Biblio

Ferroelectric capacitor memory devices with carbon-free Hf0.5Zr0.5O2 (HZO) ferroelectric films are fabricated and characterized. The HZO ferroelectric films are deposited by ALD at temperatures from 225 to 300°C, with HfCl4 and ZrCl4 as the precursors. Residual chlorine from the precursors is measured and studied systematically with various process temperatures. 10nm HZO films with optimal ALD growth temperature at 275°C exhibit remanent polarization of 25µC/cm2 and cycle endurance of 5×1011. Results will be compared with those from HZO films deposited with carbon containing metal-organic precursors.

The network security problem brought by the cloud computing has become an important issue to be dealt with in information construction. Since anomaly detection and attack detection in cloud environment need to find the vulnerability through the reverse analysis of data flow, it is of great significance to carry out the reverse analysis of unknown network protocol in the security application of cloud environment. To solve this problem, an improved mining method on bitstream protocol association rules with unknown type and format is proposed. The method combines the location information of the protocol framework to make the frequent extraction process more concise and accurate. In addition, for the frame separation problem of unknown protocol, we design a hierarchical clustering algorithm based on Jaccard distance and a frame field delimitation method based on the proximity of information entropy between bytes. The experimental results show that this technology can correctly resolve the protocol format and realize the purpose of anomaly detection in cloud computing, and ensure the security of cloud services.

Machine learning can be misled by adversarial examples, which is formed by making small changes to the original data. Nowadays, there are kinds of methods to produce adversarial examples. However, they can not apply non-differentiable models, reduce the amount of calculations, and shorten the sample generation time at the same time. In this paper, we propose a new black box attack generating adversarial examples based on reinforcement learning. By using deep Q-learning network, we can train the substitute model and generate adversarial examples at the same time. Experimental results show that this method only needs 7.7ms to produce an adversarial example, which solves the problems of low efficiency, large amount of calculation and inapplicable to non-differentiable model.

In this work, robust readout of the data (232 English characters) stored in high-security structural colour QR codes, was achieved by using multiple image processing techniques, specifically, histogram equalization and decorrelation stretching. The decoded structural colour QR codes are generic diffractive RGB-pixelated periodic nanocones selectively activated by laser exposure to obtain the particular design of interest. The samples were imaged according to the criteria determined by the diffraction grating equation for the lighting and viewing angles given the red, green, and blue periodicities of the grating. However, illumination variations all through the samples, cross-module and cross-channel interference effects result in acquiring images with dissimilar lighting conditions which cannot be directly retrieved by the decoding script and need significant preprocessing. According to the intensity plots, even if the intensity values are very close (above 200) at some typical regions of the images with different lighting conditions, their inconsistencies (below 100) at the pixels of one representative region may lead to the requirement for using different methods for recovering the data from all red, green, and blue channels. In many cases, a successful data readout could be achieved by downscaling the images to 300-pixel dimensions (along with bilinear interpolation resampling), histogram equalization (HE), linear spatial low-pass mean filtering, and gamma function, each used either independently or with other complementary processes. The majority of images, however, could be fully decoded using decorrelation stretching (DS) either as a standalone or combinational process for obtaining a more distinctive colour definition.

In order to improve the limitation of single-mode biometric identification technology, a bimodal biometric verification system based on deep learning is proposed in this paper. A modified CNN architecture is used to generate better facial feature for bimodal fusion. The obtained facial feature and acoustic feature extracted by the acoustic feature extraction model are fused together to form the fusion feature on feature layer level. The fusion feature obtained by this method are used to train a neural network of identifying the target person who have these corresponding features. Experimental results demonstrate the superiority and high performance of our bimodal biometric in comparison with single-mode biometrics for identity authentication, which are tested on a bimodal database consists of data coherent from TED-LIUM and CASIA-WebFace. Compared with using facial feature or acoustic feature alone, the classification accuracy of fusion feature obtained by our method is increased obviously.