The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot ...The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.展开更多
The rapid advancement of biomedicine in the twenty-first century has been facilitated by the constant innovation in biomedical technology.The most crucial issue in the field of medicine is to use sensor technology to ...The rapid advancement of biomedicine in the twenty-first century has been facilitated by the constant innovation in biomedical technology.The most crucial issue in the field of medicine is to use sensor technology to gather information from primitive organisms,particularly the human body.Design,development,and application of biomedical sensors in the study of clinical diseases’diagnosis and therapy have all been significantly aided by the advancement of medicine.The interest in creating sensors significantly increased in the 1960s.Chemical and biological sensors have been swiftly created in response to an urgent practical necessity,enabling the creation of selective sensors for the direct detection of diverse ions and compounds.The traditional large-size sensors are quickly turning into miniature sensors and are rapidly applied in biological and medical fields.Currently,wearable electronic blood pressure monitors,home blood glucose meters,and quick body surface digital thermometers are commonly used.The advent of a wide variety of medical-grade wearable sensors that will enable real-time biometric data tracking of a large range of physiological characteristics will likely be one of the most revolutionary,exciting,and difficult changes to come to medicine over the next several years.For possible uses in the entertainment,health monitoring,and medical care industries,high-performance flexible strain sensors connected to clothing or human skin are necessary.The use of sensors in the development of biomedical diagnostic tools and medical equipment will enhance human quality of life in the twenty-first century.This article will introduce the current medical sensor field related to sensors for physical quantities,sensors for chemical quantities,sensors for biological quantities such as electronic nose,electronic tongue,and their applications.展开更多
A system of impact damage detection for composite material structures by using an intelligent sensor embedded in composite material is described. In the course of signal processing, wavelet transform has the exception...A system of impact damage detection for composite material structures by using an intelligent sensor embedded in composite material is described. In the course of signal processing, wavelet transform has the exceptional property of temporal frequency localization, whereas Kohonen artificial neural networks have excellent characteristics of self-learning and fault-tolerance. By combining the merits of abstracting time-frequency domain eigenvalues and improving the ratio of signal to noise in this system, impact damage in composite material can be properly recognized.展开更多
Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and mo...Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment,we proposed a more effective and robust target detection framework based on deep learning,which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection.Firstly,the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence,so as to obtain accurate acoustic shadow boxes.Further,the acoustic shadow box is cut down to get the feature map containing the acoustic shadow information,and then the acoustic shadow feature map and the target information feature map are adaptively fused to make full use of the acoustic shadow feature information.In addition,we introduce a threshold processing module to improve the attention of the model to important feature information.Through the underwater sonar dataset provided by Pengcheng Laboratory,the proposed method improved the average accuracy by 3.14%at the IoU threshold of 0.7,which is better than the current traditional target detection model.展开更多
Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable...Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.展开更多
Auditory systems are the most efficient and direct strategy for communication between human beings and robots.In this domain,flexible acoustic sensors with magnetic,electric,mechanical,and optic foundations have attra...Auditory systems are the most efficient and direct strategy for communication between human beings and robots.In this domain,flexible acoustic sensors with magnetic,electric,mechanical,and optic foundations have attracted significant attention as key parts of future voice user interfaces(VUIs)for intuitive human–machine interaction.This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS_(2) flexible vibration sensor(FVS)with high sensitivity for acoustic recognition.The performance of the MXene/MoS_(2) FVS was systematically investigated both theoretically and experimentally,and the MXene/MoS_(2) FVS exhibited high sensitivity(25.8 mV/dB).An MXene/MoS_(2) FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization.This study also investigated a machine learning-based speaker recognition process,for which a machine-learning-based artificial neural network was designed and trained.The developed neural network achieved high speaker recognition accuracy(99.1%).展开更多
基金supported in part by the Hainan Provincial Natural Science Foundation of China (No.620MS067)the Intelligent Medical Project of Chongqing Medical University (ZHYXQNRC202101)the Student Scientific Research and Innovation Experiment Project of the Medical Information College of Chongqing Medical University (No.2020C006).
文摘The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.
文摘The rapid advancement of biomedicine in the twenty-first century has been facilitated by the constant innovation in biomedical technology.The most crucial issue in the field of medicine is to use sensor technology to gather information from primitive organisms,particularly the human body.Design,development,and application of biomedical sensors in the study of clinical diseases’diagnosis and therapy have all been significantly aided by the advancement of medicine.The interest in creating sensors significantly increased in the 1960s.Chemical and biological sensors have been swiftly created in response to an urgent practical necessity,enabling the creation of selective sensors for the direct detection of diverse ions and compounds.The traditional large-size sensors are quickly turning into miniature sensors and are rapidly applied in biological and medical fields.Currently,wearable electronic blood pressure monitors,home blood glucose meters,and quick body surface digital thermometers are commonly used.The advent of a wide variety of medical-grade wearable sensors that will enable real-time biometric data tracking of a large range of physiological characteristics will likely be one of the most revolutionary,exciting,and difficult changes to come to medicine over the next several years.For possible uses in the entertainment,health monitoring,and medical care industries,high-performance flexible strain sensors connected to clothing or human skin are necessary.The use of sensors in the development of biomedical diagnostic tools and medical equipment will enhance human quality of life in the twenty-first century.This article will introduce the current medical sensor field related to sensors for physical quantities,sensors for chemical quantities,sensors for biological quantities such as electronic nose,electronic tongue,and their applications.
基金Funded by Hubei Natural Science Foundation ( No. 2000J161)
文摘A system of impact damage detection for composite material structures by using an intelligent sensor embedded in composite material is described. In the course of signal processing, wavelet transform has the exceptional property of temporal frequency localization, whereas Kohonen artificial neural networks have excellent characteristics of self-learning and fault-tolerance. By combining the merits of abstracting time-frequency domain eigenvalues and improving the ratio of signal to noise in this system, impact damage in composite material can be properly recognized.
基金This work is supported by National Natural Science Foundation of China(Grant:62272109).
文摘Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment,we proposed a more effective and robust target detection framework based on deep learning,which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection.Firstly,the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence,so as to obtain accurate acoustic shadow boxes.Further,the acoustic shadow box is cut down to get the feature map containing the acoustic shadow information,and then the acoustic shadow feature map and the target information feature map are adaptively fused to make full use of the acoustic shadow feature information.In addition,we introduce a threshold processing module to improve the attention of the model to important feature information.Through the underwater sonar dataset provided by Pengcheng Laboratory,the proposed method improved the average accuracy by 3.14%at the IoU threshold of 0.7,which is better than the current traditional target detection model.
基金partially funded by Sao Paulo Research Foundation(FAPESP),Brazil,grant numbers#2015/18808-0,#2018/23064-8,#2019/23382-2.
文摘Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.
基金supported by the National Natural Science Foundation of China(Nos.51972025,61888102,and 62174152)the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(CAST)(No.2018QNRC001)+1 种基金the Strategic Priority Program of the Chinese Academy of Sciences(No.XDA16021100)the Science and Technology Development Plan of Jilin Province(No.20210101168JC).
文摘Auditory systems are the most efficient and direct strategy for communication between human beings and robots.In this domain,flexible acoustic sensors with magnetic,electric,mechanical,and optic foundations have attracted significant attention as key parts of future voice user interfaces(VUIs)for intuitive human–machine interaction.This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS_(2) flexible vibration sensor(FVS)with high sensitivity for acoustic recognition.The performance of the MXene/MoS_(2) FVS was systematically investigated both theoretically and experimentally,and the MXene/MoS_(2) FVS exhibited high sensitivity(25.8 mV/dB).An MXene/MoS_(2) FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization.This study also investigated a machine learning-based speaker recognition process,for which a machine-learning-based artificial neural network was designed and trained.The developed neural network achieved high speaker recognition accuracy(99.1%).
