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
The progressive failure characteristics of geomaterial are a remarkable and challenging topic in geotechnical engineering.To study the effect of salt content and temperature on the progressive failure characteristics ...The progressive failure characteristics of geomaterial are a remarkable and challenging topic in geotechnical engineering.To study the effect of salt content and temperature on the progressive failure characteristics of frozen sodium sulfate saline sandy soil,a series of uniaxial compression tests were performed by integrating digital image correlation(DIC)technology into the testing apparatus.The evolution law of the uniaxial compression strength(UCS),the failure strain,and the formation of the shear band of the frozen sodium sulfate saline sandy soil were analyzed.The test results show that within the scope of this study,with the increase of salt content,both the UCS and the shear band angle initially decrease with increasing salt content before showing an increase.In contrast,the failure strain and the width of the shear band exhibit an initial increase followed by a decrease in the samples.In addition,to investigate the brittle failure characteristics of frozen sodium sulfate saline sandy soil,two classic brittleness evaluation methods were employed to quantitatively assess the brittleness level for the soil samples.The findings suggest that the failure characteristics under all test conditions in this study belong to the transition stage between brittle and ductile,indicating that frozen sodium sulfate saline sandy soil exhibits certain brittle behavior under uniaxial compression conditions,and the brittleness index basically decreases and then increases with the rise in salt content.展开更多
Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
The effect of deformation resistance of AlCr_(1.3)TiNi_(2) eutectic high-entropy alloys under various current densities and strain rates was investigated during electrically-assisted compression.Results show that at c...The effect of deformation resistance of AlCr_(1.3)TiNi_(2) eutectic high-entropy alloys under various current densities and strain rates was investigated during electrically-assisted compression.Results show that at current density of 60 A/mm^(2) and strain rate of 0.1 s^(−1),the ultimate tensile stress shows a significant decrease from approximately 3000 MPa to 1900 MPa with reduction ratio of about 36.7%.However,as current density increases,elongation decreases due to intermediate temperature embrittlement.This is because the current induces Joule effect,which then leads to stress concentration and more defect formation.Moreover,the flow stress is decreased with the increase in strain rate at constant current density.展开更多
The mechanical behavior of cemented gangue backfill materials(CGBMs)is closely related to particle size distribution(PSD)of aggregates and properties of cementitious materials.Consequently,the true triaxial compressio...The mechanical behavior of cemented gangue backfill materials(CGBMs)is closely related to particle size distribution(PSD)of aggregates and properties of cementitious materials.Consequently,the true triaxial compression tests,CT scanning,SEM,and EDS tests were conducted on cemented gangue backfill samples(CGBSs)with various carbon nanotube concentrations(P_(CNT))that satisfied fractal theory for the PSD of aggregates.The mechanical properties,energy dissipations,and failure mechanisms of the CGBSs under true triaxial compression were systematically analyzed.The results indicate that appropriate carbon nanotubes(CNTs)effectively enhance the mechanical properties and energy dissipations of CGBSs through micropore filling and microcrack bridging,and the optimal effect appears at P_(CNT)of 0.08wt%.Taking PSD fractal dimension(D)of 2.500 as an example,compared to that of CGBS without CNT,the peak strength(σ_(p)),axial peak strain(ε_(1,p)),elastic strain energy(Ue),and dissipated energy(U_(d))increased by 12.76%,29.60%,19.05%,and90.39%,respectively.However,excessive CNTs can reduce the mechanical properties of CGBSs due to CNT agglomeration,manifesting a decrease inρ_(p),ε_(1,p),and the volumetric strain increment(Δε_(v))when P_(CNT)increases from 0.08wt%to 0.12wt%.Moreover,the addition of CNTs improved the integrity of CGBS after macroscopic failure,and crack extension in CGBSs appeared in two modes:detour and pass through the aggregates.Theσ_(p)and U_(d)firstly increase and then decrease with increasing D,and porosity shows the opposite trend.Theε_(1,p)andΔε_(v)are negatively correlated with D,and CGBS with D=2.150 has the maximum deformation parameters(ε_(1,p)=0.05079,Δε_(v)=0.01990)due to the frictional slip effect caused by coarse aggregates.With increasing D,the failure modes of CGBSs are sequentially manifested as oblique shear failure,"Y-shaped"shear failure,and conjugate shear failure.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
AZ31/Al/Ta composites were prepared using the vacuum hot compression bonding(VHCB)method.The effect of hot compressing temperature on the interface microstructure evolution,phase constitution,and shear strength at the...AZ31/Al/Ta composites were prepared using the vacuum hot compression bonding(VHCB)method.The effect of hot compressing temperature on the interface microstructure evolution,phase constitution,and shear strength at the interface was investigated.Moreover,the interface bonding mechanisms of the AZ31/Al/Ta composites during the VHCB process were explored.The results demonstrate that as the VHCB temperature increases,the phase composition of the interface between Mg and Al changes from the Mg-Al brittle intermetallic compounds(Al_(12)Mg_(17)and Al_(3)Mg_(2))to the Al-Mg solid solution.Meanwhile,the width of the Al/Ta interface diffusion layer at 450℃increases compared to that at 400℃.The shear strengths are 24 and 46 MPa at 400 and 450℃,respectively.The interfacial bonding mechanism of AZ31/Al/Ta composites involves the coexistence of diffusion and mechanical meshing.Avoiding the formation of brittle phases at the interface can significantly improve interfacial bonding strength.展开更多
Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive da...Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.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
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
With the increase in the quantity and scale of Static Random-Access Memory Field Programmable Gate Arrays (SRAM-based FPGAs) for aerospace application, the volume of FPGA configuration bit files that must be stored ha...With the increase in the quantity and scale of Static Random-Access Memory Field Programmable Gate Arrays (SRAM-based FPGAs) for aerospace application, the volume of FPGA configuration bit files that must be stored has increased dramatically. The use of compression techniques for these bitstream files is emerging as a key strategy to alleviate the burden on storage resources. Due to the severe resource constraints of space-based electronics and the unique application environment, the simplicity, efficiency and robustness of the decompression circuitry is also a key design consideration. Through comparative analysis current bitstream file compression technologies, this research suggests that the Lempel Ziv Oberhumer (LZO) compression algorithm is more suitable for satellite applications. This paper also delves into the compression process and format of the LZO compression algorithm, as well as the inherent characteristics of configuration bitstream files. We propose an improved algorithm based on LZO for bitstream file compression, which optimises the compression process by refining the format and reducing the offset. Furthermore, a low-cost, robust decompression hardware architecture is proposed based on this method. Experimental results show that the compression speed of the improved LZO algorithm is increased by 3%, the decompression hardware cost is reduced by approximately 60%, and the compression ratio is slightly reduced by 0.47%.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
An additional hot compression process was applied to a dilute Mg−Mn−Zn alloy post-extrusion.The alloy was extruded at 150℃ with an extrusion ratio of 15:1 and subsequently hot-compressed at 180℃ with a true strain o...An additional hot compression process was applied to a dilute Mg−Mn−Zn alloy post-extrusion.The alloy was extruded at 150℃ with an extrusion ratio of 15:1 and subsequently hot-compressed at 180℃ with a true strain of 0.9 along the extrusion direction.The microstructure,mechanical properties and thermal conductivity of as-extruded and as-hot compressed Mg−Mn−Zn alloys were investigated using optical microscopy,scanning electron microscopy,electron backscattering diffraction,and transmission electron microscopy.The aim was to concurrently enhance both strength and thermal conductivity by fostering uniform and refined microstructures while mitigating basal texture intensity.Substantial improvements were observed in yield strength(YS),ultimate tensile strength(UTS),and elongation(EL),with increase of 77%,53% and 10%,respectively.Additionally,thermal conductivity demonstrated a notable enhancement,rising from 111 to 125 W/(m·K).The underlying mechanism driving these improvements through the supplementary hot compression step was thoroughly elucidated.This study presents a promising pathway for the advancement of Mg alloys characterized by superior thermal and mechanical properties.展开更多
In recent years,video coding has been widely applied in the field of video image processing to remove redundant information and improve data transmission efficiency.However,during the video coding process,irrelevant o...In recent years,video coding has been widely applied in the field of video image processing to remove redundant information and improve data transmission efficiency.However,during the video coding process,irrelevant objects such as background elements are often encoded due to environmental disturbances,resulting in the wastage of computational resources.Existing research on video coding efficiency optimization primarily focuses on optimizing encoding units during intra-frame or inter frame prediction after the generation of coding units,neglecting the optimization of video images before coding unit generation.To address this challenge,This work proposes an image semantic segmentation compression algorithm based on macroblock encoding,called image semantic segmentation compression algorithm based on macroblock encoding(ISSC-ME),which consists of three modules.(1)The semantic label generation module generates interesting object labels using a grid-based approach to reduce redundant coding of consecutive frames.(2)The image segmentation network module generates a semantic segmentation image using U-Net.(3)The macroblock coding module,is a block segmentation-based video encoding and decoding algorithm used to compress images and improve video transmission efficiency.Experimental results show that the proposed image semantic segmentation optimization algorithm can reduce the computational costs,and improve the overall accuracy by 1.00%and the mean intersection over union(IoU)by 1.20%.In addition,the proposed compression algorithm utilizes macroblock fusion,resulting in the image compression rate achieving 80.64%.It has been proven that the proposed algorithm greatly reduces data storage and transmission,and enables fast image compression processing at the millisecond level.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
This study investigates the volumetric behaviors of various soils during freeze-thaw(FT)cycles and subsequent one-dimensional(1D)compression from experimental and theoretical studies.Experimental studies were performe...This study investigates the volumetric behaviors of various soils during freeze-thaw(FT)cycles and subsequent one-dimensional(1D)compression from experimental and theoretical studies.Experimental studies were performed on saturated expansive soil specimens with varying compaction conditions and soil structures under different stress states.Experimental results demonstrate that the specimens expand during freezing and contract during thawing.All specimens converge to the same residual void ratio after seven FT cycles,irrespective of their different initial void ratio,stress state,and soil structure.The compression index of the expansive soil specimens increases with the initial void ratio,whereas their swelling index remains nearly constant.A model extending the disturbed state concept(DSC)is proposed to predict the 1D compression behaviors of FT-impacted soils.The model incorporates a parameter,b,to account for the impacts of FT cycles.Empirical equations have been developed to link the key model parameters(i.e.the normalized yield stress and parameter b)to the soil state parameter(i.e.the normalized void ratio)in order to simplify the prediction approach.The proposed model well predicts the results of the tested expansive soil.In addition,the model’s feasibility for other types of soils,including low-and high-plastic clays,and high-plastic organic soils,has been validated using published data from the literature.The proposed model is simple yet reliable for predicting the compression behaviors of soils subjected to FT cycles.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
Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facili...Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.展开更多
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 Nos.42372312,and 42172299)the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture(Grant No.JDYC20220807).
文摘The progressive failure characteristics of geomaterial are a remarkable and challenging topic in geotechnical engineering.To study the effect of salt content and temperature on the progressive failure characteristics of frozen sodium sulfate saline sandy soil,a series of uniaxial compression tests were performed by integrating digital image correlation(DIC)technology into the testing apparatus.The evolution law of the uniaxial compression strength(UCS),the failure strain,and the formation of the shear band of the frozen sodium sulfate saline sandy soil were analyzed.The test results show that within the scope of this study,with the increase of salt content,both the UCS and the shear band angle initially decrease with increasing salt content before showing an increase.In contrast,the failure strain and the width of the shear band exhibit an initial increase followed by a decrease in the samples.In addition,to investigate the brittle failure characteristics of frozen sodium sulfate saline sandy soil,two classic brittleness evaluation methods were employed to quantitatively assess the brittleness level for the soil samples.The findings suggest that the failure characteristics under all test conditions in this study belong to the transition stage between brittle and ductile,indicating that frozen sodium sulfate saline sandy soil exhibits certain brittle behavior under uniaxial compression conditions,and the brittleness index basically decreases and then increases with the rise in salt content.
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金National Natural Science Foundation of China(52305349)Heilongjiang Touyan Team(HITTY-20190036)+2 种基金Heilongjiang Provincial Natural Science Foundation of China(LH2023E033)CGN-HIT Advanced Nuclear and New Energy Research Institute(CGN-HIT202305)Natural Science Basic Research Program of Shaanxi Province(2023-JC-QN-0518)。
文摘The effect of deformation resistance of AlCr_(1.3)TiNi_(2) eutectic high-entropy alloys under various current densities and strain rates was investigated during electrically-assisted compression.Results show that at current density of 60 A/mm^(2) and strain rate of 0.1 s^(−1),the ultimate tensile stress shows a significant decrease from approximately 3000 MPa to 1900 MPa with reduction ratio of about 36.7%.However,as current density increases,elongation decreases due to intermediate temperature embrittlement.This is because the current induces Joule effect,which then leads to stress concentration and more defect formation.Moreover,the flow stress is decreased with the increase in strain rate at constant current density.
基金financially supported by the National Natural Science Foundation of China(Nos.52174092,51904290,and 52374147)the Natural Science Foundation of Jiangsu Province,China(No.BK20220157)+2 种基金the Fundamental Research Funds for the Central Universities,China(No.2022YCPY0202)the National Key Research and Development Program of China(No.2023YFC3804204)the Major Program of Xinjiang Uygur Autonomous Region S cience and Technology(No.2023A01002)。
文摘The mechanical behavior of cemented gangue backfill materials(CGBMs)is closely related to particle size distribution(PSD)of aggregates and properties of cementitious materials.Consequently,the true triaxial compression tests,CT scanning,SEM,and EDS tests were conducted on cemented gangue backfill samples(CGBSs)with various carbon nanotube concentrations(P_(CNT))that satisfied fractal theory for the PSD of aggregates.The mechanical properties,energy dissipations,and failure mechanisms of the CGBSs under true triaxial compression were systematically analyzed.The results indicate that appropriate carbon nanotubes(CNTs)effectively enhance the mechanical properties and energy dissipations of CGBSs through micropore filling and microcrack bridging,and the optimal effect appears at P_(CNT)of 0.08wt%.Taking PSD fractal dimension(D)of 2.500 as an example,compared to that of CGBS without CNT,the peak strength(σ_(p)),axial peak strain(ε_(1,p)),elastic strain energy(Ue),and dissipated energy(U_(d))increased by 12.76%,29.60%,19.05%,and90.39%,respectively.However,excessive CNTs can reduce the mechanical properties of CGBSs due to CNT agglomeration,manifesting a decrease inρ_(p),ε_(1,p),and the volumetric strain increment(Δε_(v))when P_(CNT)increases from 0.08wt%to 0.12wt%.Moreover,the addition of CNTs improved the integrity of CGBS after macroscopic failure,and crack extension in CGBSs appeared in two modes:detour and pass through the aggregates.Theσ_(p)and U_(d)firstly increase and then decrease with increasing D,and porosity shows the opposite trend.Theε_(1,p)andΔε_(v)are negatively correlated with D,and CGBS with D=2.150 has the maximum deformation parameters(ε_(1,p)=0.05079,Δε_(v)=0.01990)due to the frictional slip effect caused by coarse aggregates.With increasing D,the failure modes of CGBSs are sequentially manifested as oblique shear failure,"Y-shaped"shear failure,and conjugate shear failure.
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National Natural Science Foundation of China(52275308,52301146)Fundamental Research Funds for the Central Universities(2023JG007)Supported by Shi Changxu Innovation Center for Advanced Materials(SCXKFJJ202207)。
文摘AZ31/Al/Ta composites were prepared using the vacuum hot compression bonding(VHCB)method.The effect of hot compressing temperature on the interface microstructure evolution,phase constitution,and shear strength at the interface was investigated.Moreover,the interface bonding mechanisms of the AZ31/Al/Ta composites during the VHCB process were explored.The results demonstrate that as the VHCB temperature increases,the phase composition of the interface between Mg and Al changes from the Mg-Al brittle intermetallic compounds(Al_(12)Mg_(17)and Al_(3)Mg_(2))to the Al-Mg solid solution.Meanwhile,the width of the Al/Ta interface diffusion layer at 450℃increases compared to that at 400℃.The shear strengths are 24 and 46 MPa at 400 and 450℃,respectively.The interfacial bonding mechanism of AZ31/Al/Ta composites involves the coexistence of diffusion and mechanical meshing.Avoiding the formation of brittle phases at the interface can significantly improve interfacial bonding strength.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Data compression plays a vital role in datamanagement and information theory by reducing redundancy.However,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and misuse.With the exponential growth of digital data,robust security measures are essential.Data encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key control.Despite their individual benefits,both require significant computational resources.Additionally,performing them separately for the same data increases complexity and processing time.Recognizing the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and efficiency.Thealgorithmoperates on 128-bit block sizes and a 256-bit secret key length.It combines Huffman coding for compression and a Tent map for encryption.Additionally,an iterative Arnold cat map further enhances cryptographic confusion properties.Experimental analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.
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 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supported in part by the National Key Laboratory of Science and Technology on Space Microwave(Grant Nos.HTKJ2022KL504009 and HTKJ2022KL5040010).
文摘With the increase in the quantity and scale of Static Random-Access Memory Field Programmable Gate Arrays (SRAM-based FPGAs) for aerospace application, the volume of FPGA configuration bit files that must be stored has increased dramatically. The use of compression techniques for these bitstream files is emerging as a key strategy to alleviate the burden on storage resources. Due to the severe resource constraints of space-based electronics and the unique application environment, the simplicity, efficiency and robustness of the decompression circuitry is also a key design consideration. Through comparative analysis current bitstream file compression technologies, this research suggests that the Lempel Ziv Oberhumer (LZO) compression algorithm is more suitable for satellite applications. This paper also delves into the compression process and format of the LZO compression algorithm, as well as the inherent characteristics of configuration bitstream files. We propose an improved algorithm based on LZO for bitstream file compression, which optimises the compression process by refining the format and reducing the offset. Furthermore, a low-cost, robust decompression hardware architecture is proposed based on this method. Experimental results show that the compression speed of the improved LZO algorithm is increased by 3%, the decompression hardware cost is reduced by approximately 60%, and the compression ratio is slightly reduced by 0.47%.
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financially supported by the National Key Research and Development Program of China(No.2022YFE0109600)the National Natural Science Foundation of China(No.52150710544)。
文摘An additional hot compression process was applied to a dilute Mg−Mn−Zn alloy post-extrusion.The alloy was extruded at 150℃ with an extrusion ratio of 15:1 and subsequently hot-compressed at 180℃ with a true strain of 0.9 along the extrusion direction.The microstructure,mechanical properties and thermal conductivity of as-extruded and as-hot compressed Mg−Mn−Zn alloys were investigated using optical microscopy,scanning electron microscopy,electron backscattering diffraction,and transmission electron microscopy.The aim was to concurrently enhance both strength and thermal conductivity by fostering uniform and refined microstructures while mitigating basal texture intensity.Substantial improvements were observed in yield strength(YS),ultimate tensile strength(UTS),and elongation(EL),with increase of 77%,53% and 10%,respectively.Additionally,thermal conductivity demonstrated a notable enhancement,rising from 111 to 125 W/(m·K).The underlying mechanism driving these improvements through the supplementary hot compression step was thoroughly elucidated.This study presents a promising pathway for the advancement of Mg alloys characterized by superior thermal and mechanical properties.
文摘In recent years,video coding has been widely applied in the field of video image processing to remove redundant information and improve data transmission efficiency.However,during the video coding process,irrelevant objects such as background elements are often encoded due to environmental disturbances,resulting in the wastage of computational resources.Existing research on video coding efficiency optimization primarily focuses on optimizing encoding units during intra-frame or inter frame prediction after the generation of coding units,neglecting the optimization of video images before coding unit generation.To address this challenge,This work proposes an image semantic segmentation compression algorithm based on macroblock encoding,called image semantic segmentation compression algorithm based on macroblock encoding(ISSC-ME),which consists of three modules.(1)The semantic label generation module generates interesting object labels using a grid-based approach to reduce redundant coding of consecutive frames.(2)The image segmentation network module generates a semantic segmentation image using U-Net.(3)The macroblock coding module,is a block segmentation-based video encoding and decoding algorithm used to compress images and improve video transmission efficiency.Experimental results show that the proposed image semantic segmentation optimization algorithm can reduce the computational costs,and improve the overall accuracy by 1.00%and the mean intersection over union(IoU)by 1.20%.In addition,the proposed compression algorithm utilizes macroblock fusion,resulting in the image compression rate achieving 80.64%.It has been proven that the proposed algorithm greatly reduces data storage and transmission,and enables fast image compression processing at the millisecond level.
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support from the Natural Sciences and Engineering Research Council of Canada(NSERC)through the Discovery Grant(Grant No.5808)received in 2019 for his research programsThe third author appreciates the funding from the National Natural Science Foundation of China(Grant No.52378365)Hubei Key Research&Development Program(Grant No.2023BCB112).
文摘This study investigates the volumetric behaviors of various soils during freeze-thaw(FT)cycles and subsequent one-dimensional(1D)compression from experimental and theoretical studies.Experimental studies were performed on saturated expansive soil specimens with varying compaction conditions and soil structures under different stress states.Experimental results demonstrate that the specimens expand during freezing and contract during thawing.All specimens converge to the same residual void ratio after seven FT cycles,irrespective of their different initial void ratio,stress state,and soil structure.The compression index of the expansive soil specimens increases with the initial void ratio,whereas their swelling index remains nearly constant.A model extending the disturbed state concept(DSC)is proposed to predict the 1D compression behaviors of FT-impacted soils.The model incorporates a parameter,b,to account for the impacts of FT cycles.Empirical equations have been developed to link the key model parameters(i.e.the normalized yield stress and parameter b)to the soil state parameter(i.e.the normalized void ratio)in order to simplify the prediction approach.The proposed model well predicts the results of the tested expansive soil.In addition,the model’s feasibility for other types of soils,including low-and high-plastic clays,and high-plastic organic soils,has been validated using published data from the literature.The proposed model is simple yet reliable for predicting the compression behaviors of soils subjected to FT cycles.
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.
基金supported in part by the National Natural Science Foundation of China(No.31470714 and 61701105).
文摘Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.
