Methods for coating conducting polymer

US7510745; US7510745 B2; US7510745B2; US7,510,745; US 7,510,745 B2; 7510745; Application No. 11/222,179Inventor name used in this publication: Xiao-ming TaoInventor name used in this publication: Xiao-yin ChengUSVersion of Recor

Design and implementation of a neural-network-controlled UPS inverter

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Neural-network-controlled single-phase UPS inverters with improved transient response and adaptability to various loads

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Polypyrrole-coated fabric strain sensor with high sensitivity and good stability

Author name used in this publication: Hing Yee J. TsangAuthor name used in this publication: C. W. M. YuenVersion of RecordPublishe

Fog Radio Access Networks: Mobility management, interference mitigation and resource optimization

In order to make Internet connections ubiquitous and autonomous in our daily lives, maximizing the utilization of radio resources and social information is one of the major research topics in future mobile communication technologies. Fog radio access network (FRAN) is regarded as a promising paradigm for the fifth generation (5G) of mobile networks. FRAN integrates fog computing with RAN and makes full use of the edge of networks. FRAN would be different in networking, computing, storage and control as compared with conventional radio access networks (RAN) and the emerging cloud RAN. In this article, we provide a description of the FRAN architecture, and discuss how the distinctive characteristics of FRAN make it possible to efficiently alleviate the burden on the fronthaul, backhaul and backbone networks, as well as reduce content delivery latencies. We will focus on the mobility management, interference mitigation, and resource optimization in FRAN. Our simulation results show that the proposed FRAN architecture and the associated mobility and resource management mechanisms can reduce the signaling cost and increase the net utility for the RAN

Analogue implementation of a neural network controller for UPS inverter applications

2001-2002 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Since the read lengths of high throughput sequencing (HTS) technologies are short, de novo assembly which plays significant roles in many applications remains a great challenge. Most of the state-of-the-art approaches base on de Bruijn graph strategy and overlap-layout strategy. However, these approaches which depend on k-mers or read overlaps do not fully utilize information of paired-end and single-end reads when resolving branches. Since they treat all single-end reads with overlapped length larger than a fix threshold equally, they fail to use the more confident long overlapped reads for assembling and mix up with the relative short overlapped reads. Moreover, these approaches have not been special designed for handling tandem repeats (repeats occur adjacently in the genome) and they usually break down the contigs near the tandem repeats. We present PERGA (Paired-End Reads Guided Assembler), a novel sequence-reads-guided de novo assembly approach, which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds using paired-end reads and different read overlap size ranging from Omax to Omin to resolve the gaps and branches. By constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. When the correct extension cannot be determined, PERGA will try to extend the contig by all feasible extensions and determine the correct extension by using look-ahead approach. Many difficult-resolved branches are due to tandem repeats which are close in the genome. PERGA detects such different copies of the repeats to resolve the branches to make the extension much longer and more accurate. We evaluated PERGA on both Illumina real and simulated datasets ranging from small bacterial genomes to large human chromosome, and it constructed longer and more accurate contigs and scaffolds than other state-of-the-art assemblers. PERGA can be freely downloaded at https://github.com/hitbio/PERGA.published_or_final_versio

TEDD: a database of temporal gene expression patterns during multiple developmental periods in human and model organisms

Characterization of the specific expression and chromatin profiles of genes enables understanding how they contribute to tissue/organ development and the mechanisms leading to diseases. Whilst the number of single-cell sequencing studies is increasing dramatically; however, data mining and reanalysis remains challenging. Herein, we systematically curated the up-to-date and most comprehensive datasets of sequencing data originating from 2760 bulk samples and over 5.1 million single-cells from multiple developmental periods from humans and multiple model organisms. With unified and systematic analysis, we profiled the gene expression and chromatin accessibility among 481 cell-types, 79 tissue-types and 92 timepoints, and pinpointed cells with the co-expression of target genes. We also enabled the detection of gene(s) with a temporal and cell-type specific expression profile that is similar to or distinct from that of a target gene. Additionally, we illustrated the potential upstream and downstream gene−gene regulation interactions, particularly under the same biological process(es) or KEGG pathway(s). Thus, TEDD (Temporal Expression during Development Database), a value-added database with a user-friendly interface, not only enables researchers to identify cell-type/tissue-type specific and temporal gene expression and chromatin profiles but also facilitates the association of genes with undefined biological functions in development and diseases. The database URL is https://TEDD.obg.cuhk.edu.hk/