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Stutzerimonas kunmingensis 7850S is a piezotolerant bacterium isolated from the sediment of the Mariana trench. Here, we described the complete genome of strain 7850S, which contains a single circular chromosome of 4,775,870 base pairs with 62.66% G + C content, and harbors 4494 protein-coding genes, 65 transfer RNA genes, and 12 ribosomal RNA genes. The experimental results showed that strain 7850S could grow under hydrostatic pressure of 0.1-70 MPa. Genomic analyses led to identifications of numbers of high hydrostatic pressure-associated genes, such as the ones associated with unsaturated fatty acids, betaine, and ectoine. A complete set of denitrification genes and some heavy metal detoxification genes were also found in this strain. The presence of these genes suggests metabolic characteristics for adaption to hadal environments and provides insights to further understand adaption strategies and ecological roles of Stutzerimonas in hadal environments.

Among the security issues of embedded systems, electromagnetic (EM) information leakage problem is difficult to detect and prevent. When the embedded system is operating, different commands will emit different EM signals. In this paper, we propose a NN architecture to analyze the EM leakage and effectively improve the accuracy. Comparing with a previous research, the accuracy is greatly improved from 38% up to 66%.

Most of the IC-electromagnetic interference researches get information in the frequency domain. However, in the electromagnetic interference (EMI) leakage research field, the measurement of the time domain is crucial. We have already proposed a single point time-domain EMI signal for information leakage analysis, but a single point measurement is incompetent. This paper proposes an automated measurement platform that can measure, visualize, and plot a time-domain two-dimensional (muti-point) EMI data on Microchip dsPIC33EP512MC202. This proposed automation process can be conveniently extended and applied to a variety of different devices under test.

Combined with the characteristics of forest image, the paper puts forward a new forest fire image segmentation method based on YCBCR and local fractal dimension. First of all, convert the image in RGB color space into YCBCR. Secondly, original triangle prism model algorithm which calculates the square of block matrix is replaced with four pyramid model. This algorithm is easy to be realized, reduce the amount of calculation model, and speed up the image segmentation speed. Segmentation effect significantly is improved, and the simulation experimental results show that this algorithm can be very good for the segmentation image of the flame extracted from complex background. It has obvious, accurate and fast effect and provides a good foundation for the future image analysis and recognition processing.