This article is devoted to developing a deep learning method for the numerical solution of the partial differential equations (PDEs). Graph kernel neural networks (GKNN) approach to embedding graphs into a computation...This article is devoted to developing a deep learning method for the numerical solution of the partial differential equations (PDEs). Graph kernel neural networks (GKNN) approach to embedding graphs into a computationally numerical format has been used. In particular, for investigation mathematical models of the dynamical system of cancer cell invasion in inhomogeneous areas of human tissues have been considered. Neural operators were initially proposed to model the differential operator of PDEs. The GKNN mapping features between input data to the PDEs and their solutions have been constructed. The boundary integral method in combination with Green’s functions for a large number of boundary conditions is used. The tools applied in this development are based on the Fourier neural operators (FNOs), graph theory, theory elasticity, and singular integral equations.展开更多
In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal de...In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.展开更多
The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some use...The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some users to the nearby small cells so that the user could get the expected service.For the MBS to offload data traffic to an Access Point(AP),it should offer an optimal economic incentive in a way its utility is maximized.Similarly,the APs should choose an optimal traffic to admit load for the price that it gets from MBS.To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading,Software Defined Networking(SDN)assisted Stackelberg Game(SaSG)model is proposed.In this model,the MBS selects the users carefully to aggregate the service with AP,so that the user experiencing least service gets aggregated first.The MBS uses the Received Signal Strength Indicator(RSSI)value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi.Each player involved in the game tries to maximize their payoff utilities,and thus,while incorporating those utilities in real-time scenario,we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience(QoE).Thus,the proposed SaSG model proves better when compared with other game theory models,and hence an optimal data offloading is achieved.展开更多
Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentat...Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.展开更多
Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational h...Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.展开更多
The mechanical properties and intrinsic hardness of the α-Ga boron phase （α-Ga-B） are studied by using the combination of first-principles calculations and a semiempirieal macroscopic hardness model. It is found t...The mechanical properties and intrinsic hardness of the α-Ga boron phase （α-Ga-B） are studied by using the combination of first-principles calculations and a semiempirieal macroscopic hardness model. It is found that α- Ga-B is mechanically stable and possesses higher bulk/shear modulus as compared with γ-B28, a newly discovered high-pressure boron phase. The theoretical hardness of α-Ga-B is estimated to be 45 GPa, which is much higher than 38 GPa for γ-B28. The results strongly indicate that α-Ga-B is a potential superhard boron phase. To further obtain insight into the superhard nature of α-Ga-B, we simulate stress-strain curves under tensile and shear deformation. Meanwhile, the microscopic mechanism driving the tensile and shear deformation modes in α-Ga-B is discussed in detail.展开更多
This paper presents the problem of control of anti-aircraft missile launcher mounted on a moving carrier-vehicle. Direct excitations on the vehicle from the road cause adverse vibrations of the launcher. In order to i...This paper presents the problem of control of anti-aircraft missile launcher mounted on a moving carrier-vehicle. Direct excitations on the vehicle from the road cause adverse vibrations of the launcher. In order to increase the precision of the guiding system in the conditions of self-propelled movement of the setup on a bumpy road, the adaptive control algorithm was proposed. Some research results of computer simulation are presented in a graphical form.展开更多
This paper is directed to study the isotope effects of some superconducting materials that have a strong coupling coefficient <i>λ</i> > 1.5, and focuses on new superconducting materials whose critical...This paper is directed to study the isotope effects of some superconducting materials that have a strong coupling coefficient <i>λ</i> > 1.5, and focuses on new superconducting materials whose critical temperature is close to room temperature, specifically LaH<sub>10</sub>-LaD<sub>10</sub> and H<sub>3</sub>S-D<sub>3</sub>S systems. The Eliashberg-McMillan (EM) model and the recent Gor’kov-Kresin (GK) model for evaluating the isotope effects coefficient α were examined for these systems. The predicted values of α as a function of pressure, as compared to experimental values led to inference that these two models, despite their importance and simplicity, cannot be considered complete. These models can be used to calculate isotope effect of most superconducting materials with strong coupling coefficients but with critical reliability. The significance of studying the isotope effect lies in the possibility of identifying the interatomic forces that control the properties of superconducting materials such as electrons-mediated phonons and Coulomb interactions.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital...In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare costs.Traditional reminder systems often fail due to a lack of personalization and real-time intervention.To address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive solution.MediServe features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare providers.The system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health conditions.Furthermore,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive impairments.Cloud-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication mismanagement.Additionally,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health patterns.By combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication adherence.This innovative approach not only improves the quality of life for elderly individuals but also reduces the burden on caregivers and healthcare systems,ultimately fostering independent and efficient health management.展开更多
Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learni...Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.展开更多
Wireless sensor networks(WSNs)are the major contributors to big data acquisition.The authenticity and integrity of the data are two most important basic requirements for various services based on big data.Data aggrega...Wireless sensor networks(WSNs)are the major contributors to big data acquisition.The authenticity and integrity of the data are two most important basic requirements for various services based on big data.Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs.However,the process of data acquisitions in WSNs are in open environments,data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence,such as coalition attack.Aimed to provide data authenticity and integrity protection for WSNs,an efficient and secure identity-based aggregate signature scheme(EIAS)is proposed in this paper.Rigorous security proof shows that our proposed scheme can be secure against all kinds of attacks.The performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.展开更多
Cloud computing has reached the peak of Gartner hype cycle,and now the focus of the whole telecom industry is the ability to scale data storage with minimal investment.But data privacy and communication issues will oc...Cloud computing has reached the peak of Gartner hype cycle,and now the focus of the whole telecom industry is the ability to scale data storage with minimal investment.But data privacy and communication issues will occur with the increment of the cloud data storage.The key privacy concern for scalability is caused by the dynamic membership allocation and multi-owner data sharing.This paper addresses the issues faced by multiple owners through a mutual authentication mechanism using the Enhanced Elliptic Curve Diffie-Hellman(EECDH)key exchange protocol along with the Elliptic Curve Digital Signature Algorithm(ECDSA).The proposed EECDH scheme is used to exchange the secured shared key among multiple owners and also to eliminate the Man-In-The-Middle(MITM)attacks with less computational complexity.By leveraging these algorithms,the integrity of data sharing among multiple owners is ensured.The EECDH improves the level of security only slightly increasing the time taken to encrypt and decrypt the data,and it is secured against the MITM attacks,which is experimented using the AVISPA tool.展开更多
A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,...A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.展开更多
Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty i...Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty in finding the routes that the packets use when they are routed.This study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol efficiency.The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination.The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization.Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network overhead.Moreover,GALAR was compared to other algorithms in terms of different network evaluation measures.The GALAR algorithm significantly outperformed the other algorithms that were used in the study.展开更多
Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critical applications that seeks a path connecting source node and destination node that satisfies multiple QoS constrains such...Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critical applications that seeks a path connecting source node and destination node that satisfies multiple QoS constrains such as energy cost, delay, and reliability imposes a great challenge in Wireless Sensor Networks (WSNs). In such challenging dynamic environment, traditional routing and layered infrastructure are inefficient and sometimes even infeasible. In recent research works, the opportunistic routing paradigm which delays the forwarding decision until reception of packets in forwarders by utilizing the broadcast nature of the wireless medium has been exploited to overcome the limitations of traditional routing. However, to guarantee the balance between the energy, delay and reliability requires the refinement of opportunistic routing through interaction between underlying layers known as cross-layer opportunistic routing. Indeed, these schemes fail to achieve optimal performance and hence require a new method to facilitate the adoption of the routing protocol to the dynamic challenging environments. In this paper, we propose a universal cross-layered opportunistic based communication protocol for WSNs for guaranteeing the user set constraints on multi-constrained QoS in low-duty-cycle WSN. Extensive simulation results show that the proposed work, Multi-Constrained QoS Opportunistic routing by optimal Power Tuning (MOR-PT) effectively achieves the feasible QoS trade-off constraints set by user by jointly considering the power control and selection diversity over established algorithms like DSF [1] and DTPC [2].展开更多
The consensus protocol is one of the core technologies in blockchain,which plays a crucial role in ensuring the block generation rate,consistency,and safety of the blockchain system.Blockchain systems mainly adopt the...The consensus protocol is one of the core technologies in blockchain,which plays a crucial role in ensuring the block generation rate,consistency,and safety of the blockchain system.Blockchain systems mainly adopt the Byzantine Fault Tolerance(BFT)protocol,which often suffers fromslow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus.In this paper,this paper proposes a new dual-mode consensus protocol based on node identity authentication.It divides the consensus process into two subprotocols:Check_BFT and Fast_BFT.In Check_BFT,the replicas authenticate the primary’s identity by monitoring its behaviors.First,assume that the systemis in a pessimistic environment,Check_BFT protocol detects whether the current environment is safe and whether the primary is an honest node;Enter the fast consensus stage after confirming the environmental safety,and implement Fast_BFT protocol.It is assumed that there are 3f+1 nodes in total.If more than 2f+1 nodes identify that the primary is honest,it will enter the Fast_BFT process.In Fast_BFT,the primary is allowed to handle transactions alone,and the replicas can only receive the messages sent by the primary.The experimental results show that the CF-BFT protocol significantly reduces the communication overhead and improves the throughput and scalability of the consensus protocol.Compared with the SAZyzz protocol,the throughput is increased by 3 times in the best case and 60%in the worst case.展开更多
We study analytically and numerically the propagation of spatial solitons in a two-dimensional stronglynonlocal nonlinear medium. Exact analytical solutions in the form of self-similar spatial solitons are obtained in...We study analytically and numerically the propagation of spatial solitons in a two-dimensional stronglynonlocal nonlinear medium. Exact analytical solutions in the form of self-similar spatial solitons are obtained involvinghigher-order Hermite-Gaussian functions. Our theoretical predictions provide new insights into the low-energy spatialsoliton transmission with high fidelity.展开更多
The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of m...The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of much techno-logical advancement,phishing acts as thefirst step in a series of attacks.With technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of security.Considering these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.展开更多
Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavel...Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet （MMW） method. A novel threshold algorithm, which compromises the hard-threshold wavelet （HTW） and soft-threshold wavelet （STW） methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.展开更多
文摘This article is devoted to developing a deep learning method for the numerical solution of the partial differential equations (PDEs). Graph kernel neural networks (GKNN) approach to embedding graphs into a computationally numerical format has been used. In particular, for investigation mathematical models of the dynamical system of cancer cell invasion in inhomogeneous areas of human tissues have been considered. Neural operators were initially proposed to model the differential operator of PDEs. The GKNN mapping features between input data to the PDEs and their solutions have been constructed. The boundary integral method in combination with Green’s functions for a large number of boundary conditions is used. The tools applied in this development are based on the Fourier neural operators (FNOs), graph theory, theory elasticity, and singular integral equations.
基金supported by Qinghai Natural Science Foundation under No.2020-ZJ-943Q.
文摘In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.
文摘The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some users to the nearby small cells so that the user could get the expected service.For the MBS to offload data traffic to an Access Point(AP),it should offer an optimal economic incentive in a way its utility is maximized.Similarly,the APs should choose an optimal traffic to admit load for the price that it gets from MBS.To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading,Software Defined Networking(SDN)assisted Stackelberg Game(SaSG)model is proposed.In this model,the MBS selects the users carefully to aggregate the service with AP,so that the user experiencing least service gets aggregated first.The MBS uses the Received Signal Strength Indicator(RSSI)value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi.Each player involved in the game tries to maximize their payoff utilities,and thus,while incorporating those utilities in real-time scenario,we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience(QoE).Thus,the proposed SaSG model proves better when compared with other game theory models,and hence an optimal data offloading is achieved.
文摘Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.
基金National Natural Science Foundation of China(Grant No.62073227)Liaoning Provincial Science and Technology Department Foundation(Grant No.2023JH2/101300212).
文摘Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.
基金Supported by the National Natural Science Foundation of China under Grant Nos 21303156,21201148,210303156 and 21403185the Natural Science Foundation of Hebei Province under Grant Nos B2011203121 and B2012203005
文摘The mechanical properties and intrinsic hardness of the α-Ga boron phase （α-Ga-B） are studied by using the combination of first-principles calculations and a semiempirieal macroscopic hardness model. It is found that α- Ga-B is mechanically stable and possesses higher bulk/shear modulus as compared with γ-B28, a newly discovered high-pressure boron phase. The theoretical hardness of α-Ga-B is estimated to be 45 GPa, which is much higher than 38 GPa for γ-B28. The results strongly indicate that α-Ga-B is a potential superhard boron phase. To further obtain insight into the superhard nature of α-Ga-B, we simulate stress-strain curves under tensile and shear deformation. Meanwhile, the microscopic mechanism driving the tensile and shear deformation modes in α-Ga-B is discussed in detail.
基金supported by the National Centre for Research and Development over the period 2011-2014
文摘This paper presents the problem of control of anti-aircraft missile launcher mounted on a moving carrier-vehicle. Direct excitations on the vehicle from the road cause adverse vibrations of the launcher. In order to increase the precision of the guiding system in the conditions of self-propelled movement of the setup on a bumpy road, the adaptive control algorithm was proposed. Some research results of computer simulation are presented in a graphical form.
文摘This paper is directed to study the isotope effects of some superconducting materials that have a strong coupling coefficient <i>λ</i> > 1.5, and focuses on new superconducting materials whose critical temperature is close to room temperature, specifically LaH<sub>10</sub>-LaD<sub>10</sub> and H<sub>3</sub>S-D<sub>3</sub>S systems. The Eliashberg-McMillan (EM) model and the recent Gor’kov-Kresin (GK) model for evaluating the isotope effects coefficient α were examined for these systems. The predicted values of α as a function of pressure, as compared to experimental values led to inference that these two models, despite their importance and simplicity, cannot be considered complete. These models can be used to calculate isotope effect of most superconducting materials with strong coupling coefficients but with critical reliability. The significance of studying the isotope effect lies in the possibility of identifying the interatomic forces that control the properties of superconducting materials such as electrons-mediated phonons and Coulomb interactions.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
文摘In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare costs.Traditional reminder systems often fail due to a lack of personalization and real-time intervention.To address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive solution.MediServe features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare providers.The system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health conditions.Furthermore,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive impairments.Cloud-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication mismanagement.Additionally,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health patterns.By combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication adherence.This innovative approach not only improves the quality of life for elderly individuals but also reduces the burden on caregivers and healthcare systems,ultimately fostering independent and efficient health management.
文摘Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.
基金The work was supported in part by the National Natural Science Foundation of China(61572370)and the National Natural Science Function of Qinghai Province(2019-ZJ-7065,2017-ZJ-959Q)+1 种基金the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(17YJCZH203)and the Natural Science Foundation of Hubei Province in China(2016CFB652).
文摘Wireless sensor networks(WSNs)are the major contributors to big data acquisition.The authenticity and integrity of the data are two most important basic requirements for various services based on big data.Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs.However,the process of data acquisitions in WSNs are in open environments,data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence,such as coalition attack.Aimed to provide data authenticity and integrity protection for WSNs,an efficient and secure identity-based aggregate signature scheme(EIAS)is proposed in this paper.Rigorous security proof shows that our proposed scheme can be secure against all kinds of attacks.The performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.
文摘Cloud computing has reached the peak of Gartner hype cycle,and now the focus of the whole telecom industry is the ability to scale data storage with minimal investment.But data privacy and communication issues will occur with the increment of the cloud data storage.The key privacy concern for scalability is caused by the dynamic membership allocation and multi-owner data sharing.This paper addresses the issues faced by multiple owners through a mutual authentication mechanism using the Enhanced Elliptic Curve Diffie-Hellman(EECDH)key exchange protocol along with the Elliptic Curve Digital Signature Algorithm(ECDSA).The proposed EECDH scheme is used to exchange the secured shared key among multiple owners and also to eliminate the Man-In-The-Middle(MITM)attacks with less computational complexity.By leveraging these algorithms,the integrity of data sharing among multiple owners is ensured.The EECDH improves the level of security only slightly increasing the time taken to encrypt and decrypt the data,and it is secured against the MITM attacks,which is experimented using the AVISPA tool.
文摘A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty in finding the routes that the packets use when they are routed.This study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol efficiency.The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination.The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization.Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network overhead.Moreover,GALAR was compared to other algorithms in terms of different network evaluation measures.The GALAR algorithm significantly outperformed the other algorithms that were used in the study.
文摘Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critical applications that seeks a path connecting source node and destination node that satisfies multiple QoS constrains such as energy cost, delay, and reliability imposes a great challenge in Wireless Sensor Networks (WSNs). In such challenging dynamic environment, traditional routing and layered infrastructure are inefficient and sometimes even infeasible. In recent research works, the opportunistic routing paradigm which delays the forwarding decision until reception of packets in forwarders by utilizing the broadcast nature of the wireless medium has been exploited to overcome the limitations of traditional routing. However, to guarantee the balance between the energy, delay and reliability requires the refinement of opportunistic routing through interaction between underlying layers known as cross-layer opportunistic routing. Indeed, these schemes fail to achieve optimal performance and hence require a new method to facilitate the adoption of the routing protocol to the dynamic challenging environments. In this paper, we propose a universal cross-layered opportunistic based communication protocol for WSNs for guaranteeing the user set constraints on multi-constrained QoS in low-duty-cycle WSN. Extensive simulation results show that the proposed work, Multi-Constrained QoS Opportunistic routing by optimal Power Tuning (MOR-PT) effectively achieves the feasible QoS trade-off constraints set by user by jointly considering the power control and selection diversity over established algorithms like DSF [1] and DTPC [2].
基金supported by the Key Laboratory of Network Password Technology in Henan Province,China(LNCT2022-A20)the Major Science and Technology Special Project of Henan Province,China(Nos.201300210100,201300210200)+2 种基金the Key Scientific Research Project of Higher Education Institutions in Henan Province,China(No.23ZX017)the Key Special Project for Science and Technology Collaborative Innovation in Zhengzhou City,Henan Province,China(No.21ZZXTCX07)and the Key Science and Technology Project of Henan Province,China(No.232102211082).
文摘The consensus protocol is one of the core technologies in blockchain,which plays a crucial role in ensuring the block generation rate,consistency,and safety of the blockchain system.Blockchain systems mainly adopt the Byzantine Fault Tolerance(BFT)protocol,which often suffers fromslow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus.In this paper,this paper proposes a new dual-mode consensus protocol based on node identity authentication.It divides the consensus process into two subprotocols:Check_BFT and Fast_BFT.In Check_BFT,the replicas authenticate the primary’s identity by monitoring its behaviors.First,assume that the systemis in a pessimistic environment,Check_BFT protocol detects whether the current environment is safe and whether the primary is an honest node;Enter the fast consensus stage after confirming the environmental safety,and implement Fast_BFT protocol.It is assumed that there are 3f+1 nodes in total.If more than 2f+1 nodes identify that the primary is honest,it will enter the Fast_BFT process.In Fast_BFT,the primary is allowed to handle transactions alone,and the replicas can only receive the messages sent by the primary.The experimental results show that the CF-BFT protocol significantly reduces the communication overhead and improves the throughput and scalability of the consensus protocol.Compared with the SAZyzz protocol,the throughput is increased by 3 times in the best case and 60%in the worst case.
基金Supported by the Science Research Foundation of Shunde Polytechnic under Grant No. 2008-KJ06Supported by the NPRP 25-6-7-2 Project with the Qatar National Research Foundation
文摘We study analytically and numerically the propagation of spatial solitons in a two-dimensional stronglynonlocal nonlinear medium. Exact analytical solutions in the form of self-similar spatial solitons are obtained involvinghigher-order Hermite-Gaussian functions. Our theoretical predictions provide new insights into the low-energy spatialsoliton transmission with high fidelity.
文摘The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of much techno-logical advancement,phishing acts as thefirst step in a series of attacks.With technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of security.Considering these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.
文摘Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet （MMW） method. A novel threshold algorithm, which compromises the hard-threshold wavelet （HTW） and soft-threshold wavelet （STW） methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.
