Excimer laser annealing at 308nm in UV and semiconductor blue laser-diode annealing at 445nm were performed and compared in term of the crystallization depending on electrical properties of Si films. As a result for the thin Si films of 50nm thickness, both lasers are very effective to enlarge the grain size and to activate electrically the dopant atoms in the CVD Si film. Smooth Si surface can be obtained using blue-laser annealing of scanned CW mode. By improving the film quality of amorphous Si deposited by sputtering for subsequent crystallization, both laser annealing techniques are effective for LTPS applications not only on conventional glass but also on flexible sheet. By conducting the latter advanced annealing technique, small grain size as well as large grains can be controlled. As blue laser is effective to crystallize even rather thicker Si films of 1µm, high performance thin-film photo-sensor or photo-voltaic applications are also expected.

In this paper, a new finger vein recognition method based on Gabor wavelet and Local Binary Pattern (GLBP) is proposed. In the new scheme, Gabor wavelet magnitude and Local Binary Pattern operator are combined, so the new feature vector has excellent stability. We introduce Block-based Linear Discriminant Analysis (BLDA) to reduce the dimensionality of the GLBP feature vector and enhance its discriminability at the same time. The results of an experiment show that the proposed approach has excellent performance compared to other competitive approaches in current literatures.

Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised subspace learning in statistical pattern recognition. In this paper, a novel method of LDA based on a new L1-norm optimization technique and its variances are proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers, since it used the L2-norm to measure the between-class and within-class distances. In addition, the conventional LDA often suffers from the so-called small sample size (3S) problem since the number of samples is always smaller than the dimension of the feature space in many applications, such as face recognition. Based on L1-norm, the proposed methods have several advantages, first they are robust to outliers because they utilize the L1-norm, which is less sensitive to outliers. Second, they have no 3S problem. Third, they are invariant to rotations as well. The proposed methods are capable of reducing the influence of outliers substantially, resulting in a robust classification. Performance assessment in face application shows that the proposed approaches are more effectiveness to address outliers issue than traditional ones.

Opto-Thermal analysis of Semiconductor Blue-Multi-Laser-Diode Annealing (BLDA) for amorphous Si (a-Si) film is conducted by varying the irradiation power, the scanning velocity and the beam shape of blue-laser of 445 nm. Thermal profiles, maximum temperature of the a-Si film and the melting duration are evaluated. By comparing the simulated results with the experimental results, the excellent controllability of BLDA for arbitrary grain size can be explained consistently by the relation between irradiation time and melting duration. The results are useful to estimate poly-crystallized phase such as micro-polycrystalline Si, polycrystalline Si and anisotropic lateral growth of single-crystal-like Si.