The virtual reality based motion simulation of the guide wire and the catheter inside specific vascular structures can benefit a lot for the endovascular intervention. A fast and well-performed collision cancellation algorithm is proposed based on the geometric analysis and the angular propagation （AP）, and a 3-D real-time interactive system is developed for the motion simulation of the guide wire and the catheter inside the specific patient vascular. The guide wire or the catheter is modeled as the ＂multi-body＂ representation and properties are defined by its intrinsic characteristics. The motion of the guide wire or the catheter inside the vascular is guided by the collision detection and the collision cancellation algorithm. Finally, a relaxation procedure is used to achieve more realistic status. Experimental results show that the behavior of the guide wire or the catheter depends on the defined parameters. The real-time simulation can be achieved. The result shows that the simulation system is effective and promising.
In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
An algorithm for recovering the quaternion signals in both noiseless and noise contaminated scenarios by solving an L1-norm minimization problem is presented. The L1-norm minimization problem over the quaternion number field is solved by converting it to an equivalent second-order cone programming problem over the real number field, which can be readily solved by convex optimization solvers like SeDuMi. Numerical experiments are provided to illustrate the effectiveness of the proposed algorithm. In a noiseless scenario, the experimental results show that under some practically acceptable conditions, exact signal recovery can be achieved. With additive noise contamination in measurements, the experimental results show that the proposed algorithm is robust to noise. The proposed algorithm can be applied in compressed-sensing-based signal recovery in the quaternion domain.
