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: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues

Abstract: Wireless networks have been widely deployed for many Internet-of-Things (IoT) applications, like smart cities and precision agriculture. Low Power Wide Area Networking (LPWAN) is an emerging IoT networking paradigm to meet three key requirements of IoT applications, i.e., low cost, large scale deployment and high energy efficiency. Among all available LPWAN technologies, LoRa networking has attracted much attention from both academia and industry, since it specifies an open standard and allows us to build autonomous LPWAN networks without any third-party infrastructure. Many LoRa networks have been developed recently, e.g., managing solar plants in Carson City, Nevada, USA and power monitoring in Lyon and Grenoble, France. However, there are still many research challenges to develop practical LoRa networks, e.g., link coordination, resource allocation, reliable transmissions and security. This article provides a comprehensive survey on LoRa networks, including the technical challenges of deploying LoRa networks and recent solutions. Based on our detailed analysis of current solutions, some open issues of LoRa networking are discussed. The goal of this survey paper is to inspire more works on improving the performance of LoRa networks and enabling more practical deployments.

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

Abstract: With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

A reduced graphene oxide/mixed-valence manganese oxide composite electrode for tailorable and surface mountable supercapacitors with high capacitance and super-long life

Abstract: Developing supercapacitor electrodes with an ultra-long cycle life and a high specific capacitance is critical to the future energy storage devices. Herein, we report a scalable synthesis technology of mixed-valence manganese oxide nanoparticles anchored to reduced graphene oxide (rGO/MnOx) as the high-performance supercapacitor electrodes. First, 2-dimensional (2D) δ-MnO2 nanosheets are formed on a graphene oxide (GO) template, which is then in situ reduced by hydrazine vapour to mixed-valence manganese oxide nanoparticles evenly distributed on a rGO conductive network. The obtained rGO/MnOx electrode material exhibits a high specific capacitance of 202 F g−1 (mass loading of 2 mg cm−2), a large areal specific capacitance of 2.5 F cm−2 (mass loading of up to 19 mg cm−2), and a super-long-life stability of 106% capacitance retention after 115 000 charge/discharge cycles. By using an ionic liquid electrolyte and an activated carbon anode, asymmetric supercapacitors (AScs) are also constructed and can be packaged into a high performance miniaturized energy storage component in either a tailorable or surface mountable configuration. Our ASc shows superior performance characteristics, with typical figures of merit including maximum energy densities of 47.9 W h kg−1 at 270 W kg−1 and 19.1 W h kg−1 at the maximum power density of 20.8 kW kg−1. The capacitance retention of the ASc is 96% after 80 000 charge/discharge cycles, which is the most excellent stability performance in an ionic liquid electrolyte as compared with the recently reported pseudo-supercapacitors. This technology may find vast applications in future miniaturized portable and wearable electronics.