Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

MMDistill:Multi-Modal BEV Distillation Framework for Multi-View 3D Object Detection

Multi-modal 3D object detection has achieved remarkable progress,but it is often limited in practical industrial production because of its high cost and low efficiency.The multi-view camera-based method provides a feasible solution due to its low cost.However,camera data lacks geometric depth,and only using camera data to obtain high accuracy is challenging.This paper proposes a multi-modal Bird-Eye-View(BEV)distillation framework(MMDistill)to make a trade-off between them.MMDistill is a carefully crafted two-stage distillation framework based on teacher and student models for learning cross-modal knowledge and generating multi-modal features.It can improve the performance of unimodal detectors without introducing additional costs during inference.Specifically,our method can effectively solve the cross-gap caused by the heterogeneity between data.Furthermore,we further propose a Light Detection and Ranging(LiDAR)-guided geometric compensation module,which can assist the student model in obtaining effective geometric features and reduce the gap between different modalities.Our proposed method generally requires fewer computational resources and faster inference speed than traditional multi-modal models.This advancement enables multi-modal technology to be applied more widely in practical scenarios.Through experiments,we validate the effectiveness and superiority of MMDistill on the nuScenes dataset,achieving an improvement of 4.1%mean Average Precision(mAP)and 4.6%NuScenes Detection Score(NDS)over the baseline detector.In addition,we also present detailed ablation studies to validate our method.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

Genetic Variation Analysis of 3D Gene and Molecular Detection of Porcine Kobuvirus in 2013-2014

[Objective] This study aimed to investigate the prevalence and variation of porcine kobuvirus （PKV） in suckling piglets in China. [Method] In 2013-2014, 224 feces samples from suckling piglets with diarrhea in 27 pig farms of five provinces in China were collected to detect 3D genes of PKV with RT-PCR method; the sequences and genetic variation of 29 PKV 3D genes were analyzed. [Result] Total positive rate of PKV in feces samples from suckling piglets with diarrhea was 65.18% （146/224）; total positive rate of PKV in pig farms was 85,2% （23/27）; nucleotide sequences and the deduced amino acid sequences of 29 PKV 3D genes shared 87.0%-100% and 92.7%-100% homologies with six PKV-related 3D sequences, respectively. [Conclusion] PKV infection is prevalent in suckling piglets in China; PKV 3D genes exhibit high diversity.

Heterogeneous Cu_(x)O Nano‑Skeletons from Waste Electronics for Enhanced Glucose Detection

Electronic waste(e-waste)and diabetes are global challenges to modern societies.However,solving these two challenges together has been challenging until now.Herein,we propose a laser-induced transfer method to fabricate portable glucose sensors by recycling copper from e-waste.We bring up a laser-induced full-automatic fabrication method for synthesizing continuous heterogeneous Cu_(x)O(h-Cu_(x)O)nano-skeletons electrode for glucose sensing,offering rapid(<1 min),clean,air-compatible,and continuous fabrication,applicable to a wide range of Cu-containing substrates.Leveraging this approach,h-Cu_(x)O nanoskeletons,with an inner core predominantly composed of Cu_(2)O with lower oxygen content,juxtaposed with an outer layer rich in amorphous Cu_(x)O(a-Cu_(x)O)with higher oxygen content,are derived from discarded printed circuit boards.When employed in glucose detection,the h-Cu_(x)O nano-skeletons undergo a structural evolution process,transitioning into rigid Cu_(2)O@CuO nano-skeletons prompted by electrochemical activation.This transformation yields exceptional glucose-sensing performance(sensitivity:9.893 mA mM^(-1) cm^(-2);detection limit:0.34μM),outperforming most previously reported glucose sensors.Density functional theory analysis elucidates that the heterogeneous structure facilitates gluconolactone desorption.This glucose detection device has also been downsized to optimize its scalability and portability for convenient integration into people’s everyday lives.

Mural Anomaly Region Detection Algorithm Based on Hyperspectral Multiscale Residual Attention Network

Mural paintings hold significant historical information and possess substantial artistic and cultural value.However,murals are inevitably damaged by natural environmental factors such as wind and sunlight,as well as by human activities.For this reason,the study of damaged areas is crucial for mural restoration.These damaged regions differ significantly from undamaged areas and can be considered abnormal targets.Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections.Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods.Thus,this study employs hyperspectral imaging to obtain mural information and proposes a mural anomaly detection algorithm based on a hyperspectral multi-scale residual attention network(HM-MRANet).The innovations of this paper include:(1)Constructing mural painting hyperspectral datasets.(2)Proposing a multi-scale residual spectral-spatial feature extraction module based on a 3D CNN(Convolutional Neural Networks)network to better capture multiscale information and improve performance on small-sample hyperspectral datasets.(3)Proposing the Enhanced Residual Attention Module(ERAM)to address the feature redundancy problem,enhance the network’s feature discrimination ability,and further improve abnormal area detection accuracy.The experimental results show that the AUC(Area Under Curve),Specificity,and Accuracy of this paper’s algorithm reach 85.42%,88.84%,and 87.65%,respectively,on this dataset.These results represent improvements of 3.07%,1.11%and 2.68%compared to the SSRN algorithm,demonstrating the effectiveness of this method for mural anomaly detection.

Flexible Tactile Electronic Skin Sensor with 3D Force Detection Based on Porous CNTs/PDMS Nanocomposites

Flexible tactile sensors have broad applications in human physiological monitoring,robotic operation and human-machine interaction.However,the research of wearable and flexible tactile sensors with high sensitivity,wide sensing range and ability to detect three-dimensional(3D)force is still very challenging.Herein,a flexible tactile electronic skin sensor based on carbon nanotubes(CNTs)/polydimethylsiloxane(PDMS)nanocomposites is presented for 3D contact force detection.The 3D forces were acquired from combination of four specially designed cells in a sensing element.Contributed from the double-sided rough porous structure and specific surface morphology of nanocomposites,the piezoresistive sensor possesses high sensitivity of 12.1 kPa?1 within the range of 600 Pa and 0.68 kPa?1 in the regime exceeding 1 kPa for normal pressure,as well as 59.9 N?1 in the scope of<0.05 N and>2.3 N?1 in the region of<0.6 N for tangential force with ultra-low response time of 3.1 ms.In addition,multi-functional detection in human body monitoring was employed with single sensing cell and the sensor array was integrated into a robotic arm for objects grasping control,indicating the capacities in intelligent robot applications.

3D cavity detection technique and its application based on cavity auto scanning laser system

Ground constructions and mines are severely threatened by ones. Safe and precise cavity detection is vital for reasonable cavity underground cavities especially those unsafe or inaccessible evaluation and disposal. The conventional cavity detection methods and their limitation were analyzed. Those methods cannot form 3D model of underground cavity which is used for instructing the cavity disposal; and their precisions in detection are always greatly affected by the geological circumstance. The importance of 3D cavity detection in metal mine for safe exploitation was pointed out; and the 3D cavity laser detection method and its principle were introduced. A cavity auto scanning laser system was recommended to actualize the cavity 3D detection after comparing with the other laser detection systems. Four boreholes were chosen to verify the validity of the cavity auto scanning laser system. The results show that the cavity auto scanning laser system is very suitable for underground 3D cavity detection, especially for those inaccessible ones.

A new approach for salt dome detection using a 3D multidirectional edge detector

Accurate salt dome detection from 3D seismic data is crucial to different seismic data analysis applications. We present a new edge based approach for salt dome detection in migrated 3D seismic data. The proposed algorithm overcomes the drawbacks of existing edge-based techniques which only consider edges in the x （crossline） and y （inline） directions in 2D data and the x （crossline）, y （inline）, and z （time） directions in 3D data. The algorithm works by combining 3D gradient maps computed along diagonal directions and those computed in x, y, and z directions to accurately detect the boundaries of salt regions. The combination of x, y, and z directions and diagonal edges ensures that the proposed algorithm works well even if the dips along the salt boundary are represented only by weak reflectors. Contrary to other edge and texture based salt dome detection techniques, the proposed algorithm is independent of the amplitude variations in seismic data. We tested the proposed algorithm on the publicly available Netherlands offshore F3 block. The results suggest that the proposed algorithm can detect salt bodies with high accuracy than existing gradient based and texture-based techniques when used separately. More importantly, the proposed approach is shown to be computationally efficient allowing for real time implementation and deployment.

Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors.The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach.In this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters.Cycling data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried out.Compared with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky data.The case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track forecasts.The detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise structures.This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

General and robust voxel feature learning with Transformer for 3D object detection

The self-attention networks and Transformer have dominated machine translation and natural language processing fields,and shown great potential in image vision tasks such as image classification and object detection.Inspired by the great progress of Transformer,we propose a novel general and robust voxel feature encoder for 3D object detection based on the traditional Transformer.We first investigate the permutation invariance of sequence data of the self-attention and apply it to point cloud processing.Then we construct a voxel feature layer based on the self-attention to adaptively learn local and robust context of a voxel according to the spatial relationship and context information exchanging between all points within the voxel.Lastly,we construct a general voxel feature learning framework with the voxel feature layer as the core for 3D object detection.The voxel feature with Transformer(VFT)can be plugged into any other voxel-based 3D object detection framework easily,and serves as the backbone for voxel feature extractor.Experiments results on the KITTI dataset demonstrate that our method achieves the state-of-the-art performance on 3D object detection.

Development of a Client-Server System for 3D Scene Change Detection

In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change cor- rectly.

3D obstacle detection of indoor mobile robots by floor detection and rejection

Obstacle detection is essential for mobile robots to avoid collision with obstacles.Mobile robots usually operate in indoor environments,where they encounter various kinds of obstacles;however,2D range sensor can sense obstacles only in 2D plane.In contrast,by using 3D range sensor,it is possible to detect ground and aerial obstacles that 2D range sensor cannot sense.In this paper,we present a 3D obstacle detection method that will help overcome the limitations of 2D range sensor with regard to obstacle detection.The indoor environment typically consists of a flat floor.The position of the floor can be determined by estimating the plane using the least squares method.Having determined the position of the floor,the points of obstacles can be known by rejecting the points of the floor.In the experimental section,we show the results of this approach using a Kinect sensor.

3D Vehicle Detection Algorithm Based onMultimodal Decision-Level Fusion

3D vehicle detection based on LiDAR-camera fusion is becoming an emerging research topic in autonomous driving.The algorithm based on the Camera-LiDAR object candidate fusion method(CLOCs)is currently considered to be a more effective decision-level fusion algorithm,but it does not fully utilize the extracted features of 3D and 2D.Therefore,we proposed a 3D vehicle detection algorithm based onmultimodal decision-level fusion.First,project the anchor point of the 3D detection bounding box into the 2D image,calculate the distance between 2D and 3D anchor points,and use this distance as a new fusion feature to enhance the feature redundancy of the network.Subsequently,add an attention module:squeeze-and-excitation networks,weight each feature channel to enhance the important features of the network,and suppress useless features.The experimental results show that the mean average precision of the algorithm in the KITTI dataset is 82.96%,which outperforms previous state-ofthe-art multimodal fusion-based methods,and the average accuracy in the Easy,Moderate and Hard evaluation indicators reaches 88.96%,82.60%,and 77.31%,respectively,which are higher compared to the original CLOCs model by 1.02%,2.29%,and 0.41%,respectively.Compared with the original CLOCs algorithm,our algorithm has higher accuracy and better performance in 3D vehicle detection.

MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection

In complex traffic environment scenarios,it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance.The accuracy of 3D object detection will be affected by problems such as illumination changes,object occlusion,and object detection distance.To this purpose,we face these challenges by proposing a multimodal feature fusion network for 3D object detection(MFF-Net).In this research,this paper first uses the spatial transformation projection algorithm to map the image features into the feature space,so that the image features are in the same spatial dimension when fused with the point cloud features.Then,feature channel weighting is performed using an adaptive expression augmentation fusion network to enhance important network features,suppress useless features,and increase the directionality of the network to features.Finally,this paper increases the probability of false detection and missed detection in the non-maximum suppression algo-rithm by increasing the one-dimensional threshold.So far,this paper has constructed a complete 3D target detection network based on multimodal feature fusion.The experimental results show that the proposed achieves an average accuracy of 82.60%on the Karlsruhe Institute of Technology and Toyota Technological Institute(KITTI)dataset,outperforming previous state-of-the-art multimodal fusion networks.In Easy,Moderate,and hard evaluation indicators,the accuracy rate of this paper reaches 90.96%,81.46%,and 75.39%.This shows that the MFF-Net network has good performance in 3D object detection.

Engagement Detection Based on Analyzing Micro Body Gestures Using 3D CNN

This paper proposes a novel,efficient and affordable approach to detect the students’engagement levels in an e-learning environment by using webcams.Our method analyzes spatiotemporal features of e-learners’micro body gestures,which will be mapped to emotions and appropriate engagement states.The proposed engagement detection model uses a three-dimensional convolutional neural network to analyze both temporal and spatial information across video frames.We follow a transfer learning approach by using the C3D model that was trained on the Sports-1M dataset.The adopted C3D model was used based on two different approaches;as a feature extractor with linear classifiers and a classifier after applying fine-tuning to the pretrained model.Our model was tested and its performance was evaluated and compared to the existing models.It proved its effectiveness and superiority over the other existing methods with an accuracy of 94%.The results of this work will contribute to the development of smart and interactive e-learning systems with adaptive responses based on users’engagement levels.

Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous driving.However,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks.Therefore,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection.We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence.Then,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud.Finally,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as input.From the experimental results,our method achieves satisfactory results in monocular 3D object detection.

3D Object Detection with Attention:Shell-Based Modeling

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.

An improved bicubic imaging fitting algorithm for 3D radar detection target

3D ground-penetrating radar has been widely used in urban road underground disease detection due to its nondestructive,efficient,and intuitive results.However,the 3D imaging of the underground target body presents the edge plate phenomenon due to the space between the 3D radar array antennas.Consequently,direct 3D imaging using detection results cannot reflect underground spatial distribution characteristics.Due to the wide-beam polarization of the ground-penetrating radar antenna,the emission of electromagnetic waves with a specific width decreases the strong middle energy on both sides gradually.Therefore,a bicubic high-precision 3D target body slice-imaging fitting algorithm with changing trend characteristics is constructed by combining the subsurface target characteristics with the changing spatial morphology trends.Using the wide-angle polarization antenna’s characteristics in the algorithm to build the trend factor between the measurement lines,the target body change trend and the edge detail portrayal achieve a 3D ground-penetrating radar-detection target high-precision fitting.Compared with other traditional fitting techniques,the fitting error is small.This paper conducts experiments and analyses on GpaMax 3D forward modeling and 3D ground-penetrating measured radar data.The experiments show that the improved bicubic fitting algorithm can eff ectively improve the accuracy of underground target slice imaging and the 3D ground-penetrating radar’s anomaly interpretation.