The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute th...The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.展开更多
We consider the linear and non-linear enhancement of diffusion weighted magnetic resonance images(DW-MRI)to use contextual information in denoising and inferring fiber crossings.We describe the space of DW-MRI images ...We consider the linear and non-linear enhancement of diffusion weighted magnetic resonance images(DW-MRI)to use contextual information in denoising and inferring fiber crossings.We describe the space of DW-MRI images in a moving frame of reference,attached to fiber fragments which allows for convection-diffusion along the fibers.Because of this approach,our method is naturally able to handle crossings in data.We will perform experiments showing the ability of the enhancement to infer information about crossing structures,even in diffusion tensor images(DTI)which are incapable of representing crossings themselves.We will present a novel non-linear enhancement technique which performs better than linear methods in areas around ventricles,thereby eliminating the need for additional preprocessing steps to segment out the ventricles.We pay special attention to the details of implementation of the various numeric schemes.展开更多
基金supported by the National Natural Science Foundation of China(61101173)
文摘The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.
文摘We consider the linear and non-linear enhancement of diffusion weighted magnetic resonance images(DW-MRI)to use contextual information in denoising and inferring fiber crossings.We describe the space of DW-MRI images in a moving frame of reference,attached to fiber fragments which allows for convection-diffusion along the fibers.Because of this approach,our method is naturally able to handle crossings in data.We will perform experiments showing the ability of the enhancement to infer information about crossing structures,even in diffusion tensor images(DTI)which are incapable of representing crossings themselves.We will present a novel non-linear enhancement technique which performs better than linear methods in areas around ventricles,thereby eliminating the need for additional preprocessing steps to segment out the ventricles.We pay special attention to the details of implementation of the various numeric schemes.
