Shenzhen University

About: Shenzhen University is a education organization based out in Shenzhen, China. It is known for research contribution in the topics: Computer science & Laser. The organization has 28054 authors who have published 35378 publications receiving 522023 citations.

High-entropy ceramics: Present status, challenges, and a look forward

Abstract: High-entropy ceramics (HECs) are solid solutions of inorganic compounds with one or more Wyckoff sites shared by equal or near-equal atomic ratios of multi-principal elements. Although in the infant stage, the emerging of this new family of materials has brought new opportunities for material design and property tailoring. Distinct from metals, the diversity in crystal structure and electronic structure of ceramics provides huge space for properties tuning through band structure engineering and phonon engineering. Aside from strengthening, hardening, and low thermal conductivity that have already been found in high-entropy alloys, new properties like colossal dielectric constant, super ionic conductivity, severe anisotropic thermal expansion coefficient, strong electromagnetic wave absorption, etc., have been discovered in HECs. As a response to the rapid development in this nascent field, this article gives a comprehensive review on the structure features, theoretical methods for stability and property prediction, processing routes, novel properties, and prospective applications of HECs. The challenges on processing, characterization, and property predictions are also emphasized. Finally, future directions for new material exploration, novel processing, fundamental understanding, in-depth characterization, and database assessments are given.

Channel coding and decoding in a relay system operated with physical-layer network coding

Abstract: This paper investigates link-by-link channel-coded PNC (physical layer network coding), in which a critical process at the relay is to transform the superimposed channel-coded packets received from the two end nodes (plus noise), Y3 = X1+ X2+W3, to the network-coded combination of the source packets, S1 oplus S2. This is in contrast to the traditional multiple-access problem, in which the goal is to obtain both S1 and S2 explicitly at the relay node. Trying to obtain S1 and S2 explicitly is an overkill if we are only interested in S1oplusS2. In this paper, we refer to the transformation Y3 rarr S1 oplus S2 as the channel-decoding- network-coding process (CNC) in that it involves both channel decoding and network coding operations. This paper shows that if we adopt the repeat accumulate (RA) channel code at the two end nodes, then there is a compatible decoder at the relay that can perform the transformation Y3 rarr S1oplusS2 efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional point-to-point channel to suit the need of the PNC multiple-access channel. Simulation results show that our new scheme outperforms the previously proposed schemes significantly in terms of BER without added complexity.

Emerging combination strategies with phototherapy in cancer nanomedicine

Abstract: Optical techniques using developed laser and optical devices have made a profound impact on modern medicine, with "biomedical optics" becoming an emerging field. Sophisticated technologies have been developed in cancer nanomedicine, such as photothermal therapy and photodynamic therapy, among others. However, single-mode phototherapy cannot completely treat persistent tumors, with the challenges of relapse or metastasis remaining; therefore, combinatorial strategies are being developed. In this review, the role of light in cancer therapy and the challenges of phototherapy are discussed. The development of combinatorial strategies with other therapeutic methods, including chemotherapy, immunotherapy, gene therapy, and radiotherapy, is presented and future directions are further discussed. This review aims to highlight the significance of light in cancer therapy and discuss the combinatorial strategies that show promise in addressing the challenges of phototherapy.

Deep residual learning for image steganalysis

Abstract: Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.