Infrared false target is an important mean to induce the infrared-guided weapons,and the key issue is how to keep the surface temperature of the infrared false target to be the same as that of the object to be protect...Infrared false target is an important mean to induce the infrared-guided weapons,and the key issue is how to keep the surface temperature of the infrared false target to be the same as that of the object to be protected.One-dimensional heat transfer models of a metal plate and imitative material were established to explore the influences of the thermophysical properties of imitative material on the surface temperature difference（STD） between the metal plate and imitative material which were subjected to periodical ambient conditions.It is elucidated that the STD is determined by the imitative material’s dimensionless thickness（dim*,） and the thermal inertia(Pim).When dim* is above 1.0,the STD is invariable as long as Pim is a constant.And if the dimensionless thickness of metal plate（d,m*） is also larger than 1.0,the STD approaches to zero as long as Pimis the same as the thermal inertia of metal plate（Pm）.When dim* is between 0.08 and 1,the STD varies irregularly with Pim and dim*.However,if dm* is also in the range of 0.08-1,the STD approaches to zero on condition that Pim=Pm and dim*= dm*.If dim*,is below 0.08,the STD is unchanged when Pimdim* is a constant.And if dm* is also less than 0.08,the STD approaches to zero as long as Pimdim* = Pmdm*.Furthermore,an applicationoriented discussion indicates that the imitative material can be both light and thin via the application of the phase change material with a preset STD because of its high specific heat capacity during the phase transition process.展开更多
巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image ...巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)informati...Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.展开更多
Despite advancements in neuroimaging,false positive diagnoses of intracranial aneurysms remain a significant concern.This article examines the causes,prevalence,and implications of such false-positive diagnoses.We dis...Despite advancements in neuroimaging,false positive diagnoses of intracranial aneurysms remain a significant concern.This article examines the causes,prevalence,and implications of such false-positive diagnoses.We discuss how conditions like arterial occlusion with vascular stump formation and infundibular widening can mimic aneurysms,particularly in the anterior circulation.The article compares various imaging modalities,including computer tomography angiogram,magnetic resonance imaging/angiography,and digital subtraction angiogram,highlighting their strengths and limitations.We emphasize the im-portance of accurate differentiation to avoid unnecessary surgical interventions.The potential of emerging technologies,such as high-resolution vessel wall ima-ging and deep neural networks for automated detection,is explored as promising avenues for improving diagnostic accuracy.This manuscript underscores the need for continued research and clinical vigilance in the diagnosis of intracranial aneurysms.展开更多
This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
BACKGROUND Certain subgroups are at an increased risk of false fecal immunochemical test(FIT)results;however,related studies are limited,and the available evidence is conflicting.AIM To evaluate factors associated wit...BACKGROUND Certain subgroups are at an increased risk of false fecal immunochemical test(FIT)results;however,related studies are limited,and the available evidence is conflicting.AIM To evaluate factors associated with false-positive and false-negative FIT results.METHODS This retrospective study was based on the database of the Tianjin Colorectal Cancer Screening Program from 2012 to 2020.A total of 4129947 residents aged 40-74 years completed at least one FIT.Of these,24890 asymptomatic participants who underwent colonoscopy examinations and completed lifestyle questionnaires were included in the analysis.Multivariable logistic regression was performed to identify the factors associated with false FIT results.RESULTS Among the overall screening population,88687(2.15%)participants tested positive for FIT.The sensitivity,specificity,positive predictive value,and negative predictive value of FIT for advanced neoplasms were 58.2%,44.8%,9.7%,and 91.3%,respectively.Older age,female sex,smoking,alcohol consumption,higher body mass index,and hemorrhoids were significantly associated with increased odds of false-positive and lower odds of falsenegative FIT results.Moreover,features of high-grade dysplasia or villous for advanced adenoma and the presence of cancer were also associated with lower odds of false-negative results,while irregular exercise and diverticulum were associated with higher odds of false-positive results.CONCLUSION FIT results may be inaccurate in certain subgroups.Our results provide important evidence for further individualization of screening strategies.展开更多
Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications,and the major challenge is false positives that occur during pedestrian detection.Th...Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications,and the major challenge is false positives that occur during pedestrian detection.The emergence of various Convolutional Neural Network-based detection strategies substantially enhances pedestrian detection accuracy but still does not solve this problem well.This paper deeply analyzes the detection framework of the two-stage CNN detection methods and finds out false positives in detection results are due to its training strategy misclassifying some false proposals,thus weakening the classification capability of the following subnetwork and hardly suppressing false ones.To solve this problem,this paper proposes a pedestrian-sensitive training algorithm to help two-stage CNN detection methods effectively learn to distinguish the pedestrian and non-pedestrian samples and suppress the false positives in the final detection results.The core of the proposed algorithm is to redesign the training proposal generating scheme for the two-stage CNN detection methods,which can avoid a certain number of false ones that mislead its training process.With the help of the proposed algorithm,the detection accuracy of the MetroNext,a smaller and more accurate metro passenger detector,is further improved,which further decreases false ones in its metro passenger detection results.Based on various challenging benchmark datasets,experiment results have demonstrated that the feasibility of the proposed algorithm is effective in improving pedestrian detection accuracy by removing false positives.Compared with the existing state-of-the-art detection networks,PSTNet demonstrates better overall prediction performance in accuracy,total number of parameters,and inference time;thus,it can become a practical solution for hunting pedestrians on various hardware platforms,especially for mobile and edge devices.展开更多
The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach...The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach the warning target area,and carry out automatic disposal of lifeline engineering facilities.Through the construction of the National Earthquake Intensity Rapid Reporting and Early Warning Project,an earthquake early warning network consisting of over 1900 monitoring stations has been established in the Beijing-Tianjin-Hebei Urban Agglomeration.The early warning system has achieved second level earthquake warning and minute level intensity rapid reporting.The implementation of these functions relies on the system's ability to timely,accurately,and reliably identify seismic waves.But with the development of social economy,the background noise of earthquake observation environment is becoming increasingly complex,which brings certain challenges to earthquake wave recognition,some interference events have the risk of triggering the earthquake warning system incorrectly.Therefore,this article focuses on seismic wave recognition in complex noise environments and proposes a seismic wave detection method based on triangulation to enhance the antiinterference ability and recognition accuracy of early warning systems.展开更多
Previous studies have identified two group-level processes,neural representations of interracial between-group difference and intraracial within-group similarity,that contribute to the racial categorization of faces.W...Previous studies have identified two group-level processes,neural representations of interracial between-group difference and intraracial within-group similarity,that contribute to the racial categorization of faces.What remains unclear is how the brain tracks race-related information that varies across different faces as an individual-level neural process involved in race perception.In three studies,we recorded functional MRI signals when Chinese adults performed different tasks on morphed faces in which proportions of pixels contributing to perceived racial identity(Asian vs White)and expression(pain vs neutral)varied independently.We found that,during a pain expression judgment task,tracking other-race and same-race-related information in perceived faces recruited the ventral occipitotemporal cortices and medial prefrontal/anterior temporal cortices,respectively.However,neural tracking of race-related information tended to be weakened during explicit race judgments on perceived faces.During a donation task,the medial prefrontal activity also tracked race-related information that distinguished between two perceived faces for altruistic decision-making and encoded the Euclidean distance between the two faces that predicted decision-making speeds.Our findings revealed task-dependent neural mechanisms underlying the tracking of race-related information during face perception and altruistic decision-making.展开更多
In interpersonal communication,the principle of politeness is an important communicative principle that is widely applied in people’s daily life.However,the communication patterns of the principle of politeness and f...In interpersonal communication,the principle of politeness is an important communicative principle that is widely applied in people’s daily life.However,the communication patterns of the principle of politeness and face theory in the travel planning process among friends still need further exploration.This study aims to analyze the specific manifestations of these principles in the communication patterns of travel planning among friends through a pragmatic interpretation of the principle of politeness and face theory,providing a new perspective for understanding linguistic behavior in interpersonal relationships.展开更多
Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by de...Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.展开更多
False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
Flower-infecting fungi have caused many economically important diseases in crop production.The fungal pathogen Ustilaginoidea virens infects developing rice florets,causing false smut disease,which leads to reduced gr...Flower-infecting fungi have caused many economically important diseases in crop production.The fungal pathogen Ustilaginoidea virens infects developing rice florets,causing false smut disease,which leads to reduced grain yield and quality,as well as contamination with mycotoxins that pose hazards to human health and food security.To ensure rice production,substantial efforts have been made to understand the interaction between rice and U.virens.In this review,we summarize the current understanding of rice resistance mechanisms to U.virens.We discuss the evaluation of false smut resistance,quantitative resistance loci,potential defense strategies of rice panicles,pathogen effector-driven identification of resistance-related genes,and engineering of false smut resistance.We conclude by proposing an integrated defense system that includes disease avoidance,immune response,metabolic adaptation,and the inhibition of susceptibility factors.Furthermore,we outline four critical stages of interaction between rice and U.virens that are essential for understanding and enhancing organ-specific rice resistance to false smut disease.展开更多
基金funded by the National Natural Science Foundation of China (No. 51576188)
文摘Infrared false target is an important mean to induce the infrared-guided weapons,and the key issue is how to keep the surface temperature of the infrared false target to be the same as that of the object to be protected.One-dimensional heat transfer models of a metal plate and imitative material were established to explore the influences of the thermophysical properties of imitative material on the surface temperature difference（STD） between the metal plate and imitative material which were subjected to periodical ambient conditions.It is elucidated that the STD is determined by the imitative material’s dimensionless thickness（dim*,） and the thermal inertia(Pim).When dim* is above 1.0,the STD is invariable as long as Pim is a constant.And if the dimensionless thickness of metal plate（d,m*） is also larger than 1.0,the STD approaches to zero as long as Pimis the same as the thermal inertia of metal plate（Pm）.When dim* is between 0.08 and 1,the STD varies irregularly with Pim and dim*.However,if dm* is also in the range of 0.08-1,the STD approaches to zero on condition that Pim=Pm and dim*= dm*.If dim*,is below 0.08,the STD is unchanged when Pimdim* is a constant.And if dm* is also less than 0.08,the STD approaches to zero as long as Pimdim* = Pmdm*.Furthermore,an applicationoriented discussion indicates that the imitative material can be both light and thin via the application of the phase change material with a preset STD because of its high specific heat capacity during the phase transition process.
文摘巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported by the National Natural Science Foundation of China(Nos.U2441250,62301380,and 62231027)Natural Science Basic Research Program of Shaanxi,China(2024JC-JCQN-63)+3 种基金the Key Research and Development Program of Shaanxi,China(No.2023-YBGY-249)the Guangxi Key Research and Development Program,China(No.2022AB46002)the China Postdoctoral Science Foundation(No.2022M722504 and 2024T170696)the Innovation Capability Support Program of Shaanxi,China(No.2024RS-CXTD-01).
文摘Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.
文摘Despite advancements in neuroimaging,false positive diagnoses of intracranial aneurysms remain a significant concern.This article examines the causes,prevalence,and implications of such false-positive diagnoses.We discuss how conditions like arterial occlusion with vascular stump formation and infundibular widening can mimic aneurysms,particularly in the anterior circulation.The article compares various imaging modalities,including computer tomography angiogram,magnetic resonance imaging/angiography,and digital subtraction angiogram,highlighting their strengths and limitations.We emphasize the im-portance of accurate differentiation to avoid unnecessary surgical interventions.The potential of emerging technologies,such as high-resolution vessel wall ima-ging and deep neural networks for automated detection,is explored as promising avenues for improving diagnostic accuracy.This manuscript underscores the need for continued research and clinical vigilance in the diagnosis of intracranial aneurysms.
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
基金Supported by Natural Science Foundation of Tianjin,No.21JCZDJC00060 and No.21JCYBJC00180Tianjin Health and Medical Science and Technology Project,No.TJWJ2023QN040National Key Research and Development Program,No.2017YFC1700606 and No.2017YFC1700604.
文摘BACKGROUND Certain subgroups are at an increased risk of false fecal immunochemical test(FIT)results;however,related studies are limited,and the available evidence is conflicting.AIM To evaluate factors associated with false-positive and false-negative FIT results.METHODS This retrospective study was based on the database of the Tianjin Colorectal Cancer Screening Program from 2012 to 2020.A total of 4129947 residents aged 40-74 years completed at least one FIT.Of these,24890 asymptomatic participants who underwent colonoscopy examinations and completed lifestyle questionnaires were included in the analysis.Multivariable logistic regression was performed to identify the factors associated with false FIT results.RESULTS Among the overall screening population,88687(2.15%)participants tested positive for FIT.The sensitivity,specificity,positive predictive value,and negative predictive value of FIT for advanced neoplasms were 58.2%,44.8%,9.7%,and 91.3%,respectively.Older age,female sex,smoking,alcohol consumption,higher body mass index,and hemorrhoids were significantly associated with increased odds of false-positive and lower odds of falsenegative FIT results.Moreover,features of high-grade dysplasia or villous for advanced adenoma and the presence of cancer were also associated with lower odds of false-negative results,while irregular exercise and diverticulum were associated with higher odds of false-positive results.CONCLUSION FIT results may be inaccurate in certain subgroups.Our results provide important evidence for further individualization of screening strategies.
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
文摘Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications,and the major challenge is false positives that occur during pedestrian detection.The emergence of various Convolutional Neural Network-based detection strategies substantially enhances pedestrian detection accuracy but still does not solve this problem well.This paper deeply analyzes the detection framework of the two-stage CNN detection methods and finds out false positives in detection results are due to its training strategy misclassifying some false proposals,thus weakening the classification capability of the following subnetwork and hardly suppressing false ones.To solve this problem,this paper proposes a pedestrian-sensitive training algorithm to help two-stage CNN detection methods effectively learn to distinguish the pedestrian and non-pedestrian samples and suppress the false positives in the final detection results.The core of the proposed algorithm is to redesign the training proposal generating scheme for the two-stage CNN detection methods,which can avoid a certain number of false ones that mislead its training process.With the help of the proposed algorithm,the detection accuracy of the MetroNext,a smaller and more accurate metro passenger detector,is further improved,which further decreases false ones in its metro passenger detection results.Based on various challenging benchmark datasets,experiment results have demonstrated that the feasibility of the proposed algorithm is effective in improving pedestrian detection accuracy by removing false positives.Compared with the existing state-of-the-art detection networks,PSTNet demonstrates better overall prediction performance in accuracy,total number of parameters,and inference time;thus,it can become a practical solution for hunting pedestrians on various hardware platforms,especially for mobile and edge devices.
基金supported by the Spark Program of Earthquake Science and Technology(No.XH23003C)。
文摘The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach the warning target area,and carry out automatic disposal of lifeline engineering facilities.Through the construction of the National Earthquake Intensity Rapid Reporting and Early Warning Project,an earthquake early warning network consisting of over 1900 monitoring stations has been established in the Beijing-Tianjin-Hebei Urban Agglomeration.The early warning system has achieved second level earthquake warning and minute level intensity rapid reporting.The implementation of these functions relies on the system's ability to timely,accurately,and reliably identify seismic waves.But with the development of social economy,the background noise of earthquake observation environment is becoming increasingly complex,which brings certain challenges to earthquake wave recognition,some interference events have the risk of triggering the earthquake warning system incorrectly.Therefore,this article focuses on seismic wave recognition in complex noise environments and proposes a seismic wave detection method based on triangulation to enhance the antiinterference ability and recognition accuracy of early warning systems.
基金supported by the National Natural Science Foundation of China(32230043 and 32371092)the Ministry of Science and Technology of China(2019YFA0707103)+1 种基金Das Chinesisch-Deutsche Zentrum für Wissenschaftsförderung(M-0093)the High-performance Computing Platform of Peking University.
文摘Previous studies have identified two group-level processes,neural representations of interracial between-group difference and intraracial within-group similarity,that contribute to the racial categorization of faces.What remains unclear is how the brain tracks race-related information that varies across different faces as an individual-level neural process involved in race perception.In three studies,we recorded functional MRI signals when Chinese adults performed different tasks on morphed faces in which proportions of pixels contributing to perceived racial identity(Asian vs White)and expression(pain vs neutral)varied independently.We found that,during a pain expression judgment task,tracking other-race and same-race-related information in perceived faces recruited the ventral occipitotemporal cortices and medial prefrontal/anterior temporal cortices,respectively.However,neural tracking of race-related information tended to be weakened during explicit race judgments on perceived faces.During a donation task,the medial prefrontal activity also tracked race-related information that distinguished between two perceived faces for altruistic decision-making and encoded the Euclidean distance between the two faces that predicted decision-making speeds.Our findings revealed task-dependent neural mechanisms underlying the tracking of race-related information during face perception and altruistic decision-making.
文摘In interpersonal communication,the principle of politeness is an important communicative principle that is widely applied in people’s daily life.However,the communication patterns of the principle of politeness and face theory in the travel planning process among friends still need further exploration.This study aims to analyze the specific manifestations of these principles in the communication patterns of travel planning among friends through a pragmatic interpretation of the principle of politeness and face theory,providing a new perspective for understanding linguistic behavior in interpersonal relationships.
基金supported in part by the National Science Foundation of China(62373240,62273224,U24A20259).
文摘Dear Editor,The attacker is always going to intrude covertly networked control systems(NCSs)by dynamically changing false data injection attacks(FDIAs)strategy,while the defender try their best to resist attacks by designing defense strategy on the basis of identifying attack strategy,maintaining stable operation of NCSs.To solve this attack-defense game problem,this letter investigates optimal secure control of NCSs under FDIAs.First,for the alterations of energy caused by false data,a novel attack-defense game model is constructed,which considers the changes of energy caused by the actions of the defender and attacker in the forward and feedback channels.
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
基金supported by grants from the Sichuan Natural Science Foundation(2024NSFSC0335 and SCCXTD-2024-SD-4 to D.-Q.L.)the National Natural Science Foundation of China(32372490 and 32072503 to J.F.)。
文摘Flower-infecting fungi have caused many economically important diseases in crop production.The fungal pathogen Ustilaginoidea virens infects developing rice florets,causing false smut disease,which leads to reduced grain yield and quality,as well as contamination with mycotoxins that pose hazards to human health and food security.To ensure rice production,substantial efforts have been made to understand the interaction between rice and U.virens.In this review,we summarize the current understanding of rice resistance mechanisms to U.virens.We discuss the evaluation of false smut resistance,quantitative resistance loci,potential defense strategies of rice panicles,pathogen effector-driven identification of resistance-related genes,and engineering of false smut resistance.We conclude by proposing an integrated defense system that includes disease avoidance,immune response,metabolic adaptation,and the inhibition of susceptibility factors.Furthermore,we outline four critical stages of interaction between rice and U.virens that are essential for understanding and enhancing organ-specific rice resistance to false smut disease.
