Little information is available about the effects of exposure to pulsed microwaves on neuronal Ca^2＋ signaling under non-thermal conditions. In this study, rat pheochromocytoma （PC12） cells were exposed to pulsed m...Little information is available about the effects of exposure to pulsed microwaves on neuronal Ca^2＋ signaling under non-thermal conditions. In this study, rat pheochromocytoma （PC12） cells were exposed to pulsed microwaves for 6 min at a specific absorption rate （SAR） of 4 W/kg to assess possible real-time effects. During microwave exposure, free calcium dynamics in the cytosol, mitochondria, and nucleus of cells were monitored by time-lapse microfluorimetry using a genetically encoded calcium indicator （ratiometric-pericam, ratiometric-10ericam-mt,展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding ...Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding types.To achieve direct communication between the devices with different quantum encoding types,in this paper,we propose encoding conversion schemes between the polarization bases(rectilinear,diagonal and circular bases)and the time-bin phase bases(two phase bases and time-bin basis)and design the quantum encoding converters.The theoretical analysis of the encoding conversion schemes is given in detail,and the basis correspondence of encoding conversion and the property of bit flip are revealed.The conversion relationship between polarization bases and time-bin phase bases can be easily selected by controlling a phase shifter.Since no optical switches are used in our scheme,the converter can be operated with high speed.The converters can also be modularized,which may be utilized to realize miniaturization in the future.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to comm...Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to communication security.This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding.The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K,thus ensuring effective concealment of the message.The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message.Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering,achieving commendable concealment and embedding rates.展开更多
High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal im...High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.展开更多
On the basis of considering the present condition of China and referring the methods of the West, a real time video encoder of digital HDTV based on the MPEG 2 Standard is developed. In this paper, the division and ...On the basis of considering the present condition of China and referring the methods of the West, a real time video encoder of digital HDTV based on the MPEG 2 Standard is developed. In this paper, the division and the realization of every function unit of HDTV are discussed, and the test results of this system are given.展开更多
The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geomet...The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geometries.We investigate the range of validity of the approximation by random walk simulations of restricted diffusion in a cylinder using isotropic diffusion encoding sequences as well as conventional single gradient pulse pairs and oscillating gradient waveforms.The results show that clear deviations from the approximation may be observed at relative signal attenuations below 0.1 for onedimensional sequences with few oscillation periods.Increasing the encoding dimensionality and/or number of oscillations while extending the total duration of the waveform diminishes the non-Gaussian effects while preserving the low apparent diffusivities characteristic of restriction.展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.展开更多
Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilize...Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.展开更多
Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the vary...Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.展开更多
In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing perme...In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the fea...In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.展开更多
Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requi...Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.展开更多
Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the t...Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.展开更多
Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.T...Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.This includes the analysis of BIM and AI technologies and their integration advantages,real-time monitoring and alarm strategies for construction site safety based on BIM and AI integration,as well as the development direction of BIM and AI integration in real-time monitoring and alarm for construction site safety.It is hoped that through this analysis,a scientific reference can be provided for the digital and intelligent management of construction site safety,promoting the digital and intelligent development of its safety management work.展开更多
基金supported by the National Natural Science Foundation of China[No.31400716 and No.61401497]
文摘Little information is available about the effects of exposure to pulsed microwaves on neuronal Ca^2＋ signaling under non-thermal conditions. In this study, rat pheochromocytoma （PC12） cells were exposed to pulsed microwaves for 6 min at a specific absorption rate （SAR） of 4 W/kg to assess possible real-time effects. During microwave exposure, free calcium dynamics in the cytosol, mitochondria, and nucleus of cells were monitored by time-lapse microfluorimetry using a genetically encoded calcium indicator （ratiometric-pericam, ratiometric-10ericam-mt,
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
基金supported by the National Natural Science Foundation of China(Grant No.62001440).
文摘Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding types.To achieve direct communication between the devices with different quantum encoding types,in this paper,we propose encoding conversion schemes between the polarization bases(rectilinear,diagonal and circular bases)and the time-bin phase bases(two phase bases and time-bin basis)and design the quantum encoding converters.The theoretical analysis of the encoding conversion schemes is given in detail,and the basis correspondence of encoding conversion and the property of bit flip are revealed.The conversion relationship between polarization bases and time-bin phase bases can be easily selected by controlling a phase shifter.Since no optical switches are used in our scheme,the converter can be operated with high speed.The converters can also be modularized,which may be utilized to realize miniaturization in the future.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
基金sponsored by the National Natural Science Foundation of China No.U24B201114,6247070859,62302114 and No.62172353Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1331007 and No.1311022Natural Science Foundation of Guangdong Province No.2024A1515010177.
文摘Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to communication security.This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding.The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K,thus ensuring effective concealment of the message.The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message.Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering,achieving commendable concealment and embedding rates.
基金supported by the Fundamental Research Funds for the Central Universities(No.2024JBZX027)the National Natural Science Foundation of China(No.52375078).
文摘High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.
文摘On the basis of considering the present condition of China and referring the methods of the West, a real time video encoder of digital HDTV based on the MPEG 2 Standard is developed. In this paper, the division and the realization of every function unit of HDTV are discussed, and the test results of this system are given.
基金financially supported by the Swedish Research Council(2022-04422_VR)。
文摘The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geometries.We investigate the range of validity of the approximation by random walk simulations of restricted diffusion in a cylinder using isotropic diffusion encoding sequences as well as conventional single gradient pulse pairs and oscillating gradient waveforms.The results show that clear deviations from the approximation may be observed at relative signal attenuations below 0.1 for onedimensional sequences with few oscillation periods.Increasing the encoding dimensionality and/or number of oscillations while extending the total duration of the waveform diminishes the non-Gaussian effects while preserving the low apparent diffusivities characteristic of restriction.
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
基金supported by the National Natural Science Foundation of China(No.22306076)the Natural Science Foundation of Jiangsu Province(No.BK20230676)the Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.22KJB610011).
文摘Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.
基金supported by the Collaborative Tackling Project of the Yangtze River Delta SciTech Innovation Community(Nos.2024CSJGG01503,2024CSJGG01500)Guangxi Key Research and Development Program(No.AB24010317)Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics(Jiangxi Police College)(No.2025JXJYKFJJ002).
文摘Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks.
基金supported by the National Natural Science Foundation of China (Grant No.52122405)Science and Technology Major Project of Shanxi Province,China (Grant No.202101060301024)Science and Technology Major Project of Xizang Autonomous Region,China (Grant No.XZ202201ZD0004G0204).
文摘In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
基金the financial support of the Natural Science Foundation of Hubei Province,China (Grant No.2022CFB770)。
文摘In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.
基金the research result of the 2024 Guangxi Higher Education Undergraduate Teaching Reform Project“OBE-Guided,Digitally Empowered‘Hadoop Big Data Development Technology’Course Ideological and Political Construction Innovation Exploration and Practice”(Project No.:2024JGA396).
文摘Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.
基金supported by the National Key Research and Development Project(Grant No.2023YFB4705300)the National Natural Science Foundation of China(NSFC)(Grant Nos.62205200 and 62375168)the Natural Science Foundation of Shanghai(Grant No.22ZR1431600)。
文摘Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.
基金“Research on AI-Intelligent Management Technology for Construction Safety Based on BIM Technology and Smart Construction Site Scenarios”(Project No.:KJQN202401904)“Research on Intelligent Monitoring System for Construction Quality and Safety Based on BIM and AI Technologies”(Project No.:202412608006)。
文摘Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.This includes the analysis of BIM and AI technologies and their integration advantages,real-time monitoring and alarm strategies for construction site safety based on BIM and AI integration,as well as the development direction of BIM and AI integration in real-time monitoring and alarm for construction site safety.It is hoped that through this analysis,a scientific reference can be provided for the digital and intelligent management of construction site safety,promoting the digital and intelligent development of its safety management work.
