AIM:To investigate the effects of binocular fusional C-optotypes(positive/negative)and 2D planar C-optotypes on the amplitude and stability of transient accommodation(TAC)in adults,and to provide a basis for non-conta...AIM:To investigate the effects of binocular fusional C-optotypes(positive/negative)and 2D planar C-optotypes on the amplitude and stability of transient accommodation(TAC)in adults,and to provide a basis for non-contact myopia intervention.METHODS:This was a self-controlled study.Using redblue 3D technology,four experimental stages were set up:Test A[fixating on the 1 m negative fusional C-optotypes,8△base-in(BI)],Test B(fixating on the 5 m planar C-optotypes),Test C(fixating on the 1 m planar C-optotypes),and Test D[fixating on the 1 m positive fusional C-optotypes,20△base-out(BO)].A WAM-5500 open-field autorefractor was used to measure TAC and accommodative microfluctuations[evaluated via interquartile range(IQR)and median-based coefficient of variation(CVmed)].Additionally,the convergence accommodation to convergence(CA/C)ratio was calculated,and a visual fatigue questionnaire was administered to assess participants’subjective visual comfort.RESULTS:A total of 21 subjects(7 males,14 females;aged 23-41y)with normal binocular visual function were enrolled.The results showed that the TAC increased gradually across the four stages,and these values were Test A(-0.35±0.26 D)<Test B(-0.46±0.24 D)<Test C(-0.77±0.32 D)<Test D(-1.38±0.31 D).There were significant overall differences(F=56.136,P<0.001).Compared with Test C,Test A reduced TAC by 0.42 D(P<0.05),while Test D increased it by 0.61 D(P<0.001).There was no significant intergroup difference in accommodative fluctuation amplitude(all P>0.05),but the fluctuation stability of Test D showed a significant difference between the first 20s and the second 20s(P=0.017).The CA/C ratio was significantly higher in Test D(0.05±0.02 D/△)than in Test A(0.03±0.02 D/△,P=0.007),indicating stronger accommodation-convergence linkage during positive fusional fixation.The visual fatigue scores of all stages were low(median 0-1),with Test D slightly higher than Test B and Test C(P<0.05).No linear correlation was found between TAC and age(all r<0.1,P>0.05).CONCLUSION:Negative fusional C-optotypes induce ciliary muscle relaxation to reduce TAC,while positive fusional C-optotypes enhance accommodation-convergence coordination to increase TAC.The red-blue 3D-based noncontact training mode exhibits good safety(median visual fatigue scores:0-1 across all tests)and provides a novel dual-directional(relaxation-activation)strategy for myopia prevention and control.展开更多
This paper presents a reality-virtual fusional campus environment.It is an online 3D platform with some aspects of real information merged together.The whole platform is based on OpenSimulator with detailed geo-models...This paper presents a reality-virtual fusional campus environment.It is an online 3D platform with some aspects of real information merged together.The whole platform is based on OpenSimulator with detailed geo-models to represent the university campus.Some preliminary experiments were done to integrate the realistic information with the virtual campus for making the geo-environment not only with detailed indoor and outdoor models,but also with the real representations of the physical world.The overall motivation is to provide a framework with strong support for reality-virtuality fusional modeling in a collaborative 3D online platform for research purposes.展开更多
AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy s...AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy subjects.METHODS:A cross-sectional comparative study was conducted,enrolling two cohorts:a PD group and a healthy control group.The PD group was recruited via non-random convenience sampling,while the control group was selected randomly from individuals without PD.All participants were screened according to predefined inclusion and exclusion criteria before undergoing a comprehensive optometric assessment,which included measurements of uncorrected visual acuity,corrected visual acuity,and objective and subjective refraction.Subsequently,binocular vision function evaluations were performed,covering NPC measurement,fusional vergence reserve assessment at both distance and near,saccadic eye movement testing,and versional eye movement and heterophoria assessment.RESULTS:A total of 42 PD patients and 41 healthy controls were included in the final analysis.The two groups were well-matched in terms of sex distribution[29 males(69.0%)in the PD group vs 29 males(70.7%)in the control group,P=0.867]and mean age(55.3±9.6y in the PD group vs 54.9±9.8y in the control group,P=0.866).The prevalence of abnormal versional eye movements was significantly higher in the PD group than in the control group(23.81%,95%CI:12.05%-39.45%vs 7.32%,95%CI:1.54%-19.92%;P=0.025).Near exophoria was more prevalent in PD patients(61.90%,95%CI:45.64%-76.43%)than in controls(17.07%,95%CI:7.15%-32.06%),with a significant difference[odds ratio(OR)=7.99;95%CI:2.83-21.99;P<0.001].The mean NPC was significantly greater(more receded)in the PD group than in the control group(9.01±3.74 cm vs 7.20±2.15 cm;P=0.007).A statistically significant positive correlation was observed between PD severity and NPC values(Pearson’s correlation coefficient=0.309;P=0.046).Except for distance baseout break and distance base-out recovery values,all other fusional vergence parameters were significantly lower in the PD group than in the control group(P<0.05).The mean saccadic test score was significantly lower in PD patients than in controls(3.29±0.57 vs 3.78±0.42;P<0.001).Among all fusional vergence indices,near base-in blur yielded the highest area under the curve(AUC=0.877),with a sensitivity of 69%and specificity of 90%,followed by distance base-out blur(AUC=0.824,sensitivity=97.6%,specificity=66.7%),near base-out blur(AUC=0.814,sensitivity=76.2%,specificity=72.7%),near base-out break(AUC=0.749,sensitivity=78.6%,specificity=67.6%),and near base-out recovery(AUC=0.749,sensitivity=95.2%,specificity=50%).CONCLUSION:PD is associated with significant binocular vision function impairment,with receded NPC and reduced near fusional vergence reserves being the most prominent disorders.These findings highlight the potential value of binocular vision assessment as a non-invasive biomarker for the early detection and clinical monitoring of PD.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5w...A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.展开更多
Copper manufactured by laser powder bed fusion(LPBF)process typically exhibits poor strength-ductility coordination,and the addition of strengthening phases is an effective way to address this issue.To explore the eff...Copper manufactured by laser powder bed fusion(LPBF)process typically exhibits poor strength-ductility coordination,and the addition of strengthening phases is an effective way to address this issue.To explore the effects of strengthening phases on Cu,Cu-carbon nanotubes(CNTs)composites were prepared using LPBF technique with Cu-CNTs mixed powder as the matrix.The formability,microstructure,mechanical properties,electrical conductivity,and thermal properties were studied.The result shows that the prepared composites have high relative density.The addition of CNTs results in inhomogeneous equiaxed grains at the edges of the molten pool and columnar grains at the center.Compared with pure copper,the overall mechanical properties of the composite are improved:tensile strength increases by 52.8%and elongation increases by 146.4%;the electrical and thermal properties are also enhanced:thermal conductivity increases by 10.8%and electrical conductivity increases by 12.7%.展开更多
Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely us...Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely used in fields such as injection molding,die casting,and stamping dies.Adding reinforcing particles into steel is an effective means to improve its performance.Nb/18Ni300 composites were fabricated by LPBF using two kinds of Nb powders with different particle sizes,and their microstructures and properties were studied.The results show that the unmelted Nb particles are uniformly distributed in the 18Ni300 matrix and the grains are refined,which is particularly pronounced with fine Nb particles.In addition,element diffusion occurs between the particles and the matrix.The main phases of the base alloy are α-Fe and a small amount of γ-Fe.With the addition of Nb,part of the α-Fe is transformed into γ-Fe,and unmelted Nb phases appear.The addition of Nb also enhances the hardness and wear resistance of the composites but slightly reduces their tensile properties.After aging treatment,the molten pools and grain boundaries become blurred,grains are further refined,and the interfaces around the particles are thinned.The aging treatment also promotes the formation of reverted austenite.The hardness,ultimate tensile strength,and volumetric wear rate of the base alloy reach 51.9 HRC,1704 MPa,and 17.8×10^(-6) mm^(3)/(N·m),respectively.In contrast,the sample added with fine Nb particles has the highest hardness(56.1 HRC),ultimate tensile strength(1892 MPa)and yield strength(1842 MPa),and the volume wear rate of the sample added with coarse Nb particles is reduced by 90%to 1.7×10^(-6) mm^(3)/(N·m).展开更多
2025年12月22日,上海交通大学医学院医学技术学院李文杰在人工智能与信息融合领域国际顶级期刊Information Fusion发表了一项研究成果“CX-Mind:a pioneering multimodal large language model for interleaved reasoning in chest X-ra...2025年12月22日,上海交通大学医学院医学技术学院李文杰在人工智能与信息融合领域国际顶级期刊Information Fusion发表了一项研究成果“CX-Mind:a pioneering multimodal large language model for interleaved reasoning in chest X-ray via curriculum-guided reinforcement learning”。展开更多
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini...Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.展开更多
Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learni...Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.展开更多
Achieving uniform X-ray irradiation in indirect-drive inertial confinement fusion(ICF)is a key challenge for successful capsule implosion.Spherical hohlraums,particularly those with octahedral laser entrance holes(LEH...Achieving uniform X-ray irradiation in indirect-drive inertial confinement fusion(ICF)is a key challenge for successful capsule implosion.Spherical hohlraums,particularly those with octahedral laser entrance holes(LEHs),are an alternative to the cylindrical hohlraums currently considered for ICF at NIF(USA)and LMJ(France).These spherical hohlraums are advantageous in terms of irradiation uniformity on the fusion capsule because,owing to their octahedral symmetry,low-order asymmetries cancel out intrinsically.However,they may be less favorable from an energetic point of view,primarily owing to radiation losses through their multiple LEHs.The net balance of these advantages and disadvantages is difficult to determine,because,unlike cylindrical hohlraums,they require fully 3D modeling.To address this,a new version of the MULTI-3D simulation code has been developed.MULTI-3D is a 3D radiation-hydrodynamics code with arbitrary Langrangian-Eulerian(ALE)hydrodynamics,multigroup SN radiation transport,and ray-tracing laser deposition.Using this tool,several aspects of the behavior of spherical hohlraums have been analyzed,with special attention to phenomena inaccessible to 2D modeling.In these targets,laser beams strike the inner walls at very oblique angles,and the expansion of plasma significantly alters the locations where primary X rays are produced.Furthermore,the complex distribution of laser hot spots leads to mutual interactions,where plasma bubbles from one beam intersect the path of another.The laser-to-X-ray energy conversion efficiency has been analyzed as a function of key parameters.The symmetry on the capsule has also been evaluated,revealing nonuniformities of less than 1%.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
Inertial confinement fusion(ICF)requires a constant search for the most effective materials to improve the efficiency of compression of the capsule and of laser-to-target energy transfer.Foams could provide a solution...Inertial confinement fusion(ICF)requires a constant search for the most effective materials to improve the efficiency of compression of the capsule and of laser-to-target energy transfer.Foams could provide a solution,but they require further experimental and theoretical investigation.The new 3D-printing technologies,such as two-photon polymerization,are opening a new era in the production of foams,allowing fine control of material morphology.Very few detailed studies of the interaction of foams with high-power lasers in regimes relevant for ICF have been described in the literature to date,and more investigation is needed.In this work,we present the results of an experimental campaign performed at the ABC laser facility at ENEA Centro Ricerche Frascati in which 3D-printed microstructured materials were irradiated at high power.3D simulations of the laser-target interaction performed with the FLASH code reveal that the laser is scattered by plasma density gradients and channeled into the structure when the center of the focal spot is on the through hole.The time required for the laser to completely ablate the structure given by the simulations is in good agreement with the experimental measurement.Measurements of the reflected and transmitted laser light indicate that scattering occurred during the irradiation,in accordance with the simulations.Two-plasmon decay has also been found to be active during irradiation.展开更多
The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-e...The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.展开更多
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ...Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.展开更多
Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks ...Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios(SNRs)and non-Gaussian impulsive noise,where observations may be distorted differently across feature modalities,making conventional fusion unstable and degrading detection reliability.To address this challenge,the generalized Gaussian distribution(GGD)is adopted as the noise model,and a multimodal fusion framework termed BCAM-Net(bidirectional cross-attention multimodal network)is proposed.BCAM-Net adopts a parallel dual-branch architecture:a time-frequency branch that leverages the continuous wavelet transform(CWT)to extract time-frequency representations,and a temporal branch that learns long-range dependencies from raw signals.BCAM-Net utilizes a bidirectional cross-attention mechanism to achieve deep alignment and mutual calibration of temporal and time-frequency features,generating a fused representation that is highly robust to complex noise.Simulation results show that,under GGD noise with shape parameterβ=0.5,BCAM-Net achieves high detection probabilities in the low-SNR regime and outperforms representative baselines.At a false alarm probability Pf=0.1 and SNR of−14 dB,it attains a detection probability of 0.9020,exceeding the CNN-Transformer,WT-ResNet,TFCFN,and conventional CNN benchmarks by 5.75%,6.98%,33.3%,and 21.1%,respectively.These results indicate that BCAM-Net can effectively improve spectrum sensing performance in low-SNR impulsive-noise scenarios,and provides a lightweight,high-performance solution for practical cognitive radio spectrum sensing.展开更多
Unmanned aerial vehicles(UAVs),especially quadcopters,have become indispensable in numerous industrial and scientific applications due to their flexibility,lowcost,and capability to operate in dynamic environments.Thi...Unmanned aerial vehicles(UAVs),especially quadcopters,have become indispensable in numerous industrial and scientific applications due to their flexibility,lowcost,and capability to operate in dynamic environments.This paper presents a complete design and implementation of a compact autonomous quadcopter capable of trajectory tracking,object detection,precision landing,and real-time telemetry via long-range communication protocols.The system integrates an onboard flight controller running real-time sensor fusion algorithms,a vision-based detection system on a companion single-board computer,and a telemetry unit using Long Range(LoRa)communication.Extensive flight tests were conducted to validate the system’s stability,communication range,and autonomous capabilities.Potential applications in law enforcement,agriculture,search and rescue,and environmental monitoring are also discussed.展开更多
文摘AIM:To investigate the effects of binocular fusional C-optotypes(positive/negative)and 2D planar C-optotypes on the amplitude and stability of transient accommodation(TAC)in adults,and to provide a basis for non-contact myopia intervention.METHODS:This was a self-controlled study.Using redblue 3D technology,four experimental stages were set up:Test A[fixating on the 1 m negative fusional C-optotypes,8△base-in(BI)],Test B(fixating on the 5 m planar C-optotypes),Test C(fixating on the 1 m planar C-optotypes),and Test D[fixating on the 1 m positive fusional C-optotypes,20△base-out(BO)].A WAM-5500 open-field autorefractor was used to measure TAC and accommodative microfluctuations[evaluated via interquartile range(IQR)and median-based coefficient of variation(CVmed)].Additionally,the convergence accommodation to convergence(CA/C)ratio was calculated,and a visual fatigue questionnaire was administered to assess participants’subjective visual comfort.RESULTS:A total of 21 subjects(7 males,14 females;aged 23-41y)with normal binocular visual function were enrolled.The results showed that the TAC increased gradually across the four stages,and these values were Test A(-0.35±0.26 D)<Test B(-0.46±0.24 D)<Test C(-0.77±0.32 D)<Test D(-1.38±0.31 D).There were significant overall differences(F=56.136,P<0.001).Compared with Test C,Test A reduced TAC by 0.42 D(P<0.05),while Test D increased it by 0.61 D(P<0.001).There was no significant intergroup difference in accommodative fluctuation amplitude(all P>0.05),but the fluctuation stability of Test D showed a significant difference between the first 20s and the second 20s(P=0.017).The CA/C ratio was significantly higher in Test D(0.05±0.02 D/△)than in Test A(0.03±0.02 D/△,P=0.007),indicating stronger accommodation-convergence linkage during positive fusional fixation.The visual fatigue scores of all stages were low(median 0-1),with Test D slightly higher than Test B and Test C(P<0.05).No linear correlation was found between TAC and age(all r<0.1,P>0.05).CONCLUSION:Negative fusional C-optotypes induce ciliary muscle relaxation to reduce TAC,while positive fusional C-optotypes enhance accommodation-convergence coordination to increase TAC.The red-blue 3D-based noncontact training mode exhibits good safety(median visual fatigue scores:0-1 across all tests)and provides a novel dual-directional(relaxation-activation)strategy for myopia prevention and control.
基金the Direct Grant of the Chinese University of Hong Kong (No.2021064)the National High Technology Research and Development Program of China (No.2010AA122202)
文摘This paper presents a reality-virtual fusional campus environment.It is an online 3D platform with some aspects of real information merged together.The whole platform is based on OpenSimulator with detailed geo-models to represent the university campus.Some preliminary experiments were done to integrate the realistic information with the virtual campus for making the geo-environment not only with detailed indoor and outdoor models,but also with the real representations of the physical world.The overall motivation is to provide a framework with strong support for reality-virtuality fusional modeling in a collaborative 3D online platform for research purposes.
基金Supported by Mashhad University of Medical Sciences.
文摘AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy subjects.METHODS:A cross-sectional comparative study was conducted,enrolling two cohorts:a PD group and a healthy control group.The PD group was recruited via non-random convenience sampling,while the control group was selected randomly from individuals without PD.All participants were screened according to predefined inclusion and exclusion criteria before undergoing a comprehensive optometric assessment,which included measurements of uncorrected visual acuity,corrected visual acuity,and objective and subjective refraction.Subsequently,binocular vision function evaluations were performed,covering NPC measurement,fusional vergence reserve assessment at both distance and near,saccadic eye movement testing,and versional eye movement and heterophoria assessment.RESULTS:A total of 42 PD patients and 41 healthy controls were included in the final analysis.The two groups were well-matched in terms of sex distribution[29 males(69.0%)in the PD group vs 29 males(70.7%)in the control group,P=0.867]and mean age(55.3±9.6y in the PD group vs 54.9±9.8y in the control group,P=0.866).The prevalence of abnormal versional eye movements was significantly higher in the PD group than in the control group(23.81%,95%CI:12.05%-39.45%vs 7.32%,95%CI:1.54%-19.92%;P=0.025).Near exophoria was more prevalent in PD patients(61.90%,95%CI:45.64%-76.43%)than in controls(17.07%,95%CI:7.15%-32.06%),with a significant difference[odds ratio(OR)=7.99;95%CI:2.83-21.99;P<0.001].The mean NPC was significantly greater(more receded)in the PD group than in the control group(9.01±3.74 cm vs 7.20±2.15 cm;P=0.007).A statistically significant positive correlation was observed between PD severity and NPC values(Pearson’s correlation coefficient=0.309;P=0.046).Except for distance baseout break and distance base-out recovery values,all other fusional vergence parameters were significantly lower in the PD group than in the control group(P<0.05).The mean saccadic test score was significantly lower in PD patients than in controls(3.29±0.57 vs 3.78±0.42;P<0.001).Among all fusional vergence indices,near base-in blur yielded the highest area under the curve(AUC=0.877),with a sensitivity of 69%and specificity of 90%,followed by distance base-out blur(AUC=0.824,sensitivity=97.6%,specificity=66.7%),near base-out blur(AUC=0.814,sensitivity=76.2%,specificity=72.7%),near base-out break(AUC=0.749,sensitivity=78.6%,specificity=67.6%),and near base-out recovery(AUC=0.749,sensitivity=95.2%,specificity=50%).CONCLUSION:PD is associated with significant binocular vision function impairment,with receded NPC and reduced near fusional vergence reserves being the most prominent disorders.These findings highlight the potential value of binocular vision assessment as a non-invasive biomarker for the early detection and clinical monitoring of PD.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+8 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)2024 Scientific Research Project of Shaanxi National Defense Industry Vocational and Technical College(Gfy24-07)Shaanxi Vocational and Technical Education Association 2024 Vocational Education Teaching Reform Research Topic(2024SZX354)National Natural Science Foundation of China(U24A20115)2024 Shaanxi Provincial Education Department Service Local Special Scientific Research Program Project-Industrialization Cultivation Project(24JC005,24JC063)Shaanxi Province“14th Five-Year Plan”Education Science Plan,2024 Project(SGH24Y3181)National Key Research and Development Program of China(2023YFB4606400)Longmen Laboratory Frontier Exploration Topics Project(LMQYTSKT003)。
文摘A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.
基金National Key Research and Development Program of China(2023YFB4606400)Supported by Longmen Laboratory Frontier Exploration Topics(LMQYTSKT003)。
文摘Copper manufactured by laser powder bed fusion(LPBF)process typically exhibits poor strength-ductility coordination,and the addition of strengthening phases is an effective way to address this issue.To explore the effects of strengthening phases on Cu,Cu-carbon nanotubes(CNTs)composites were prepared using LPBF technique with Cu-CNTs mixed powder as the matrix.The formability,microstructure,mechanical properties,electrical conductivity,and thermal properties were studied.The result shows that the prepared composites have high relative density.The addition of CNTs results in inhomogeneous equiaxed grains at the edges of the molten pool and columnar grains at the center.Compared with pure copper,the overall mechanical properties of the composite are improved:tensile strength increases by 52.8%and elongation increases by 146.4%;the electrical and thermal properties are also enhanced:thermal conductivity increases by 10.8%and electrical conductivity increases by 12.7%.
基金Key-Area Research and Development Program of Guangdong Province(2023B0909020004)Project of Innovation Research Team in Zhongshan(CXTD2023006)+1 种基金Natural Science Foundation of Guangdong Province(2023A1515011573)Zhongshan Social Welfare Science and Technology Research Project(2024B2022)。
文摘Laser powder bed fusion(LPBF)is highly suitable for forming 18Ni300 mold steel,thanks to its excellent capability in manufacturing complex shapes and outstanding capacity for regulating microstructures.It is widely used in fields such as injection molding,die casting,and stamping dies.Adding reinforcing particles into steel is an effective means to improve its performance.Nb/18Ni300 composites were fabricated by LPBF using two kinds of Nb powders with different particle sizes,and their microstructures and properties were studied.The results show that the unmelted Nb particles are uniformly distributed in the 18Ni300 matrix and the grains are refined,which is particularly pronounced with fine Nb particles.In addition,element diffusion occurs between the particles and the matrix.The main phases of the base alloy are α-Fe and a small amount of γ-Fe.With the addition of Nb,part of the α-Fe is transformed into γ-Fe,and unmelted Nb phases appear.The addition of Nb also enhances the hardness and wear resistance of the composites but slightly reduces their tensile properties.After aging treatment,the molten pools and grain boundaries become blurred,grains are further refined,and the interfaces around the particles are thinned.The aging treatment also promotes the formation of reverted austenite.The hardness,ultimate tensile strength,and volumetric wear rate of the base alloy reach 51.9 HRC,1704 MPa,and 17.8×10^(-6) mm^(3)/(N·m),respectively.In contrast,the sample added with fine Nb particles has the highest hardness(56.1 HRC),ultimate tensile strength(1892 MPa)and yield strength(1842 MPa),and the volume wear rate of the sample added with coarse Nb particles is reduced by 90%to 1.7×10^(-6) mm^(3)/(N·m).
文摘2025年12月22日,上海交通大学医学院医学技术学院李文杰在人工智能与信息融合领域国际顶级期刊Information Fusion发表了一项研究成果“CX-Mind:a pioneering multimodal large language model for interleaved reasoning in chest X-ray via curriculum-guided reinforcement learning”。
基金funded by the Natural Science Foundation of Chongqing Municipality,grant number CSTB2022NSCQ-MSX0503.
文摘Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/03/32440).
文摘Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.
基金supported by the Project Nos.PID2022-137339OB-C22 of the“Plan Estatal 2021-2023R”of the Spanish Government and ENR-IFE.01.CEA of EUROFUSION.
文摘Achieving uniform X-ray irradiation in indirect-drive inertial confinement fusion(ICF)is a key challenge for successful capsule implosion.Spherical hohlraums,particularly those with octahedral laser entrance holes(LEHs),are an alternative to the cylindrical hohlraums currently considered for ICF at NIF(USA)and LMJ(France).These spherical hohlraums are advantageous in terms of irradiation uniformity on the fusion capsule because,owing to their octahedral symmetry,low-order asymmetries cancel out intrinsically.However,they may be less favorable from an energetic point of view,primarily owing to radiation losses through their multiple LEHs.The net balance of these advantages and disadvantages is difficult to determine,because,unlike cylindrical hohlraums,they require fully 3D modeling.To address this,a new version of the MULTI-3D simulation code has been developed.MULTI-3D is a 3D radiation-hydrodynamics code with arbitrary Langrangian-Eulerian(ALE)hydrodynamics,multigroup SN radiation transport,and ray-tracing laser deposition.Using this tool,several aspects of the behavior of spherical hohlraums have been analyzed,with special attention to phenomena inaccessible to 2D modeling.In these targets,laser beams strike the inner walls at very oblique angles,and the expansion of plasma significantly alters the locations where primary X rays are produced.Furthermore,the complex distribution of laser hot spots leads to mutual interactions,where plasma bubbles from one beam intersect the path of another.The laser-to-X-ray energy conversion efficiency has been analyzed as a function of key parameters.The symmetry on the capsule has also been evaluated,revealing nonuniformities of less than 1%.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
基金framework of the EUROfusion Consortium,funded by the European Union via the Euratom Research and Training Programme(Grant Agreement No.101052200—EUROfusion)The CRESCO-ENEAGRID High Performance Computing infrastructure is funded by ENEA+3 种基金the Italian National Agency for New Technologies,Energy and Sustainable Economic Developmentby Italian and European research programmesthe framework of the“Universities’Excellence Initiative”programme by the Ministry of Education,Science and Sports of the Republic of Lithuania under an agreement with the Research Council of Lithuania(Project No.S-A-UEI-23-6)support was received through EU LASERLAB-EUROPE JRAextension(Grant Agreement No.871124,Horizon 2020 Research and Innovation Programme).
文摘Inertial confinement fusion(ICF)requires a constant search for the most effective materials to improve the efficiency of compression of the capsule and of laser-to-target energy transfer.Foams could provide a solution,but they require further experimental and theoretical investigation.The new 3D-printing technologies,such as two-photon polymerization,are opening a new era in the production of foams,allowing fine control of material morphology.Very few detailed studies of the interaction of foams with high-power lasers in regimes relevant for ICF have been described in the literature to date,and more investigation is needed.In this work,we present the results of an experimental campaign performed at the ABC laser facility at ENEA Centro Ricerche Frascati in which 3D-printed microstructured materials were irradiated at high power.3D simulations of the laser-target interaction performed with the FLASH code reveal that the laser is scattered by plasma density gradients and channeled into the structure when the center of the focal spot is on the through hole.The time required for the laser to completely ablate the structure given by the simulations is in good agreement with the experimental measurement.Measurements of the reflected and transmitted laser light indicate that scattering occurred during the irradiation,in accordance with the simulations.Two-plasmon decay has also been found to be active during irradiation.
基金supported by the National Natural Science Foundation of China(Nos.12405288,12374241,12474484,U2330401,12088101)the Natural Science Foundation of Top Talent of SZTU(No.GDRC202526)。
文摘The process of nuclear fusion in the presence of a laser field was theoretically analyzed.The analysis is applicable to most fusion reactions and different types of currently available intense lasers,from X-ray free-electron lasers to solid-state near-infrared lasers.Laser fields were shown to enhance the fusion yields,and the mechanism of this enhancement was explained.Low-frequency lasers are more efficient in enhancing fusion than high-frequency lasers.The calculation results show enhancements of fusion yields by orders of magnitude with currently available intense low-frequency laser fields.The temperature requirement for controlled nuclear fusion may be reduced with the aid of intense laser fields.
基金General Program of National Natural Science Foundation of China(82474390)Construction Project of Pudong New Area Famous TCM Studios(National Pilot Zone for TCM Development,Shanghai)(PDZY-2025-0716)Shanghai Municipal Science and Technology Program Project Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000).
文摘Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.
基金supported in part by JSPS Grants-in-Aid for Scientific Research 25K07742 and 25K23457.
文摘Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios(SNRs)and non-Gaussian impulsive noise,where observations may be distorted differently across feature modalities,making conventional fusion unstable and degrading detection reliability.To address this challenge,the generalized Gaussian distribution(GGD)is adopted as the noise model,and a multimodal fusion framework termed BCAM-Net(bidirectional cross-attention multimodal network)is proposed.BCAM-Net adopts a parallel dual-branch architecture:a time-frequency branch that leverages the continuous wavelet transform(CWT)to extract time-frequency representations,and a temporal branch that learns long-range dependencies from raw signals.BCAM-Net utilizes a bidirectional cross-attention mechanism to achieve deep alignment and mutual calibration of temporal and time-frequency features,generating a fused representation that is highly robust to complex noise.Simulation results show that,under GGD noise with shape parameterβ=0.5,BCAM-Net achieves high detection probabilities in the low-SNR regime and outperforms representative baselines.At a false alarm probability Pf=0.1 and SNR of−14 dB,it attains a detection probability of 0.9020,exceeding the CNN-Transformer,WT-ResNet,TFCFN,and conventional CNN benchmarks by 5.75%,6.98%,33.3%,and 21.1%,respectively.These results indicate that BCAM-Net can effectively improve spectrum sensing performance in low-SNR impulsive-noise scenarios,and provides a lightweight,high-performance solution for practical cognitive radio spectrum sensing.
文摘Unmanned aerial vehicles(UAVs),especially quadcopters,have become indispensable in numerous industrial and scientific applications due to their flexibility,lowcost,and capability to operate in dynamic environments.This paper presents a complete design and implementation of a compact autonomous quadcopter capable of trajectory tracking,object detection,precision landing,and real-time telemetry via long-range communication protocols.The system integrates an onboard flight controller running real-time sensor fusion algorithms,a vision-based detection system on a companion single-board computer,and a telemetry unit using Long Range(LoRa)communication.Extensive flight tests were conducted to validate the system’s stability,communication range,and autonomous capabilities.Potential applications in law enforcement,agriculture,search and rescue,and environmental monitoring are also discussed.
