Memristive Non-Volatile Memory Based on Graphene Materials

Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices

Screening and Expression Analysis of Key Regulator Genes Associated with (Z)-3-Hexenal and (E)-2-Hexenal Transformation during Manufacturing Process of Oolong Tea

In this study, the contents of (Z)-3-hexenal and (E)-2-hexenal during oolong tea processing were measured and four (3Z):(2E)-hexenal isomerase (HI) genes were selected based on transcriptomic data. Meanwhile, the correlation between the changes of (Z)-3-hexenal and (E)-2-hexenal contents and related gene expression was analyzed. The results indicated that during oolong tea processing, one of the two compounds fell, while the other rose. Mechanical damage caused by tossing increased and reduced the contents of (Z)-3-hexenal and (E)-2-hexenal, respectively. Subsequent spreading contributed to the transformation of (Z)-3-hexenal into (E)-2-hexenal, resulting in an increase in the content of (E)-2-hexenal. The four selected genes all responded to mechanical stress and water deficit stress. The constructed phylogenetic tree indicated that CsHI was closely related to many germin-like proteins in plants such as tea (Camellia sinensis) and carrot (Daucus carota). This study provides a reference for clarifying the formation and transformation mechanism of volatile substances during oolong tea processing and improving the quality of oolong tea

Effect of Annealing Temperature for Ni/AlOx/Pt RRAM Devices Fabricated with Solution-Based Dielectric

Resistive random access memory (RRAM) devices with Ni/AlOx/Pt-structure were manufactured by deposition of a solution-based aluminum oxide (AlOx) dielectric layer which was subsequently annealed at temperatures from 200 °C to 300 °C, in increments of 25 °C. The devices displayed typical bipolar resistive switching characteristics. Investigations were carried out on the effect of different annealing temperatures for associated RRAM devices to show that performance was correlated with changes of hydroxyl group concentration in the AlOx thin films. The annealing temperature of 250 °C was found to be optimal for the dielectric layer, exhibiting superior performance of the RRAM devices with the lowest operation voltage (104), the narrowest resistance distribution, the longest retention time (>104 s) and the most endurance cycles (>150)

Hierarchical Recognition System for Target Recognition from Sparse Representations

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants, L1-RNM, L2-RBM, and L1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM

Feature-transferable Pyramid Network for Cross-scale Object Detection in SAR Images

Multiscale object detection in Synthetic Aperture Radar (SAR) images can locate and recognize key objects in large-scene SAR images, and it is one of the key technologies in SAR image interpretation. However, for the simultaneous detection of SAR objects with large size differences, that is, cross-scale object detection, existing object detection methods are difficult to extract the features of cross-scale objects, and also difficult to realize cross-scale object simultaneous detection. In this study, we propose a multiscale object detection method based on the Feature-Transferable Pyramid Network (FTPN) for SAR images. In the feature extraction stage, the feature migration method is used to obtain an effective mosaic of the feature images of each layer and extract feature images with different scales. Simultaneously, the void convolution method is used to increase the receptive field of feature extraction and aid the network in extracting large object features. These steps can effectively preserve the features of objects of different sizes, to realize the simultaneous detection of cross-scale objects in SAR images. The experiments based on the GaoFen-3 SAR dataset, SAR Ship Detection Dataset (SSDD), and high-resolution SSDD-2.0 show that the proposed method can detect cross-scale objects, such as airports and ships in SAR images, and the mean Average Precision (mAP) can reach 96.5% on the existing dataset, which is 8.1% higher than that of the characteristic pyramid network algorithm. Moreover, the overall performance of the proposed method is better than that of the latest YOLOv4 and other object detection algorithms

Speed Measurement of the Moving Targets Using the Stepping Equivalent Range-Gate Method

In this paper, we proposed a stepping equivalent range-gate method (S-ERG method) to measure the speed and the distance of the moving target for range-gated imaging lidar. In this method, the speed is obtained by recording the time at which the moving target passes the front and back edges of the range gate, the distance information can also be obtained by the front and back edges of the range gate at the same time. To verify the feasibility of this method, a stationary target and a moving target with different speeds were measured by the S-ERG method. By using the S-ERG method, we not only obtained the distance information of the stationary target and the moving target at the front and back edges of the range gate, respectively, but also obtained the speed of the moving target. Compared to speeds measured by rotational displacement sensors, the speed measurement error of the S-ERG method is less than 5%, whether the target is far away or close to the range-gated lidar system, and this method is almost independent of the delay step time. The theoretical analysis and experimental results indicate range-gated imaging lidar using the S-ERG method has high practicality and wide applications

Adjacent Frame Difference with Dynamic Threshold Method in Underwater Flash Imaging LiDAR

During the underwater LiDAR imaging process, the images achieved by the conventional constant threshold adjacent frame difference (AFD) method normally loses the distance information of targets. This is mainly due to the Gaussian distribution of the laser light intensity field, which leads to the inhomogeneous intensity distribution in the frame from the target acquired by intensity charge-coupled devices (ICCD). In order to overcome this issue, the novel dynamic threshold adjacent frame difference (DTAFD) method was proposed in this paper. The DTAFD method modifies the intensity threshold following the pixel intensities in the different parts of the single frame intensity image acquired by ICCD. After the detailed theoretical demonstration of the DTAFD method, with the purpose of verifying its feasibility, the self-developed range-gated flash imaging LiDAR has been employed to construct the distance images of the rectangular and circular shaped targets at different distances. The distance between the rectangular target and the LiDAR system is 25.7 m, and the circular target is 70 cm further away from the rectangular target. The full distance information of these two targets is obtained by the DTAFD method with an effectively suppressing noise and the PSNR is increased from 6.95±0.0426 dB to 7.62±0.0264 dB. The experimental results indicate that the DTAFD method efficiently solves the AFD method’s drawback on the target information loss caused by the unequal optical field distribution, which makes it more suitable for the scenarios with uneven laser distribution such as the underwater imaging environment

An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach

The current status and effects of emergency drug shortages in China: Perceptions of emergency department physicians.

OBJECTIVES:The shortage of emergency drugs in China is severe. This study aimed to characterize emergency drug shortages in China and to measure their effects. METHODS:An online questionnaire based on a literature review was sent to emergency department physicians in Chinese secondary and tertiary hospitals from November 2016 to February 2017. The survey asked physicians questions about their experiences with emergency drug shortages. RESULTS:In total, 236 physicians from 29 provinces participated in the survey. According to their responses, 90.7% of the respondents experienced drug shortages during the last year. More than half of the physicians (65.7%) reported that drug shortages occurred at least once a month. Hospitals in the eastern and western regions of China had more emergency drugs in shortage than hospitals in central China, especially those with many inpatient beds (≥800). In addition, the shortage situation was more serious in secondary hospitals than in tertiary hospitals. More respondents agreed that original medicines, injections, essential medicines, medicines without alternative agents and cheap medicines were more susceptible to shortages than generics, oral medicines, nonessential medicines, medicines with alternative agents and expensive medicines, respectively. Most respondents thought that drug shortages always, often or sometimes affected patients [delayed therapy (62.6%), longer rescue and recovery times (58.9%) and higher costs (58.7%)] and physicians [inconvenience (81.0%), higher pressure (76.5%) and harm to patient-doctor relationships (72%)] and compromised hospital reputations (55.1%). CONCLUSIONS:The shortage of emergency drugs in China is serious, especially in secondary hospitals located in eastern and western China. Emergency drug shortages have significant effects on patients and physicians