Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ul...Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.展开更多
Mega Low Earth Orbit(LEO)satellite constellations can provide pervasive intelligent services in the forthcoming Six-Generation(6G)network via the Free-Space Optical(FSO)InterSatellite Link(ISL).However,the challenges ...Mega Low Earth Orbit(LEO)satellite constellations can provide pervasive intelligent services in the forthcoming Six-Generation(6G)network via the Free-Space Optical(FSO)InterSatellite Link(ISL).However,the challenges posed by the mega LEO satellite constellations,such as limited onboard resources,high-speed movement and the vibration of satellite platforms,present significant obstacles for the existing Pointing,Acquisition and Tracking(PAT)schemes of FSOISL.To address these challenges,we propose a beaconless PAT scheme under satellite platform vibrations,employing a composite scanning approach combining an inner Archimedean spiral scan with an outer regular hexagon step scan.The proposed composite scanning approach covers a wide range of the Field of Uncertainty(FOU)and reduces the required scans by actuator,which can ensure a high Acquisition Probability(AP)while reducing the Average Acquisition Time(AAT)for the inner scan.Specifically,we model and analyze the effect of satellite platform vibrations on the acquisition performance of our beaconless PAT scheme,and derive closed-form expressions for both AP and AAT by utilizing a 2-track model where the acquisition happens on two adjacent spiral scan tracks.By utilizing the theoretical derivations,we can achieve a minimum AAT under diverse APs by selecting appropriate values of overlapping region and scanning range.Simulation results validate that our optimized composite scanning approach for beaconless PAT scheme outperforms the existing schemes.展开更多
1.Introduction From the first-generation(1G)through the second-generation(2G)Global System for Mobile Communications(GSM),the third-generation(3G)wideband code division multiple access(WCDMA)to the fourth-generation(4...1.Introduction From the first-generation(1G)through the second-generation(2G)Global System for Mobile Communications(GSM),the third-generation(3G)wideband code division multiple access(WCDMA)to the fourth-generation(4G)long-term evolution(LTE)wireless networks,terrestrial networks(TNs)have demonstrated significant success in increasing communication speeds and improving quality of service(QoS)for users.展开更多
Ensemble learning,a pivotal branch of machine learning,amalgamates multiple base models to enhance the overarching performance of predictive models,capitalising on the diversity and collective wisdom of the ensemble t...Ensemble learning,a pivotal branch of machine learning,amalgamates multiple base models to enhance the overarching performance of predictive models,capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and mitigate overfitting.In this review,a four-layer research framework is established for the research of ensemble learning,which can offer a comprehensive and structured review of ensemble learning from bottom to top.Firstly,this survey commences by introducing fundamental ensemble learning techniques,including bagging,boosting,and stacking,while also exploring the ensemble's diversity.Then,deep ensemble learning and semi-supervised ensemble learning are studied in detail.Furthermore,the utilisation of ensemble learning techniques to navigate challenging datasets,such as imbalanced and highdimensional data,is discussed.The application of ensemble learning techniques across various research domains,including healthcare,transportation,finance,manufacturing,and the Internet,is also examined.The survey concludes by discussing challenges intrinsic to ensemble learning.展开更多
The Global Positioning System(GPS)plays an indispensable role in the control of Unmanned Aerial Vehicle(UAV).However,the civilian GPS signals,transmitted over the air without any encryption,are vulnerable to spoofing ...The Global Positioning System(GPS)plays an indispensable role in the control of Unmanned Aerial Vehicle(UAV).However,the civilian GPS signals,transmitted over the air without any encryption,are vulnerable to spoofing attacks,which further guides the UAV on deviated positions or trajectories.To counter the GPS,,m spoofing on UAV system and to detect the position/trajectory anomaly in real time,a motion state vector based stack long short-term memory trajectory prediction scheme is firstly proposed,leveraging the temporal and spatial features of UAV kinematics.Based on the predicted results,an ensemble voting-based trajectory anomaly detection scheme is proposed to detect the position anomalies in real time with the information of motion state sequences.The proposed prediction-based trajectory anomaly detection scheme outperforms the existing offline detection schemes designed for fixed trajectories.Software In The Loop(SITL)based online prediction and online anomaly detection are demonstrated with random 3D flight trajectories.Results show that the coefficient of determination(R^(2))and Root Mean Square Error(RMSE)of the prediction scheme can reach 0.996 and 3.467,respectively.The accuracy,recall,and F1-score of the proposed anomaly detection scheme can reach 0.984,0.988,and 0.983,respectively,which outperform deep ensemble learning,LSTM-based classifier,machine learning classifier and GA-XGBoost based schemes.Moreover,results show that compared with LSTM-based classifier,the average duration(from the moment starting an attack to the moment the attack being detected)and distance of the proposed scheme are reduced by 24.4%and 19.5%,respectively.展开更多
Implicit neural representation(INR)networks break through the accuracy and resolution limitations of traditional discrete representations by modeling high-dimensional data as continuously differentiable implicit neura...Implicit neural representation(INR)networks break through the accuracy and resolution limitations of traditional discrete representations by modeling high-dimensional data as continuously differentiable implicit neural networks,enabling lossless compression and efficient reconstruction of details in a compact form.However,an optical-assisted INR network has yet to be demonstrated.INR networks require high nonlinearity,whereas implementing analog nonlinear activation in photonic neural networks is a challenge.Inspired by the inherent physical properties of modulators,we propose an optoelectronic nonlinear activation and implement it on the image reconstruction task.Simulations and experiments demonstrate that the proposed optoelectronic periodic neural network can represent images and perform image reconstruction with excellent results.This approach empowers complex image reconstruction with high-frequency details and reduces the amount of required hardware.Our method enables the development of compact,efficient optoelectronic neural networks,utilizing repeatable modular units for scalable and practical high-performance computing.It can enable scene generation and compression in biomedicine,autonomous driving,and augmented reality/virtual reality.展开更多
Visual perception is critical in robotic operations,particularly in collaborative and autonomous robot systems.Through efficient visual systems,robots can acquire and process environmental information in real-time,rec...Visual perception is critical in robotic operations,particularly in collaborative and autonomous robot systems.Through efficient visual systems,robots can acquire and process environmental information in real-time,recognise objects,assess spatial relationships,and make adaptive decisions.This review aims to provide a comprehensive overview of the latest advancements in the field of vision as applied to robotic perception,focusing primarily on visual applications in the areas of object perception,self-perception,human-robot collaboration,and multi-robot collaboration.By summarising the current state of development and analysing the challenges and opportunities that remain in these areas,this paper offers a thorough examination of the integration of visual perception with operational robotics.It further inspires future research and drives the application and development of visual perception across various robotic domains,enabling operational robots to better adapt to complex environments and reliably accomplish tasks.展开更多
Robotics plays an increasingly important role in all areas of human activity.Teleoperation robots can effectively ensure the safety of operators when operating in difficult and high‐risk industrial scenarios,which ob...Robotics plays an increasingly important role in all areas of human activity.Teleoperation robots can effectively ensure the safety of operators when operating in difficult and high‐risk industrial scenarios,which obviously requires instant and efficient signal compression and transmission in the system.However,most of the existing algorithms cannot fully explore the correlation within the signal,which mostly limits the compression efficiency.In this paper,a novel prediction‐aided kinaestheticsignal compression framework is proposed,which uses semantic communication methods to explore the temporal and spatial correlations of signals and employs neural network predictions to uncover their internal correlations.Specifically,the signal is first divided into two groups:the base part and the predictable part,and then a series of transformation matrices are introduced to establish the correlation between the two groups of the signal,which can be automatically optimised by a well‐designed neural network.This strategy of using learnable transformation matrices for prediction can not only accurately construct the correlation within the signal through massive data mining but also efficiently execute inference in a simple matrix multiplication computing form.Experimental results demonstrate that the proposed method outperforms the existing traditional tactile codecs and the latest tactile semantic communication methods.展开更多
Thermophilic proteins maintain their structure and function at high temperatures,making them widely useful in industrial applications.Due to the complexity of experimental measurements,predicting the melting temperatu...Thermophilic proteins maintain their structure and function at high temperatures,making them widely useful in industrial applications.Due to the complexity of experimental measurements,predicting the melting temperature(T_(m))of proteins has become a research hotspot.Previous methods rely on amino acid composition,physicochemical properties of proteins,and the optimal growth temperature(OGT)of hosts for T_(m)prediction.However,their performance in predicting T_(m)values for thermophilic proteins(T_(m)>60℃)are generally unsatisfactory due to data scarcity.Herein,we introduce T_(m)Pred,a T_(m)prediction model for thermophilic proteins,that combines protein language model,graph convolutional network and Graphormer module.For performance evaluation,T_(m)Pred achieves a root mean square error(RMSE)of 5.48℃,a pearson correlation coefficient(P)of 0.784,and a coefficient of determination(R~2)of 0.613,representing improvements of 19%,15%,and 32%,respectively,compared to the state-of-the-art predictive models like DeepTM.Furthermore,T_(m)Pred demonstrated strong generalization capability on independent blind test datasets.Overall,T_(m)Pred provides an effective tool for the mining and modification of thermophilic proteins by leveraging deep learning.展开更多
High-speed imaging is crucial for understanding the transient dynamics of the world,but conventional frame-by-frame video acquisition is limited by specialized hardware and substantial data storage requirements.We int...High-speed imaging is crucial for understanding the transient dynamics of the world,but conventional frame-by-frame video acquisition is limited by specialized hardware and substantial data storage requirements.We introduce“SpeedShot,”a computational imaging framework for efficient high-speed video imaging.SpeedShot features a low-speed dual-camera setup,which simultaneously captures two temporally coded snapshots.Cross-referencing these two snapshots extracts a multiplexed temporal gradient image,producing a compact and multiframe motion representation for video reconstruction.Recognizing the unique temporal-only modulation model,we propose an explicable motion-guided scale-recurrent transformer for video decoding.It exploits cross-scale error maps to bolster the cycle consistency between predicted and observed data.Evaluations on both simulated datasets and real imaging setups demonstrate SpeedShot’s effectiveness in video-rate up-conversion,with pronounced improvement over video frame interpolation and deblurring methods.The proposed framework is compatible with commercial low-speed cameras,offering a versatile low-bandwidth alternative for video-related applications,such as video surveillance and sports analysis.展开更多
In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the tran...In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the transmission delay.To address this problem,in this paper,we propose an age-optimal caching distribution mechanism for the high-timeliness data collection in S-IoT by adopting a freshness metric,as called age of information(AoI)through the caching-based single-source multidestinations(SSMDs)transmission,namely Multi-AoI,with a well-designed cross-slot directed graph(CSG).With the proposed CSG,we make optimizations on the locations of cache nodes by solving a nonlinear integer programming problem on minimizing Multi-AoI.In particular,we put up forward three specific algorithms respectively for improving the Multi-AoI,i.e.,the minimum queuing delay algorithm(MQDA)based on node deviation from average level,the minimum propagation delay algorithm(MPDA)based on the node propagation delay reduction,and a delay balanced algorithm(DBA)based on node deviation from average level and propagation delay reduction.The simulation results show that the proposed mechanism can effectively improve the freshness of information compared with the random selection algorithm.展开更多
The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)model...The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)models.However,existing dual-pol radar data assimilation(DA)methods exhibit limitations in terms of computational efficiency and data utilization.In this study,a new dual-pol radar DA approach is developed that utilizes a UNet-based model to retrieve mixing ratio information for four hydrometeor species from dual-pol radar data.The validation results for the UNet-based model indicate that the distributions of the retrieved hydrometeor mixing ratios provided by the model align well with the labeled data,yielding a reasonable range of root mean square errors(RMSEs).On this basis,the hydrometeor analysis increments retrieved by the UNet-based model are incorporated into the model integration process through the incremental analysis update(IAU)scheme,establishing a complete dual-pol radar DA framework for the CMA-MESO model.To evaluate the efficacy of this DA scheme,comparative simulation experiments were conducted for Typhoon Lekima(2019).Verification results indicate that using the hydrometeor DA scheme generally improves the threat scores(TSs)for 3-hour accumulated precipitation during medium-and heavy-rainfall events.Additionally,the 24-hour accumulated rainfall TSs for the medium-,heavy-,and extreme-precipitation categories in the DA experiment are all superior to those in the control experiment.The DA method also yields superior predictions of the spatial distribution of extremerainfall events.These results demonstrate that the proposed dual-pol radar DA approach effectively enhances the precipitation forecasting capabilities of numerical weather models.展开更多
Radio spectrum has become a rare resource due to the rapid development of wireless communication technique. Cognitive radio is one of important techniques to deal with this radio spectrum problem. But the resource all...Radio spectrum has become a rare resource due to the rapid development of wireless communication technique. Cognitive radio is one of important techniques to deal with this radio spectrum problem. But the resource allocation in cognitive radio also has its own issues, such as the flexibility of the allocation algorithm, the performance of resource allocation, and so on. In order to increase the flexibility of the allocation algorithm for cognitive radio, more and more researches are focusing on the evolutionary algorithms, such as genetic algorithm(GA), particle swarm optimization(PSO). Evolutionary algorithm can greatly improve the flexibility of the allocation algorithm for cognitive radio system in different communication scenarios, but the performances are relatively lower than the original mathematical methods. So in this paper, we proposed an adaptive resource allocation algorithm based on modified PSO for cognitive radio system to solve these problems. Modified particle swarm optimization(Modified PSO) has both genetic algorithm(GA) and particle swarm optimization(PSO)’s updating processes which makes this modified PSO overcame PSO’s own disadvantages and keep advantages. Simulation results showed our proposed algorithm has enough flexibility to meet cognitive radio systems’ requirements, and also has a better performance than original PSO.展开更多
To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,a...To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,and they are applied to the prediction modeling of refined gasoline blending conservative formula.Firstly,based on historical measured data of refined gasoline blending and according to the characteristics of the data set,we use bootstrapping to divide the training data set and the test data set.Secondly,considering that parameter selection for the network is difficult,particle swarm optimization is adopted to improve the related optimal parameters and replace the tedious process of manually selecting parameters,greatly improving optimization efficiency.In addition,the contrastive divergence algorithm is used for unsupervised forward feature learning and supervised reverse fine-tuning of the network,so as to construct a more accurate prediction model for conservative formula.Finally,in order to evaluate the effectiveness of this method,the simulation results are compared with those of traditional modeling methods,which show that the DBNs has better prediction performance than error back propagation and support vector machines,and can provide production guidance for refined gasoline blending formula.展开更多
Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(...Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.展开更多
Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the spars...Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation,and improve system design and performance analysis.Compared with the traditional sub6 GHz channel,millimeter wave(mmWave)channel has been considered to be more sparse in existing researches.However,most research only assume that the mmWave channel is sparse,without providing quantitative analysis and evaluation.Therefore,this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements.A vector network analyzer(VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz,and multi-scenario channel measurements are conducted.The Gini index,Rician𝐾factor and rootmean-square(RMS)delay spread are used to measure channel sparsity.Then,the key factors affecting mmWave channel sparsity are explored.It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel.In addition,the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.展开更多
Cooperative utilization of multidimensional resources including cache, power and spectrum in satellite-terrestrial integrated networks(STINs) can provide a feasible approach for massive streaming media content deliver...Cooperative utilization of multidimensional resources including cache, power and spectrum in satellite-terrestrial integrated networks(STINs) can provide a feasible approach for massive streaming media content delivery over the seamless global coverage area. However, the on-board supportable resources of a single satellite are extremely limited and lack of interaction with others. In this paper, we design a network model with two-layered cache deployment, i.e., satellite layer and ground base station layer, and two types of sharing links, i.e., terrestrial-satellite sharing(TSS) links and inter-satellite sharing(ISS) links, to enhance the capability of cooperative delivery over STINs. Thus, we use rateless codes for the content divided-packet transmission, and derive the total energy efficiency(EE) in the whole transmission procedure, which is defined as the ratio of traffic offloading and energy consumption. We formulate two optimization problems about maximizing EE in different sharing scenarios(only TSS and TSS-ISS),and propose two optimized algorithms to obtain the optimal content placement matrixes, respectively.Simulation results demonstrate that, enabling sharing links with optimized cache placement have more than 2 times improvement of EE performance than other traditional placement schemes. Particularly, TSS-ISS schemes have the higher EE performance than only TSS schemes under the conditions of enough number of satellites and smaller inter-satellite distances.展开更多
The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such...The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.展开更多
Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based ...Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture interaction.Methods The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine.By establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was created.Using code script logic,particle systems,as well as other systems,chemical phenomena were simulated.Furthermore,we created an online teaching platform using streaming media and databases to address the problems of distance teaching.Results The proposed system was evaluated against two mainstream products in the market.In the experiments,the proposed system outperformed the other products in terms of fidelity and practicality.Conclusions The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.展开更多
Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strate...Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.展开更多
基金upported by the National Natural Science Foundation of China(Grant No.62305184)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.WDZC20220818100259004).
文摘Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.
基金supported in part by the Major Key Project of PCL of China(No.PCL2024A01)in part by the National Natural Science Foundation of China(Nos.62071141,62027802)+1 种基金in part by the Shenzhen Science and Technology Program of China(Nos.JCYJ20241202123904007,GXWD20231127123203001,JSGG20220831110801003)in part by the Fundamental Research Funds for the Central Universities of China(No.HIT.OCEF.2024046)。
文摘Mega Low Earth Orbit(LEO)satellite constellations can provide pervasive intelligent services in the forthcoming Six-Generation(6G)network via the Free-Space Optical(FSO)InterSatellite Link(ISL).However,the challenges posed by the mega LEO satellite constellations,such as limited onboard resources,high-speed movement and the vibration of satellite platforms,present significant obstacles for the existing Pointing,Acquisition and Tracking(PAT)schemes of FSOISL.To address these challenges,we propose a beaconless PAT scheme under satellite platform vibrations,employing a composite scanning approach combining an inner Archimedean spiral scan with an outer regular hexagon step scan.The proposed composite scanning approach covers a wide range of the Field of Uncertainty(FOU)and reduces the required scans by actuator,which can ensure a high Acquisition Probability(AP)while reducing the Average Acquisition Time(AAT)for the inner scan.Specifically,we model and analyze the effect of satellite platform vibrations on the acquisition performance of our beaconless PAT scheme,and derive closed-form expressions for both AP and AAT by utilizing a 2-track model where the acquisition happens on two adjacent spiral scan tracks.By utilizing the theoretical derivations,we can achieve a minimum AAT under diverse APs by selecting appropriate values of overlapping region and scanning range.Simulation results validate that our optimized composite scanning approach for beaconless PAT scheme outperforms the existing schemes.
基金support from the Development Program from Institute for Communication Systems(ICS),the 5G&6G Innovation Centre(5GIC&6GIC)at University of Surreythe China Scholarship Council,the National Natural Science Foundation of China(62371158)the Major Key Project of Pengcheng Laboratory(PCL2024A01).
文摘1.Introduction From the first-generation(1G)through the second-generation(2G)Global System for Mobile Communications(GSM),the third-generation(3G)wideband code division multiple access(WCDMA)to the fourth-generation(4G)long-term evolution(LTE)wireless networks,terrestrial networks(TNs)have demonstrated significant success in increasing communication speeds and improving quality of service(QoS)for users.
基金supported in part by National Natural Science Foundation of China No.92467109,U21A20478National Key R&D Program of China 2023YFA1011601the Major Key Project of PCL(Grant PCL2024A05).
文摘Ensemble learning,a pivotal branch of machine learning,amalgamates multiple base models to enhance the overarching performance of predictive models,capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and mitigate overfitting.In this review,a four-layer research framework is established for the research of ensemble learning,which can offer a comprehensive and structured review of ensemble learning from bottom to top.Firstly,this survey commences by introducing fundamental ensemble learning techniques,including bagging,boosting,and stacking,while also exploring the ensemble's diversity.Then,deep ensemble learning and semi-supervised ensemble learning are studied in detail.Furthermore,the utilisation of ensemble learning techniques to navigate challenging datasets,such as imbalanced and highdimensional data,is discussed.The application of ensemble learning techniques across various research domains,including healthcare,transportation,finance,manufacturing,and the Internet,is also examined.The survey concludes by discussing challenges intrinsic to ensemble learning.
基金supported in part by the National Natural Science Foundation of China(No.62271076)in part by the Fundamental Research Funds for the Central Universities,China(No.2242022k60006).
文摘The Global Positioning System(GPS)plays an indispensable role in the control of Unmanned Aerial Vehicle(UAV).However,the civilian GPS signals,transmitted over the air without any encryption,are vulnerable to spoofing attacks,which further guides the UAV on deviated positions or trajectories.To counter the GPS,,m spoofing on UAV system and to detect the position/trajectory anomaly in real time,a motion state vector based stack long short-term memory trajectory prediction scheme is firstly proposed,leveraging the temporal and spatial features of UAV kinematics.Based on the predicted results,an ensemble voting-based trajectory anomaly detection scheme is proposed to detect the position anomalies in real time with the information of motion state sequences.The proposed prediction-based trajectory anomaly detection scheme outperforms the existing offline detection schemes designed for fixed trajectories.Software In The Loop(SITL)based online prediction and online anomaly detection are demonstrated with random 3D flight trajectories.Results show that the coefficient of determination(R^(2))and Root Mean Square Error(RMSE)of the prediction scheme can reach 0.996 and 3.467,respectively.The accuracy,recall,and F1-score of the proposed anomaly detection scheme can reach 0.984,0.988,and 0.983,respectively,which outperform deep ensemble learning,LSTM-based classifier,machine learning classifier and GA-XGBoost based schemes.Moreover,results show that compared with LSTM-based classifier,the average duration(from the moment starting an attack to the moment the attack being detected)and distance of the proposed scheme are reduced by 24.4%and 19.5%,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.62305184)the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)+1 种基金the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.JCYJ20241202123919027)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.WDZC20220818100259004).
文摘Implicit neural representation(INR)networks break through the accuracy and resolution limitations of traditional discrete representations by modeling high-dimensional data as continuously differentiable implicit neural networks,enabling lossless compression and efficient reconstruction of details in a compact form.However,an optical-assisted INR network has yet to be demonstrated.INR networks require high nonlinearity,whereas implementing analog nonlinear activation in photonic neural networks is a challenge.Inspired by the inherent physical properties of modulators,we propose an optoelectronic nonlinear activation and implement it on the image reconstruction task.Simulations and experiments demonstrate that the proposed optoelectronic periodic neural network can represent images and perform image reconstruction with excellent results.This approach empowers complex image reconstruction with high-frequency details and reduces the amount of required hardware.Our method enables the development of compact,efficient optoelectronic neural networks,utilizing repeatable modular units for scalable and practical high-performance computing.It can enable scene generation and compression in biomedicine,autonomous driving,and augmented reality/virtual reality.
基金supported by the National Natural Science Foundation of China(Grant 62306185)the Guangdong Basic and Applied Basic Research Foundation(Grant 2024A1515012065)the Shenzhen Science and Technology Program(Grants JSGGKQTD20221101115656029 and KJZD20230923113801004).
文摘Visual perception is critical in robotic operations,particularly in collaborative and autonomous robot systems.Through efficient visual systems,robots can acquire and process environmental information in real-time,recognise objects,assess spatial relationships,and make adaptive decisions.This review aims to provide a comprehensive overview of the latest advancements in the field of vision as applied to robotic perception,focusing primarily on visual applications in the areas of object perception,self-perception,human-robot collaboration,and multi-robot collaboration.By summarising the current state of development and analysing the challenges and opportunities that remain in these areas,this paper offers a thorough examination of the integration of visual perception with operational robotics.It further inspires future research and drives the application and development of visual perception across various robotic domains,enabling operational robots to better adapt to complex environments and reliably accomplish tasks.
基金supported in part by the National Natural Science Foundation of China(NSFC)(Grants 62302128 and 624B2049)supported by Shenzhen Science and Technology Innovation Committee(Grant RCBS20231211090749086).
文摘Robotics plays an increasingly important role in all areas of human activity.Teleoperation robots can effectively ensure the safety of operators when operating in difficult and high‐risk industrial scenarios,which obviously requires instant and efficient signal compression and transmission in the system.However,most of the existing algorithms cannot fully explore the correlation within the signal,which mostly limits the compression efficiency.In this paper,a novel prediction‐aided kinaestheticsignal compression framework is proposed,which uses semantic communication methods to explore the temporal and spatial correlations of signals and employs neural network predictions to uncover their internal correlations.Specifically,the signal is first divided into two groups:the base part and the predictable part,and then a series of transformation matrices are introduced to establish the correlation between the two groups of the signal,which can be automatically optimised by a well‐designed neural network.This strategy of using learnable transformation matrices for prediction can not only accurately construct the correlation within the signal through massive data mining but also efficiently execute inference in a simple matrix multiplication computing form.Experimental results demonstrate that the proposed method outperforms the existing traditional tactile codecs and the latest tactile semantic communication methods.
基金financially supported by the National Key R&D Program of China(Nos.2020YFA0908100 and 2023YFF1204401)Shenzhen Medical Research Fund(No.B2302037)+1 种基金the National Natural Science Foundation of China(Nos.22331003 and 21925102)Beijing National Laboratory for Molecular Sciences(No.BNLMS-CXXM-202006)。
文摘Thermophilic proteins maintain their structure and function at high temperatures,making them widely useful in industrial applications.Due to the complexity of experimental measurements,predicting the melting temperature(T_(m))of proteins has become a research hotspot.Previous methods rely on amino acid composition,physicochemical properties of proteins,and the optimal growth temperature(OGT)of hosts for T_(m)prediction.However,their performance in predicting T_(m)values for thermophilic proteins(T_(m)>60℃)are generally unsatisfactory due to data scarcity.Herein,we introduce T_(m)Pred,a T_(m)prediction model for thermophilic proteins,that combines protein language model,graph convolutional network and Graphormer module.For performance evaluation,T_(m)Pred achieves a root mean square error(RMSE)of 5.48℃,a pearson correlation coefficient(P)of 0.784,and a coefficient of determination(R~2)of 0.613,representing improvements of 19%,15%,and 32%,respectively,compared to the state-of-the-art predictive models like DeepTM.Furthermore,T_(m)Pred demonstrated strong generalization capability on independent blind test datasets.Overall,T_(m)Pred provides an effective tool for the mining and modification of thermophilic proteins by leveraging deep learning.
基金supported by the National Natural Science Foundation of China(Grant No.62305184)the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)+7 种基金the Science,Technology,and Innovation Commission of Shenzhen Municipality(Grant No.JCYJ20241202123919027)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)the Science Fund for Distinguished Young Scholars of Zhejiang Province(Grant No.LR23F010001)the Research Center for Industries of the Future(RCIF)at Westlake University and and the Key Project of Westlake Institute for Optoelectronics(Grant No.2023GD007)the Zhejiang“Pioneer”and“Leading Goose”R&D Program(Grant Nos.2024SDXHDX0006 and 2024C03182)the Ningbo Science and Technology Bureau“Science and Technology Yongjiang 2035”Key Technology Breakthrough Program(Grant No.2024Z126)the Research Grants Council of the Hong Kong Special Administrative Region,China(Grant Nos.C5031-22G,CityU11310522,and CityU11300123)the City University of Hong Kong(Grant No.9610628).
文摘High-speed imaging is crucial for understanding the transient dynamics of the world,but conventional frame-by-frame video acquisition is limited by specialized hardware and substantial data storage requirements.We introduce“SpeedShot,”a computational imaging framework for efficient high-speed video imaging.SpeedShot features a low-speed dual-camera setup,which simultaneously captures two temporally coded snapshots.Cross-referencing these two snapshots extracts a multiplexed temporal gradient image,producing a compact and multiframe motion representation for video reconstruction.Recognizing the unique temporal-only modulation model,we propose an explicable motion-guided scale-recurrent transformer for video decoding.It exploits cross-scale error maps to bolster the cycle consistency between predicted and observed data.Evaluations on both simulated datasets and real imaging setups demonstrate SpeedShot’s effectiveness in video-rate up-conversion,with pronounced improvement over video frame interpolation and deblurring methods.The proposed framework is compatible with commercial low-speed cameras,offering a versatile low-bandwidth alternative for video-related applications,such as video surveillance and sports analysis.
基金supports from the Major Key Project of PCL (PCL2021A031)Shenzhen Science Technology Program (GXWD20201230155427003-20200824093323001)
文摘In the Satellite-integrated Internet of Things(S-IoT),data freshness in the time-sensitive scenarios could not be guaranteed over the timevarying topology with current distribution strategies aiming to reduce the transmission delay.To address this problem,in this paper,we propose an age-optimal caching distribution mechanism for the high-timeliness data collection in S-IoT by adopting a freshness metric,as called age of information(AoI)through the caching-based single-source multidestinations(SSMDs)transmission,namely Multi-AoI,with a well-designed cross-slot directed graph(CSG).With the proposed CSG,we make optimizations on the locations of cache nodes by solving a nonlinear integer programming problem on minimizing Multi-AoI.In particular,we put up forward three specific algorithms respectively for improving the Multi-AoI,i.e.,the minimum queuing delay algorithm(MQDA)based on node deviation from average level,the minimum propagation delay algorithm(MPDA)based on the node propagation delay reduction,and a delay balanced algorithm(DBA)based on node deviation from average level and propagation delay reduction.The simulation results show that the proposed mechanism can effectively improve the freshness of information compared with the random selection algorithm.
基金Major Key Project of PCL(PCL2025A10)Open Research Project of the China Meteorological Administration Hydro-Meteorology Key Laboratory(23SWQXM036)+2 种基金National Natural Science Foundation of China(42375160)Project of the Key Laboratory of Atmospheric Sounding of China Meteorological Administration(2022KLAS06M)Science and Technology Research Project of the Guangdong Provincial Meteorological Bureau(GRMC2024M04)。
文摘The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)models.However,existing dual-pol radar data assimilation(DA)methods exhibit limitations in terms of computational efficiency and data utilization.In this study,a new dual-pol radar DA approach is developed that utilizes a UNet-based model to retrieve mixing ratio information for four hydrometeor species from dual-pol radar data.The validation results for the UNet-based model indicate that the distributions of the retrieved hydrometeor mixing ratios provided by the model align well with the labeled data,yielding a reasonable range of root mean square errors(RMSEs).On this basis,the hydrometeor analysis increments retrieved by the UNet-based model are incorporated into the model integration process through the incremental analysis update(IAU)scheme,establishing a complete dual-pol radar DA framework for the CMA-MESO model.To evaluate the efficacy of this DA scheme,comparative simulation experiments were conducted for Typhoon Lekima(2019).Verification results indicate that using the hydrometeor DA scheme generally improves the threat scores(TSs)for 3-hour accumulated precipitation during medium-and heavy-rainfall events.Additionally,the 24-hour accumulated rainfall TSs for the medium-,heavy-,and extreme-precipitation categories in the DA experiment are all superior to those in the control experiment.The DA method also yields superior predictions of the spatial distribution of extremerainfall events.These results demonstrate that the proposed dual-pol radar DA approach effectively enhances the precipitation forecasting capabilities of numerical weather models.
基金supported in part by the National Natural Sciences Foundation of China(NSFC)under Grant 61525103,the National Natural Sciences Foundation of China(NSFC)under Grant 61501140,the National Natural Sciences Foundation of China under Grant 61831008the Shenzhen Fundamental Research Project under Grant JCYJ20150930150304185+1 种基金the Guangdong Science and Technology Planning Project 2018B030322004in part by the Shenzhen Basic Research Program under Grant ZDSYS201707280903305
文摘Radio spectrum has become a rare resource due to the rapid development of wireless communication technique. Cognitive radio is one of important techniques to deal with this radio spectrum problem. But the resource allocation in cognitive radio also has its own issues, such as the flexibility of the allocation algorithm, the performance of resource allocation, and so on. In order to increase the flexibility of the allocation algorithm for cognitive radio, more and more researches are focusing on the evolutionary algorithms, such as genetic algorithm(GA), particle swarm optimization(PSO). Evolutionary algorithm can greatly improve the flexibility of the allocation algorithm for cognitive radio system in different communication scenarios, but the performances are relatively lower than the original mathematical methods. So in this paper, we proposed an adaptive resource allocation algorithm based on modified PSO for cognitive radio system to solve these problems. Modified particle swarm optimization(Modified PSO) has both genetic algorithm(GA) and particle swarm optimization(PSO)’s updating processes which makes this modified PSO overcame PSO’s own disadvantages and keep advantages. Simulation results showed our proposed algorithm has enough flexibility to meet cognitive radio systems’ requirements, and also has a better performance than original PSO.
基金support from the National Natural Science Foundation of China (61763027)the Young Ph.D.Program Foundation of Gansu EducationalCommittee (2021QB-044)
文摘To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,and they are applied to the prediction modeling of refined gasoline blending conservative formula.Firstly,based on historical measured data of refined gasoline blending and according to the characteristics of the data set,we use bootstrapping to divide the training data set and the test data set.Secondly,considering that parameter selection for the network is difficult,particle swarm optimization is adopted to improve the related optimal parameters and replace the tedious process of manually selecting parameters,greatly improving optimization efficiency.In addition,the contrastive divergence algorithm is used for unsupervised forward feature learning and supervised reverse fine-tuning of the network,so as to construct a more accurate prediction model for conservative formula.Finally,in order to evaluate the effectiveness of this method,the simulation results are compared with those of traditional modeling methods,which show that the DBNs has better prediction performance than error back propagation and support vector machines,and can provide production guidance for refined gasoline blending formula.
基金Major Key Project of Pengcheng Laboratory,Grant/Award Number:PCL2022A03。
文摘Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.
基金supported by National Key R&D Program of China under Grant 2022YFF0608103the National Natural Science Foundation of China under Grant 61922012+1 种基金the Science and Technology Program of State Administration for Market Regulation under Grant 2021MK155the Fundamental Funds of National Institute of Metrology under Grant AKYZD2116-2.
文摘Wireless channel characteristics have significant impacts on channel modeling,estimation,and communication performance.While the channel sparsity is an important characteristic of wireless channels.Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation,and improve system design and performance analysis.Compared with the traditional sub6 GHz channel,millimeter wave(mmWave)channel has been considered to be more sparse in existing researches.However,most research only assume that the mmWave channel is sparse,without providing quantitative analysis and evaluation.Therefore,this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements.A vector network analyzer(VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz,and multi-scenario channel measurements are conducted.The Gini index,Rician𝐾factor and rootmean-square(RMS)delay spread are used to measure channel sparsity.Then,the key factors affecting mmWave channel sparsity are explored.It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel.In addition,the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.
基金supported by National Natural Sciences Foundation of China(No.62271165,62027802,61831008)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515030297,2021A1515011572)Shenzhen Science and Technology Program ZDSYS20210623091808025,Stable Support Plan Program GXWD20231129102638002.
文摘Cooperative utilization of multidimensional resources including cache, power and spectrum in satellite-terrestrial integrated networks(STINs) can provide a feasible approach for massive streaming media content delivery over the seamless global coverage area. However, the on-board supportable resources of a single satellite are extremely limited and lack of interaction with others. In this paper, we design a network model with two-layered cache deployment, i.e., satellite layer and ground base station layer, and two types of sharing links, i.e., terrestrial-satellite sharing(TSS) links and inter-satellite sharing(ISS) links, to enhance the capability of cooperative delivery over STINs. Thus, we use rateless codes for the content divided-packet transmission, and derive the total energy efficiency(EE) in the whole transmission procedure, which is defined as the ratio of traffic offloading and energy consumption. We formulate two optimization problems about maximizing EE in different sharing scenarios(only TSS and TSS-ISS),and propose two optimized algorithms to obtain the optimal content placement matrixes, respectively.Simulation results demonstrate that, enabling sharing links with optimized cache placement have more than 2 times improvement of EE performance than other traditional placement schemes. Particularly, TSS-ISS schemes have the higher EE performance than only TSS schemes under the conditions of enough number of satellites and smaller inter-satellite distances.
基金supported in part by the National Key R&D Program of China under Project 2020YFB1006004the Guangxi Natural Science Foundation under Grants 2019GXNSFFA245015 and 2019GXNSFGA245004+2 种基金the National Natural Science Foundation of China under Projects 62162017,61862012,61962012,and 62172119the Major Key Project of PCL under Grants PCL2021A09,PCL2021A02 and PCL2022A03the Innovation Project of Guangxi Graduate Education YCSW2021175.
文摘The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.
基金National Innovation and Entrepreneurship Program for College Students(202218213001)Science and Technology Innovation Strategy of Guangdong Province(Science and Technology Innovation Cultivation of University Students 2020329182130C000002).
文摘Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture interaction.Methods The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine.By establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was created.Using code script logic,particle systems,as well as other systems,chemical phenomena were simulated.Furthermore,we created an online teaching platform using streaming media and databases to address the problems of distance teaching.Results The proposed system was evaluated against two mainstream products in the market.In the experiments,the proposed system outperformed the other products in terms of fidelity and practicality.Conclusions The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.
基金the Strategic research and consulting project of Chinese Academy of Engineering(2023-HY-14).
文摘Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.
