation Commission(JCYJ20180507183424383)National Natural Science Foundation of China(61851402).
文摘Conventional frame-based image sensors suffer greatly from high energy consumption and latency.Mimicking neurobiological structures and functionalities of the retina provides a promising way to build a neuromorphic vision sensor with highly efficient image processing.In this review article,we will start with a brief introduction to explain the working mechanism and the challenges of conventional frame-based image sensors,and introduce the structure and functions of biological retina.In the main section,we will overview recent developments in neuromorphic vision sensors,including the silicon retina based on conventional Si CMOS digital technologies,and the neuromorphic vision sensors with the implementation of emerging devices.Finally,we will provide a brief outline of the prospects and outlook for the development of this field.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2019R1A2C2002447)This research also was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.NRF-2014R1A6A1030419)This work also was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0020967,Advanced Training Program for Smart Sensor Engineers).
文摘The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords:smaller,faster,and smarter.(1)Smaller:Devices are becoming more compact by integrating previously separated components such as sensors,memory,and processing units.As a prime example,the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits,such as simpler circuitry,lower power consumption,and less data redundancy.(2)Swifter:Owing to the nature of physics,smaller and more integrated devices can detect,process,and react to input more quickly.In addition,the methods for sensing and processing optical information using various materials(such as oxide semiconductors)are evolving.(3)Smarter:Owing to these two main research directions,we can expect advanced applications such as adaptive vision sensors,collision sensors,and nociceptive sensors.This review mainly focuses on the recent progress,working mechanisms,image pre-processing techniques,and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.
文摘现有的前向碰撞预警系统大多采用多个毫米波雷达叠加或毫米波雷达与视觉传感器融合等方式,存在成本高、算法受限等问题。在比较多种传感器的性能及应用优、缺点后,选择双目视觉传感器作为前向碰撞预警系统传感器。将改进后的碰撞时间(Time to Collision,TTC)算法与卡尔曼滤波融合,结合双目视觉传感器,比较TTC值与适应性阈值,评估风险等级,确保行车安全,降低事故率。在Matlab环境下,基于改进算法对两个不同行车场景进行仿真分析。结果表明,与传统的TTC算法相比,融合卡尔曼滤波TTC算法的碰撞时间预警响应及时性和可靠性显著提高。
