An Implementation of List Successive Cancellation Decoder with Large List Size for Polar Codes

Polar codes are the first class of forward error correction (FEC) codes with a provably capacity-achieving capability. Using list successive cancellation decoding (LSCD) with a large list size, the error correction performance of polar codes exceeds other well-known FEC codes. However, the hardware complexity of LSCD rapidly increases with the list size, which incurs high usage of the resources on the field programmable gate array (FPGA) and significantly impedes the practical deployment of polar codes. To alleviate the high complexity, in this paper, two low-complexity decoding schemes and the corresponding architectures for LSCD targeting FPGA implementation are proposed. The architecture is implemented in an Altera Stratix V FPGA. Measurement results show that, even with a list size of 32, the architecture is able to decode a codeword of 4096-bit polar code within 150 us, achieving a throughput of 27MbpsComment: 4 pages, 4 figures, 4 tables, Published in 27th International Conference on Field Programmable Logic and Applications (FPL), 201

The effect of CO2 phase on drainage process by analysis of transient differential pressure

Authors gratefully acknowledge the financial supports from China Scholarship Council (CSC) and UK India Education & Research Initiative (UKIERI).Peer reviewedPostprin

AlGaInP light-emitting diodes with SACNTs as current-spreading layer

Transparent conductive current-spreading layer is important for quantum efficiency and thermal performance of light-emitting diodes (LEDs). The increasing demand for tin-doped indium oxide (ITO) caused the price to greatly increase. Super-aligned carbon nanotubes (SACNTs) and Au-coated SACNTs as current-spreading layer were applied on AlGaInP LEDs. The LEDs with Au-coated SACNTs showed good current spreading effect. The voltage bias at 20 mA dropped about 0.15 V, and the optical power increased about 10% compared with the LEDs without SACNTs

THE EXTRACTS OF PACIFIC OYSTER (CRASSOSTREA GIGAS) ALLEVIATE OVARIAN FUNCTIONAL DISORDERS OF FEMALE RATS WITH EXPOSURE TO BISPHENOL A THROUGH DECREASING FSHR EXPRESSION IN OVARIAN TISSUES

Background: Bisphenol-A (BPA) is one of the widespread industrial compounds, which has adverse effects on animal and human health. The study was aimed to explore the effects of Crassostrea gigas extracts (CGE) in alleviating ovarian functional disorders of female rats with exposure to BPA and the underlying possible mechanism. Materials and methods: Eighteen four-week-old female Sprague-Dawley (SD) rats were randomly divided into BPA group (50mg/kg BPA), BPA+CGE group (50mg/kg BPA+50mg/kg CGE), and control group (equivalent dosage of vehicle) with 6 rats in each group. After a 6-week treatment ended, the serum levels of estradiol (E2), follicle stimulating hormone (FSH), luteinizing hormone (LH) were measured by using commercial standard assay kits. The expression levels of FSH receptor (FSHR) in the rat ovarian tissues were respectively detected by immunohistochemistry and Real-time PCR. Results: CGE treatment markedly increased E2 levels and decreased FSH levels in the serum (P0.05). The protein and mRNA expression levels of FSHR were the lowest in the ovaries of control rats and the highest in BPA rats (

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion