瑞典MALA探地雷达在管线探测中的应用

应用瑞典MALA探地雷达进行了地下管线探测的研究。根据地下管线的材质、周围介质及埋设深度的不同选择,不同频率的天线同时设置必要的工作参数,对不同类型的地下管线进行了探测,并对工程实例中典型的管线异常特征进行了分析。     

Large-area, high-speed patterning of carbon nanotubes using material-assisted excimer laser photoablation

Large-area patterning of carbon nanotubes (CNTs) using a nonlithographic process is demonstrated. Projection imaging with deep ultraviolet radiation from 248 nm KrF excimer laser and material-assisted photoablation were used to pattern the CNTs. A matrix of CNTs dissolved in a DMF solution was deposited on a silicon wafer by spin coating, followed by coating of photodefinable polyimide on the CNTs. The CNTs and the polyimide layer were simultaneously patterned by the excimer laser projection photoablation process. Even though CNTs cannot be directly photoablated by low-fluence excimer laser radiation, simultaneous patterning of the illuminated CNT-polyimide combination region occurred due to the physical force of dissociated fragments of polyimide layer. We have demonstrated clean, large-area patterning of CNTs on 100 mm diameter Si wafers. Additionally, this patterning process is economical and provides higher throughput compared with conventional methods.     

Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation

Estimation in the deformable template model is a big challenge in image analysis. The issue is to estimate an atlas of a population. This atlas contains a template and the corresponding geometrical variability of the observed shapes. The goal is to propose an accurate estimation algorithm with low computational cost and with theoretical guaranties of relevance. This becomes very demanding when dealing with high dimensional data, which is particularly the case of medical images. The use of an optimized Monte Carlo Markov Chain method for a stochastic Expectation Maximization algorithm, is proposed to estimate the model parameters by maximizing the likelihood. A new Anisotropic Metropolis Adjusted Langevin Algorithm is used as transition in the MCMC method. First it is proven that this new sampler leads to a geometrically uniformly ergodic Markov chain. Furthermore, it is proven also that under mild conditions, the estimated parameters converge almost surely and are asymptotically Gaussian distributed. The methodology developed is then tested on handwritten digits and some 2D and 3D medical images for the deformable model estimation. More widely, the proposed algorithm can be used for a large range of models in many fields of applications such as pharmacology or genetic. The technical proofs are detailed in an appendix.¹