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Most reversible data hiding (RDH) algorithms for color images directly embed secret information in the luminance channel. As a result, the grayscale version of the color image is usually distorted. Since many color image processing algorithms work on the grayscale version, their performance on marked color images may get seriously impacted. Therefore, RD H with grayscale invariance (RDH-GI) is advanced to eliminate such shortcomings and has received great attention thereafter. However, their performance is still far from satisfactory. This paper presents a novel RDH-GI method by exploiting the pixel clustering technique to segment host pixels into different complexity levels. Data embedding tends to take place in less complex regions, and thus image distortion is reduced while maintaining grayscale invariance. Experiments prove the effectiveness of the proposed work.

Conventional 3D printing methods often require supporting structures to maintain the structure of the suspended parts in the manufacture of metamaterials, micronano arrays, micro-channels. The remaining uncured resin in the microstructure and the removal of the sacrificial layer material by post-processing are a key challenge for structural molding. Therefore, we propose a free-form-based direct writing 3D printing technology for manufacturing flexible microstructures without self-supporting materials. We proved that the 3D printed microstructure can withstand the bending moment it generates to prevent the collapse of the flexible microstructure. This method provides a new way for the manufacture of flexible microstructure devices such as soft robots, flexible sensing, biological tissues, micro-nano fluidics, and metamaterials.

A major problem in the detection of fine dust is that the calculation of the relationship between the number concentration and the mass concentration is too complicated and the particle distribution in each place is different. It is difficult to find a unified calculation method. Based on the above considerations, this topic proposes a neural-network-based atmospheric dust concentration data processing system design, which enables the neural network to learn autonomously to explore the relationship between the two concentrations in order to achieve the results of the mass concentration measurement device for the calculation of number concentrations. This system is mainly composed of data measurement module and data processing module. On the basis of completing the overall design, a data processing platform based on a neural network is built to achieve a targeted solution to specific problems. The experimental results show that the system can effectively analyze and process the measurement data, meet the original design function requirements, and verify the correctness of the system design program.

In the current failure analysis of rail vehicles, the aging of electrical components, the reduction of reliability and various direct and indirect failures caused by temperature rise account for a very high proportion. They have the characteristics of complex effects, hidden and changeable, unobvious initial phenomena, slow accumulation and development, which makes failure analysis and diagnosis very difficult. In order to meet the needs of in-depth research and systematic testing of the temperature rise characteristics and related effects of various electrical components used in vehicle systems, a set of low resistance circuit temperature rise test system is developed. This paper introduces the working principle, system composition and functions of the test system. The test system is technologically advanced, reliable, safe, easy to operate and flexible. It is of great significance for the temperature rise and temperature control of electrical components in the actual working environment and conditions of vehicles.

The temperature rise test of rail vehicle electrical connector terminal is the key to ensure the safe operation of rail vehicle. In this paper, a new test method of connector terminal temperature rise based on new test platform is proposed on the basis of summarizing the existing test methods. The factors affecting the accuracy of temperature rise test are discussed. Experimental results verify the effectiveness of the new method.

In recent years, the quantity of big scholarly data has a rapid growth, which brings new problems and challenges to the big scholarly data analysis. This paper employs the probability theory and correlation analysis methods to analyze the dynamics of publicly available research meta-data from IEEE official site. The experiment gives several visual evolution results of the correlations about journals and subjects, which provides some indications for future related research.

In prediction-error-expansion and pixel sorting based reversible data hiding (RDH) works, only part of the neighboring pixels are used in pixel prediction and local complexity calculation. To fully exploit the local correlations, we propose a novel efficient RDH using filter selection. Firstly, we divide the cover image into multiple non-overlapping layers following the concept of image down-sampling. Then data is embedded in a layer-wise manner. For data embedding, the prediction-error and the local complexity are estimated using the idea of block-based image filtering, the related techniques of which have been widely studied in the area of image processing. Experimental results validate the applicability of the proposed work in the perspectives of both embedding capacity and image qualitv.

When the missile is rolling, the roll angular velocity of missile will be coupled with the stable platform of seeker, causing the output of the seeker gyro has an additional signal related to the roll angular velocity of missile based on the real LOS (line of sight) angular velocity. Based on the principle of LOS synthesis, the coupling mechanism between the LOS angular velocity of the two frame seeker and the roll angular velocity of the missile is analyzed. In order to eliminate the coupling effect, the Kalman filter technology is used to extract the LOS angular velocity, and the Kalman filter equation is established, which is verified by simulation. The results show that the missile roll movement affects the gyro output, and the extraction of LOS angular velocity by Kalman filter technology can meet the requirements of guidance information accuracy.

Aiming at the defects of chicken swarm optimization algorithm, such as easy to fall into local optimal, premature convergence and slow convergence, a chicken swarm optimization algorithm based on quantum behavior is proposed in this paper. A quantized potential well model is established based on the individual information of chicken swarm. According to the existing individual extremum and global extremum obtained by the original updating formula, Monte Carlo random sampling is adopted to complete the updating of individual extremum, and the search is conducted at a parallel Angle near individual extremum and global extremum, which improves the local search performance of the algorithm. At the same time, the convergence of quantum-behavior chicken swarm optimization algorithm is discussed in this paper, and QCSO is proved to be a globally convergent optimization algorithm. The optimization capability of QCSO is tested by using basic test function, and the results show that the optimization performance of this algorithm is greatly improved compared with the original algorithm.

In recent years, clock speed and power density have increased rapidly, improving the performance of mobile devices, and thus, thermal management issues for local hot spots of chips are increasing. Dynamic thermal management (DTM) through frequency modulation and passive cooling components such as heat pipes is currently being used as a cooling strategy for mobile devices. However, DTM has an inverse relationship with chip performance, and passive cooler occupy too much space for local hot spot cooling. From the point of view of local hot spot cooling, a solid-state thermoelectric cooler (TEC) has the advantage of enabling on/off control and site-specific cooling. However, most of the research on TECs focuses on the material or interfacial properties of TECs, not the design rule or structure of TECs for practical applications. This study proposes an active cooling device using a TEC with a unique planar-radial structure that can selectively cool only locally generated hot spots and minimize form factor designs with 2D structures, making this cooling device highly applicable to mobile devices. A 5-pair TEC using the optimized TE leg thickness (n-Bi2Te3 5.05 μm and p-Sb2Te3 5.45 μm) and radial structure enhanced the Peltier effect and confirmed the maximum junction temperature difference of 2.4 °C. By adopting a hybrid cooling system combining active and passive coolers in a mobile device, we obtained an effective cooling area ratio of 4 and a cooling effect (~3.87 °C).

Due to the light interference, mobile terminal iris recognition on the visible light cannot accurately extract texture information that used for iris recognition. In order to solve the problem, image quality evaluation is used to ensure that the iris image quality meets the recognition needs, find an excellent and complete iris structure, and extract the iris texture information to complete the matching. In this paper, a multi-level quality evaluation cascade framework is designed. This paper preprocesses the iris image, and then uses the Tenegrad evaluation function to complete the first level image quality evaluation, and then uses the wavelet transform and BP neural network to accomplish the second level image quality evaluation. To test the effectiveness of the algorithm, 900 iris images in the UBIRIS.v2 iris gallery are used for testing. The accuracy of quality evaluation is 96.1%. The result shows that the method can correctly evaluate the iris image quality, exclude the images that cannot be used for iris recognition, and effectively improve iris recognition on the mobile terminal.