Bit-plane decomposition makes images obtain a number of layers. According to the amount of data information, images are encrypted, and the paper proposes image encryption method with Chaotic Mapping based on multi-lay...Bit-plane decomposition makes images obtain a number of layers. According to the amount of data information, images are encrypted, and the paper proposes image encryption method with Chaotic Mapping based on multi-layer parameter disturbance. The advantage of multi-layer parameter disturbance is that it not only scrambles pixel location of images, but also changes pixel values of images. Bit-plane decomposition can increase the space of key. And using chaotic sequence generated by chaotic system with different complexities to encrypt layers with different information content can save operation time. The simulation experiments show that using chaotic mapping in image encryption method based on multi-layer parameter disturbance can cover plaintext effectively and safely, which makes it achieve ideal encryption effect.展开更多
This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the cha...This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the charge,a peak overpressure prediction model for the composite charge under singlepoint detonation and simultaneous detonation was established.The effects of the charge structure and initiation method on the overpressure field characteristics were investigated in AUTODYN simulation.The accuracy of the prediction model and the reliability of the numerical simulation method were subsequently verified in a series of static explosion experiments.The results reveal that the mass of the inner charge was the key factor determining the peak overpressure of the composite charge under single-point detonation.The peak overpressure in the radial direction improved apparently with an increase in the aspect ratio of the charge.The overpressure curves in the axial direction exhibited a multi-peak phenomenon,and the secondary peak overpressure even exceeded the primary peak at distances of 30D and 40D(where D is the charge diameter).The difference in peak overpressure among azimuth angles of 0-90°gradually decreased with an increase in the propagation distance of the shock wave.The coupled effect of the detonation energy of the inner and outer charge under simultaneous detonation improved the overpressure in both radial and axial directions.The difference in peak overpressure obtained from model prediction and experimental measurements was less than 16.4%.展开更多
When estimating ionospheric Total Electron Content(TEC)using Global Navigation Satellite System(GNSS)observations,one of the signifcant error sources is the mapping error introduced by slant to vertical TEC conversion...When estimating ionospheric Total Electron Content(TEC)using Global Navigation Satellite System(GNSS)observations,one of the signifcant error sources is the mapping error introduced by slant to vertical TEC conversion and vice versa.A single-layer Mapping Function(MF)based on a thin-shell assumption of the Earth’s ionosphere is commonly used for TEC conversion.However,the accuracy of single-layer MF is susceptible to the inaccurate fxing of the ionospheric single-layer height.In order to fnd a mapping approach less sensitive to the choice of ionospheric efective height we defned a multi-layer ionosphere mapping function and investigated its performance in comparison with the single-layer model.We found that the multi-layer MF outperforms the single-layer MF when computing GNSS receiver Diferential Code Biases(DCBs)especially at low latitude and equatorial regions where ionosphere is highly dynamic and difcult to model.When compared with the International GNSS Services(IGS)products,we found that the mean receiver DCB estimation is improved(closer to benchmark)by about 0.14-0.27 ns and 0.30-0.78 ns during days in 2019 and 2023,respectively.We found that the receiver DCB estimation improves for about 66-87%receivers.This is also refected in Global Ionosphere Maps(GIMs)showing better performance for the multi-layer MF when comparing with IGS GIMs.Our investigation using GNSS observations onboard Low Earth Orbiting(LEO)satellites shows that the multi-layer MF can be successfully applied in computing satellite and receiver DCBs accurately.展开更多
Dear Editor,This letter proposes an innovative open-vocabulary 3D scene understanding model based on visual-language model.By efficiently integrating 3D point cloud data,image data,and text data,our model effectively ...Dear Editor,This letter proposes an innovative open-vocabulary 3D scene understanding model based on visual-language model.By efficiently integrating 3D point cloud data,image data,and text data,our model effectively overcomes the segmentation problem[1],[2]of traditional models dealing with unknown categories[3].By deeply learning the deep semantic mapping between vision and language,the network significantly improves its ability to recognize unlabeled categories and exceeds current state-of-the-art methods in the task of scene understanding in open-vocabulary.展开更多
同步定位与建图(simultaneous localization and mapping, SLAM)技术是移动机器人研究及应用的关键问题,旨在解决机器人在复杂环境中实现自主定位与地图构建等功能。对SLAM的系统组成、关键技术及应用进行了简要介绍;重点围绕特征点法...同步定位与建图(simultaneous localization and mapping, SLAM)技术是移动机器人研究及应用的关键问题,旨在解决机器人在复杂环境中实现自主定位与地图构建等功能。对SLAM的系统组成、关键技术及应用进行了简要介绍;重点围绕特征点法、滤波法、图优化法、多传感器融合和动态场景5个方面,综述了SLAM系统的关键技术、国内外研究现状及标志性应用进展;并结合代表性系统,比较分析了不同方法之间的优缺点,详细阐述了多传感器融合SLAM系统,同时对复杂场景下的SLAM技术进行了展望。展开更多
By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and...By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.展开更多
针对HIT-TENA体系结构在试验过程中对试验场景的显示需求,以Visual Studio 2008为开发平台,利用面向对象编程语言C++,基于Map X控件,开发二维场景显示软件。阐述了HIT-TENA体系结构的组成及二维场景显示软件在HIT-TENA中的地位与作用,...针对HIT-TENA体系结构在试验过程中对试验场景的显示需求,以Visual Studio 2008为开发平台,利用面向对象编程语言C++,基于Map X控件,开发二维场景显示软件。阐述了HIT-TENA体系结构的组成及二维场景显示软件在HIT-TENA中的地位与作用,根据二维场景显示软件的功能要求,使用UML语言对该软件进行了需求分析、概要设计及详细设计,详细介绍了该软件与HIT-TENA中间件的数据交互接口实现以及设备信息的实时绘制的实现,并利用一个虚拟试验系统对软件进行测试,软件运行良好。展开更多
为实现实景和三维空间信息模型的融合和精细化表达,文中以陕西理工大学南校区为例,基于二、三维矢量数据、实景模型和纹理贴图,借助MapGIS三维平台和精细建模工具,将室内和室外三维场景表达和功能展示结合,实现了校园构筑物三维空间信...为实现实景和三维空间信息模型的融合和精细化表达,文中以陕西理工大学南校区为例,基于二、三维矢量数据、实景模型和纹理贴图,借助MapGIS三维平台和精细建模工具,将室内和室外三维场景表达和功能展示结合,实现了校园构筑物三维空间信息模型和实景的精细化模型表达。三维空间信息模型根据语义环境,可自适应性地建立三维构筑物表面纹理、色调、分辨率等表达,也可在三维GIS(Geographic Information System)功能基础上,实现可视域分析、表面分析、粒子效果、天际线分析等功能。展开更多
Scene recognition is a fundamental task in computer vision,which generally includes three vital stages,namely feature extraction,feature transformation and classification.Early research mainly focuses on feature extra...Scene recognition is a fundamental task in computer vision,which generally includes three vital stages,namely feature extraction,feature transformation and classification.Early research mainly focuses on feature extraction,but with the rise of Convolutional Neural Networks(CNNs),more and more feature transformation methods are proposed based on CNN features.In this work,a novel feature transformation algorithm called Graph Encoded Local Discriminative Region Representation(GEDRR)is proposed to find discriminative local representations for scene images and explore the relationship between the discriminative regions.In addition,we propose a method using the multi-head attention module to enhance and fuse convolutional feature maps.Combining the two methods and the global representation,a scene recognition framework called Global and Graph Encoded Local Discriminative Region Representation(G2ELDR2)is proposed.The experimental results on three scene datasets demonstrate the effectiveness of our model,which outperforms many state-of-the-arts.展开更多
In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” with the extension of its object space, expression space an...In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” with the extension of its object space, expression space and information source, which challenges the theory of cartographic visualization. This paper discusses the ubiquitous map visualization from the object content and expression form. Oriented to the ternary space, it divides the object dimension of ubiquitous map visualization and analyzes the expression characteristics of ubiquitous map visualization. Based on that, it constructs the variable system, symbol system and method system of ubiquitous map visualization. With three cases of the metro roadmap, the tag map, and the three-dimensional (3D) city map, the application of the proposed content is explained to illustrate its effectiveness. The research in this paper is expected to further enrich the theoretical basis of cartographic visualization and provide theoretical support for the expression and application of ubiquitous map visualization.展开更多
<div style="text-align:justify;"> In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” due to th...<div style="text-align:justify;"> In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” due to the extension of its object space and the geographic information it contains, which brings new challenges to map information organization. This paper analyzes the concept and information characteristics of the ubiquitous map. Based on that, it proposes a ubiquitous map information organization model oriented to location-based aggregation. This new model includes three parts as “ubiquitous map instance”, “location-based aggregation mode” and “map scene”. This paper focuses on the “map scene” part which is the core of the model and contains two mutually mapped aspects as “content scene” and “representation scene”. And both aspects are divided into three levels as “features” ←→ “elements” ←→ “scenes” according to ubiquitous map information characteristics and location-based aggregation mode. With cases of map decomposition, the application of the model is explained to illustrate its effectiveness. The model is expected to provide powerful data organization and management capabilities for ubiquitous map production and use. </div>展开更多
In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those ...In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .展开更多
Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance o...Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.展开更多
文摘Bit-plane decomposition makes images obtain a number of layers. According to the amount of data information, images are encrypted, and the paper proposes image encryption method with Chaotic Mapping based on multi-layer parameter disturbance. The advantage of multi-layer parameter disturbance is that it not only scrambles pixel location of images, but also changes pixel values of images. Bit-plane decomposition can increase the space of key. And using chaotic sequence generated by chaotic system with different complexities to encrypt layers with different information content can save operation time. The simulation experiments show that using chaotic mapping in image encryption method based on multi-layer parameter disturbance can cover plaintext effectively and safely, which makes it achieve ideal encryption effect.
基金funded by the National Natural Science Foundation of China(Grant No.11972018,No.12002336)China Postdoctoral Science Foundation(Grant No.2021M701710)。
文摘This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the charge,a peak overpressure prediction model for the composite charge under singlepoint detonation and simultaneous detonation was established.The effects of the charge structure and initiation method on the overpressure field characteristics were investigated in AUTODYN simulation.The accuracy of the prediction model and the reliability of the numerical simulation method were subsequently verified in a series of static explosion experiments.The results reveal that the mass of the inner charge was the key factor determining the peak overpressure of the composite charge under single-point detonation.The peak overpressure in the radial direction improved apparently with an increase in the aspect ratio of the charge.The overpressure curves in the axial direction exhibited a multi-peak phenomenon,and the secondary peak overpressure even exceeded the primary peak at distances of 30D and 40D(where D is the charge diameter).The difference in peak overpressure among azimuth angles of 0-90°gradually decreased with an increase in the propagation distance of the shock wave.The coupled effect of the detonation energy of the inner and outer charge under simultaneous detonation improved the overpressure in both radial and axial directions.The difference in peak overpressure obtained from model prediction and experimental measurements was less than 16.4%.
基金funded by the LMAP(LEO Ionospheric Mapping Assessment and Derivation For Precise PVT Applications)project under the ESA Contract No.4000142821/23/NL/MGu/my。
文摘When estimating ionospheric Total Electron Content(TEC)using Global Navigation Satellite System(GNSS)observations,one of the signifcant error sources is the mapping error introduced by slant to vertical TEC conversion and vice versa.A single-layer Mapping Function(MF)based on a thin-shell assumption of the Earth’s ionosphere is commonly used for TEC conversion.However,the accuracy of single-layer MF is susceptible to the inaccurate fxing of the ionospheric single-layer height.In order to fnd a mapping approach less sensitive to the choice of ionospheric efective height we defned a multi-layer ionosphere mapping function and investigated its performance in comparison with the single-layer model.We found that the multi-layer MF outperforms the single-layer MF when computing GNSS receiver Diferential Code Biases(DCBs)especially at low latitude and equatorial regions where ionosphere is highly dynamic and difcult to model.When compared with the International GNSS Services(IGS)products,we found that the mean receiver DCB estimation is improved(closer to benchmark)by about 0.14-0.27 ns and 0.30-0.78 ns during days in 2019 and 2023,respectively.We found that the receiver DCB estimation improves for about 66-87%receivers.This is also refected in Global Ionosphere Maps(GIMs)showing better performance for the multi-layer MF when comparing with IGS GIMs.Our investigation using GNSS observations onboard Low Earth Orbiting(LEO)satellites shows that the multi-layer MF can be successfully applied in computing satellite and receiver DCBs accurately.
基金supported by CAFUC(ZHMH 2022-005)Key Laboratory of Flight Techniques and Flight Safety(FZ2022ZZ06)Flight Technology and Flight Safety of Civil Aviation Administration of China(FZ2022KF10).
文摘Dear Editor,This letter proposes an innovative open-vocabulary 3D scene understanding model based on visual-language model.By efficiently integrating 3D point cloud data,image data,and text data,our model effectively overcomes the segmentation problem[1],[2]of traditional models dealing with unknown categories[3].By deeply learning the deep semantic mapping between vision and language,the network significantly improves its ability to recognize unlabeled categories and exceeds current state-of-the-art methods in the task of scene understanding in open-vocabulary.
文摘同步定位与建图(simultaneous localization and mapping, SLAM)技术是移动机器人研究及应用的关键问题,旨在解决机器人在复杂环境中实现自主定位与地图构建等功能。对SLAM的系统组成、关键技术及应用进行了简要介绍;重点围绕特征点法、滤波法、图优化法、多传感器融合和动态场景5个方面,综述了SLAM系统的关键技术、国内外研究现状及标志性应用进展;并结合代表性系统,比较分析了不同方法之间的优缺点,详细阐述了多传感器融合SLAM系统,同时对复杂场景下的SLAM技术进行了展望。
基金Supported by Major Science and Technology Project of Hubei Province(2022AAA009)。
文摘By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.
文摘针对HIT-TENA体系结构在试验过程中对试验场景的显示需求,以Visual Studio 2008为开发平台,利用面向对象编程语言C++,基于Map X控件,开发二维场景显示软件。阐述了HIT-TENA体系结构的组成及二维场景显示软件在HIT-TENA中的地位与作用,根据二维场景显示软件的功能要求,使用UML语言对该软件进行了需求分析、概要设计及详细设计,详细介绍了该软件与HIT-TENA中间件的数据交互接口实现以及设备信息的实时绘制的实现,并利用一个虚拟试验系统对软件进行测试,软件运行良好。
文摘为实现实景和三维空间信息模型的融合和精细化表达,文中以陕西理工大学南校区为例,基于二、三维矢量数据、实景模型和纹理贴图,借助MapGIS三维平台和精细建模工具,将室内和室外三维场景表达和功能展示结合,实现了校园构筑物三维空间信息模型和实景的精细化模型表达。三维空间信息模型根据语义环境,可自适应性地建立三维构筑物表面纹理、色调、分辨率等表达,也可在三维GIS(Geographic Information System)功能基础上,实现可视域分析、表面分析、粒子效果、天际线分析等功能。
基金This research is partially supported by the Programme for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,and also partially supported by JSPS KAKENHI Grant No.15K00159.
文摘Scene recognition is a fundamental task in computer vision,which generally includes three vital stages,namely feature extraction,feature transformation and classification.Early research mainly focuses on feature extraction,but with the rise of Convolutional Neural Networks(CNNs),more and more feature transformation methods are proposed based on CNN features.In this work,a novel feature transformation algorithm called Graph Encoded Local Discriminative Region Representation(GEDRR)is proposed to find discriminative local representations for scene images and explore the relationship between the discriminative regions.In addition,we propose a method using the multi-head attention module to enhance and fuse convolutional feature maps.Combining the two methods and the global representation,a scene recognition framework called Global and Graph Encoded Local Discriminative Region Representation(G2ELDR2)is proposed.The experimental results on three scene datasets demonstrate the effectiveness of our model,which outperforms many state-of-the-arts.
文摘In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” with the extension of its object space, expression space and information source, which challenges the theory of cartographic visualization. This paper discusses the ubiquitous map visualization from the object content and expression form. Oriented to the ternary space, it divides the object dimension of ubiquitous map visualization and analyzes the expression characteristics of ubiquitous map visualization. Based on that, it constructs the variable system, symbol system and method system of ubiquitous map visualization. With three cases of the metro roadmap, the tag map, and the three-dimensional (3D) city map, the application of the proposed content is explained to illustrate its effectiveness. The research in this paper is expected to further enrich the theoretical basis of cartographic visualization and provide theoretical support for the expression and application of ubiquitous map visualization.
文摘<div style="text-align:justify;"> In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” due to the extension of its object space and the geographic information it contains, which brings new challenges to map information organization. This paper analyzes the concept and information characteristics of the ubiquitous map. Based on that, it proposes a ubiquitous map information organization model oriented to location-based aggregation. This new model includes three parts as “ubiquitous map instance”, “location-based aggregation mode” and “map scene”. This paper focuses on the “map scene” part which is the core of the model and contains two mutually mapped aspects as “content scene” and “representation scene”. And both aspects are divided into three levels as “features” ←→ “elements” ←→ “scenes” according to ubiquitous map information characteristics and location-based aggregation mode. With cases of map decomposition, the application of the model is explained to illustrate its effectiveness. The model is expected to provide powerful data organization and management capabilities for ubiquitous map production and use. </div>
文摘In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .
文摘Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.
