Institute of Electronics

Based on the achievement of synthesis of ZnO nanowires in mass production, ZnO nanowires gas sensors were fabricated with microelectromechanical system technology and ethanol-sensing characteristics were investigated. The sensor exhibited high sensitivity and fast response to ethanol gas at a work temperature of 300 °C. Our results demonstrate the potential application of ZnO nanowires for fabricating highly sensitive gas sensors.

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

Nanogenerators capable of converting energy from mechanical sources to electricity with high effective efficiency using low-cost, nonsemiconducting, organic nanomaterials are attractive for many applications, including energy harvesters. In this work, near-field electrospinning is used to direct-write poly(vinylidene fluoride) (PVDF) nanofibers with in situ mechanical stretch and electrical poling characteristics to produce piezoelectric properties. Under mechanical stretching, nanogenerators have shown repeatable and consistent electrical outputs with energy conversion efficiency an order of magnitude higher than those made of PVDF thin films. The early onset of the nonlinear domain wall motions behavior has been identified as one mechanism responsible for the apparent high piezoelectricity in nanofibers, rendering them potentially advantageous for sensing and actuation applications.

Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This paper presents a novel multi-category rotation detector for small, cluttered and rotated objects, namely SCRDet. Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects. Meanwhile, the supervised pixel attention network and the channel attention network are jointly explored for small and cluttered object detection by suppressing the noise and highlighting the objects feature. For more accurate rotation estimation, the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the state-of-the-art performance of our detector. The code and models will be available at https://github.com/DetectionTeamUCAS.

Abstract Resistive switching (RS) is an interesting property shown by some materials systems that, especially during the last decade, has gained a lot of interest for the fabrication of electronic devices, with electronic nonvolatile memories being those that have received the most attention. The presence and quality of the RS phenomenon in a materials system can be studied using different prototype cells, performing different experiments, displaying different figures of merit, and developing different computational analyses. Therefore, the real usefulness and impact of the findings presented in each study for the RS technology will be also different. This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained. The idea is to help the scientific community to evaluate the real usefulness and impact of an RS study for the development of RS technology.

A novel autofocusing technique is developed for random translational motion compensation in inverse synthetic aperture radar (ISAR) imaging of objects. This technique is based on an entropy minimization principle and validated via a nonparametric estimation method. Images of a simulation and a real flying aircraft are used for illustration. Images of encouraging quality confirm the feasibility of autofocusing the radar images by just the requirement of minimizing the image entropy.

Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and photosynthetically active radiation (PAR). The first three products cover the years 1982–2012 (LAI) and 1981–2010 (albedo and emissivity) at 1–5 km and 8-day resolutions, and the last two radiation products span the period 2008–2010 at 5 km and 3-h resolutions. These products have been evaluated and validated, and the preliminary results indicate that they are of higher quality and accuracy than the existing products. In particular, the first three products have much longer time series, and are therefore highly suitable for various environmental studies. This paper outlines the algorithms, product characteristics, preliminary validation results, potential applications and some examples of initial analysis of these products.

In this letter, we propose a novel supervised change detection method based on a deep siamese convolutional network for optical aerial images. We train a siamese convolutional network using the weighted contrastive loss. The novelty of the method is that the siamese network is learned to extract features directly from the image pairs. Compared with hand-crafted features used by the conventional change detection method, the extracted features are more abstract and robust. Furthermore, because of the advantage of the weighted contrastive loss function, the features have a unique property: the feature vectors of the changed pixel pair are far away from each other, while the ones of the unchanged pixel pair are close. Therefore, we use the distance of the feature vectors to detect changes between the image pair. Simple threshold segmentation on the distance map can even obtain good performance. For improvement, we use a k-nearest neighbor approach to update the initial result. Experimental results show that the proposed method produces results comparable, even better, with the two state-of-the-art methods in terms of F-measure.

Ship detection has been playing a significant role in the field of remote sensing for a long time, but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection, and the redundancy of the detection region. In order to solve these problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ships in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving problems resulting from the narrow width of the ship. Compared with previous multiscale detectors such as Feature Pyramid Network (FPN), DFPN builds high-level semantic feature-maps for all scales by means of dense connections, through which feature propagation is enhanced and feature reuse is encouraged. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multiscale region of interest (ROI) Align for the purpose of maintaining the completeness of the semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has state-of-the-art performance.

Abstract Hierarchical assembly of hollow microstructures is of great scientific and practical value and remains a great challenge. This paper presents a facile and one‐pot synthesis of Cu 2 O microspheres with multilayered and porous shells, which were organized by nanocrystals. The time‐dependent experiments revealed a two‐step organization process, in which hollow microspheres of Cu 2 (OH) 3 NO 3 were formed first due to the Ostwald ripening and then reduced by glutamic acid, the resultant Cu 2 O nanocrystals were deposited on the hollow intermediate microspheres and organized into finally multishell structures. The special microstructures actually recorded the evolution process of materials morphologies and microstructures in space and time scales, implying an intermediate‐templating route, which is important for understanding and fabricating complex architectures. The Cu 2 O microspheres obtained were used to fabricate a gas sensor, which showed much higher sensitivity than solid Cu 2 O microspheres.

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.

Two novel channel-assignment strategies are proposed: the locally optimized dynamic assignment (LODA) strategy and the borrowing with directional channel-locking (BDCL) strategy. Their performance is compared with the fixed-assignment (FA) strategy (currently used on certain systems) and the borrowing with channel ordering (BCO) strategy (the strategy that has given the lowest blocking probability in previous research). Computer simulations on a 49-cell network for both uniform and nonuniform traffic showed that the average call-blocking probability of the BDCL strategy is always the lowest. The LODA performance is comparable with that of BCO under nonuniform traffic conditions but is inferior under uniform traffic conditions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A polyol process was developed to synthesize Co3O4 with controllable superstructures. By tuning the reaction conditions, the prepared Co3O4 were readily regulated in its morphologies, which could vary from nanosphere to two-dimensional (2D) nanoplates and 3D hierarchical structures, and finally to microspheres. The growth kinetics of such a process was also studied. The synthesized Co3O4 exhibited good sensitivity, remarkable selectivity, and high stability as an alcohol sensor material.

The exploration of efficient electrocatalysts for energy conversion is important for green energy development. Owing to their high surface areas and unusual electronic structure, 2D electrocatalysts have attracted increasing interest. Among them, non-van der Waals (non-vdW) 2D materials with numerous chemical bonds in all three dimensions and novel chemical and electronic properties beyond those of vdW 2D materials have been studied increasingly over the past decades. Herein, the progress of non-vdW 2D electrocatalysts is critically reviewed, with a special emphasis on electronic structure modulation. Strategies for heteroatom doping, vacancy engineering, pore creation, alloying, and heterostructure engineering are analyzed for tuning electronic structures and achieving intrinsically enhanced electrocatalytic performances. Lastly, a roadmap for the future development of non-vdW 2D electrocatalysts is provided from material, mechanism, and performance viewpoints.

Radar electronic countermeasure (ECM) and electronic counter-countermeasures (ECCM) form the principal conflicting pair in modern electronic warfare. The technical history and state of art of both the radar ECM and ECCM are surveyed and their development trends are predicted. The classification of ECCM techniques, compatibility of various ECCM devices in a radar system and effectiveness evaluation of radar ECM and ECCM systems are also discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

Abstract This paper introduces a differential vibrating beam MEMS accelerometer demonstrating excellent long-term stability for applications in gravimetry and seismology. The MEMS gravimeter module demonstrates an output Allan deviation of 9 μGal for a 1000 s integration time, a noise floor of 100 μGal/√Hz, and measurement over the full ±1 g dynamic range (1 g = 9.81 ms −2 ). The sensitivity of the device is demonstrated through the tracking of Earth tides and recording of ground motion corresponding to a number of teleseismic events over several months. These results demonstrate that vibrating beam MEMS accelerometers can be employed for measurements requiring high levels of stability and resolution with wider implications for precision measurement employing other resonant-output MEMS devices such as gyroscopes and magnetometers.

Although the scale-invariant feature transform (SIFT) algorithm has been successfully applied to both optical image registration and synthetic aperture radar (SAR) image registration, SIFT-like algorithms have failed to register high-resolution (HR) optical and SAR images due to large geometric differences and intensity differences. In this paper, to perform optical-to-SAR (OS) image registration, we proposed an advanced SIFT-like algorithm (OS-SIFT) that consists of three main modules: keypoint detection in two Harris scale spaces, orientation assignment and descriptor extraction, and keypoint matching. Considering the inherent properties of SAR images and optical images, the multiscale ratio of exponentially weighted averages and multiscale Sobel operators are used to calculate consistent gradients for the SAR images and optical images on the basis of which, as a result, two Harris scale spaces can be constructed. Keypoints are detected by finding the local maxima in the scale space followed by a localization refinement method based on the spatial relationship of the keypoints. Moreover, gradient location orientation histogram-like descriptors are extracted using multiple image patches to increase the distinctiveness. The experimental results on simulated images and several HR satellite images show that the proposed OS-SIFT algorithm gives a robust registration result for optical-to-SAR images and outperforms other state-of-the-art algorithms in terms of registration accuracy.

This article reviews the recent developments in microfluidic technologies for in vitro cancer diagnosis. We summarize the working principles and experimental results of key microfluidic platforms for cancer cell detection, characterization, and separation based on cell-affinity micro-chromatography, magnetic activated micro-sorting, and cellular biophysics (e.g., cell size and mechanical and electrical properties). We examine the advantages and limitations of each technique and discuss future research opportunities for improving device throughput and purity, and for enabling on-chip analysis of captured cancer cells.

Synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods of SAR ship detection are difficult to detect small scale ships and avoid the interference of inshore complex background. Deep learning detection methods have shown great performance on various object detection tasks recently but using deep learning methods for SAR ship detection does not show an excellent performance it should have. One of the important reasons is that there is no effective model to handle the detection of multiscale ships in multiresolution SAR images. Another important reason is it is difficult to handle multiscene SAR ship detection including offshore and inshore, especially it cannot effectively distinguish between inshore complex background and ships. In this paper, we propose a densely connected multiscale neural network based on faster-RCNN framework to solve multiscale and multiscene SAR ship detection. Instead of using a single feature map to generate proposals, we densely connect one feature map to every other feature maps from top to down and generate proposals from each fused feature map. In addition, we propose a training strategy to reduce the weight of easy examples in the loss function, so that the training process more focus on the hard examples to reduce false alarm. Experiments on expanded public SAR ship detection dataset, verify the proposed method can achieve an excellent performance on multiscale SAR ship detection in multiscene.