Controlling thickness to tune performance of high-mobility transparent conducting films deposited from Ga-doped ZnO ceramic target

Structure modification has been found to tune significantly the transparent-conducting performance, especially mobility and conductivity of hydrogenated Ga-doped ZnO (HGZO) films. The strong correlation between film thickness and mobility of the films is revealed. The mobility increases quickly with increasing the thickness from 350 to 900 nm, and then tends to be saturated at further thicknesses. A higher mobility than 50 cm²/Vs can be achieved, which is an extra-high value for polycrystalline ZnO films deposited by using the sputtering technique. The thickness-dependent mobility originates from scatterings on grain boundaries and dislocation-induced defects controlled by thin-film growth. Based on the Volmer-Weber model, an expansion model is built up to describe the thickness-dependent crystal growth of the HGZO films, especially at the thick films. As a result, the 800 nm-thick HGZO film obtains the highest performance with high mobility of 51.5 cm²/Vs, low resistivity of 5.3 × 10^?4 Ωcm, and good transmittance of 83.3 %.     

Synthesis and Application of Novel Nano Fe-BTC/GO Composites as Highly Efficient Photocatalysts in the Dye Degradation

Topics in Catalysis - Nano Fe-BTC/graphene oxide (GO) composites were successfully synthesized by hydrothermal treatment with a microwave-assisted method. Samples were characterized by X-ray...     

Integrating local action elements for action analysis

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial–temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

尾矿和Na_2O对包钢稀选尾矿微晶玻璃的影响

利用熔融浇注法对包钢稀选尾矿微晶玻璃在生产过程中的流动性进行了研究,利用DSC,XRD,SEM等手段研究了尾矿和Na2O含量对微晶玻璃的析晶特性及其他物理性能的影响.结果表明,当其它成分不变时,随着尾矿含量的增加,流动性增强;当其它成分不变时,随着Na2O含量的增加,流动性增强;在本实验条件下,尾矿加入量较佳范围为40%~50%;Na2O较佳范围为3%~6%.     

Micro cam system driven by electrostatic comb-drive actuators based on SOI-MEMS technology

Microsystem Technologies - This paper reports a design and fabrication process of a micro cam system (MCS) with a flat-faced translating follower. The cam rim with cover diameter of 2.4 mm...     

Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.