Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Analysis and design of L-probe proximity fed-patch antennas

Abstract: In this paper, the finite-difference time-domain (FDTD) method is employed to analyze L-probe proximity-fed rectangular patch antennas. Numerical results for the input impedance, co- and cross-polarization radiation patterns are presented and compared with the measurements. Good agreement between the computed and measured results is obtained. The effects of geometric parameters on the characteristics of the L-probe patch antenna are extensively studied. For design purposes, the variation of input impedance at resonance with different geometric parameters is plotted on a Smith chart. Mutual coupling between two L-probe patch antennas is also investigated.

The Main Progress of Perovskite Solar Cells in 2020-2021.

Abstract: Perovskite solar cells (PSCs) emerging as a promising photovoltaic technology with high efficiency and low manufacturing cost have attracted the attention from all over the world. Both the efficiency and stability of PSCs have increased steadily in recent years, and the research on reducing lead leakage and developing eco-friendly lead-free perovskites pushes forward the commercialization of PSCs step by step. This review summarizes the main progress of PSCs in 2020 and 2021 from the aspects of efficiency, stability, perovskite-based tandem devices, and lead-free PSCs. Moreover, a brief discussion on the development of PSC modules and its challenges toward practical application is provided.

Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification

Abstract: In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-theart methods on five benchmark datasets.

Artificial-Noise-Aided Secure Transmission With Directional Modulation Based on Random Frequency Diverse Arrays

Abstract: In this paper, a random frequency diverse array-based directional modulation with artificial noise (RFDA-DM-AN) scheme is proposed to enhance physical layer security of wireless communications. Specifically, we first design the RFDA-DM-AN scheme by randomly allocating frequencies to transmit antennas, thereby achieving 2-D (i.e., angle and range) secure transmissions, and outperforming the state-of-the-art 1-D (i.e., angle) phase array (PA)-based DM scheme. Then we derive the closed-form expression of a lower bound on the ergodic secrecy capacity (ESC) of our RFDA-DM-AN scheme. Based on the theoretical lower bound derived, we further optimize the transmission power allocation between the useful signal and artificial noise (AN) in order to improve the ESC. Simulation results show that: 1) our RFDA-DM-AN scheme achieves a higher secrecy capacity than that of the PA-based DM scheme; 2) the lower bound derived is shown to approach the ESC as the number of transmit antennas $N$ increases and precisely matches the ESC when $N$ is sufficiently large; and 3) the proposed optimum power allocation achieves the highest ESC of all power allocations schemes in the RFDA-DM-AN.