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.

General synthesis of single-atom catalysts with high metal loading using graphene quantum dots.

Abstract: Transition-metal single-atom catalysts present extraordinary activity per metal atomic site, but suffer from low metal-atom densities (typically less than 5 wt% or 1 at.%), which limits their overall catalytic performance. Here we report a general method for the synthesis of single-atom catalysts with high transition-metal-atom loadings of up to 40 wt% or 3.8 at.%, representing several-fold improvements compared to benchmarks in the literature. Graphene quantum dots, later interweaved into a carbon matrix, were used as a support, providing numerous anchoring sites and thus facilitating the generation of high densities of transition-metal atoms with sufficient spacing between the metal atoms to avoid aggregation. A significant increase in activity in electrochemical CO2 reduction (used as a representative reaction) was demonstrated on a Ni single-atom catalyst with increased Ni loading. Transition-metal single-atom catalysts display excellent activity per metal atom site, but suffer from low metal atom densities (typically less than 5 wt% or 1 at.%), which limits their overall catalytic performance. Now, the use of a graphene-quantum-dot primary support, later interweaved into a carbon matrix, has enabled the synthesis of single-atom catalysts with high transition-metal atom loadings of up to 40 wt% or 3.84 at.%.

Performance Analysis and Optimization in Downlink NOMA Systems With Cooperative Full-Duplex Relaying

Abstract: We study a downlink non-orthogonal multiple access system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability and ergodic sum rate with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Furthermore, by taking the fairness between the near user and far user into account, we characterize the optimal power allocations with closed-form expressions at the BS and relay to maximize the minimum achievable rate of users. Simulation results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of the art.

Paper-Based Sensors for Gas, Humidity, and Strain Detections: A Review.

Abstract: Paper, as a flexible, low-cost, lightweight, tailorable, environmental-friendly, degradable, and renewable material, is emerging in electronic devices Especially, many kinds of paper-based (PB) sensors have been reported for wearable applications in recent years Among them, the PB gas, humidity, and strain sensors are widely studied for monitoring gas, humidity, and strain from the human body and the environment However, gas, humidity, and strain often coexist and interact, and the paper itself is hydrophilic and flexible, resulting in that it is still challenging to develop high-performance PB sensors specialized for gas, humidity, and strain detections Therefore, it is necessary to summarize and discuss them systematically In this review, we focus on summarizing the state-of-art studies of the PB gas, humidity, and strain sensors Specifically, the fabrications (electrodes and sensing materials) and applications of PB gas, humidity, and strain sensors are summarized and discussed The current challenges and the potential trends of PB sensors for gas, humidity, and strain detections are also outlined This review not only can help readers to understand the development status of the PB gas, humidity, and strain sensors but also is helpful for readers to find out and solve the problems in this field through comparative reading

HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation

Abstract: With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher resolution SAR imageries, which benefits ship detection and instance segmentation. Meanwhile, object detectors based on convolutional neural network (CNN) show high performance on SAR ship detection even without land-ocean segmentation; but with respective shortcomings, such as the relatively small size of SAR images for ship detection, limited SAR training samples, and inappropriate annotations, in existing SAR ship datasets, related research is hampered. To promote the development of CNN based ship detection and instance segmentation, we have constructed a High-Resolution SAR Images Dataset (HRSID). In addition to object detection, instance segmentation can also be implemented on HRSID. As for dataset construction, under the overlapped ratio of 25%, 136 panoramic SAR imageries with ranging resolution from 1m to 5m are cropped to $800 \times 800$ pixels SAR images. To reduce wrong annotation and missing annotation, optical remote sensing imageries are applied to reduce the interferes from harbor constructions. There are 5604 cropped SAR images and 16951 ships in HRSID, and we have divided HRSID into a training set (65% SAR images) and test set (35% SAR images) with the format of Microsoft Common Objects in Context (MS COCO). 8 state-of-the-art detectors are experimented on HRSID to build the baseline; MS COCO evaluation metrics are applicated for comprehensive evaluation. Experimental results reveal that ship detection and instance segmentation can be well implemented on HRSID.

Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data

Abstract: Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.