Capital Normal University

About: Capital Normal University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Terahertz radiation & Quantum entanglement. The organization has 11441 authors who have published 11988 publications receiving 159071 citations. The organization is also known as: Shǒudū Shīfàn Dàxué.

Concurrence of arbitrary dimensional bipartite quantum states.

Abstract: We derive an analytical lower bound for the concurrence of a bipartite quantum state in arbitrary dimension. A functional relation is established relating concurrence, the Peres-Horodecki criterion, and the realignment criterion. We demonstrate that our bound is exact for some mixed quantum states. The significance of our method is illustrated by giving a quantitative evaluation of entanglement for many bound entangled states, some of which fail to be identified by the usual concurrence estimation method.

UAV-based multispectral remote sensing for precision agriculture: a comparison between different cameras.

Abstract: Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown great potential for precision agriculture However, there are many problems in data acquisition, processing and application, which have stunted its development In this study, a narrowband Mini-MCA6 multispectral camera and a sunshine-sensor-equipped broadband Sequoia multispectral camera were mounted on a multirotor micro-UAV They were used to simultaneously collect multispectral imagery and soil–plant analysis development (SPAD) values of maize at multiple sampling points in the field, in addition to the spectral reflectances of six standard diffuse reflectance panels with different reflectance values (45%, 20%, 30%, 40%, 60% and 65%) The accuracies of the reflectance and vegetation indices (VIs) derived from the imagery were compared, and the effectiveness and accuracy of the SPAD prediction from the normalized difference vegetation index (NDVI) and red-edge NDVI (reNDVI) under different nitrogen treatments were examined at the plot level The results show that the narrowband Mini-MCA6 camera could produce more accurate reflectance values than the broadband Sequoia camera, but only if the appropriate calibration method (the nonlinear subband empirical line method) was adopted, especially in visible (blue, green and red) bands However, the accuracy of the VIs was not completely dependent on the accuracy of the reflectance, ie, the NDVI from Mini-MCA6 was slightly better than that from Sequoia, whereas Sequoia produced more accurate reNDVI than did Mini-MCA6 At the plot level, reNDVI performed better than NDVI in SPAD prediction regardless of which camera was employed Moreover, the reNDVI had relatively low sensitivity to the vegetation coverage and was insignificantly affected by environmental factors (eg, exposed sandy soil) This study indicates that UAV multispectral remote sensing technology is instructive for precision agriculture, but more effort is needed regarding calibration methods for vegetation, postprocessing techniques and robust quantitative studies

Multiplex sequencing of pooled mitochondrial genomes—a crucial step toward biodiversity analysis using mito-metagenomics

Abstract: The advent in high-throughput-sequencing (HTS) technologies has revolutionized conventional biodiversity research by enabling parallel capture of DNA sequences possessing species-level diagnosis. However, polymerase chain reaction (PCR)-based implementation is biased by the efficiency of primer binding across lineages of organisms. A PCR-free HTS approach will alleviate this artefact and significantly improve upon the multi-locus method utilizing full mitogenomes. Here we developed a novel multiplex sequencing and assembly pipeline allowing for simultaneous acquisition of full mitogenomes from pooled animals without DNA enrichment or amplification. By concatenating assemblies from three de novo assemblers, we obtained high-quality mitogenomes for all 49 pooled taxa, with 36 species >15 kb and the remaining >10 kb, including 20 complete mitogenomes and nearly all protein coding genes (99.6%). The assembly quality was carefully validated with Sanger sequences, reference genomes and conservativeness of protein coding genes across taxa. The new method was effective even for closely related taxa, e.g. three Drosophila spp., demonstrating its broad utility for biodiversity research and mito-phylogenomics. Finally, the in silico simulation showed that by recruiting multiple mito-loci, taxon detection was improved at a fixed sequencing depth. Combined, these results demonstrate the plausibility of a multi-locus mito-metagenomics approach as the next phase of the current single-locus metabarcoding method.

From IoT to 5G I-IoT: The Next Generation IoT-Based Intelligent Algorithms and 5G Technologies

Abstract: The Internet of Things is a novel paradigm with access to wireless communication systems and artificial intelligence technologies, which is considered to be applicable to a variety of promising fields and applications. Meanwhile, the development of the fifth-generation cellular network technologies creates the possibility to deploy enormous sensors in the framework of the IoT and to process massive data, challenging the technologies of communications and data mining. In this article, we propose a novel paradigm, 5G Intelligent Internet of Things (5G I-IoT), to process big data intelligently and optimize communication channels. First, we articulate the concept of the 5G I-IoT and introduce three major components of the 5G I-IoT. Then we expound the interaction among these components and introduce the key methods and techniques based on our proposed paradigm, including big data mining, deep learning, and reinforcement learning. In addition, an experimental result evaluates the performance of 5G I-IoT, and the effective utilization of channels and QoS have been greatly improved. Finally, several application fields and open issues are discussed.