The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation...The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.展开更多
Complex urban scenery is generally composed of gigantic amount of detailed buildings, efficient representation and rendering are essential for its visualization. We present an accelerating method for urban visualizati...Complex urban scenery is generally composed of gigantic amount of detailed buildings, efficient representation and rendering are essential for its visualization. We present an accelerating method for urban visualization. Our approach can optimize the organization of models in accordance with the quadtree based terrain, which makes the parallelization easier. Through minimizing the draw call within one rendering process, our approach can reduce the time cost of each frame and improve the framerate greatly. Hereby, our system can handle large-scale detailed models with high diversity and also can afford the ability to adjust the rendering strategy automatically according to the state of the hardware.展开更多
In recent years,with the continuous development of DDoS attacks,DDoS attacks are becoming easier to implement.More and more servers and even personal computers are under the threat of DDoS attacks,especially DDoS floo...In recent years,with the continuous development of DDoS attacks,DDoS attacks are becoming easier to implement.More and more servers and even personal computers are under the threat of DDoS attacks,especially DDoS flood attacks.Its main purpose is to cause the target host’s TCP/IP protocol layer to become congested.In this paper,we propose a real-time visualization defense framework for DDoS attack.Our framework is based on spark-streaming so that it allows for parallel and distributed traffic analysis that can be deployed at high speed network links.Moreover,this framework includes a cylindrical coordinates Visualization Model,which enables users to recognize DDoS threats promptly and clearly.The experiments show that our framework is able to detect and visualize DDoS flooding attacks timely and efficiently.展开更多
Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their ...Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.展开更多
Two-dimensional cell culture may be insufficient when it comes to understanding human disease.The redox behavior of complex,three-dimensional tissue is critical to understanding disease genesis and propagation.Unfortu...Two-dimensional cell culture may be insufficient when it comes to understanding human disease.The redox behavior of complex,three-dimensional tissue is critical to understanding disease genesis and propagation.Unfortunately,few measurement tools are available for such three-dimensional models to yield quantitative insight into how reactive oxygen species(ROS)form over time.Here,we demonstrate an imaging platform for the real-time visualization of H_(2)O_(2) formation for mammalian spheroids made of noncancerous human embryonic kidney cells(HEK-293)and metastatic breast cancer cells(MCF-7 and MDA-MB-231).We take advantage of the luminol and H_(2)O_(2) electrochemiluminescence reaction on a transparent tin-doped indium oxide electrode.The luminescence of this reaction as a function of[H_(2)O_(2)]is linear(R^(2)=0.98)with a dynamic range between 0.5μM to 0.1 mM,and limit of detection of 2.26±0.58μM.Our method allows for the observation of ROS activity in growing spheroids days in advance of current techniques without the need to sacrifice the sample postanalysis.Finally,we use our procedure to demonstrate how key ROS pathways in cancerous spheroids can be up-regulated and downregulated through the addition of common metabolic drugs,rotenone and carbonyl cyanide-p-trifluoromethoxyphenylhydrazone.Our results suggest that the Warburg Effect can be studied for single mammalian cancerous spheroids,and the use of metabolic drugs allows one to implicate specific metabolic pathways in ROS formation.We expect this diagnostic tool to have wide applications in understanding the real-time propagation of human disease in a system more closely related to human tissue.展开更多
With the dramatic development of spatial data in- frastructure, CyberGIS has become significant for geospatial data sharing. Due to the large number of concurrent users and large volume of vector data, CyberGIS faces ...With the dramatic development of spatial data in- frastructure, CyberGIS has become significant for geospatial data sharing. Due to the large number of concurrent users and large volume of vector data, CyberGIS faces a great chal- lenge in how to improve performance. The real-time visual- ization of vector maps is the most common function in Cyber- GIS applications, and it is time-consuming especially when the data volume becomes large. So, how to improve the effi- ciency of visualization of large vector maps is still a signif- icant research direction for GIScience scientists. In this re- search, we review the existing three optimization strategies, and determine that the third category strategy （i.e., parallel optimization） is appropriate for the real-time visualization of large vector maps. One of the key issues of parallel optimiza- tion is how to decompose the real-time visualization tasks into balanced sub tasks while taking into consideration the spatial heterogeneous characteristics. We put forward some rules that the decomposition should conform to, and design a real-time visualization framework for large vector maps. We focus on a balanced decomposition approach that can assure efficiency and effectiveness. Considering the spatial hetero- geneous characteristic of vector data, we use a ＂horizontal grid, vertical multistage＂ approach to construct a spatial point distribution information grid. The load balancer analyzes the spatial characteristics of the map requests and decomposes the real-time viewshed into multiple balanced sub viewsheds.Then, all the sub viewsheds are distributed to multiple server nodes to be executed in parallel, so as to improve the real- time visualization efficiency of large vector maps. A group of experiments have been conducted by us. The analysis results demonstrate that the approach proposed in this research has the ability of balanced decomposition, and it is efficient and effective for all geometry types of vector data.展开更多
People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces.Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial aw...People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces.Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users.This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects.The system architecture includes four main layers:Wearable Internet of Things(IoT),Network,Cloud,and Indoor Object Detection Layers.The wearable hardware prototype is assembled using a Raspberry Pi 4,while the indoor object detection approach exploits YOLOv11.YOLOv11 represents the cutting edge of deep learning models optimized for both speed and accuracy in recognizing objects and powers the research prototype.In this work,we used a prototype implementation,comparative experiments,and two datasets compiled from Furniture Detection(i.e.,from Roboflow Universe)and Kaggle,which comprises 3000 images evenly distributed across three object categories,including bed,sofa,and table.In the evaluation process,the Raspberry Pi is only used for a feasibility demonstration of real-time inference performance(e.g.,latency and memory consumption)on embedded hardware.We also evaluated YOLOv11 by comparing its performance with other current methodologies,which involved a Convolutional Neural Network(CNN)(MobileNet-Single Shot MultiBox Detector(SSD))model together with the RTDETR Vision Transformer.The experimental results show that YOLOv11 stands out by reaching an average of 99.07%,98.51%,97.96%,and 98.22%for the accuracy,precision,recall,and F1-score,respectively.This feasibility study highlights the effectiveness of Raspberry Pi 4 and YOLOv11 in real-time indoor object detection,paving the way for structured user studies with visually impaired people in the future to evaluate their real-world use and impact.展开更多
With the continuous development of digital medicine,minimally invasive precision and safety have become the primary development trends in hepatobiliary surgery.Due to the specificity and complexity of hepatobiliary su...With the continuous development of digital medicine,minimally invasive precision and safety have become the primary development trends in hepatobiliary surgery.Due to the specificity and complexity of hepatobiliary surgery,traditional preoperative imaging techniques such as computed tomography and magnetic resonance imaging cannot meet the need for identification of fine anatomical regions.Imaging-based three-dimensional(3D)reconstruction,virtual simulation of surgery and 3D printing optimize the surgical plan through preoperative assessment,improving the controllability and safety of intraoperative operations,and in difficult-to-reach areas of the posterior and superior liver,assistive robots reproduce the surgeon’s natural movements with stable cameras,reducing natural vibrations.Electromagnetic navigation in abdominal surgery solves the problem of conventional surgery still relying on direct visual observation or preoperative image assessment.We summarize and compare these recent trends in digital medical solutions for the future development and refinement of digital medicine in hepatobiliary surgery.展开更多
Street-level visualization is an important application of 3D city models.Challenges to street-level visualization include the cluttering of buildings due to fine detail and visualization performance.In this paper,a no...Street-level visualization is an important application of 3D city models.Challenges to street-level visualization include the cluttering of buildings due to fine detail and visualization performance.In this paper,a novel method is proposed for streetlevel visualization based on visual saliency evaluation.The basic idea of the method is to preserve these salient buildings in a scene while removing those that are non-salient.The method can be divided into pre-processing procedures and real-time visualization.The first step in pre-processing is to convert 3D building models at higher Levels of Detail(Lo Ds) into LoD 1 models with simplified ground plans.Then,a number of index viewpoints are created along the streets; these indices refer to both the position and the direction of each street site.A visual saliency value is computed for each building,with respect to the index site,based on a visual difference between the original model and the generalized model.We calculate and evaluate three methods for visual saliency:local difference,global difference and minimum projection area.The real-time visualization process begins by mapping the observer to its closest indices.The street view is then generated based on the building information stored in those indexes.A user study shows that the local visual saliency method performs better than do the global visual saliency,area and image-based methods and that the framework proposed in this paper may improve the performance of 3D visualization.展开更多
In order to ensure the safety,quality and efficiency of computer numerical control(CNC)machine tool processing,a real-time monitoring and visible solution for CNC machine tools based on hyper text markup language(HTML...In order to ensure the safety,quality and efficiency of computer numerical control(CNC)machine tool processing,a real-time monitoring and visible solution for CNC machine tools based on hyper text markup language(HTML)5 is proposed.The characteristics of the real-time monitoring technology of CNC machine tools under the traditional Client/Server(C/S)structure are compared and analyzed,and the technical drawbacks are proposed.Web real-time communication technology and browser drawing technology are deeply studied.A real-time monitoring and visible system for CNC machine tool data is developed based on Metro platform,combining WebSocket real-time communication technology and Canvas drawing technology.The system architecture is given,and the functions and implementation methods of the system are described in detail.The practical application results show that the WebSocket real-time communication technology can effectively reduce the bandwidth and network delay and save server resources.The numerical control machine data monitoring system can intuitively reflect the machine data,and the visible effect is good.It realizes timely monitoring of equipment alarms and prompts maintenance and management personnel.展开更多
AIM:To evaluate the usefulness of real-time virtual sonography(RVS)in biliary and pancreatic diseases.METHODS:This study included 15 patients with biliary and pancreatic diseases.RVS can be used to observe an ultrasou...AIM:To evaluate the usefulness of real-time virtual sonography(RVS)in biliary and pancreatic diseases.METHODS:This study included 15 patients with biliary and pancreatic diseases.RVS can be used to observe an ultrasound image in real time by merging the ultrasound image with a multiplanar reconstruction computed tomography(CT)image,using pre-scanned CT volume data.The ultrasound used was EUB-8500with a convex probe EUP-C514.The RVS images were evaluated based on 3 levels,namely,excellent,good and poor,by the displacement in position.RESULTS:By combining the objectivity of CT with free scanning using RVS,it was possible to easily interpret the relationship between lesions and the surrounding organs as well as the position of vascular structures.The resulting evaluation levels of the RVS images were12 excellent(pancreatic cancer,bile duct cancer,cholecystolithiasis and cholangiocellular carcinoma)and 3 good(pancreatic cancer and gallbladder cancer).Compared with conventional B-mode ultrasonography and CT,RVS images achieved a rate of 80%superior visualization and 20%better visualization.CONCLUSION:RVS has potential usefulness in objective visualization and diagnosis in the field of biliary and pancreatic diseases.展开更多
One of the most indispensable needs of life is food and its worldwide availability endorsement has made agriculture an essential sector in recent years. As the technology evolved, the need to maintain a good and suita...One of the most indispensable needs of life is food and its worldwide availability endorsement has made agriculture an essential sector in recent years. As the technology evolved, the need to maintain a good and suitable climate in the greenhouse became imperative to ensure that the indoor plants are more productive hence the agriculture sector was not left behind. That notwithstanding, the introduction and deployment of IoT technology in agriculture solves many problems and increases crop production. This paper focuses mainly on the deployment of the Internet of Things (IoT) in acquiring real- time data of environmental parameters in the greenhouse. Various IoT technologies that can be applicable in greenhouse monitoring system was presented and in the proposed model, a method is developed to send the air temperature and humidity data obtained by the DHT11 sensor to the cloud using an ESP8266-based NodeMCU and firstly to the cloud platform Thing- Speak, and then to Adafruit.IO in which MQTT protocol was used for the reception of sensor data to the application layer referred as Human-Machine Interface. The system has been completely implemented in an actual prototype, allowing the acquiring of data and the publisher/subscriber concept used for communication. The data is published with a broker’s aid, which is responsible for transferring messages to the intended clients based on topic choice. Lastly, the functionality testing of MQTT was carried out and the results showed that the messages are successfully published.展开更多
A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model ...A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.展开更多
The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wir...The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wireless networking technologies, the improvement in computational power of handheld devices such as smartphones, tablet PCs, ultra-mobile personal computers (UMPCs) and netbook computers allow field users to connect, store and stream large amounts of geospatial data from the web-server. Nowadays, geospatial data collection is more flexible and timely manner. In this paper we discuss field data collection using a smartphone and web-based GIS system, which collects, integrates, visualizes and analyzes the collected data in real-time. We built a web-GIS system for creating a user account, acquiring coordinates from GPS embedded devices or wireless access points, and providing a user-friendly survey form. The collected data can be visualized and analyzed by performing thematic mapping, labeling, symbolizing, querying and generating a summary report. We tested this system on a university campus management system, in which we collected information on illegal disposal sites and parking events within the university campus.展开更多
●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of th...●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of the first-year postgraduate were included.All the residents were novices to cataract surgery.Real-time cataract surgical observations were performed using a custom-built 3D visualization system.The training lasted 4wk(32h)in all.A modified International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric(ICO-OSCAR)containing 4 specific steps of cataract surgery was applied.The self-assessment(self)and expert-assessment(expert)were performed through the microsurgical attempts in the wet lab for each participant.●RESULTS:Compared with pre-training assessments(self 3.2±0.8,expert 2.5±0.6),the overall mean scores of posttraining(self 5.2±0.4,expert 4.7±0.6)were significantly improved after real-time observation training of 3D visualization system(P<0.05).Scores of 4 surgical items were significantly improved both self and expert assessment after training(P<0.05).●CONCLUSION:The 3D observation training provides novice ophthalmic residents with a better understanding of intraocular microsurgical techniques.It is a useful tool to improve teaching efficiency of surgical education.展开更多
LWD（logging while drilling） data has been used to explore complex subtle reservoirs by realtime visual interpretation and geo-steering.The method comprises of computer communication,well log data processing,formatio...LWD（logging while drilling） data has been used to explore complex subtle reservoirs by realtime visual interpretation and geo-steering.The method comprises of computer communication,well log data processing,formation recognition,reservoir modeling and model updating in real time.We studied the key technologies related to real-time LWD data visual interpretation and geo-steering and developed computer software with Chinese intellectual property rights covering the following important aspects： 1） real-time computer communication of well site LWD data;2） visualization of geological model and borehole information;3） real-time interpretation of LWD data;4） real-time geological model updating and geo-steering technology.We use field application examples to demonstrate the feasibility and validity of the proposed technologies.展开更多
An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method...An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.展开更多
Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginn...Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.展开更多
Self-localization and orientation estimation are the essential capabilities for mobile robot navigation.In this article,a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System)tightly coupled po...Self-localization and orientation estimation are the essential capabilities for mobile robot navigation.In this article,a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System)tightly coupled pose estimation(RRVPE)method for aerial robot navigation is presented.The aerial robot carries a front-facing stereo camera for self-localization and an RGB-D camera to generate 3D voxel map.Ulteriorly,a GNSS receiver is used to continuously provide pseudorange,Doppler frequency shift and universal time coordinated(UTC)pulse signals to the pose estimator.The proposed system leverages the Kanade Lucas algorithm to track Shi-Tomasi features in each video frame,and the local factor graph solution process is bounded in a circumscribed container,which can immensely abandon the computational complexity in nonlinear optimization procedure.The proposed robot pose estimator can achieve camera-rate(30 Hz)performance on the aerial robot companion computer.We thoroughly experimented the RRVPE system in both simulated and practical circumstances,and the results demonstrate dramatic advantages over the state-of-the-art robot pose estimators.展开更多
基金The National Natural Science Foundation of China (62176048)provided funding for this research.
文摘The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.
基金Supported by National Natural Science Foundation of China(Nos.61170205,61232014,61472010 and 61421062)National Key Technology Support Program of China(No.2013BAK03B07)
文摘Complex urban scenery is generally composed of gigantic amount of detailed buildings, efficient representation and rendering are essential for its visualization. We present an accelerating method for urban visualization. Our approach can optimize the organization of models in accordance with the quadtree based terrain, which makes the parallelization easier. Through minimizing the draw call within one rendering process, our approach can reduce the time cost of each frame and improve the framerate greatly. Hereby, our system can handle large-scale detailed models with high diversity and also can afford the ability to adjust the rendering strategy automatically according to the state of the hardware.
文摘In recent years,with the continuous development of DDoS attacks,DDoS attacks are becoming easier to implement.More and more servers and even personal computers are under the threat of DDoS attacks,especially DDoS flood attacks.Its main purpose is to cause the target host’s TCP/IP protocol layer to become congested.In this paper,we propose a real-time visualization defense framework for DDoS attack.Our framework is based on spark-streaming so that it allows for parallel and distributed traffic analysis that can be deployed at high speed network links.Moreover,this framework includes a cylindrical coordinates Visualization Model,which enables users to recognize DDoS threats promptly and clearly.The experiments show that our framework is able to detect and visualize DDoS flooding attacks timely and efficiently.
文摘Automatic analysis of student behavior in classrooms has gained importance with the rise of smart education and vision technologies.However,the limited real-time accuracy of existing methods severely constrains their practical classroom deployment.To address this issue of low accuracy,we propose an improved YOLOv11-based detector that integrates CARAFE upsampling,DySnakeConv,DyHead,and SMFA fusion modules.This new model for real-time classroom behavior detection captures fine-grained student behaviors with low latency.Additionally,we have developed a visualization system that presents data through intuitive dashboards.This system enables teachers to dynamically grasp classroom engagement by tracking student participation and involvement.The enhanced YOLOv11 model achieves an mAP@0.5 of 87.2%on the evaluated datasets,surpassing baseline models.This significance lies in two aspects.First,it provides a practical technical route for deployable live classroom behavior monitoring and engagement feedback systems.Second,by integrating this proposed system,educators could make data-informed and fine-grained teaching decisions,ultimately improving instructional quality and learning outcomes.
基金support from the National Institutes of Health under Grant No.R35-GM138133-01Vanshika Gupta is supported by the National Science Foundation through the Graduate Research Fellowship Program(NSF-GRFP)+1 种基金Brady R.Layman is supported by the Department of Defense(DoD)through the National Defense Engineering Graduate(NDSEG)FellowshipMegan L.Hill is supported by the Department of Energy(DOE)through the University Nuclear Leadership Program Graduate Fellowship(UNLP).
文摘Two-dimensional cell culture may be insufficient when it comes to understanding human disease.The redox behavior of complex,three-dimensional tissue is critical to understanding disease genesis and propagation.Unfortunately,few measurement tools are available for such three-dimensional models to yield quantitative insight into how reactive oxygen species(ROS)form over time.Here,we demonstrate an imaging platform for the real-time visualization of H_(2)O_(2) formation for mammalian spheroids made of noncancerous human embryonic kidney cells(HEK-293)and metastatic breast cancer cells(MCF-7 and MDA-MB-231).We take advantage of the luminol and H_(2)O_(2) electrochemiluminescence reaction on a transparent tin-doped indium oxide electrode.The luminescence of this reaction as a function of[H_(2)O_(2)]is linear(R^(2)=0.98)with a dynamic range between 0.5μM to 0.1 mM,and limit of detection of 2.26±0.58μM.Our method allows for the observation of ROS activity in growing spheroids days in advance of current techniques without the need to sacrifice the sample postanalysis.Finally,we use our procedure to demonstrate how key ROS pathways in cancerous spheroids can be up-regulated and downregulated through the addition of common metabolic drugs,rotenone and carbonyl cyanide-p-trifluoromethoxyphenylhydrazone.Our results suggest that the Warburg Effect can be studied for single mammalian cancerous spheroids,and the use of metabolic drugs allows one to implicate specific metabolic pathways in ROS formation.We expect this diagnostic tool to have wide applications in understanding the real-time propagation of human disease in a system more closely related to human tissue.
文摘With the dramatic development of spatial data in- frastructure, CyberGIS has become significant for geospatial data sharing. Due to the large number of concurrent users and large volume of vector data, CyberGIS faces a great chal- lenge in how to improve performance. The real-time visual- ization of vector maps is the most common function in Cyber- GIS applications, and it is time-consuming especially when the data volume becomes large. So, how to improve the effi- ciency of visualization of large vector maps is still a signif- icant research direction for GIScience scientists. In this re- search, we review the existing three optimization strategies, and determine that the third category strategy （i.e., parallel optimization） is appropriate for the real-time visualization of large vector maps. One of the key issues of parallel optimiza- tion is how to decompose the real-time visualization tasks into balanced sub tasks while taking into consideration the spatial heterogeneous characteristics. We put forward some rules that the decomposition should conform to, and design a real-time visualization framework for large vector maps. We focus on a balanced decomposition approach that can assure efficiency and effectiveness. Considering the spatial hetero- geneous characteristic of vector data, we use a ＂horizontal grid, vertical multistage＂ approach to construct a spatial point distribution information grid. The load balancer analyzes the spatial characteristics of the map requests and decomposes the real-time viewshed into multiple balanced sub viewsheds.Then, all the sub viewsheds are distributed to multiple server nodes to be executed in parallel, so as to improve the real- time visualization efficiency of large vector maps. A group of experiments have been conducted by us. The analysis results demonstrate that the approach proposed in this research has the ability of balanced decomposition, and it is efficient and effective for all geometry types of vector data.
基金funded by the King Salman Center for Disability Research through Research Group No.KSRG-2024-140.
文摘People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces.Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users.This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects.The system architecture includes four main layers:Wearable Internet of Things(IoT),Network,Cloud,and Indoor Object Detection Layers.The wearable hardware prototype is assembled using a Raspberry Pi 4,while the indoor object detection approach exploits YOLOv11.YOLOv11 represents the cutting edge of deep learning models optimized for both speed and accuracy in recognizing objects and powers the research prototype.In this work,we used a prototype implementation,comparative experiments,and two datasets compiled from Furniture Detection(i.e.,from Roboflow Universe)and Kaggle,which comprises 3000 images evenly distributed across three object categories,including bed,sofa,and table.In the evaluation process,the Raspberry Pi is only used for a feasibility demonstration of real-time inference performance(e.g.,latency and memory consumption)on embedded hardware.We also evaluated YOLOv11 by comparing its performance with other current methodologies,which involved a Convolutional Neural Network(CNN)(MobileNet-Single Shot MultiBox Detector(SSD))model together with the RTDETR Vision Transformer.The experimental results show that YOLOv11 stands out by reaching an average of 99.07%,98.51%,97.96%,and 98.22%for the accuracy,precision,recall,and F1-score,respectively.This feasibility study highlights the effectiveness of Raspberry Pi 4 and YOLOv11 in real-time indoor object detection,paving the way for structured user studies with visually impaired people in the future to evaluate their real-world use and impact.
基金Supported by National Natural Science Foundation of China,No.82070638 and No.81770621and JSPS KAKENHI,No.JP18H02866.
文摘With the continuous development of digital medicine,minimally invasive precision and safety have become the primary development trends in hepatobiliary surgery.Due to the specificity and complexity of hepatobiliary surgery,traditional preoperative imaging techniques such as computed tomography and magnetic resonance imaging cannot meet the need for identification of fine anatomical regions.Imaging-based three-dimensional(3D)reconstruction,virtual simulation of surgery and 3D printing optimize the surgical plan through preoperative assessment,improving the controllability and safety of intraoperative operations,and in difficult-to-reach areas of the posterior and superior liver,assistive robots reproduce the surgeon’s natural movements with stable cameras,reducing natural vibrations.Electromagnetic navigation in abdominal surgery solves the problem of conventional surgery still relying on direct visual observation or preoperative image assessment.We summarize and compare these recent trends in digital medical solutions for the future development and refinement of digital medicine in hepatobiliary surgery.
基金supported by the National Natural Science Foundation of China(Grant No.41201486)the National Key Technologies R&D Program of China(Grant No.SQ2013GX07E00985)the project of the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) in the Collaborative Innovation Center of Modern Grain Circulation and Security,Nanjing University of Finance and Economics
文摘Street-level visualization is an important application of 3D city models.Challenges to street-level visualization include the cluttering of buildings due to fine detail and visualization performance.In this paper,a novel method is proposed for streetlevel visualization based on visual saliency evaluation.The basic idea of the method is to preserve these salient buildings in a scene while removing those that are non-salient.The method can be divided into pre-processing procedures and real-time visualization.The first step in pre-processing is to convert 3D building models at higher Levels of Detail(Lo Ds) into LoD 1 models with simplified ground plans.Then,a number of index viewpoints are created along the streets; these indices refer to both the position and the direction of each street site.A visual saliency value is computed for each building,with respect to the index site,based on a visual difference between the original model and the generalized model.We calculate and evaluate three methods for visual saliency:local difference,global difference and minimum projection area.The real-time visualization process begins by mapping the observer to its closest indices.The street view is then generated based on the building information stored in those indexes.A user study shows that the local visual saliency method performs better than do the global visual saliency,area and image-based methods and that the framework proposed in this paper may improve the performance of 3D visualization.
文摘In order to ensure the safety,quality and efficiency of computer numerical control(CNC)machine tool processing,a real-time monitoring and visible solution for CNC machine tools based on hyper text markup language(HTML)5 is proposed.The characteristics of the real-time monitoring technology of CNC machine tools under the traditional Client/Server(C/S)structure are compared and analyzed,and the technical drawbacks are proposed.Web real-time communication technology and browser drawing technology are deeply studied.A real-time monitoring and visible system for CNC machine tool data is developed based on Metro platform,combining WebSocket real-time communication technology and Canvas drawing technology.The system architecture is given,and the functions and implementation methods of the system are described in detail.The practical application results show that the WebSocket real-time communication technology can effectively reduce the bandwidth and network delay and save server resources.The numerical control machine data monitoring system can intuitively reflect the machine data,and the visible effect is good.It realizes timely monitoring of equipment alarms and prompts maintenance and management personnel.
文摘AIM:To evaluate the usefulness of real-time virtual sonography(RVS)in biliary and pancreatic diseases.METHODS:This study included 15 patients with biliary and pancreatic diseases.RVS can be used to observe an ultrasound image in real time by merging the ultrasound image with a multiplanar reconstruction computed tomography(CT)image,using pre-scanned CT volume data.The ultrasound used was EUB-8500with a convex probe EUP-C514.The RVS images were evaluated based on 3 levels,namely,excellent,good and poor,by the displacement in position.RESULTS:By combining the objectivity of CT with free scanning using RVS,it was possible to easily interpret the relationship between lesions and the surrounding organs as well as the position of vascular structures.The resulting evaluation levels of the RVS images were12 excellent(pancreatic cancer,bile duct cancer,cholecystolithiasis and cholangiocellular carcinoma)and 3 good(pancreatic cancer and gallbladder cancer).Compared with conventional B-mode ultrasonography and CT,RVS images achieved a rate of 80%superior visualization and 20%better visualization.CONCLUSION:RVS has potential usefulness in objective visualization and diagnosis in the field of biliary and pancreatic diseases.
文摘One of the most indispensable needs of life is food and its worldwide availability endorsement has made agriculture an essential sector in recent years. As the technology evolved, the need to maintain a good and suitable climate in the greenhouse became imperative to ensure that the indoor plants are more productive hence the agriculture sector was not left behind. That notwithstanding, the introduction and deployment of IoT technology in agriculture solves many problems and increases crop production. This paper focuses mainly on the deployment of the Internet of Things (IoT) in acquiring real- time data of environmental parameters in the greenhouse. Various IoT technologies that can be applicable in greenhouse monitoring system was presented and in the proposed model, a method is developed to send the air temperature and humidity data obtained by the DHT11 sensor to the cloud using an ESP8266-based NodeMCU and firstly to the cloud platform Thing- Speak, and then to Adafruit.IO in which MQTT protocol was used for the reception of sensor data to the application layer referred as Human-Machine Interface. The system has been completely implemented in an actual prototype, allowing the acquiring of data and the publisher/subscriber concept used for communication. The data is published with a broker’s aid, which is responsible for transferring messages to the intended clients based on topic choice. Lastly, the functionality testing of MQTT was carried out and the results showed that the messages are successfully published.
基金Supported by the Innovative Research Groups of the National Natural Science Foundation of China(No.51321065)the National Natural Science Foundation of China(No.51339003 and No.51439005)
文摘A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.
文摘The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wireless networking technologies, the improvement in computational power of handheld devices such as smartphones, tablet PCs, ultra-mobile personal computers (UMPCs) and netbook computers allow field users to connect, store and stream large amounts of geospatial data from the web-server. Nowadays, geospatial data collection is more flexible and timely manner. In this paper we discuss field data collection using a smartphone and web-based GIS system, which collects, integrates, visualizes and analyzes the collected data in real-time. We built a web-GIS system for creating a user account, acquiring coordinates from GPS embedded devices or wireless access points, and providing a user-friendly survey form. The collected data can be visualized and analyzed by performing thematic mapping, labeling, symbolizing, querying and generating a summary report. We tested this system on a university campus management system, in which we collected information on illegal disposal sites and parking events within the university campus.
基金Supported by research grants from the National Key Research and Development Program of China(No.2020YFE0204400)the National Natural Science Foundation of China(No.82271042+1 种基金No.52203191)the Zhejiang Province Key Research and Development Program(No.2023C03090).
文摘●AIM:To determine the teaching effects of a real-time three dimensional(3D)visualization system in the operating room for early-stage phacoemulsification training.●METHODS:A total of 10 ophthalmology residents of the first-year postgraduate were included.All the residents were novices to cataract surgery.Real-time cataract surgical observations were performed using a custom-built 3D visualization system.The training lasted 4wk(32h)in all.A modified International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric(ICO-OSCAR)containing 4 specific steps of cataract surgery was applied.The self-assessment(self)and expert-assessment(expert)were performed through the microsurgical attempts in the wet lab for each participant.●RESULTS:Compared with pre-training assessments(self 3.2±0.8,expert 2.5±0.6),the overall mean scores of posttraining(self 5.2±0.4,expert 4.7±0.6)were significantly improved after real-time observation training of 3D visualization system(P<0.05).Scores of 4 surgical items were significantly improved both self and expert assessment after training(P<0.05).●CONCLUSION:The 3D observation training provides novice ophthalmic residents with a better understanding of intraocular microsurgical techniques.It is a useful tool to improve teaching efficiency of surgical education.
基金funded by several Co. of CNPC and SINOPECChina National Science and Technology Major Projects of Oil & Gas (2011ZX05009-003)"863" Projects (2006AA060105)
文摘LWD（logging while drilling） data has been used to explore complex subtle reservoirs by realtime visual interpretation and geo-steering.The method comprises of computer communication,well log data processing,formation recognition,reservoir modeling and model updating in real time.We studied the key technologies related to real-time LWD data visual interpretation and geo-steering and developed computer software with Chinese intellectual property rights covering the following important aspects： 1） real-time computer communication of well site LWD data;2） visualization of geological model and borehole information;3） real-time interpretation of LWD data;4） real-time geological model updating and geo-steering technology.We use field application examples to demonstrate the feasibility and validity of the proposed technologies.
基金supported by the National Natural Science Foundation of China(60835004 60775047+2 种基金 60872130)the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214)
文摘An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.
文摘Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.
基金Supported by the Guizhou Provincial Science and Technology Projects([2020]2Y044)the Science and Technology Projects of China Southern Power Grid Co.Ltd.(066600KK52170074)the National Natural Science Foundation of China(61473144)。
文摘Self-localization and orientation estimation are the essential capabilities for mobile robot navigation.In this article,a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System)tightly coupled pose estimation(RRVPE)method for aerial robot navigation is presented.The aerial robot carries a front-facing stereo camera for self-localization and an RGB-D camera to generate 3D voxel map.Ulteriorly,a GNSS receiver is used to continuously provide pseudorange,Doppler frequency shift and universal time coordinated(UTC)pulse signals to the pose estimator.The proposed system leverages the Kanade Lucas algorithm to track Shi-Tomasi features in each video frame,and the local factor graph solution process is bounded in a circumscribed container,which can immensely abandon the computational complexity in nonlinear optimization procedure.The proposed robot pose estimator can achieve camera-rate(30 Hz)performance on the aerial robot companion computer.We thoroughly experimented the RRVPE system in both simulated and practical circumstances,and the results demonstrate dramatic advantages over the state-of-the-art robot pose estimators.
