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.

Electrocatalytic hydrogen evolution under neutral pH conditions: current understandings, recent advances, and future prospects

Abstract: Hydrogen production from direct water electrolysis has long been pursued as a key that may revolutionize the hydrogen economy. With the rapid availability of electricity generated using renewable energy resources, this long-pursued target is now closer to reality than ever before. To date, most studies regarding electrocatalytic hydrogen evolution reaction (HER) are carried out in strong acidic/alkali electrolytes. However, hydrogen production from HER under extreme pH conditions has several drawbacks, including a corrosive working environment, the requirement of expensive anion/cation exchange membranes, and acidic/alkali withstanding electrocatalysts. The more sustainable approach to address these drawbacks is to deploy neutral/near-neutral electrolytes for HER. Regretfully, both theoretical discussions and practical applications regarding HER under neutral/near-neutral conditions are relatively rare and very elusive. In this review, we systematically discuss the current understandings of HER mechanisms under neutral conditions and analyze the influences of different types of neutral electrolytes. The most recent advances in the development of neutral HER electrocatalysts are summarized and exemplified, and general electrocatalyst design principles are highlighted. Lastly, we provide our perspective on the potential future research direction. We hope that this review inspires future endeavors to realize efficient HER for hydrogen production under neutral conditions.

A modified real GA for the sparse linear array synthesis with multiple constraints

Abstract: This paper describes a modified real genetic algorithm (MGA) for the synthesis of sparse linear arrays. The MGA has been utilized to optimize the element positions to reduce the peak sidelobe level (PSLL) of the array. And here the multiple optimization constraints include the number of elements, the aperture and the minimum element spacing. Unlike standard GA using fixed corresponding relationship between the gene variables and their coding, the MGA utilized the coding resetting of gene variables to avoid infeasible solution during the optimization process. Also, the proposed approach has reduced the size of the searching area of the GA by means of indirect description of individual. The simulated results confirming the great efficiency and the robustness of this algorithm are provided in this paper.

Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems

Abstract: We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the MS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramer-Rao bound (CRB) results for channel parameters and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs and present a clear advantage over the compressed sensing-based method.

Enabling Efficient Multi-Keyword Ranked Search Over Encrypted Mobile Cloud Data Through Blind Storage

Abstract: In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and ${k}$ -nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.