Hybrid vector beams with non-uniform orbital angular momentum density induced by designed azimuthal polarization gradient

Based on angular amplitude modulation of orthogonal base vectors in common-path interference method, we propose an interesting type of hybrid vector beams with unprecedented azimuthal polarization gradient and demonstrate in experiment. Geometrically, the configured azimuthal polarization gradient is indicated by intriguing mapping tracks of angular polarization states on Poincaré sphere, more than just conventional circles for previously reported vector beams. Moreover, via tailoring relevant parameters, more special polarization mapping tracks can be handily achieved. More noteworthily, the designed azimuthal polarization gradients are found to be able to induce azimuthally non-uniform orbital angular momentum density, while generally uniform for circle-track cases, immersing in homogenous intensity background whatever base states are. These peculiar features may open alternative routes for new optical effects and applications.

Transfer Learning for Disease Diagnosis from Myocardial Perfusion SPECT Imaging

Coronary artery disease(CAD)is one of the most common pathological conditions and the major global cause of death.Myocardial perfusion imaging(MPI)using single-photon emission computed tomography(SPECT)is a non-invasive method and plays an essential role in diagnosing CAD.However,there is currently a shortage of doctors who can diagnose using SPECT-MPI in developing countries,especially Vietnam.Research on deploying machine learning and deep learning in supporting CAD diagnosis has been noticed for a long time.However,these methods require a large dataset and are therefore time-consuming and labor-intensive.This study aims to develop a cost-effective and high-performance CAD classification model to support doctors in these countries.In this paper,we propose a transfer learning framework for a multi-stage training process with different learning rates.The process consists of two training stages:a warming up stage in which all layers of a pre-trained model(on ImageNet dataset)are frozen;and a finetuning stage in which a small amount of the top layers are unfrozen and then retrained with a lower learning rate.The dataset for this study consists of the polar maps from 218 patients.Various popular CNN-based pre-trained models have been investigated,and ResNet152V2-based model has obtained the highest performances with an accuracy of 95.5%,area under the receiver operating characteristic curve(AUC)score 0.932,sensitivity 94.4%,precision 96.4%,and F1-score 95.2%.These performances are competitive or even better than all state-of-the-art approaches in terms of classification accuracy and sensitivity.We also apply the class activation mapping technique to help explain the model’s predictions and increase the model’s reliability,proving capable of assisting the SPECT image readers in the CAD diagnosis.

A novel bit mapping scheme for high-order polar coded modulation systems

In order to improve the spectrum efficiency of the high-order polar coded modulation systems, the polar code is used as the component code of the bit-interleave coded modulation(BICM) system, a novel bit mapping scheme is proposed considering of the channel polarization and successive cancellation(SC) decoding principle of polar codes as well as the unequal protection of equivalent channels by modulator. In this scheme, the frozen bits on the unreliable split channel are allocated to the equivalent channel with the low protection of the modulator, while the equivalent channels with the high protection are used to transmit the information bits. Thus, the error-correcting performance of polar codes is improved. Compared with some bit mapping schemes, the proposed bit mapping scheme only needs to divide and choose the parameters of the split channels reliability measurement, the complexity does not increase obviously, and simulation results show that the proposed scheme has the better performance under the quadrature amplitude modulation(QAM) modulation based on the Gray mapping.