A power system with a high wind power integration requires extra transmission capacity to accommodate the intermittency inherent to wind power production.Storage can smooth out this intermittency and reduce transmissi...A power system with a high wind power integration requires extra transmission capacity to accommodate the intermittency inherent to wind power production.Storage can smooth out this intermittency and reduce transmission requirements.This paper proposes a stochastic optimization model to coordinate the long-term planning of both transmission and storage facilities to efficiently integrate wind power.Both longterm and short-term uncertainties are considered in this model.Long-term uncertainty is described via scenarios,while shortterm uncertainty is described via operating conditions.Garver’s 6-node system and a system representing Northwest China in 2030 are used to illustrate the proposed model.Results indicate that storage reduces transmission requirement and the overall investment,and allows the efficient integration of wind power.展开更多
The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabli...The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.展开更多
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t...In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.展开更多
Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by reta...Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior per展开更多
Dear Editor,In this letter,we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data.Globally,more than 373 millio...Dear Editor,In this letter,we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data.Globally,more than 373 million people have been diagnosed with COVID-19 and 5.66 million have died from this disease by 2022.It continues to have a negative impact on human daily life and the global economic development till now,due to the lack of effective treatment of COVID-19 induced issues and prevention of transmission methods.展开更多
The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environme...The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments.They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area.The data have to be picked up by the sensor,and then sent to the sink node where they may be processed.The nodes of the WSNs are powered by batteries,therefore they eventually run out of power.This energy restriction has an effect on the network life span and environmental sustainability.The objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of energy.The lifespan of WSNs is being extended often using clustering and routing strategies.The Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do clustering.The cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node centrality.Based on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS receives.The overall experimentation is carried out under the MATLAB environment.From the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.展开更多
Purpose-In this research work,brief quantum-dot cellular automata(QCA)concepts are discussed through arithmetic and logic units.This work is most useful for nanoelectronic applications,VLSI industry mainly depends on ...Purpose-In this research work,brief quantum-dot cellular automata(QCA)concepts are discussed through arithmetic and logic units.This work is most useful for nanoelectronic applications,VLSI industry mainly depends on this type of fault-tolerant QCA based arithmetic logic unit(ALU)design.The ALU design is mainly depending on set instructions and rules;these are maintained through low-power ultra-functional tricks only possible with QCA-based reversible arithmetic and logic unit for nanoelectronics.The main objective of this investigation is to design an ultra-low power and ultra-high-speed ALU design with QCA technology.The following QCA method has been implemented through reversible logic.Design/methodology/approach-QCA logic is the main and critical condition for realizing NANO-scale design that delivers considerably fast integrate module,effective performable computation and is less energy efficiency at the nano-scale(QCA).Processors need an ALU in order to process and calculate data.Faultresistant ALU in QCA technology utilizing reverse logic is the primary objective of this study.There are now two sections,i.e.reversible ALU(RAU),logical(LAU)and arithmetical(RAU).Findings-A reversible 231 multiplexer based on the Fredkin gate(FRG)was developed to allow users to choose between arithmetic and logical operations.QCA full adders are also implemented to improve arithmetic operations’performance.The ALU is built using reversible logic gates that are fault-tolerant.Originality/value-In contrast to earlier research,the suggested reversible multilayeredALU with reversible QCA operation is imported.The 8-and 16-bit ALU,as well as logical unit functioning,is designed through fewer gates,constant inputs and outputs.This implementation is designed on the Mentor Graphics QCA tool and verifies all functionalities.展开更多
With increasing penetration of wind energy,the variability and uncertainty of wind resources have become important factors for power systems operation.In particular,an effective method is required for identifying the ...With increasing penetration of wind energy,the variability and uncertainty of wind resources have become important factors for power systems operation.In particular,an effective method is required for identifying the stochastic range of wind power output,in order to better guide the operational security of power systems.This paper proposes a metric to determine accurate wind power output ranges so that the probability of actual wind power outputs being out of the range would be less than a small pre-defined value.A mixed-integer linear programming(MILP)based chance-constrained optimization model is proposed for efficiently determining optimal wind power output ranges,which are quantified via maximum and the minimum wind generation levels with respect to a certain time interval.The derived wind power range is then used to construct dynamic uncertainty intervals for the robust securityconstrained unit commitment(SCUC)model.A comparison with the deterministic SCUC model and the traditional robust SCUC model with presumed static uncertainty interval demonstrates that the proposed approach can offer more accurate wind power variabilities(i.e.,different variability degrees with respect to different wind power output levels at different time periods).The proposed approach is also shown to offer more effective and robust SCUC solutions,guaranteeing operational security and economics of power systems.Numerical case studies on a 6-bus system and the modified IEEE 118-bus system with realworld wind power data illustrate the effectiveness of the proposed approach.展开更多
One of the main drawbacks of Digital Holography(DH)is the coherent nature of the light source,which severely corrupts the quality of holographic reconstructions.Although numerous techniques to reduce noise in DH have ...One of the main drawbacks of Digital Holography(DH)is the coherent nature of the light source,which severely corrupts the quality of holographic reconstructions.Although numerous techniques to reduce noise in DH have provided good results,holographic noise suppression remains a challenging task.We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms,block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions.The optimized joint action of these different image-denoising methods permits the removal of up to 98%of the noise while preserving the image contrast.The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH.Experimental validation is provided for both singlewavelength and multi-wavelength DH,and a comparison with the most used holographic denoising methods is performed.展开更多
In the digital era,the Narrowband Internet of Things(Nb-IoT)influ-ences the massive Machine-Type-Communication(mMTC)features to establish secure routing among the 5G/6G mobile networks.It supports global coverage to th...In the digital era,the Narrowband Internet of Things(Nb-IoT)influ-ences the massive Machine-Type-Communication(mMTC)features to establish secure routing among the 5G/6G mobile networks.It supports global coverage to the low-cost IoT devices distributed in terrestrial networks.Its key traffic char-acteristics include robust uplink,moderate data rate/device,extremely high energy efficiency,prolonging device lifetime,and Quality of Service(QoS).This paper proposes a Deep Reinforcement Learning(DRL)combined software-defined air interface algorithm applied on the switching system,satisfying the user require-ment and enabling them with the network resources to extend quality of service by choosing the most appropriate quality of service metric.In this framework,Non-Orthogonal Multiple Accesses(NOMA)and Rate-Splitting Multiple Access(RSMA)are combined to accommodate massive(Nb-IoT)devices that can be uti-lized the entire resource(frequency band)for tackling the unknown dynamics pro-hibitive.The proposed algorithm instantly assigns the network resources per user requirements and enhances selecting the best quality of service metric optimiza-tion.Therefore,it has potential benefits of high scalability,low latency,energy efficiency,and spectrum utility.展开更多
Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads ...Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.展开更多
Motion planning is a vital module for unmanned aerial vehicles(UAVs),especially in scenarios of autonomous navigation and operation.This survey delivers some recent state-of-the-art UAV motion planning algorithms and ...Motion planning is a vital module for unmanned aerial vehicles(UAVs),especially in scenarios of autonomous navigation and operation.This survey delivers some recent state-of-the-art UAV motion planning algorithms and related applications.The logic flow of this survey is divided as the path finding,which is the front-end of most motion planning systems,and the trajectory optimisation,which usually serves as the back-end.Motivation,methodology,problem formulation and derivation are given in this survey,in detail.Finally,a section about real-world applications is given,where roles and effectiveness of most popular motion planning methods are revealed.展开更多
Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel metho...Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.展开更多
基金supported jointly by US NSF grant(No.1548015)National Science Foundation of China(No.51325702)Scientific&Technical Project of State Grid(No.52020114026C).
文摘A power system with a high wind power integration requires extra transmission capacity to accommodate the intermittency inherent to wind power production.Storage can smooth out this intermittency and reduce transmission requirements.This paper proposes a stochastic optimization model to coordinate the long-term planning of both transmission and storage facilities to efficiently integrate wind power.Both longterm and short-term uncertainties are considered in this model.Long-term uncertainty is described via scenarios,while shortterm uncertainty is described via operating conditions.Garver’s 6-node system and a system representing Northwest China in 2030 are used to illustrate the proposed model.Results indicate that storage reduces transmission requirement and the overall investment,and allows the efficient integration of wind power.
基金support from the National Science Foundation under Grants 1443894,1560437,and 1731017Louisiana Board of Regents under Grant LEQSF(2017-20)-RD-A-29a research gift from Intel Corporation
文摘The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.
基金supported by the Center for Mining,Electro-Mechanical Research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnam。
文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.
文摘Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior per
基金This work was funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia(GCV19-37-1441).
文摘Dear Editor,In this letter,we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data.Globally,more than 373 million people have been diagnosed with COVID-19 and 5.66 million have died from this disease by 2022.It continues to have a negative impact on human daily life and the global economic development till now,due to the lack of effective treatment of COVID-19 induced issues and prevention of transmission methods.
基金supported via funding from Prince Sattam Bin Abdulaziz University(No.PSAU/2023/R/1444).
文摘The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments.They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area.The data have to be picked up by the sensor,and then sent to the sink node where they may be processed.The nodes of the WSNs are powered by batteries,therefore they eventually run out of power.This energy restriction has an effect on the network life span and environmental sustainability.The objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of energy.The lifespan of WSNs is being extended often using clustering and routing strategies.The Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do clustering.The cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node centrality.Based on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS receives.The overall experimentation is carried out under the MATLAB environment.From the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.
文摘Purpose-In this research work,brief quantum-dot cellular automata(QCA)concepts are discussed through arithmetic and logic units.This work is most useful for nanoelectronic applications,VLSI industry mainly depends on this type of fault-tolerant QCA based arithmetic logic unit(ALU)design.The ALU design is mainly depending on set instructions and rules;these are maintained through low-power ultra-functional tricks only possible with QCA-based reversible arithmetic and logic unit for nanoelectronics.The main objective of this investigation is to design an ultra-low power and ultra-high-speed ALU design with QCA technology.The following QCA method has been implemented through reversible logic.Design/methodology/approach-QCA logic is the main and critical condition for realizing NANO-scale design that delivers considerably fast integrate module,effective performable computation and is less energy efficiency at the nano-scale(QCA).Processors need an ALU in order to process and calculate data.Faultresistant ALU in QCA technology utilizing reverse logic is the primary objective of this study.There are now two sections,i.e.reversible ALU(RAU),logical(LAU)and arithmetical(RAU).Findings-A reversible 231 multiplexer based on the Fredkin gate(FRG)was developed to allow users to choose between arithmetic and logical operations.QCA full adders are also implemented to improve arithmetic operations’performance.The ALU is built using reversible logic gates that are fault-tolerant.Originality/value-In contrast to earlier research,the suggested reversible multilayeredALU with reversible QCA operation is imported.The 8-and 16-bit ALU,as well as logical unit functioning,is designed through fewer gates,constant inputs and outputs.This implementation is designed on the Mentor Graphics QCA tool and verifies all functionalities.
基金supported in part by the U.S.National Science Foundation under Grant ECCS-1254310.
文摘With increasing penetration of wind energy,the variability and uncertainty of wind resources have become important factors for power systems operation.In particular,an effective method is required for identifying the stochastic range of wind power output,in order to better guide the operational security of power systems.This paper proposes a metric to determine accurate wind power output ranges so that the probability of actual wind power outputs being out of the range would be less than a small pre-defined value.A mixed-integer linear programming(MILP)based chance-constrained optimization model is proposed for efficiently determining optimal wind power output ranges,which are quantified via maximum and the minimum wind generation levels with respect to a certain time interval.The derived wind power range is then used to construct dynamic uncertainty intervals for the robust securityconstrained unit commitment(SCUC)model.A comparison with the deterministic SCUC model and the traditional robust SCUC model with presumed static uncertainty interval demonstrates that the proposed approach can offer more accurate wind power variabilities(i.e.,different variability degrees with respect to different wind power output levels at different time periods).The proposed approach is also shown to offer more effective and robust SCUC solutions,guaranteeing operational security and economics of power systems.Numerical case studies on a 6-bus system and the modified IEEE 118-bus system with realworld wind power data illustrate the effectiveness of the proposed approach.
基金supported by DATABENC_Progetto SNECS-PON03PE_00163_1 Social Network delle Entitàdei Centri Storici.
文摘One of the main drawbacks of Digital Holography(DH)is the coherent nature of the light source,which severely corrupts the quality of holographic reconstructions.Although numerous techniques to reduce noise in DH have provided good results,holographic noise suppression remains a challenging task.We propose a novel framework that combines the concepts of encoding multiple uncorrelated digital holograms,block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions.The optimized joint action of these different image-denoising methods permits the removal of up to 98%of the noise while preserving the image contrast.The resulting quality of the hologram reconstructions is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH.Experimental validation is provided for both singlewavelength and multi-wavelength DH,and a comparison with the most used holographic denoising methods is performed.
文摘In the digital era,the Narrowband Internet of Things(Nb-IoT)influ-ences the massive Machine-Type-Communication(mMTC)features to establish secure routing among the 5G/6G mobile networks.It supports global coverage to the low-cost IoT devices distributed in terrestrial networks.Its key traffic char-acteristics include robust uplink,moderate data rate/device,extremely high energy efficiency,prolonging device lifetime,and Quality of Service(QoS).This paper proposes a Deep Reinforcement Learning(DRL)combined software-defined air interface algorithm applied on the switching system,satisfying the user require-ment and enabling them with the network resources to extend quality of service by choosing the most appropriate quality of service metric.In this framework,Non-Orthogonal Multiple Accesses(NOMA)and Rate-Splitting Multiple Access(RSMA)are combined to accommodate massive(Nb-IoT)devices that can be uti-lized the entire resource(frequency band)for tackling the unknown dynamics pro-hibitive.The proposed algorithm instantly assigns the network resources per user requirements and enhances selecting the best quality of service metric optimiza-tion.Therefore,it has potential benefits of high scalability,low latency,energy efficiency,and spectrum utility.
文摘Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.
基金This work was supported in parts by the National Natural Science Foundation of China under grant no.61973270the Foundation or Innovative Research Groups of the National Natural Science Foundation of China under grant no.61621002the Fundamental Research Funds for Central Universities。
文摘Motion planning is a vital module for unmanned aerial vehicles(UAVs),especially in scenarios of autonomous navigation and operation.This survey delivers some recent state-of-the-art UAV motion planning algorithms and related applications.The logic flow of this survey is divided as the path finding,which is the front-end of most motion planning systems,and the trajectory optimisation,which usually serves as the back-end.Motivation,methodology,problem formulation and derivation are given in this survey,in detail.Finally,a section about real-world applications is given,where roles and effectiveness of most popular motion planning methods are revealed.
文摘Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.
