The resource allocation of the federated learning(FL)for unmanned aerial vehicle(UAV)swarm systems are investigated.The UAV swarms based on FL realize the artificial intelligence(AI)applications by means of distribute...The resource allocation of the federated learning(FL)for unmanned aerial vehicle(UAV)swarm systems are investigated.The UAV swarms based on FL realize the artificial intelligence(AI)applications by means of distributed training on the basis of ensuring the security of private data.However,the direct application of the FL in UAV swarms will incur high overhead.Therefore,in this article,we consider the resource allocation problem in FL for UAV swarms.To avoid the high communication overhead between UAVs and the central server,we proposed an FL framework for UAV swarms based on mobile edge computing(MEC)in which model aggregation is migrated to edge servers.In the proposed framework,the total cost of the FL is defined as the weighted sum of the total delay of UAV swarms to complete the FL and system energy consumption.In order to minimize the total cost of FL,we propose a resource allocation algorithm for joint optimization of computing resources and multi-UAV association based on deep reinforcement learning(DRL).The simulation result shows that:(1)compared with the benchmark algorithm,the proposed algorithm can effectively reduce the total cost of FL;(2)the proposed algorithm can realize the trade-off between task completion delay and system energy consumption through weight changes.展开更多
Background Standard views in two-dimensional echocardiography are well established but the qualities of acquired images are highly dependent on operator skills and are assessed subjectively.This study was aimed at pro...Background Standard views in two-dimensional echocardiography are well established but the qualities of acquired images are highly dependent on operator skills and are assessed subjectively.This study was aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality indicators.Consequently,image quality assessment can thus be automated to enhance clinical measurements,interpretation,and real-time optimization.Methods We developed deep neural networks for the automated assessment of echocardiographic frames that were randomly sampled from 11,262 adult patients.The private echocardiography dataset consists of 33,784 frames,previously acquired between 2010 and 2020.Unlike non-medical images where full-reference metrics can be applied for image quality,echocardiogram's data are highly heterogeneous and requires blind-reference(IQA)metrics.Therefore,deep learning approaches were used to extract the spatiotemporal features and the image's quality indicators were evaluated against the mean absolute error.Our quality indicators encapsulate both anatomical and pathological elements to provide multivariate assessment scores for anatomical visibility,clarity,depth-gain and foreshortedness.Results The model performance accuracy yielded 94.4%,96.8%,96.2%,97.4%for anatomical visibility,clarity,depth-gain and foreshortedness,respectively.The mean model error of 0.375±0.0052 with computational speed of 2.52 ms per frame(real-time performance)was achieved.Conclusion The novel approach offers new insight to the objective assessment of transthoracic echocardiogram image quality and clinical quantification in A4C and PLAX views.It also lays stronger foundations for the operator's guidance system which can leverage the learning curve for the acquisition of optimum quality images during the transthoracic examination.展开更多
基金supported by Beijing Natural Science Foundation under Grant 4222010.
文摘The resource allocation of the federated learning(FL)for unmanned aerial vehicle(UAV)swarm systems are investigated.The UAV swarms based on FL realize the artificial intelligence(AI)applications by means of distributed training on the basis of ensuring the security of private data.However,the direct application of the FL in UAV swarms will incur high overhead.Therefore,in this article,we consider the resource allocation problem in FL for UAV swarms.To avoid the high communication overhead between UAVs and the central server,we proposed an FL framework for UAV swarms based on mobile edge computing(MEC)in which model aggregation is migrated to edge servers.In the proposed framework,the total cost of the FL is defined as the weighted sum of the total delay of UAV swarms to complete the FL and system energy consumption.In order to minimize the total cost of FL,we propose a resource allocation algorithm for joint optimization of computing resources and multi-UAV association based on deep reinforcement learning(DRL).The simulation result shows that:(1)compared with the benchmark algorithm,the proposed algorithm can effectively reduce the total cost of FL;(2)the proposed algorithm can realize the trade-off between task completion delay and system energy consumption through weight changes.
文摘Background Standard views in two-dimensional echocardiography are well established but the qualities of acquired images are highly dependent on operator skills and are assessed subjectively.This study was aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality indicators.Consequently,image quality assessment can thus be automated to enhance clinical measurements,interpretation,and real-time optimization.Methods We developed deep neural networks for the automated assessment of echocardiographic frames that were randomly sampled from 11,262 adult patients.The private echocardiography dataset consists of 33,784 frames,previously acquired between 2010 and 2020.Unlike non-medical images where full-reference metrics can be applied for image quality,echocardiogram's data are highly heterogeneous and requires blind-reference(IQA)metrics.Therefore,deep learning approaches were used to extract the spatiotemporal features and the image's quality indicators were evaluated against the mean absolute error.Our quality indicators encapsulate both anatomical and pathological elements to provide multivariate assessment scores for anatomical visibility,clarity,depth-gain and foreshortedness.Results The model performance accuracy yielded 94.4%,96.8%,96.2%,97.4%for anatomical visibility,clarity,depth-gain and foreshortedness,respectively.The mean model error of 0.375±0.0052 with computational speed of 2.52 ms per frame(real-time performance)was achieved.Conclusion The novel approach offers new insight to the objective assessment of transthoracic echocardiogram image quality and clinical quantification in A4C and PLAX views.It also lays stronger foundations for the operator's guidance system which can leverage the learning curve for the acquisition of optimum quality images during the transthoracic examination.
