Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings.However,current intelligent monitoring systems always rely on an ext...Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings.However,current intelligent monitoring systems always rely on an external computer with the capability of machine learning rather than integrating it into the sensing device.The computer-assisted intelligent system is hampered by energy inefficiencies,privacy issues,and bandwidth restrictions.Here,a flexible,large-scale sensing array with the capability of low-power in-sensor intelligence based on a compression hypervector encoder is proposed for real-time recognition.The system with in-sensor intelligence can accommodate different individuals and learn new postures without additional computer processing.Both the communication bandwidth requirement and energy consumption of this system are significantly reduced by 1,024 and 500 times,respectively.The capability for in-sensor inference and learning eliminates the necessity to transmit raw data externally,thereby effectively addressing privacy concerns.Furthermore,the system possesses a rapid recognition speed(a few hundred milliseconds)and a high recognition accuracy(about 99%),comparing with support vector machine and other hyperdimensional computing methods.The research holds marked potential for applications in the integration of artificial intelligence of things and flexible electronics.展开更多
The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral...The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases.Traditional methods for microbial detection primarily include the plate culturing,polymerase chain reaction and enzyme-linked immunosorbent assay,which are either time-consuming or laborious.Herein,we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms.Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min.Specifically,a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis(LDA)with the accuracy up to 100%both in the solution and artificial saliva samples.Moreover,the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%.The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oralderived microorganism detection,and provide new ways for disease diagnosis associated with microbial metabolism.展开更多
Flexible sensing array integrated with multiple sensors is an attractive approach for flight parameter detection.However,the poor resolution of flexible sensors and time-consuming neural network processes mitigate the...Flexible sensing array integrated with multiple sensors is an attractive approach for flight parameter detection.However,the poor resolution of flexible sensors and time-consuming neural network processes mitigate their accuracy and adaptability in predicting flight parameters.Here we present an ultra-thin flexible sensing patch with a new configuration,comprising a differential pressure sensor array and a vector flow velocity sensor.The capacitive differential pressure sensor array is fabricated by a multilayer polyimide bonding technique,reaching a resolution of 0.14 Pa.To solve flight parameters with the flexible sensing patch,we develop an analytical pressure-velocity fusion algorithm,enabling fast response and high accuracy in flight parameter detection.The average errors in calculating the angle of attack,angle of sideslip,and airspeed are 0.22°,0.35°,and 0.73 m s^(-1),respectively.The high-resolution flexible sensors and novel analytical pressure-velocity fusion algorithm pave the way for flexible sensing patch-based air data sensing techniques.展开更多
As the basis of machine vision,the biomimetic image sensing devices are the eyes of artificial intelligence.In recent years,with the development of two-dimensional(2D)materials,many new optoelectronic devices are deve...As the basis of machine vision,the biomimetic image sensing devices are the eyes of artificial intelligence.In recent years,with the development of two-dimensional(2D)materials,many new optoelectronic devices are developed for their outstanding performance.However,there are still little sensing arrays based on 2D materials with high imaging quality,due to the poor uniformity of pixels caused by material defects and fabrication technique.Here,we propose a 2D MoS_(2)sensing array based on artificial neural network(ANN)learning.By equipping the MoS_(2)sensing array with a“brain”(ANN),the imaging quality can be effectively improved.In the test,the relative standard deviation(RSD)between pixels decreased from about 34.3%to 6.2%and 5.49%after adjustment by the back propagation(BP)and Elman neural networks,respectively.The peak signal to noise ratio(PSNR)and structural similarity(SSIM)of the image are improved by about 2.5 times,which realizes the re-recognition of the distorted image.This provides a feasible approach for the application of 2D sensing array by integrating ANN to achieve high quality imaging.展开更多
基金supported by the National Key R&D Program of China(grant no.2020YFA0405700)the NationalNatural Science Foundation of China(grant no.51925503 to Y.H.and grant no.52375568 to F.Z.)+1 种基金the Tencent Foundation(XPLORER Prize to Y.H.)the Science and Technology Innovation Team of Hubei Province.
文摘Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings.However,current intelligent monitoring systems always rely on an external computer with the capability of machine learning rather than integrating it into the sensing device.The computer-assisted intelligent system is hampered by energy inefficiencies,privacy issues,and bandwidth restrictions.Here,a flexible,large-scale sensing array with the capability of low-power in-sensor intelligence based on a compression hypervector encoder is proposed for real-time recognition.The system with in-sensor intelligence can accommodate different individuals and learn new postures without additional computer processing.Both the communication bandwidth requirement and energy consumption of this system are significantly reduced by 1,024 and 500 times,respectively.The capability for in-sensor inference and learning eliminates the necessity to transmit raw data externally,thereby effectively addressing privacy concerns.Furthermore,the system possesses a rapid recognition speed(a few hundred milliseconds)and a high recognition accuracy(about 99%),comparing with support vector machine and other hyperdimensional computing methods.The research holds marked potential for applications in the integration of artificial intelligence of things and flexible electronics.
基金financially supported by Quanzhou high-level Talents Project Fund(No.2022C033R)the National Natural Science Foundation of China(Nos.21925401,52221001)+2 种基金the Fundamental Research Funds for the Central Universities(No.2042022rc0004)the Postdoctoral Innovative Research of Hubei Province of China(No.211000025)the interdisciplinary innovative talents foundation from Renmin Hospital of Wuhan University。
文摘The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases.Traditional methods for microbial detection primarily include the plate culturing,polymerase chain reaction and enzyme-linked immunosorbent assay,which are either time-consuming or laborious.Herein,we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms.Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min.Specifically,a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis(LDA)with the accuracy up to 100%both in the solution and artificial saliva samples.Moreover,the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%.The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oralderived microorganism detection,and provide new ways for disease diagnosis associated with microbial metabolism.
基金supported financially by the National Natural Science Foundation of China(T2121003 received by X.D.,and U23A20638 received by Y.J.)the National Key Research and Development Program of China(2023YFB3208000 and 2023YFB3208001 received by Y.J.).
文摘Flexible sensing array integrated with multiple sensors is an attractive approach for flight parameter detection.However,the poor resolution of flexible sensors and time-consuming neural network processes mitigate their accuracy and adaptability in predicting flight parameters.Here we present an ultra-thin flexible sensing patch with a new configuration,comprising a differential pressure sensor array and a vector flow velocity sensor.The capacitive differential pressure sensor array is fabricated by a multilayer polyimide bonding technique,reaching a resolution of 0.14 Pa.To solve flight parameters with the flexible sensing patch,we develop an analytical pressure-velocity fusion algorithm,enabling fast response and high accuracy in flight parameter detection.The average errors in calculating the angle of attack,angle of sideslip,and airspeed are 0.22°,0.35°,and 0.73 m s^(-1),respectively.The high-resolution flexible sensors and novel analytical pressure-velocity fusion algorithm pave the way for flexible sensing patch-based air data sensing techniques.
基金This project was financially supported by the Dalian Science and Technology Innovation Fund of China(No.2019J11CY011)the Science Fund for Creative Research Groups of NSFC(No.51621064).
文摘As the basis of machine vision,the biomimetic image sensing devices are the eyes of artificial intelligence.In recent years,with the development of two-dimensional(2D)materials,many new optoelectronic devices are developed for their outstanding performance.However,there are still little sensing arrays based on 2D materials with high imaging quality,due to the poor uniformity of pixels caused by material defects and fabrication technique.Here,we propose a 2D MoS_(2)sensing array based on artificial neural network(ANN)learning.By equipping the MoS_(2)sensing array with a“brain”(ANN),the imaging quality can be effectively improved.In the test,the relative standard deviation(RSD)between pixels decreased from about 34.3%to 6.2%and 5.49%after adjustment by the back propagation(BP)and Elman neural networks,respectively.The peak signal to noise ratio(PSNR)and structural similarity(SSIM)of the image are improved by about 2.5 times,which realizes the re-recognition of the distorted image.This provides a feasible approach for the application of 2D sensing array by integrating ANN to achieve high quality imaging.
