Parallel imaging techniques can help solving problems of radiofrequency energy deposition and image inhomogeneity， reducing scan time， lowering motion artifacts， and accelerating data acquisition in ultra-high field Magnetic Resonance Imaging （MRI）. To enhance feature extraction ability to MRI complex-valued data and reduce wrap-around artifacts caused by under-sampling in parallel imaging， a Residual Complex convolution scan-specific Robust Artificial-neural-networks for K-space Interpolation （RCRAKI） was proposed. In the algorithm， the raw under-sampled MRI scan data was taken as input， and the advantages of both linear and nonlinear reconstruction methods were combined with a residual structure. In the residual connection part， convolution was used to create a linear reconstruction baseline， while multiple layers of complex convolution were utilized in the main path to compensate for baseline defects， ultimately reconstructing Magnetic Resonance （MR） images with fewer artifacts. Experiments were conducted on data acquired from a 7T ultra-high field MR device developed by the Institute of Energy of Hefei Comprehensive National Science Center， and RCRAKI was compared with residual scan-specific Robust Artificial-neural-networks for K-space Interpolation （rRAKI） under a sampling rate of 40 Automatic Calibration Signals （ACSs） and 8 speedup ratio for mouse imaging quality across different anatomical planes. Experimental results show that in sagittal plane， the proposed algorithm has the Normalized Root Mean Squared Error （NRMSE） decreased by 59.74%， the Structural SIMilarity （SSIM） increased by 0.45%， and the Peak Signal-to-Noise Ratio （PSNR） increased by 13.04%； in axial plane， the proposed algorithm has the NRMSE decreased by 7.97%， the SSIM improved slightly （by 0.005%）， and the PSNR increased by 1.09%； in coronal plane， the proposed algorithm has the NRMSE decreased by 35.03%， the PSNR increased by 5.60%， and the SSIM increased by 0.98%. It can be seen that RCRAKI performs well on all the different anatomical planes of MRI data， can reduce the influence of noise amplification at high speedup ratio， and reconstruct MR images with clearer details.

Chip layer assignment is a key step in analytical placement of 3D Integrated Circuits (ICs). Analytical placement could face the conversion from 3D continuous space in z-direction to several connected 2D chip layer spaces by layer assignment. However, layer assignment may destroy the previous optimal solution in 3D continuous space. To realize the transition from an optimal 3D placement to a legalized, layer-assigned placement smoothly, a layer assignment method was proposed by using the minimum cost flow, which protected solution space and inherited optimal wirelength at most. The layer assignment method was embedded in a multilevel non-linear placement of 3D ICs which minimized the weighted sum of total wirelength and Through Silicon Via (TSV) number subject to area density constraints. The proposed placement algorithm can achieve better wirelength results, TSV number and run time in comparison with the recent 3D placement methods.

It has been proved in many researches that there are some design flaws in IEEE 802.1X standard. In order to eliminate the Denial of Service (DoS) attack, replay attack, session hijack, Man-In-the-Middle (MIM) attack and other security threats, the protocol was analyzed in view of the state machines. It is pointed out that the origin of these problems is the inequality and incompleteness of state machines as well as the lack of integrity protection and source authenticity on messages. However, an improvement proposal called Dual-way Challenge Handshake and Logoff Authentication was proposed, and a formal analysis was done on it with an improved BAN logic. It is proved that the proposal can effectively resist the security threats mentioned above.