Influence of roughness on the detection of mechanical characteristics of low-k film by the surface acoustic waves

The surface acoustic wave （SAW） technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant （low-k） film by matching the theoretical dispersion curve with the experimental dispersion curve. In this paper, the influence of sample roughness on the precision of SAW mechanical detection is inves- tigated in detail. Random roughness values at the surface of low-k film and at the interface between this low-k film and the substrate are obtained by the Monte Carlo method. The dispersive characteristic of SAW on the layered structure with rough surface and rough interface is modeled by numerical simulation of finite element method. The Young＇s moduli of the Black DiamondTM samples with different roughness values are determined by SAWs in the experiment. The results show that the influence of sample roughness is very small when the root-mean-square （RMS） of roughness is smaller than 50 nm and correlation length is smaller than 20 μm. This study indicates that the SAW technique is reliable and precise in the nondestructive mechanical detection for low-k films.

TOPOLOGICAL STRUCTURE OF THE SINGULAR POINTS OF THE THIRD ORDER PHASE LOCKED LOOP EQUATIONS WITH THE CHARACTER OF DETECTED PHASE BEING g(φ)=(1+k)sinφ/(1+kcosφ)

In this paper, we study the topological structure of the singular points of the third order phase locked loop equations with the character of detected phase being g(?) =(1+k)sin?/1+kcos?.

CTSF:An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature

The past decade has seen the rapid development of text detection based on deep learning.However,current methods of Chinese character detection and recognition have proven to be poor.The accuracy of segmenting text boxes in natural scenes is not impressive.The reasons for this strait can be summarized into two points:the complexity of natural scenes and numerous types of Chinese characters.In response to these problems,we proposed a lightweight neural network architecture named CTSF.It consists of two modules,one is a text detection network that combines CTPN and the image feature extraction modules of PVANet,named CDSE.The other is a literacy network based on spatial pyramid pool and fusion of Chinese character skeleton features named SPPCNN-SF,so as to realize the text detection and recognition,respectively.Our model performs much better than the original model on ICDAR2011 and ICDAR2013(achieved 85%and 88%F-measures)and enhanced the processing speed in training phase.In addition,our method achieves extremely performance on three Chinese datasets,with accuracy of 95.12%,95.56%and 96.01%.

Droid Detector:Android Malware Characterization and Detection Using Deep Learning

Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.