IA-OPD : an optimized orthogonal pulse design scheme for waveform division multiple access UWB systems

A new design scheme of orthogonal pulses is proposed for waveform division multiple access ultra-wideband (WDMA-UWB) systems. In order to achieve WDMA and to improve user capacity, the proposed method, termed as interference alignment based orthogonal pulse design (IA-OPD), employs combined orthogonal wavelet functions in the pulse design. The combination coefficients are optimized by using interference alignment. Due to the reciprocity between transmitted and local template signals, the iterative process based on maximum signal to interference plus noise ratio (Max-SINR) criterion can be used to solve the optimization problem in interference alignment. Numerical results demonstrate that the optimized orthogonal pulses provide excellent performances in terms of multiple access interference (MAI) suppression, user capacity and near-far resistance without using any multiuser detection (MUD) techniques. Thus, the IA-OPD scheme can be used to efficiently design a large number of orthogonal pulses for multiuser WDMA-UWB systems with low computational complexity and simple transceiver structure

Retrieving Precipitable Water Vapor From Shipborne Multi‐GNSS Observations

©2019. American Geophysical UnionPrecipitable water vapor (PWV) is an important parameter for climate research and a crucial factor to achieve high accuracy in satellite geodesy and satellite altimetry. Currently Global Navigation Satellite System (GNSS) PWV retrieval using static Precise Point Positioning is limited to ground stations. We demonstrated the PWV retrieval using kinematic Precise Point Positioning method with shipborne GNSS observations during a 20‐day experiment in 2016 in Fram Strait, the region of the Arctic Ocean between Greenland and Svalbard. The shipborne GNSS PWV shows an agreement of ~1.1 mm with numerical weather model data and radiosonde observations, and a root‐mean‐square of ~1.7 mm compared to Satellite with ARgos and ALtiKa PWV. An improvement of 10% is demonstrated with the multi‐GNSS compared to the Global Positioning System solution. The PWV retrieval was conducted under different sea state from calm water up to gale. Such shipborne GNSS PWV has the promising potential to improve numerical weather forecasts and satellite altimetry

RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution

Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the imaging effects of smartphones with limited sensors. The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network. In particular, we emphasize the role of the base-frame and utilize it as a key prompt to guide the knowledge acquisition from other frames in every recurrence. Moreover, we introduce an implicit weighting loss to improve the model's flexibility in facing input frames with variable numbers. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves better results than state-of-the-art ones. Codes and pre-trained models are available at https://github.com/ZcsrenlongZ/RBSR.Comment: 17 page

Validation of 7 Years in-Flight HY-2A Calibration Microwave Radiometer Products Using Numerical Weather Model and Radiosondes

Haiyang-2A (HY-2A) has been working in-flight for over seven years, and the accuracy of HY-2A calibration microwave radiometer (CMR) data is extremely important for the wet troposphere delay correction (WTC) in sea surface height (SSH) determination. We present a comprehensive evaluation of the HY-2A CMR observation using the numerical weather model (NWM) for all the data available period from October 2011 to February 2018, including the WTC and the precipitable water vapor (PWV). The ERA(ECMWF Re-Analysis)-Interim products from European Centre for Medium-Range Weather Forecasts (ECMWF) are used for the validation of HY-2A WTC and PWV products. In general, a global agreement of root-mean-square (RMS) of 2.3 cm in WTC and 3.6 mm in PWV are demonstrated between HY-2A observation and ERA-Interim products. Systematic biases are revealed where before 2014 there was a positive WTC/PWV bias and after that, a negative one. Spatially, HY-2A CMR products show a larger bias in polar regions compared with mid-latitude regions and tropical regions and agree better in the Antarctic than in the Arctic with NWM. Moreover, HY-2A CMR products have larger biases in the coastal area, which are all caused by the brightness temperature (TB) contamination from land or sea ice. Temporally, the WTC/PWV biases increase from October 2011 to March 2014 with a systematic bias over 1 cm in WTC and 2 mm in PWV, and the maximum RMS values of 4.62 cm in WTC and 7.61 mm in PWV occur in August 2013, which is because of the unsuitable retrieval coefficients and systematic TB measurements biases from 37 GHz band. After April 2014, the TB bias is corrected, HY-2A CMR products agree very well with NWM from April 2014 to May 2017 with the average RMS of 1.68 cm in WTC and 2.65 mm in PWV. However, since June 2017, TB measurements from the 18.7 GHz band become unstable, which led to the huge differences between HY-2A CMR products and the NWM with an average RMS of 2.62 cm in WTC and 4.33 mm in PWV. HY-2A CMR shows high accuracy when three bands work normally and further calibration for HY-2A CMR is in urgent need. Furtherly, 137 global coastal radiosonde stations were used to validate HY-2A CMR. The validation based on radiosonde data shows the same variation trend in time of HY-2A CMR compared to the results from ECMWF, which verifies the results from ECMWF

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized

Effect of Metformin on Lactate Metabolism in Normal Hepatocytes under High Glucose Stress in Vitro

Objective: To study the effect of metformin on lactate metabolism in hepatocytes in vitro under high glucose stress. Method: LO2 hepatocytes was cultured in vitro, hepatocytes were randomly divided into blank control group, 25 mmol/L glucose solution, 27 mmol/L glucose solution, 29 mmol/L glucose solution, 31 mmol/L glucose solution, 33 mmol/L glucose solution, 35 mmol/L glucose solution treatment group, after determining the optimal concentration as 31 mmol/L, use 30 mmol/L metformin solution, and then divided into blank control group, normal hepatocytes + the optimal concentration of glucose solution, normal hepatocytes + metformin solution , normal hepatocytes+. The optimal concentration of glucose solution normal hepatocytes + metformin solution, calculate the number of hepatocytes on cell count plate respectively in the 12 h, 24 h, 48 h, and use the lactic acid kit to determine the lactic acid value of the cell culture medium of normal liver cells + optimal concentration glucose solution and normal liver cells + optimal concentration glucose solution + metformin solution at 12 h, 24 h, and 48 h, respectively. Results: There was no significant change in the lactic acid concentration but significant increase in the number of surviving hepatocytes in the high-glycemic control group compared with that in the high-glycemic control group without metformin. Conclusions: Metformin has no significant effect on lactic acid metabolism of hepatocytes under high glucose stress in vitro, and has a protective effect on hepatocytes under high glucose stress. Based on this, it is preliminarily believed that metformin is not the direct factor leading to diabetic lactic acidosis

Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution

Video super-resolution (VSR) aiming to reconstruct a high-resolution (HR) video from its low-resolution (LR) counterpart has made tremendous progress in recent years. However, it remains challenging to deploy existing VSR methods to real-world data with complex degradations. On the one hand, there are few well-aligned real-world VSR datasets, especially with large super-resolution scale factors, which limits the development of real-world VSR tasks. On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results. As an attempt to address the aforementioned issues, we build a real-world 4 VSR dataset, namely MVSR4$\times$, where low- and high-resolution videos are captured with different focal length lenses of a smartphone, respectively. Moreover, we propose an effective alignment method for real-world VSR, namely EAVSR. EAVSR takes the proposed multi-layer adaptive spatial transform network (MultiAdaSTN) to refine the offsets provided by the pre-trained optical flow estimation network. Experimental results on RealVSR and MVSR4$\times$ datasets show the effectiveness and practicality of our method, and we achieve state-of-the-art performance in real-world VSR task. The dataset and code will be publicly available

Validating HY-2A CMR precipitable water vapor using ground-based and shipborne GNSS observations

The calibration microwave radiometer (CMR) on board the Haiyang-2A (HY-2A) satellite provides wet tropospheric delay correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. The ground-based global navigation satellite system (GNSS) provides precise precipitable water vapor (PWV) with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 International GNSS Service (IGS) stations along the global coastline and 56 d shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made in the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm as the root mean square (rms) within 100 km. Geographically, the rms is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an rms of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well

Involvement of NMDA-AKT-mTOR Signaling in Rapid Antidepressant-Like Activity of Chaihu-jia-Longgu-Muli-tang on Olfactory Bulbectomized Mice

Background: Fast-onset antidepressants are urgently needed. Chaihu-jia-Longgu-Muli-tang (CLM), a classic Chinese herbal medicine, has been used for antidepressant treatment with long history. Olfactory bulbectomization (OB) model is validated for identification of rapid antidepressant efficacy. Here we used OB model for investigating the rapid onset activity of CLM in mice, and also tested the involvement of prefrontal Akt-mTOR and associated AMPA/NMDA receptors as well as hippocampal BDNF in the rapid antidepressant-like effect of CLM.Methods: The OB model was first characterized with depression-like behaviors and the time course changes of the behaviors. The fast onset of antidepressant effect of CLM was evaluated using sucrose preference test, tail suspension test and forced swim test in OB mice after a single administration. The expression of synaptic proteins of AMPA and NMDA subunits as well as Akt/mTOR signaling in the prefrontal cortex, and hippocampal BDNF was evaluated with the immunoblotting method.Results: A single dose of CLM significantly improved the deficiency in the sucrose preference and decreased the immobility time in the tail suspension test in OB mice. In the prefrontal cortex (PFC) in OB mice, there was lower expression level of the AMPA receptor subunit GluR1, rescued by a single dose of CLM. Additionally, the expression of NMDA subunit NR1 was up-regulated in OB mice, whereas mTOR and its upstream Akt signalings were both down-regulated. These deficiencies were reversed by a single dose of CLM. The CLM treatment also attenuated the expressions of NMDA receptor subunits NR2A and NR2B, which did not change in OB mice. In the hippocampus, expressions of GluR1 and brain derived neurotrophic factor (BDNF) were both up-regulated in OB mice, although CLM increased GluR1, but not BDNF.Conclusion: CLM elicited rapid antidepressant-like effects in the OB model mice, and CLM reversal of the abnormality in PFC expression of AMPA and NMDA receptors and associated Akt-mTOR signaling may underlie the effects