Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to instal...Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.展开更多
The computer code for the prediction of forest fire behavior is presented. Basic concept of the study is a combined approach to the problem, including the original formulation of fire spread mathematical model, classi...The computer code for the prediction of forest fire behavior is presented. Basic concept of the study is a combined approach to the problem, including the original formulation of fire spread mathematical model, classification of natural fuels, processing of the forest inventory data and programming output of fire simulation code which is compatible with commonly used geographic information system.展开更多
In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the n...In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential.On the other hand,using intelligence in the authentication system is a significant attraction.While existing surveys broadly address vehicular security,a critical gap remains in the systematic exploration of Deep Learning(DL)-based authentication methods tailored to these communication paradigms.This survey fills that gap by offering a comprehensive analysis of DL techniques—including supervised,unsupervised,reinforcement,and hybrid learning—for vehicular authentication.This survey highlights novel contributions,such as a taxonomy of DL-driven authentication protocols,real-world case studies,and a critical evaluation of scalability and privacy-preserving techniques.Additionally,this paper identifies unresolved challenges,such as adversarial resilience and real-time processing constraints,and proposes actionable future directions,including lightweight model optimization and blockchain integration.By grounding the discussion in concrete applications,such as biometric authentication for driver safety and adaptive key management for infrastructure security,this survey bridges theoretical advancements with practical deployment needs,offering a roadmap for next-generation secure intelligent vehicular ecosystems for the modern world.展开更多
Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance b...Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains challenging.Methods This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and VR.Therefore,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image graph.Using this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of views.The rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image graph.The detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in real-time.In addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light intensities.Results Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,respectively.While achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering approaches.Conclusions Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR environments.Nevertheless,the handling of morphing artifacts in the parallax image regions remains a topic for future research.展开更多
With the advancements of the next-generation communication networking and Internet ofThings(IoT)technologies,a variety of computation-intensive applications(e.g.,autonomous driving and face recognition)have emerged.Th...With the advancements of the next-generation communication networking and Internet ofThings(IoT)technologies,a variety of computation-intensive applications(e.g.,autonomous driving and face recognition)have emerged.The execution of these IoT applications demands a lot of computing resources.Nevertheless,terminal devices(TDs)usually do not have sufficient computing resources to process these applications.Offloading IoT applications to be processed by mobile edge computing(MEC)servers with more computing resources provides a promising way to address this issue.While a significant number of works have studied task offloading,only a few of them have considered the security issue.This study investigates the problem of spectrum allocation and security-sensitive task offloading in an MEC system.Dynamic voltage scaling(DVS)technology is applied by TDs to reduce energy consumption and computing time.To guarantee data security during task offloading,we use AES cryptographic technique.The studied problem is formulated as an optimization problem and solved by our proposed efficient offloading scheme.The simulation results show that the proposed scheme can reduce system cost while guaranteeing data security.展开更多
Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental cons...Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.展开更多
Cyber-criminals target smart connected devices for spyware distribution and security breaches,but existing Internet of Things(IoT)security standards are insufficient.Major IoT industry players prioritize market share ...Cyber-criminals target smart connected devices for spyware distribution and security breaches,but existing Internet of Things(IoT)security standards are insufficient.Major IoT industry players prioritize market share over security,leading to insecure smart products.Traditional host-based protection solutions are less effective due to limited resources.Overcoming these challenges and enhancing the security of IoT Devices requires a security design at the network level that uses lightweight cryptographic parameters.In order to handle control,administration,and security concerns in traditional networking,the Gateway Node offers a contemporary networking architecture.By managing all network-level computations and complexity,the Gateway Node relieves IoT devices of these responsibilities.In this study,we introduce a novel privacy-preserving security architecture for gateway-node smart homes.Subsequently,we develop Smart Homes,An Efficient,Anonymous,and Robust Authentication Scheme(EARAS)based on the foundational principles of this security architecture.Furthermore,we formally examine the security characteristics of our suggested protocol that makes use of methodology such as ProVerif,supplemented by an informal analysis of security.Lastly,we conduct performance evaluations and comparative analyses to assess the efficacy of our scheme.Performance analysis shows that EARAS achieves up to 30%to 54%more efficient than most protocols and lower computation cost compared to Banerjee et al.’s scheme,and significantly reduces communication overhead compared to other recent protocols,while ensuring comprehensive security.Our objective is to provide robust security measures for smart homes while addressing resource constraints and preserving user privacy.展开更多
Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean,sunflower,and rapeseed.The growth of the oil palm industry causes degradation to the environment,especially when th...Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean,sunflower,and rapeseed.The growth of the oil palm industry causes degradation to the environment,especially when the expansion of plantations goes uncontrolled.Remote sensing is a useful tool to monitor the development of oil palm plantations.In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability,this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring.In addition,the existing knowledge gaps are identified and recommendations for further research are given.展开更多
Some convergence theorems of Ishikawa type iterative sequence with errors for nonlinear general quasi-contractive mapping in convex metric spaces are proved. The results not only extend and improve the corresponding r...Some convergence theorems of Ishikawa type iterative sequence with errors for nonlinear general quasi-contractive mapping in convex metric spaces are proved. The results not only extend and improve the corresponding results of L. B. Ciric, Q. H. Liu, H. E. Rhoades and H. K. Xu, et al., but also give an affirmative answer to the open question of Rhoades-Naimpally- Singh in convex metric spaces.展开更多
AIM:To investigate perception of natural orifice transluminal endoscopic surgery(NOTES)as a potential technique for appendectomy.METHODS:One hundred patients undergoing endoscopy and 100 physicians were given a questi...AIM:To investigate perception of natural orifice transluminal endoscopic surgery(NOTES)as a potential technique for appendectomy.METHODS:One hundred patients undergoing endoscopy and 100 physicians were given a questionnaire describing in detail the techniques of NOTES and laparoscopic appendectomy.They were asked about the reasons for their preference,choice of orifice,and extent of complication risk they were willing to accept.RESULTS:Fifty patients(50%)and only 21 physicians(21%)preferred NOTES(P<0.001).Patients had previously heard of NOTES less frequently(7%vs73%,P<0.001)and had undergone endoscopy more frequently(88%vs 36%,P<0.001)than physicians.Absence of hernia was the most common reason for NOTES preference in physicians(80%vs 44%,P= 0.003),whereas reduced pain was the most common reason in patients(66%vs 52%).Physicians were more likely to refuse NOTES as a novel and unsure technique(P<0.001)and having an increased risk of infection(P<0.001).The preferred access site in both groups was colon followed by stomach,with vagina being rarely preferred.In multivariable modeling,those with high-school education[odds ratio(OR):2.68,95% confidence interval(CI):1.23-5.83]and prior colonoscopy(OR:2.10,95%CI:1.05-4.19)were more likely to prefer NOTES over laparoscopic appendectomy.There was a steep decline in NOTES preference with increased rate of procedural complications.Male patients were more likely to consent to their wives vaginal NOTES appendectomy than male physicians(P=0.02).CONCLUSION:The preference of NOTES for appendectomy was greater in patients than physicians and was related to reduced pain and absence of hernia rather than lack of scarring.展开更多
Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as ...Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.展开更多
This paper points out that, due to a flaw in the sender＇s encoding, the receiver in Gao et al.＇s controlled quantum secret direct communication （CQSDC） protocol [Chin. Phys. 14 （2005）, No. 5, p. 893] can reveal ...This paper points out that, due to a flaw in the sender＇s encoding, the receiver in Gao et al.＇s controlled quantum secret direct communication （CQSDC） protocol [Chin. Phys. 14 （2005）, No. 5, p. 893] can reveal the whole secret message without permission from the controller. An improvement is proposed to avoid this flaw.展开更多
The control of underactuated mechanical systems is very complex for the loss of its control inputs. The model of underactuated mechanical systems in a potential field is built with Lagrangian method and its structural...The control of underactuated mechanical systems is very complex for the loss of its control inputs. The model of underactuated mechanical systems in a potential field is built with Lagrangian method and its structural properties are analyzed in detail. A stable control approach is proposed for the class of underactuated mechanical systems. This approach is applied to an underactuated double-pendulum-type overhead crane and the simulation results illustrate the correctness of dynamics analysis and validity of the proposed control algorithm.展开更多
Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may b...Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.展开更多
Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fr...Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fraction of agents.展开更多
This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction mode...This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction model as their inputs. Presented statistical models allow for easy-to-compute predictions, both in temporal sense and for out-of-sample individual farms. Model performance is illustrated on a sample of real photovoltaic farms located in the Czech Republic.展开更多
In this paper, a nonlinear hemivariational inequality of second order with a forcing term of subcritical growth is studied. Using techniques from multivalued analysis and the theory of nonlinear operators of monotone ...In this paper, a nonlinear hemivariational inequality of second order with a forcing term of subcritical growth is studied. Using techniques from multivalued analysis and the theory of nonlinear operators of monotone type, an existence theorem for the Dirichlet boundary value problem is proved.展开更多
Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing...Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub- actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.展开更多
Both a general domain-independent bottom-up multi-level model and an algorithm for establishing the taxonomic relation of Chinese ontology are proposed.The model consists of extracting domain vocabularies and establis...Both a general domain-independent bottom-up multi-level model and an algorithm for establishing the taxonomic relation of Chinese ontology are proposed.The model consists of extracting domain vocabularies and establishing taxonomic relation,with the consideration of characteristics unique to Chinese natural language.By establishing the semantic forests of domain vocabularies and then using the existing semantic dictionary or machine-readable dictionary(MRD),the proposed algorithm can integrate these semantic forests together to establish the taxonomic relation.Experimental results show that the proposed algorithm is feasible and effective in establishing the integrated taxonomic relation among domain vocabularies and concepts.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Support-ing Project number(PNURSP2026R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.
文摘The computer code for the prediction of forest fire behavior is presented. Basic concept of the study is a combined approach to the problem, including the original formulation of fire spread mathematical model, classification of natural fuels, processing of the forest inventory data and programming output of fire simulation code which is compatible with commonly used geographic information system.
基金funded and supported by the UCSI University Research Excellence&Innovation Grant(REIG),REIG-ICSDI-2024/044.
文摘In the rapidly evolving landscape of intelligent transportation systems,the security and authenticity of vehicular communication have emerged as critical challenges.As vehicles become increasingly interconnected,the need for robust authentication mechanisms to safeguard against cyber threats and ensure trust in an autonomous ecosystem becomes essential.On the other hand,using intelligence in the authentication system is a significant attraction.While existing surveys broadly address vehicular security,a critical gap remains in the systematic exploration of Deep Learning(DL)-based authentication methods tailored to these communication paradigms.This survey fills that gap by offering a comprehensive analysis of DL techniques—including supervised,unsupervised,reinforcement,and hybrid learning—for vehicular authentication.This survey highlights novel contributions,such as a taxonomy of DL-driven authentication protocols,real-world case studies,and a critical evaluation of scalability and privacy-preserving techniques.Additionally,this paper identifies unresolved challenges,such as adversarial resilience and real-time processing constraints,and proposes actionable future directions,including lightweight model optimization and blockchain integration.By grounding the discussion in concrete applications,such as biometric authentication for driver safety and adaptive key management for infrastructure security,this survey bridges theoretical advancements with practical deployment needs,offering a roadmap for next-generation secure intelligent vehicular ecosystems for the modern world.
基金Supported by the Bavarian Academic Forum(BayWISS),as a part of the joint academic partnership digitalization program.
文摘Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains challenging.Methods This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and VR.Therefore,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image graph.Using this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of views.The rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image graph.The detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in real-time.In addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light intensities.Results Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,respectively.While achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering approaches.Conclusions Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR environments.Nevertheless,the handling of morphing artifacts in the parallax image regions remains a topic for future research.
基金supported in part by Key Scientific Research Projects of Colleges and Universities in Anhui Province(2022AH051921)Science Research Project of Bengbu University(2024YYX47pj,2024YYX48pj)+8 种基金Anhui Province Excellent Research and Innovation Team in Intelligent Manufacturing and Information Technology(2023AH052938)Big Data and Machine Learning Research Team(BBXYKYTDxj05)Funding Project for the Cultivation of Outstanding Talents in Colleges and Universities(gxyqZD2021135)the Key Scientific Research Projects of Anhui Provincial Department of Education(2022AH051376)Start Up Funds for Scientific Research of High-Level Talents of Bengbu University(BBXY2020KYQD02)Scientific Research and Development Fund of Suzhou University(2021fzjj29)Research on Grain Logistics Data Processing and Safety Issues(ALAQ202401017)the Open Fund of State Key Laboratory of Tea Plant Biology and Utilization(SKLTOF20220131)funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabia.
文摘With the advancements of the next-generation communication networking and Internet ofThings(IoT)technologies,a variety of computation-intensive applications(e.g.,autonomous driving and face recognition)have emerged.The execution of these IoT applications demands a lot of computing resources.Nevertheless,terminal devices(TDs)usually do not have sufficient computing resources to process these applications.Offloading IoT applications to be processed by mobile edge computing(MEC)servers with more computing resources provides a promising way to address this issue.While a significant number of works have studied task offloading,only a few of them have considered the security issue.This study investigates the problem of spectrum allocation and security-sensitive task offloading in an MEC system.Dynamic voltage scaling(DVS)technology is applied by TDs to reduce energy consumption and computing time.To guarantee data security during task offloading,we use AES cryptographic technique.The studied problem is formulated as an optimization problem and solved by our proposed efficient offloading scheme.The simulation results show that the proposed scheme can reduce system cost while guaranteeing data security.
基金financially supported by the Technological Agency of the Czech Republic (TAČR), Joint Grant No SS 02030031 ARAMISby the long-term strategic development financing of the Institute of Computer Science of the Czech Academy of Sciences (RVO 67985807)
文摘Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.
基金Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Cyber-criminals target smart connected devices for spyware distribution and security breaches,but existing Internet of Things(IoT)security standards are insufficient.Major IoT industry players prioritize market share over security,leading to insecure smart products.Traditional host-based protection solutions are less effective due to limited resources.Overcoming these challenges and enhancing the security of IoT Devices requires a security design at the network level that uses lightweight cryptographic parameters.In order to handle control,administration,and security concerns in traditional networking,the Gateway Node offers a contemporary networking architecture.By managing all network-level computations and complexity,the Gateway Node relieves IoT devices of these responsibilities.In this study,we introduce a novel privacy-preserving security architecture for gateway-node smart homes.Subsequently,we develop Smart Homes,An Efficient,Anonymous,and Robust Authentication Scheme(EARAS)based on the foundational principles of this security architecture.Furthermore,we formally examine the security characteristics of our suggested protocol that makes use of methodology such as ProVerif,supplemented by an informal analysis of security.Lastly,we conduct performance evaluations and comparative analyses to assess the efficacy of our scheme.Performance analysis shows that EARAS achieves up to 30%to 54%more efficient than most protocols and lower computation cost compared to Banerjee et al.’s scheme,and significantly reduces communication overhead compared to other recent protocols,while ensuring comprehensive security.Our objective is to provide robust security measures for smart homes while addressing resource constraints and preserving user privacy.
基金supported by the Ministry of Higher Education in Malaysia and the Universiti Teknologi Malaysia under the Fundamental Research Grant Scheme[grant number R.J1300000.7827.4F725].
文摘Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean,sunflower,and rapeseed.The growth of the oil palm industry causes degradation to the environment,especially when the expansion of plantations goes uncontrolled.Remote sensing is a useful tool to monitor the development of oil palm plantations.In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability,this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring.In addition,the existing knowledge gaps are identified and recommendations for further research are given.
基金Foundation items:the National Ntural Science Foundation of China(19771058)the Natural Science Foundation of Education Department of Sichuan Province(01LA70)
文摘Some convergence theorems of Ishikawa type iterative sequence with errors for nonlinear general quasi-contractive mapping in convex metric spaces are proved. The results not only extend and improve the corresponding results of L. B. Ciric, Q. H. Liu, H. E. Rhoades and H. K. Xu, et al., but also give an affirmative answer to the open question of Rhoades-Naimpally- Singh in convex metric spaces.
基金Supported by Grant NT 11234-3 of the Czech Ministry of Healththe Institutional Research Plan AV0Z10300504
文摘AIM:To investigate perception of natural orifice transluminal endoscopic surgery(NOTES)as a potential technique for appendectomy.METHODS:One hundred patients undergoing endoscopy and 100 physicians were given a questionnaire describing in detail the techniques of NOTES and laparoscopic appendectomy.They were asked about the reasons for their preference,choice of orifice,and extent of complication risk they were willing to accept.RESULTS:Fifty patients(50%)and only 21 physicians(21%)preferred NOTES(P<0.001).Patients had previously heard of NOTES less frequently(7%vs73%,P<0.001)and had undergone endoscopy more frequently(88%vs 36%,P<0.001)than physicians.Absence of hernia was the most common reason for NOTES preference in physicians(80%vs 44%,P= 0.003),whereas reduced pain was the most common reason in patients(66%vs 52%).Physicians were more likely to refuse NOTES as a novel and unsure technique(P<0.001)and having an increased risk of infection(P<0.001).The preferred access site in both groups was colon followed by stomach,with vagina being rarely preferred.In multivariable modeling,those with high-school education[odds ratio(OR):2.68,95% confidence interval(CI):1.23-5.83]and prior colonoscopy(OR:2.10,95%CI:1.05-4.19)were more likely to prefer NOTES over laparoscopic appendectomy.There was a steep decline in NOTES preference with increased rate of procedural complications.Male patients were more likely to consent to their wives vaginal NOTES appendectomy than male physicians(P=0.02).CONCLUSION:The preference of NOTES for appendectomy was greater in patients than physicians and was related to reduced pain and absence of hernia rather than lack of scarring.
基金the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No.824019 and China Scholarship Council(CSC)the Fundamental Research Funds for Central Universities(No.2020JJ014,YY19SSK05).
文摘Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.
基金supported by the National Science Council,Taiwan,(Grant No. NSC 100-2221-E-006-152-MY3)
文摘This paper points out that, due to a flaw in the sender＇s encoding, the receiver in Gao et al.＇s controlled quantum secret direct communication （CQSDC） protocol [Chin. Phys. 14 （2005）, No. 5, p. 893] can reveal the whole secret message without permission from the controller. An improvement is proposed to avoid this flaw.
基金Supported by National Natural Science Foundation of P.R.China (60575047)
文摘The control of underactuated mechanical systems is very complex for the loss of its control inputs. The model of underactuated mechanical systems in a potential field is built with Lagrangian method and its structural properties are analyzed in detail. A stable control approach is proposed for the class of underactuated mechanical systems. This approach is applied to an underactuated double-pendulum-type overhead crane and the simulation results illustrate the correctness of dynamics analysis and validity of the proposed control algorithm.
文摘Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.
基金Acknowledgments： This work is supported by the State Key Laboratory of Software Engineering and the Natural Science Foundation of Guizhou and the Science Foundation of Guizhou Province（No. 20043029）.
基金supported by the National Key Research and Development Project of China(2020YFA0714301)the National Natural Science Foundation of China(61833005)。
文摘Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fraction of agents.
文摘This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction model as their inputs. Presented statistical models allow for easy-to-compute predictions, both in temporal sense and for out-of-sample individual farms. Model performance is illustrated on a sample of real photovoltaic farms located in the Czech Republic.
文摘In this paper, a nonlinear hemivariational inequality of second order with a forcing term of subcritical growth is studied. Using techniques from multivalued analysis and the theory of nonlinear operators of monotone type, an existence theorem for the Dirichlet boundary value problem is proved.
文摘Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub- actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60496326 and No.10671045)
文摘Both a general domain-independent bottom-up multi-level model and an algorithm for establishing the taxonomic relation of Chinese ontology are proposed.The model consists of extracting domain vocabularies and establishing taxonomic relation,with the consideration of characteristics unique to Chinese natural language.By establishing the semantic forests of domain vocabularies and then using the existing semantic dictionary or machine-readable dictionary(MRD),the proposed algorithm can integrate these semantic forests together to establish the taxonomic relation.Experimental results show that the proposed algorithm is feasible and effective in establishing the integrated taxonomic relation among domain vocabularies and concepts.
