Spatial Coded Modulation

In this paper, we propose a spatial coded modulation (SCM) scheme, which improves the accuracy of the active antenna detection by coding over the transmit antennas. Specifically, the antenna activation pattern in the SCM corresponds to a codeword in a properly designed codebook with a larger minimum Hamming distance than its counterpart conventional spatial modulation. As the minimum Hamming distance increases, the reliability of the active antenna detection is directly enhanced, which in turn improves the demodulation of the modulated symbols and yields a better system reliability. In addition to the reliability, the proposed SCM scheme also achieves a higher capacity with the identical antenna configuration compared to the conventional spatial modulation technique. Moreover, the proposed SCM scheme strikes a balance between spectral efficiency and reliability by trading off the minimum Hamming distance with the number of available codewords. The optimal maximum likelihood detector is first formulated. Then, a low-complexity suboptimal detector is proposed to reduce the computational complexity, which has a two-step detection. Theoretical derivations of the channel capacity and the bit error rate are presented in various channel scenarios, i.e., Rayleigh, Rician, Nakagami-m, imperfect channel state information, and spatial correlation. Further derivation on performance bounding is also provided to reveal the insight of the benefit of increasing the minimum Hamming distance. Numerical results validate the analysis and demonstrate that the proposed SCM outperforms the conventional spatial modulation techniques in both channel capacity and system reliability.Comment: 30 pages, 17 figure

A Two-Ray Multipath Model for Frequency Diverse Array-Based Directional Modulation in MISOME Wiretap Channels

A two-ray multipath model for frequency diverse array (FDA)-based directional modulation (DM) is proposed in multi-input single-output multi-eavesdropper (MISOME) wiretap channels for the first time. The excitation factors of the FDA and the weighting coefficients of the inserted artificial noise (AN) are jointly designed in a way which imposes no impact on the desired receiver while simultaneously distorting the received signals of eavesdroppers. Secrecy rate is analyzed for the proposed two-ray multipath FDA-based DM model. Numerical simulations verify the capability of physical layer secure (PLS) transmissions of the proposed FDA-DM model in two-ray multipath MISOME wiretap channels.Comment: accepted by IEEE VTC2019-Fall, 5 pages, 6 figure

Coherent RAKE Receiver for CPM-Based Direct Sequence Spread Spectrum

Direct sequence spread spectrum (DSSS) using continuous phase modulation (CPM) inherits the techniques’ benefits, constant envelope, anti-interference, and spectral efficiency. To get diversity gains over a Rayleigh-fading multipath channel as in conventional direct sequence spread-spectrum binary phase shift keying (DSSS-BPSK) system, a new class of coherent RAKE receivers is proposed in this work. By introducing chip branch metric to the receiver scheme, despreading and data detection can be done meanwhile based on Maximum Likelihood Sequence Detection (MLSD). Compared to the conventional RAKE receiver which sums decision metrics symbol-by-symbol, the proposed DSSS-CPM RAKE receiver accumulates symbol branch metric increments over every phase state of multiple paths after chip phase synchronization. Numerical results show that DSSS-CPM using the synchronous despreading and demodulation algorithm has no performance loss compared to CPM system that employs MLSD algorithm under the same test conditions. Moreover, the proposed RAKE receiver outperforms conventional RAKE receiver and achieves a remarkable diversity gain of bit error rate (BER) under the Rayleigh-fading multipath channel

MiR–20a-5p promotes radio-resistance by targeting Rab27B in nasopharyngeal cancer cells

Additional file 3: Figure S2. The protein level of PARP and caspase3 detected by western in NCM, 5PM, NCA, 5PA, si-NC and si-Rab27B transfected CNE-2 and CNE-1 cells respectively

Antihypertensive treatment among inpatients with hypertension at Anhui Provincial Hospital in China: a cross-sectional study

The aim of this study was to assess the prescribing pattern of antihypertensive treatment among inpatients with uncomplicated and complicated hypertension at Anhui Provincial Hospital (First Class Public Hospital) in the central region of China in accordance with the recommendations of current international guidelines. A retrospective cross-sectional study was performed from 1 January to 31 December, 2009. A total of 2010 hypertensive inpatients were included. Among 683 inpatients receiving monotherapy, calcium channel blockers (CCBs) were the most frequently drugs used in uncomplicated hypertensive patients (57.41 %) and those with stroke (61.73 %). Beta-blockers (BBs) (27.90 %) and angiotensin-converting enzyme inhibitors (ACEIs, 26.17 %) were the preferred agents in hypertensive patients with coronary heart disease (CHD). Among 1327 inpatients with combination therapy, two-drug regimen was the most popular, except for the hypertensive patients with stroke. The pattern of antihypertensive utilization appears to be partly in accordance with the recommendations of international guidelines. There was a tendency to use CCBs in hypertensive patients with stroke, whereas BBs and ACEI were the most prescribed in those with CHD.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Prior knowledge auxiliary for few-shot pest detection in the wild

One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost-effective, and responsive. However, existing deep-learning-based methods can detect only over a dozen common types of bulk agricultural pests in structured environments. Also, such methods generally require large-scale well-labeled pest data sets for their base-class training and novel-class fine-tuning, and these significantly hinder the further promotion of deep convolutional neural network approaches in pest detection for economic crops, forestry, and emergent invasive pests. In this paper, a few-shot pest detection network is introduced to detect rarely collected pest species in natural scenarios. Firstly, a prior-knowledge auxiliary architecture for few-shot pest detection in the wild is presented. Secondly, a hierarchical few-shot pest detection data set has been built in the wild in China over the past few years. Thirdly, a pest ontology relation module is proposed to combine insect taxonomy and inter-image similarity information. Several experiments are presented according to a standard few-shot detection protocol, and the presented model achieves comparable performance to several representative few-shot detection algorithms in terms of both mean average precision (mAP) and mean average recall (mAR). The results show the promising effectiveness of the proposed few-shot detection architecture