High-resolution and large-area laser interference nanomanufacturing technology

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyThe thesis systematically investigates the laser interference nanomanufacturing technology taking into account its advantages and abilities to realise various potential applications. The latest progresses have addressed the major issues hampering the cross-scale developments of structural applications, such as cost-ineffective fabrication, limited area, low efficiency and challenging integration. The studies carried out on high-resolution and large-area laser interference nanomanufacturing technology will complement the exploration of modern optical devices and extraordinary functional applications. With respect to classical interference theory and relevant references, there is still a lack of studies providing insight into the effects of polarisation on the multi-beam interference while it is found that the polarisation vector plays a key role in the formation, period and contrast of interfering patterns. Herein, the theory of multi-beam interference is developed through the integration of the polarisation vector and electric field vector. It is worth pointing out that based on the detailed analysis of the four-beam interference with the special polarisation modes, it is demonstrated that the modulation phenomenon in four-beam laser interference is the result of the misalignment of incident angles or unequal incident angles only in the case of the TE-TE-TM-TM mode. In the experiments, a straightforward method of generating various well-defined structures on material surfaces is proposed using the nanosecond laser interference system. The experimental results of two-, three- and four-beam interference show a good correspondence to the theoretical analyses and simulations. Artificial bio-structures are fabricated using the four-beam interference method with the TE-TE-TE-TE polarisation mode and the fabricated microcone structures exhibit excellent properties with both a high contact angle (CA=156.3°) and low omnidirectional reflectance (5.9-15.4%). In order to fabricate high-resolution structures, the 266nm nanosecond laser interference system is employed to treat the organic and metal-film materials. Nanograting structures with feature sizes of sub-100nm width and 2nm height are fabricated on the organic material surface. An attempt is successfully conduced to produce the nanoelectrode arrays by using laser interference lithography and chemical deposition. Finally, the advantages of the developed laser interference technology and contributions of the research are summarised, and recommendations of future work are given

Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio

A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets

In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers. This paper proposes a cost-sensitive ensemble method based on cost-sensitive support vector machine (SVM), and query-by-committee (QBC) to solve imbalanced data classification. The proposed method first divides the majority-class dataset into several subdatasets according to the proportion of imbalanced samples and trains subclassifiers using AdaBoost method. Then, the proposed method generates candidate training samples by QBC active learning method and uses cost-sensitive SVM to learn the training samples. By using 5 class-imbalanced datasets, experimental results show that the proposed method has higher area under ROC curve (AUC), F-measure, and G-mean than many existing class-imbalanced learning methods

Image Denoising Based on Artificial Bee Colony and BP Neural Network

Image is often subject to noise pollution during the process of collection, acquisition and transmission, noise is a major factor affecting the image quality, which has greatly impeded people from extracting information from the image. The purpose of image denoising is to restore the original image without noise from the noise image, and at the same time maintain the detailed information of the image as much as possible. This paper, by combining artificial bee colony algorithm and BP neural network, proposes the image denoising method based on artificial bee colony and BP neural network (ABC-BPNN), ABC-BPNN adopts the “double circulation” structure during the training process, after specifying the expected convergence speed and precision, it can adjust the rules according to the structure, automatically adjusts the number of neurons, while the weight of the neurons and relevant parameters are determined through bee colony optimization. The simulation result shows that the algorithm proposed in this paper can maintain the image edges and other important features while removing noise, so as to obtain better denoising effect