A new type of V-shaped photonic crystal fiber with elliptical air-holes is proposed to realize simultaneous high birefringence and nonlinearity at a wavelength of 1.55μm.The full vector finite element method was adop...A new type of V-shaped photonic crystal fiber with elliptical air-holes is proposed to realize simultaneous high birefringence and nonlinearity at a wavelength of 1.55μm.The full vector finite element method was adopted to investigate its characteristics,including birefringence,nonlinearity,and dispersion.The PCF exhibited a very high birefringence of 2.89×10^(-2) and very high nonlinear coefficient of 102.69 W^(-1)·km^(-1).In particular,there were two zero-dispersion wavelengths(ZDWs)in the visible(X:640-720 nm and Y:730-760 nm)and near-infrared regions(X:1050-1606 nm and Y:850-1500 nm).The combination of high birefringence and nonlinearity allowed the PCF to maintain the polarization state and generate a broadband super continuum,with potential applications in nonlinear optics.展开更多
We report a high power and widely tunable erbium-doped fiber (EDF) ring laser using 1480nm pump and high concentration EDF. Large tuning range up to 105nm (1513-1618 nm) has been obtained by optimizing of the EDF length.
A novel technique of producing azimuthal index 1>1 doughnut beam of good quality by use of multi-liquid crystal cells is presented, it has the advantages of high conversion efficiency and flexibility.
Induced acoustic wave to bare fiber through various types of horn are examined. The center wavelength and extinction ratio of the notch filter are dynamically tunable and dependent on the RF signals.
Condensation in the air-holes of photonic crystal fiber results in bubble development during the electric arc splicing is first reported. The condensation can be removed by laser splicing; and 1.4dB splice loss has be...Condensation in the air-holes of photonic crystal fiber results in bubble development during the electric arc splicing is first reported. The condensation can be removed by laser splicing; and 1.4dB splice loss has been achieved.展开更多
We measured macro-bending losses for two large mode area photonic crystal fibers. Experimental results show that macro-bending loss and loss window are dependent on the parameter d/∧ and number of air-holes ring in t...We measured macro-bending losses for two large mode area photonic crystal fibers. Experimental results show that macro-bending loss and loss window are dependent on the parameter d/∧ and number of air-holes ring in the cladding.展开更多
Background.In critical care,intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions,which has always been a challenging task....Background.In critical care,intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions,which has always been a challenging task.Recently,deep learning models such as recurrent neural networks(RNNs)have demonstrated their strong potential on predicting such events.However,in real deployment,the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.Methods.In this study,we propose a novel self-correcting mechanism for RNN to fill in this gap.Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp,so that the model can“learn”from previous predictions.We also proposed a regularization method that takes into account not only the model’s prediction errors on the labels but also its estimation errors on the input data.Results.We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury(AKI)prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.Conclusions.The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.61475029)
文摘A new type of V-shaped photonic crystal fiber with elliptical air-holes is proposed to realize simultaneous high birefringence and nonlinearity at a wavelength of 1.55μm.The full vector finite element method was adopted to investigate its characteristics,including birefringence,nonlinearity,and dispersion.The PCF exhibited a very high birefringence of 2.89×10^(-2) and very high nonlinear coefficient of 102.69 W^(-1)·km^(-1).In particular,there were two zero-dispersion wavelengths(ZDWs)in the visible(X:640-720 nm and Y:730-760 nm)and near-infrared regions(X:1050-1606 nm and Y:850-1500 nm).The combination of high birefringence and nonlinearity allowed the PCF to maintain the polarization state and generate a broadband super continuum,with potential applications in nonlinear optics.
文摘We report a high power and widely tunable erbium-doped fiber (EDF) ring laser using 1480nm pump and high concentration EDF. Large tuning range up to 105nm (1513-1618 nm) has been obtained by optimizing of the EDF length.
文摘A novel technique of producing azimuthal index 1>1 doughnut beam of good quality by use of multi-liquid crystal cells is presented, it has the advantages of high conversion efficiency and flexibility.
文摘Induced acoustic wave to bare fiber through various types of horn are examined. The center wavelength and extinction ratio of the notch filter are dynamically tunable and dependent on the RF signals.
文摘Condensation in the air-holes of photonic crystal fiber results in bubble development during the electric arc splicing is first reported. The condensation can be removed by laser splicing; and 1.4dB splice loss has been achieved.
文摘We measured macro-bending losses for two large mode area photonic crystal fibers. Experimental results show that macro-bending loss and loss window are dependent on the parameter d/∧ and number of air-holes ring in the cladding.
基金This work is supported by the National University of Singapore start-up grant with award number R-608-000-172-133.
文摘Background.In critical care,intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions,which has always been a challenging task.Recently,deep learning models such as recurrent neural networks(RNNs)have demonstrated their strong potential on predicting such events.However,in real deployment,the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.Methods.In this study,we propose a novel self-correcting mechanism for RNN to fill in this gap.Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp,so that the model can“learn”from previous predictions.We also proposed a regularization method that takes into account not only the model’s prediction errors on the labels but also its estimation errors on the input data.Results.We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury(AKI)prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.Conclusions.The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.
