The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these pro...The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.展开更多
Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.Th...Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.展开更多
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
文摘The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.
文摘Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
