Affine Frequency Division Multiplexing With Index Modulation

Affine frequency division multiplexing (AFDM) is a new multicarrier technique based on chirp signals tailored for high-mobility communications, which can achieve full diversity. In this paper, we propose an index modulation (IM) scheme based on the framework of AFDM systems, named AFDM-IM. In the proposed AFDM-IM scheme, the information bits are carried by the activation state of the subsymbols in discrete affine Fourier (DAF) domain in addition to the conventional constellation symbols. To efficiently perform IM, we divide the subsymbols in DAF domain into several groups and consider both the localized and distributed strategies. An asymptotically tight upper bound on the average bit error rate (BER) of the maximum-likelihood detection in the existence of channel estimation errors is derived in closed-form. Computer simulations are carried out to evaluate the performance of the proposed AFDM-IM scheme, whose results corroborate its superiority over the benchmark schemes in the linear time-varying channels. We also evaluate the BER performance of the index and modulated bits for the AFDM-IM scheme with and without satisfying the full diversity condition of AFDM. The results show that the index bits have a stronger diversity protection than the modulated bits even when the full diversity condition of AFDM is not satisfied

Field Emission Properties of the Graphene Double-Walled Carbon Nanotube Hybrid Films Prepared by Vacuum Filtration and Screen Printing

The graphene double-walled carbon nanotube (DWCNT) hybrid films were prepared by vacuum filtration and screen printing. Their electron field emission properties have been studied systematically. The electron emission properties of the hybrid films are much better than those of pure DWCNT films and pure graphene films. Comparing with the screen printed films, the vacuum filtered films have many advantages, such as lower turn-on field, higher emission current density, better uniformity, better long-term stability, and stronger adhesive strength with conductive substrates. The optimized hybrid films with 20% weight ratio of graphene, which were fabricated by vacuum filtration, show the best electron emission performances with a low turn-on field of 0.50 Vμm−1 (at 1 μAcm−2) and a high field enhancement factor β of 27000

Preparation of Zeolite X by the Aluminum Residue From Coal Fly Ash for the Adsorption of Volatile Organic Compounds

In China, coal fly ash is a large-scale solid waste generated by power plants. The high value utilization of coal fly ash has always been a hot research issue in China for these years. In this paper, the synthesis of zeolite X using aluminum residue from coal fly ash can not only realize the resource utilization of waste, but also achieve the effect of energy saving and emission reduction. Zeolite X prepared by hydrothermal synthesis method have been found to have higher purity and better crystallinity by chemical composition analysis. By comparing and analyzing the adsorption performance of zeolite X and activated carbon on volatile organic compounds, it is found that the adsorption capacity of zeolite X is higher than that of activated carbon, and it has stronger stability. This indicates that the zeolite X synthesized by this environmentally friendly and economical method has a good application prospect in adsorbing volatile organic compounds

FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images

In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads to inaccurate small-organ label maps. We propose a novel end-to-end deep neural network to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ sub-networks while maintaining the accuracy of large organ segmentation. A strong main network with densely connected atrous spatial pyramid pooling and squeeze-and-excitation modules is used for segmenting large organs, where large organs' label maps are directly output. For small organs, their probabilistic locations instead of label maps are estimated by the main network. High-resolution and multi-scale feature volumes for each small organ are ROI-pooled according to their locations and are fed into small-organ networks for accurate segmenting small organs. Our proposed network is extensively tested on both collected real data and the \emph{MICCAI Head and Neck Auto Segmentation Challenge 2015} dataset, and shows superior performance compared with state-of-the-art segmentation methods.Comment: MICCAI 201