Objective To determine the relationship between TSH receptor gene mutations and autonomously functioning thyroid adenomas (AFTAs). Methods The thyroid samples from 14 cases of diagnosed AFTAs were analyzed, with nor...Objective To determine the relationship between TSH receptor gene mutations and autonomously functioning thyroid adenomas (AFTAs). Methods The thyroid samples from 14 cases of diagnosed AFTAs were analyzed, with normal thyroid specimens adjacent to the tumors as controls. The 155 base pairs DNA fragments which encompassed the third cytoplasmic loop and the sixth transmembrane segments in the TSH receptor gene exon 10 were amplified by Polymerase chain reaction (PCR) and analyzed by the single-strand conformation polymorphism (SSCP). Direct sequencing of the PCR products was performed with Prism Dye Terminator Cycle Sequencing Core Kit. Results 6 of 14 AFTA specimens displayed abnormal migration in SSCP analysis. In sequence analysis of 3 abnormally migrated samples, one base substitution at nucleotide 1957 (A to C) and two same insertion mutations of one adenosine nucleotide between nucleotide 1972 and 1973 were identified. No mutations were found in controls. Conclusion This study confirmed the presence of TSH receptor gene mutations in AFTAs; both one-point substitution mutation and one-base insertion mutation were found to be responsible for the pathogenesis of AFTAs.展开更多
The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State N...The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.展开更多
Thermal cloaks offer the potential to conceal internal objects from detection or to prevent thermal shock by controlling external heat flow. However, most conventional natural materials lack the desired flexibility an...Thermal cloaks offer the potential to conceal internal objects from detection or to prevent thermal shock by controlling external heat flow. However, most conventional natural materials lack the desired flexibility and versatility required for on-demand thermal manipulation. We propose a solution in the form of homogeneous multilayer thermodynamic cloaks. Through an ingenious design, these cloaks achieve exceptional and extreme parameters, enabling the distribution of multiple materials in space. We first investigate the effects of important design parameters on the thermal shielding effectiveness of conventional thermal cloaks. Subsequently, we introduce an autonomous tuning function for the thermodynamic cloak, accomplished by leveraging two phase transition materials as thermal conductive layers. Remarkably, this tuning function does not require any energy input. Finite element analysis results demonstrate a significant reduction in the temperature gradient inside the thermal cloak compared to the surrounding background. This reduction indicates the cloak’s remarkable ability to manipulate the spatial thermal field. Furthermore, the utilization of materials undergoing phase transition leads to an increase in thermal conductivity, enabling the cloak to achieve the opposite variation of the temperature field between the object region and the background. This means that, while the temperature gradient within the cloak decreases, the temperature gradient in the background increases. This work addresses a compelling and crucial challenge in the realm of thermal metamaterials, i.e., autonomous tuning of the thermal field without energy input. Such an achievement is currently unattainable with existing natural materials. This study establishes the groundwork for the application of thermal metamaterials in thermodynamic cloaks, with potential extensions into thermal energy harvesting, thermal camouflage, and thermoelectric conversion devices.By harnessing phonons, our findings provide an unprecedented and practical approach to flexibly implementing thermal cloaks and manipulating heat flow.展开更多
Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investmen...Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck.This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data,augmented with memory of recent upand-down fluctuations occur in the data of short-term stock movement.The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process,which take care of decision-making in an ever-changing dynamic environment.Three trading strategies were implemented in this model;namely,a Price Model Strategy,a Probabilistic Model Strategy,and a Bayesian Gated Recurrent Unit Strategy,each leveraging the respective model’s outputs to optimize trading decisions.The experimental results show that,compared with the standard Gated Recurrent Unit models,the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment.The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.展开更多
Hi Kazak Autonomous Prefecture is situated in the northwestern region of Northwest China's Xinjiang Uygur Autonomous Region.Ili not only boasts beautiful natural landscapes,such as colorful oceans of flowers,vast ...Hi Kazak Autonomous Prefecture is situated in the northwestern region of Northwest China's Xinjiang Uygur Autonomous Region.Ili not only boasts beautiful natural landscapes,such as colorful oceans of flowers,vast grasslands,and lofty snow mountains,it is also known for its profound historical and cultural heritage。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
This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limita...This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.展开更多
Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimo...Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimodal sensor fusion,often struggle with noisy data and demand high-performance GPUs,leading to sensor misalignment and performance degradation.This paper introduces an Enhanced Channel Attention BEV(ECABEV),a novel approach designed to address the challenges under insufficient GPU memory conditions.ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism,which utilizes global average and max pooling to effectively filter out noise while preserving discriminative features.Furthermore,an improved fusion approach is proposed to efficiently merge categorical data across modalities.To reduce computational overhead,a bilinear interpolation layer normalizationmethod is devised to ensure spatial feature fidelity.Moreover,a scalable crossentropy loss function is further designed to handle the imbalanced classes with less computational efficiency sacrifice.Extensive experiments on the nuScenes dataset demonstrate that ECABEV achieves state-of-the-art performance with an IoU of 39.961,using a lightweight ViT-B/14 backbone and lower resolution(224×224).Our approach highlights its cost-effectiveness and practical applicability,even on low-end devices.The code is publicly available at:https://github.com/YYF-CQU/ECABEV.git.展开更多
Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been i...Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.展开更多
Embodied intelligence is redefining policing On the first day of 2026 chunyun,a period of high mobility associated with the Chinese New Year,the city of Jingzhou in Hubei Province welcomed new participants in road saf...Embodied intelligence is redefining policing On the first day of 2026 chunyun,a period of high mobility associated with the Chinese New Year,the city of Jingzhou in Hubei Province welcomed new participants in road safety:police robots capable of moving autonomously and interacting with passengers.Deployed on a trial basis on 2 February,these robots quickly demonstrated their usefulness in various urban settings.展开更多
On December 8,1995,in accordance with historical conventions and religious rituals,and with the approval of the State Council of the People's Republic of China,the enthronement ceremony of Panchen Erdeni Chos-kyi ...On December 8,1995,in accordance with historical conventions and religious rituals,and with the approval of the State Council of the People's Republic of China,the enthronement ceremony of Panchen Erdeni Chos-kyi rGyal-po,the 11 th Panchen Erdeni,was solemnly held at the Tashilhunpo Monastery in Xigaze City,southwest China's Xizang Autonomous Region.This grand event successfully completed the major religious matter of the reincarnation of the late 10th Panchen Erdeni and fully embodied the policy on freedom of religious belief in China,earning the heartfelt support of monks and lay believers from all sectors of Xizang.展开更多
As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a no...As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.展开更多
This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on th...This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.展开更多
When Dachung Wochen,a 35-year-old blind Tibetan,walks in the bustling streets of Lhasa,capital of the Xizang Autonomous Region,with his guide dog Fuju,people are amazed by their tacit cooperation.Fuju helps his master...When Dachung Wochen,a 35-year-old blind Tibetan,walks in the bustling streets of Lhasa,capital of the Xizang Autonomous Region,with his guide dog Fuju,people are amazed by their tacit cooperation.Fuju helps his master navigate every obstacle on the road.After spending only a year with him,he understands most of his commands in Tibetan.Dachung Wochen's journey with the four-year-old canine began in April 2024.After 28 days of professional training at the China Guide Dog Training Center in Dalian,Liaoning Province,Dachung,as he is called by friends and family,took Fuju to Xizang by air and rail.展开更多
Spinal cord injury(SCI) often results in permanent dysfunction of locomotion,sensation,and autonomic regulation,imposing a substantial burden on both individuals and society(Anjum et al.,2020).SCI has a complex pathop...Spinal cord injury(SCI) often results in permanent dysfunction of locomotion,sensation,and autonomic regulation,imposing a substantial burden on both individuals and society(Anjum et al.,2020).SCI has a complex pathophysiology:an initial primary injury(mechanical trauma,axonal disruption,and hemorrhage) is followed by a progressive secondary injury cascade that involves ischemia,neuronal loss,and inflammation.Given the challenges in achieving regeneration of the injured spinal cord,neuroprotection has been at the forefront of clinical research.展开更多
In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to...In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.展开更多
As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety o...As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles.In this work,we introduce a novel neural scene representation called Street Detection Gaussians(SDGs),which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection.At its core lies the dynamic Gaussian representation,where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution.The framework’s radar-enhanced perception module learns cross-modal correlations between sparse radardata anddense visual features,resulting ina22%reduction inocclusionerrors compared tovisiononly systems.A breakthrough differentiable rendering pipeline back-propagates semantic detection losses throughout the entire 3D reconstruction process,enabling the optimization of both geometric and semantic fidelity.Evaluated on the Waymo Open Dataset and the KITTI Dataset,the system achieves real-time performance(135 Frames Per Second(FPS)),photorealistic quality(Peak Signal-to-Noise Ratio(PSNR)34.9 dB),and state-of-the-art detection accuracy(78.1%Mean Average Precision(mAP)),demonstrating a 3.8×end-to-end improvement over existing hybrid approaches while enabling seamless integration with autonomous driving stacks.展开更多
Autonomous navigation is a key technology for unmanned motion platforms to perform their tasks smoothly.The current approaches for daytime polarization navigation have been extensively researched.However,the polarizat...Autonomous navigation is a key technology for unmanned motion platforms to perform their tasks smoothly.The current approaches for daytime polarization navigation have been extensively researched.However,the polarization light intensity is the fundamental information within the polarization image,and the light intensity at night is 6-8 orders of magnitude lower than that during the day,which increase the noise and the loss of local polarization information due to occlusion,resulting in a significant decrease in the polarization orientation accuracy.Aimed at the problem,a bio-inspired model is introduced to denoise and enhance weak nighttime polarization patterns.Further,to address the issue of outlier interference in the occluded environment during practical application,a fast-fitting method of the solar meridian based on the anti-symmetric distribution of the polarization angle adjusted by Proportional and Differential(PD)control is proposed.The experimental results show that the method proposed in this paper achieves a dynamic orientation error Root Mean Square Error(RMSE)of 0.7°in the weak polarization mode at night and in the presence of local occlusion.The proposed method has strong robustness under weak polarization occlusion at night,and the orientation accuracy is improved by 97%and 80%in comparison to the least squares method,which provides a new method for polarization navigation at night.This effectively improves the robustness and environmental applicability of the bionic polarization compass for nighttime applications.展开更多
PHOTO NEWS Unforgettable Winter Break Tourists take selfies in front of snow sculptures in the Fenghuang Mountain Scenic Area,Yakeshi City in Inner Mongolia Autonomous Region,on December 7,2025.
文摘Objective To determine the relationship between TSH receptor gene mutations and autonomously functioning thyroid adenomas (AFTAs). Methods The thyroid samples from 14 cases of diagnosed AFTAs were analyzed, with normal thyroid specimens adjacent to the tumors as controls. The 155 base pairs DNA fragments which encompassed the third cytoplasmic loop and the sixth transmembrane segments in the TSH receptor gene exon 10 were amplified by Polymerase chain reaction (PCR) and analyzed by the single-strand conformation polymorphism (SSCP). Direct sequencing of the PCR products was performed with Prism Dye Terminator Cycle Sequencing Core Kit. Results 6 of 14 AFTA specimens displayed abnormal migration in SSCP analysis. In sequence analysis of 3 abnormally migrated samples, one base substitution at nucleotide 1957 (A to C) and two same insertion mutations of one adenosine nucleotide between nucleotide 1972 and 1973 were identified. No mutations were found in controls. Conclusion This study confirmed the presence of TSH receptor gene mutations in AFTAs; both one-point substitution mutation and one-base insertion mutation were found to be responsible for the pathogenesis of AFTAs.
文摘The autonomously trading agents described in this paper produce a decision to act such as: buy, sell or hold, based on the input data. In this work, we have simulated autonomously trading agents using the Echo State Network (ESNs) model. We generate a collection of trading agents that use different trading strategies using Evolutionary Programming (EP). The agents are tested on EUR/ USD real market data. The main goal of this study is to test the overall performance of this collection of agents when they are active simultaneously. Simulation results show that using different agents concurrently outperform a single agent acting alone.
基金supported by the National Natural Science Foundation of China (Grant No. 11774278)the Fundamental Research Funds for Central Universities (Grant No. 2012jdgz04)。
文摘Thermal cloaks offer the potential to conceal internal objects from detection or to prevent thermal shock by controlling external heat flow. However, most conventional natural materials lack the desired flexibility and versatility required for on-demand thermal manipulation. We propose a solution in the form of homogeneous multilayer thermodynamic cloaks. Through an ingenious design, these cloaks achieve exceptional and extreme parameters, enabling the distribution of multiple materials in space. We first investigate the effects of important design parameters on the thermal shielding effectiveness of conventional thermal cloaks. Subsequently, we introduce an autonomous tuning function for the thermodynamic cloak, accomplished by leveraging two phase transition materials as thermal conductive layers. Remarkably, this tuning function does not require any energy input. Finite element analysis results demonstrate a significant reduction in the temperature gradient inside the thermal cloak compared to the surrounding background. This reduction indicates the cloak’s remarkable ability to manipulate the spatial thermal field. Furthermore, the utilization of materials undergoing phase transition leads to an increase in thermal conductivity, enabling the cloak to achieve the opposite variation of the temperature field between the object region and the background. This means that, while the temperature gradient within the cloak decreases, the temperature gradient in the background increases. This work addresses a compelling and crucial challenge in the realm of thermal metamaterials, i.e., autonomous tuning of the thermal field without energy input. Such an achievement is currently unattainable with existing natural materials. This study establishes the groundwork for the application of thermal metamaterials in thermodynamic cloaks, with potential extensions into thermal energy harvesting, thermal camouflage, and thermoelectric conversion devices.By harnessing phonons, our findings provide an unprecedented and practical approach to flexibly implementing thermal cloaks and manipulating heat flow.
文摘Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck.This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data,augmented with memory of recent upand-down fluctuations occur in the data of short-term stock movement.The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process,which take care of decision-making in an ever-changing dynamic environment.Three trading strategies were implemented in this model;namely,a Price Model Strategy,a Probabilistic Model Strategy,and a Bayesian Gated Recurrent Unit Strategy,each leveraging the respective model’s outputs to optimize trading decisions.The experimental results show that,compared with the standard Gated Recurrent Unit models,the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment.The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.
文摘Hi Kazak Autonomous Prefecture is situated in the northwestern region of Northwest China's Xinjiang Uygur Autonomous Region.Ili not only boasts beautiful natural landscapes,such as colorful oceans of flowers,vast grasslands,and lofty snow mountains,it is also known for its profound historical and cultural heritage。
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.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00245084)by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(RS-2024-00415938,HRD Program for Industrial Innovation)and Soonchunhyang University.
文摘This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.
基金funded by the National Natural Science Foundation of China,grant number 62262045the Fundamental Research Funds for the Central Universities,grant number 2023CDJYGRH-YB11the Open Funding of SUGON Industrial Control and Security Center,grant number CUIT-SICSC-2025-03.
文摘Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimodal sensor fusion,often struggle with noisy data and demand high-performance GPUs,leading to sensor misalignment and performance degradation.This paper introduces an Enhanced Channel Attention BEV(ECABEV),a novel approach designed to address the challenges under insufficient GPU memory conditions.ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism,which utilizes global average and max pooling to effectively filter out noise while preserving discriminative features.Furthermore,an improved fusion approach is proposed to efficiently merge categorical data across modalities.To reduce computational overhead,a bilinear interpolation layer normalizationmethod is devised to ensure spatial feature fidelity.Moreover,a scalable crossentropy loss function is further designed to handle the imbalanced classes with less computational efficiency sacrifice.Extensive experiments on the nuScenes dataset demonstrate that ECABEV achieves state-of-the-art performance with an IoU of 39.961,using a lightweight ViT-B/14 backbone and lower resolution(224×224).Our approach highlights its cost-effectiveness and practical applicability,even on low-end devices.The code is publicly available at:https://github.com/YYF-CQU/ECABEV.git.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.
文摘Embodied intelligence is redefining policing On the first day of 2026 chunyun,a period of high mobility associated with the Chinese New Year,the city of Jingzhou in Hubei Province welcomed new participants in road safety:police robots capable of moving autonomously and interacting with passengers.Deployed on a trial basis on 2 February,these robots quickly demonstrated their usefulness in various urban settings.
文摘On December 8,1995,in accordance with historical conventions and religious rituals,and with the approval of the State Council of the People's Republic of China,the enthronement ceremony of Panchen Erdeni Chos-kyi rGyal-po,the 11 th Panchen Erdeni,was solemnly held at the Tashilhunpo Monastery in Xigaze City,southwest China's Xizang Autonomous Region.This grand event successfully completed the major religious matter of the reincarnation of the late 10th Panchen Erdeni and fully embodied the policy on freedom of religious belief in China,earning the heartfelt support of monks and lay believers from all sectors of Xizang.
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB BremenThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP2/367/46)+1 种基金This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.
文摘This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.
文摘When Dachung Wochen,a 35-year-old blind Tibetan,walks in the bustling streets of Lhasa,capital of the Xizang Autonomous Region,with his guide dog Fuju,people are amazed by their tacit cooperation.Fuju helps his master navigate every obstacle on the road.After spending only a year with him,he understands most of his commands in Tibetan.Dachung Wochen's journey with the four-year-old canine began in April 2024.After 28 days of professional training at the China Guide Dog Training Center in Dalian,Liaoning Province,Dachung,as he is called by friends and family,took Fuju to Xizang by air and rail.
文摘Spinal cord injury(SCI) often results in permanent dysfunction of locomotion,sensation,and autonomic regulation,imposing a substantial burden on both individuals and society(Anjum et al.,2020).SCI has a complex pathophysiology:an initial primary injury(mechanical trauma,axonal disruption,and hemorrhage) is followed by a progressive secondary injury cascade that involves ischemia,neuronal loss,and inflammation.Given the challenges in achieving regeneration of the injured spinal cord,neuroprotection has been at the forefront of clinical research.
基金supported by Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01842,Artificial Intelligence Graduate School Program(GIST))supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)(RS-2025-25448249+1 种基金Automotive Industry Technology Development(R&D)Program)supported by the Regional Innovation System&Education(RISE)programthrough the(Gwangju RISE Center),funded by the Ministry of Education(MOE)and the Gwangju Metropolitan City,Republic of Korea(2025-RISE-05-001).
文摘In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.
文摘As a cornerstone for applications such as autonomous driving,3D urban perception is a burgeoning field of study.Enhancing the performance and robustness of these perception systems is crucial for ensuring the safety of next-generation autonomous vehicles.In this work,we introduce a novel neural scene representation called Street Detection Gaussians(SDGs),which redefines urban 3D perception through an integrated architecture unifying reconstruction and detection.At its core lies the dynamic Gaussian representation,where time-conditioned parameterization enables simultaneous modeling of static environments and dynamic objects through physically constrained Gaussian evolution.The framework’s radar-enhanced perception module learns cross-modal correlations between sparse radardata anddense visual features,resulting ina22%reduction inocclusionerrors compared tovisiononly systems.A breakthrough differentiable rendering pipeline back-propagates semantic detection losses throughout the entire 3D reconstruction process,enabling the optimization of both geometric and semantic fidelity.Evaluated on the Waymo Open Dataset and the KITTI Dataset,the system achieves real-time performance(135 Frames Per Second(FPS)),photorealistic quality(Peak Signal-to-Noise Ratio(PSNR)34.9 dB),and state-of-the-art detection accuracy(78.1%Mean Average Precision(mAP)),demonstrating a 3.8×end-to-end improvement over existing hybrid approaches while enabling seamless integration with autonomous driving stacks.
基金co-supported by the Excellent Youth Foundation of Shanxi Province,China(No.202103021222011)the Key Research and Development project of Shanxi Province of China(No.202202020101002)+3 种基金the Fundamental Research Program of Shanxi Province of China(No.202303021211150)the Aviation Science Foundation of China(No.2022Z0220U0002)the Graduate Education Innovation Plan Project of Shanxi Province,China(No.2023KY588)the Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement,China(No.201905D121001).
文摘Autonomous navigation is a key technology for unmanned motion platforms to perform their tasks smoothly.The current approaches for daytime polarization navigation have been extensively researched.However,the polarization light intensity is the fundamental information within the polarization image,and the light intensity at night is 6-8 orders of magnitude lower than that during the day,which increase the noise and the loss of local polarization information due to occlusion,resulting in a significant decrease in the polarization orientation accuracy.Aimed at the problem,a bio-inspired model is introduced to denoise and enhance weak nighttime polarization patterns.Further,to address the issue of outlier interference in the occluded environment during practical application,a fast-fitting method of the solar meridian based on the anti-symmetric distribution of the polarization angle adjusted by Proportional and Differential(PD)control is proposed.The experimental results show that the method proposed in this paper achieves a dynamic orientation error Root Mean Square Error(RMSE)of 0.7°in the weak polarization mode at night and in the presence of local occlusion.The proposed method has strong robustness under weak polarization occlusion at night,and the orientation accuracy is improved by 97%and 80%in comparison to the least squares method,which provides a new method for polarization navigation at night.This effectively improves the robustness and environmental applicability of the bionic polarization compass for nighttime applications.
文摘PHOTO NEWS Unforgettable Winter Break Tourists take selfies in front of snow sculptures in the Fenghuang Mountain Scenic Area,Yakeshi City in Inner Mongolia Autonomous Region,on December 7,2025.
