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
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are ofte...The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are often misjudged,and warnings lag by about 24 h without automated spatial localization.This study establishes a technical framework for requirements analysis,architectural design,and data-integration protocols.Revit parametric modelling is used to build a 3D tunnel model with structural elements,pipelines and 18 monitoring points(for displacement and joint width).Custom Revit API code integrated real-time sensor data into the BIM platform via an automated pipeline.The system achieved a spatial accuracy of±1 mm in locating deformation hotspots.Notifications are triggered within 10 s of anomaly detection,and the system renders 3D risk propagation paths in real-time.Realtime 3D visualization of risk propagation paths is also facilitated.The efficacy of the solution was validated in a Ningbo utility tunnel project,where it was demonstrated that it eliminates human-dependent judgment errors and reduces warning latency by 99.9%compared to conventional methods.The BIM-IoT integrated approach,which enables millimetre-level precision in risk identification and near-instantaneous response,establishes a new paradigm for intelligent infrastructure safety management.展开更多
With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing r...With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.Ho...Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.However,the analysis and visualization of Ribo-seq data remain challenging.Despite the availability of various analytical pipelines,improvements in comprehensiveness,accuracy,and user-friendliness are still necessary.In this study,we develop RiboParser/RiboShiny,a robust framework for analyzing and visualizing Ribo-seq data.Building on published methods,we optimize ribosome structure-based and start/stopbased models to improve the accuracy and stability of P-site detection,even in species with a high proportion of leaderless transcripts.Leveraging these improvements,RiboParser offers comprehensive analyses,including quality control,gene-level analysis,codon-level analysis,and the analysis of Ribo-seq variants.Meanwhile,RiboShiny provides a user-friendly and adaptable platform for data visualization,facilitating deeper insights into the translational landscape.Furthermore,the integration of standardized genome annotation renders our platform universally applicable to various organisms with sequenced genomes.This framework has the potential to significantly improve the precision and efficiency of Ribo-seq data interpretation,thereby deepening our understanding of translational regulation.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
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supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
基金supported by the Scientific Research Projects of the Education Department of Zhejiang Province(Grant No.Y202454744)the Ningbo Public Welfare Science and Technology Project(Grant No.2024S077)+1 种基金International Sci-tech Cooperation Projects under the“Innovation Yongjiang 2035”Key R&D Programme(No.2024H019)the Ningbo Key R&D Program(Grant No.2024Z287).
文摘The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are often misjudged,and warnings lag by about 24 h without automated spatial localization.This study establishes a technical framework for requirements analysis,architectural design,and data-integration protocols.Revit parametric modelling is used to build a 3D tunnel model with structural elements,pipelines and 18 monitoring points(for displacement and joint width).Custom Revit API code integrated real-time sensor data into the BIM platform via an automated pipeline.The system achieved a spatial accuracy of±1 mm in locating deformation hotspots.Notifications are triggered within 10 s of anomaly detection,and the system renders 3D risk propagation paths in real-time.Realtime 3D visualization of risk propagation paths is also facilitated.The efficacy of the solution was validated in a Ningbo utility tunnel project,where it was demonstrated that it eliminates human-dependent judgment errors and reduces warning latency by 99.9%compared to conventional methods.The BIM-IoT integrated approach,which enables millimetre-level precision in risk identification and near-instantaneous response,establishes a new paradigm for intelligent infrastructure safety management.
文摘With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported by the National Key Research and Development Program of China(2022YFA0912100)the National Natural Science Foundation of China(32270098 and 32470073)+1 种基金the Fundamental Research Funds for the Central Universities(2662024JC015)the National Key Laboratory of Agricultural Microbiology(AML2024D02)to Z.Z.
文摘Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.However,the analysis and visualization of Ribo-seq data remain challenging.Despite the availability of various analytical pipelines,improvements in comprehensiveness,accuracy,and user-friendliness are still necessary.In this study,we develop RiboParser/RiboShiny,a robust framework for analyzing and visualizing Ribo-seq data.Building on published methods,we optimize ribosome structure-based and start/stopbased models to improve the accuracy and stability of P-site detection,even in species with a high proportion of leaderless transcripts.Leveraging these improvements,RiboParser offers comprehensive analyses,including quality control,gene-level analysis,codon-level analysis,and the analysis of Ribo-seq variants.Meanwhile,RiboShiny provides a user-friendly and adaptable platform for data visualization,facilitating deeper insights into the translational landscape.Furthermore,the integration of standardized genome annotation renders our platform universally applicable to various organisms with sequenced genomes.This framework has the potential to significantly improve the precision and efficiency of Ribo-seq data interpretation,thereby deepening our understanding of translational regulation.
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.
