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Electrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton—Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.

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The human head pose estimation is an important and challenging problem, which provides the estimation of the head posture in 3D space from 2D image. It is a crucial technique for face recognition, gaze estimation, facial attribute recognition, etc. However, fast head pose estimation executing on the terminal for video edge computation has many challenges due to the computational complexity of the existing algorithms. In this paper, we propose a fast head pose estimation method based on a novel Rotation-Adaptive facial landmark detection powered by Local Binary Feature (RALBF). The landmark detection method is structured through fusing the prior of the rotation information provided by the Progressive Calibration Networks (PCN) face detector to a Local Binary Feature (LBF) based landmark detection method, which improves the robustness against head pose variations and simultaneously keep the computing efficiency. RALBF is trained and tested on 300W dataset and AFLW2000 dataset, it is verified by the accuracy evaluation that RALBF performs better than LBF. To improve the speed of head pose estimation, the 68, 51 and 10 landmarks distribution schemes are explored and compared on speed and accuracy. In the 10 landmarks scheme, the head pose estimation running once only takes 8.3ms on Intel i7-6700HQ CPU and takes 21.8ms on HiSilicon SoC Hi3519AV100, and the average error of Euler angle is 5.9973 • when the face yaw angle is between ±35 • on AFLW2000 3D dataset. Experiments demonstrate our approach performing well on real scenes. INDEX TERMS Head pose estimation, facial landmark detection, PnP problem, local binary features.

Electrical Impedance Tomography (EIT) is a detection imaging technology developed 30 years ago. When the conventional EIT measurement system is used, the electrode and the excitation measurement terminal are connected with a long wire, which is easily affected by external interference, and the measurement result is unstable. In this paper, we developed a flexible electrode device based on flexible electronics technology, which can be softly attached to the skin surface for real-time physiological monitoring. The flexible equipment includes an excitation measuring circuit and electrode, which eliminates the adverse effects of connecting long wires and improves the effectiveness of measuring signals. At the same time, the design also uses flexible electronic technology to make the system structure achieve ultra-low modulus and high tensile strength so that the electronic equipment has soft mechanical properties. Experiments have shown that when the flexible electrode is deformed, its function is completely unaffected, the measurement results remain stable, and the static and fatigue performances are satisfactory. The flexible electrode has high system accuracy and good anti-interference.