University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Necklace-like Multishelled Hollow Spinel Oxides with Oxygen Vacancies for Efficient Water Electrolysis.

Abstract: The durability and reactivity of catalysts can be effectively and precisely controlled through the careful design and engineering of their surface structures and morphologies. Herein, we develop a novel “adsorption–calcination–reduction” strategy to synthesize spinel transitional metal oxides with a unique necklace-like multishelled hollow structure exploiting sacrificial templates of carbonaceous microspheres, including NiCo2O4 (NCO), CoMn2O4, and NiMn2O4. Importantly, benefiting from the unique structures and reduction treatment to offer rich oxygen vacancies, the unique reduced NCO (R-NCO) as a bifunctional electrocatalyst exhibits the dual characteristics of good stability as well as high electrocatalytic activity for both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). At 1.61 V cell voltage, a 10 mA cm–2 water splitting current density is obtained from the dual-electrode, alkaline water electrolyzer. Calculations based on density functional theory (DFT) reveal a mechanism ...

Integrated Molecular, Interfacial, and Device Engineering towards High‐Performance Non‐Fullerene Based Organic Solar Cells

Abstract: High-performance non-fullerene OSCs with PCEs of up to ca. 6.0% are demonstrated based on PBDTT-F-TT polymer and a molecular di-PBI acceptor through comprehensive molecular, interfacial, and device engineering. Impressive PCEs can also be retained in devices with relatively thick BHJ layer and processed through non-halogenated solvents, indicating these high-performance non-fullerene OSCs are promising for large-area printing applications.

Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design

Abstract: Millimeter wave (MmWave) communications is capable of supporting multi-gigabit wireless access thanks to its abundant spectrum resource. However, severe path loss and high directivity make it vulnerable to blockage events, which can be frequent in indoor and dense urban environments. To address this issue, in this paper, we introduce intelligent reflecting surface (IRS) as a new technology to provide effective reflected paths to enhance the coverage of mmWave signals. In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver. Our objective is to maximize the received signal power by jointly optimizing the BS's transmit precoding vector and IRSs’ phase shift coefficients. Although such an optimization problem is generally non-convex, we show that, by exploiting some important characteristics of mmWave channels, an optimal closed-form solution can be derived for the single IRS case and a near-optimal analytical solution can be obtained for the multi-IRS case. Our analysis reveals that the received signal power increases quadratically with the number of reflecting elements for both the single IRS and multi-IRS cases. Simulation results are included to verify the optimality and near-optimality of our proposed solutions. Results also show that IRSs can help create effective virtual line-of-sight (LOS) paths and thus substantially improve robustness against blockages in mmWave communications.

VerifyNet: Secure and Verifiable Federated Learning

Abstract: As an emerging training model with neural networks, federated learning has received widespread attention due to its ability to update parameters without collecting users’ raw data. However, since adversaries can track and derive participants’ privacy from the shared gradients, federated learning is still exposed to various security and privacy threats. In this paper, we consider two major issues in the training process over deep neural networks (DNNs): 1) how to protect user’s privacy (i.e., local gradients) in the training process and 2) how to verify the integrity (or correctness) of the aggregated results returned from the server. To solve the above problems, several approaches focusing on secure or privacy-preserving federated learning have been proposed and applied in diverse scenarios. However, it is still an open problem enabling clients to verify whether the cloud server is operating correctly, while guaranteeing user’s privacy in the training process. In this paper, we propose VerifyNet, the first privacy-preserving and verifiable federated learning framework. In specific, we first propose a double-masking protocol to guarantee the confidentiality of users’ local gradients during the federated learning. Then, the cloud server is required to provide the Proof about the correctness of its aggregated results to each user. We claim that it is impossible that an adversary can deceive users by forging Proof , unless it can solve the NP-hard problem adopted in our model. In addition, VerifyNet is also supportive of users dropping out during the training process. The extensive experiments conducted on real-world data also demonstrate the practical performance of our proposed scheme.