An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Core–shell NTC materials with low thermal constant and high resistivity for wide-temperature thermistor ceramics

Core–shell structures have been proposed to improve the electrical properties of negative-temperature coefficient (NTC) thermistor ceramics. In this work, Al₂O₃-modified Co_1.5Mn_1.2Ni_0.3O₄ NTC thermistor ceramics with adjustable electrical properties were prepared through citrate-chelation followed by conventional sintering. Co_1.5Mn_1.2Ni_0.3O₄ powder was coated with a thin Al₂O₃ shell layer to form a core–shell structure. Resistivity (ρ) increased rapidly with increasing thickness of the Al₂O₃ layer, and the thermal constant (B) varied moderately between 3706 and 3846 K. In particular, Co_1.5Mn_1.2Ni_0.3O₄@Al₂O₃ ceramic with 0.08 wt% Al₂O₃ showed the increase of ρ double, and the change in its B was less than 140 K. The Co_1.5Mn_1.2Ni_0.3O₄@Al₂O₃ NTC ceramics showed high stability, and their grain size was relatively uniform due to the protection offered by the shell. The aging coefficient of the ceramic was less than 0.2% after aging for 500 hours at 125°C. Taken together, the results indicate that as-prepared Co_1.5Mn_1.2Ni_0.3O₄@Al₂O₃ NTC ceramics with a core–shell structure may be promising candidates for application as wide-temperature NTC thermistor ceramics.     

超高清电视系统关键技术浅析

本文对国内外的电视技术发展现状进行了充分的研究和分析,并对超高清电视系统的相关图像技术参数进行了分析和介绍。     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

亲水作用色谱-串联质谱法测定动物源运动食品中3种氨基糖苷类抗生素的残留量

该研究建立了一种亲水交互作用色谱-串联质谱（HILIC-MS/MS）法测定动物源运动食品中潮霉素B、新霉素、安普霉素3种氨基糖苷类抗生素残留量的方法。结果表明，样品经Sielc Obelisc R柱分离，采用0.1%甲酸水溶液-乙腈梯度洗脱，可以实现3种目标物组分的分离。在此条件下，3种氨基糖苷类抗生素在5～500 ng/mL的质量浓度范围内线性关系良好，相关系数R2为0.999 5～0.999 9，检出限均为15 μg/kg，定量限均为50 μg/kg，保留时间的日间和日内相对标准偏差（RSD）分别为3.5%～7.9%和3.5%～4.1%，峰面积的日间和日内RSD分别为3.6%～7.4%和3.2%～3.9%，加标回收率为85.7%～93.6%，回收率试验结果的RSD为3.1%～5.2%。该方法可以满足动物源运动食品中3种氨基糖苷类抗生素的检测需求。     

Engineering grain boundary anisotropy to elucidate grain growth behavior in alumina

Current grain growth models have evolved to account for the relationship between grain boundary energy/mobility anisotropy and the five degrees of grain boundary character. However, the role of grain boundary networks on overall growth kinetics remains poorly understood. To experimentally investigate this problem, a highly textured Al₂O₃ was fabricated by colloidal casting in a strong magnetic field to engineer a unique spatial distribution of grain boundary character. Microstructural evolution was quantified and compared to an untextured sample. From this comparison, a prevalence of (0001)/(0001) terminated grain boundaries with anisotropic networks were identified in the textured sample. These boundaries and their networks were found to be driving grain growth at a faster rate than predicted by models. These findings will allow better modelling of grain growth in real systems by experimentally exploring the impact thereon of grain boundary plane anisotropy and relative energy/mobility differences between neighboring boundaries.     

On global Hessenberg based methods for solving Sylvester matrix equations

In the first part of this paper, we investigate the use of Hessenberg-based methods for solving the Sylvester matrix equation $AX+XB=C$. To achieve this goal, the Sylvester form of the global generalized Hessenberg process is presented. Using this process, different methods based on a Petrov–Galerkin or on a minimal norm condition are derived. In the second part, we focus on the SGl-CMRH method which is based on the Sylvester form of the Hessenberg process with pivoting strategy combined with a minimal norm condition. In order to accelerate the SGl-CMRH method, a preconditioned framework of this method is also considered. It includes both fixed and flexible variants of the SGl-CMRH method. Moreover, the connection between the flexible preconditioned SGl-CMRH method and the fixed one is studied and some upper bounds for the residual norm are obtained. In particular, application of the obtained theoretical results is investigated for the special case of solving linear systems of equations with several right-hand sides. Finally, some numerical experiments are given in order to evaluate the effectiveness of the proposed methods.     

Simultaneously enhancing mechanical and microwave absorption properties of Cf/SiC composites via SiC nanowires additions

C_f/SiC composite is an excellent structural and functional material, silicon carbide nanowires (SiCnws) are not only a toughening material but also a great application in the field of microwave absorption. In this study, SiCnws are grown on the surface of carbon fiber (C_f) by polymer impregnation and pyrolysis, and the SiC matrix was prepared by chemical vapor osmosis method. The SiCnws are introduced to enhance the mechanical and microwave absorption properties simultaneously. After 3 impregnations, the flexural strength of the composite was 107.35 ± 10 MPa. When the thickness is 1.86 mm, the minimum reflection loss value is ?41.08 dB, and the effective absorption bandwidth (RL ≤ ?10 dB) is 3.86 GHz. Furthermore, the microwave absorption mechanism of the material is discussed. This work provides a new method to prepare lightweight, stable and high-performance microwave absorption materials, and these materials are expected to be used in high temperature environments.     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.