Face recognition using Gabor-based direct linear discriminant analysis and support vector machine

This paper presents a novel and uniform framework for face recognition. This framework is based on a combination of Gabor wavelets, direct linear discriminant analysis (DLDA) and support vector machine (SVM). First, feature vectors are extracted from raw face images using Gabor wavelets. These Gabor-based features are robust against local distortions caused by the variance of illumination, expression and pose. Next, the extracted feature vectors are projected to a low-dimensional subspace using DLDA technique. The Gabor-based DLDA feature vectors are then applied to SVM classifier. A new kernel function for SVM called hyperhemispherically normalized polynomial (HNP) is also proposed in this paper and its validity on the improvement of classification accuracy is theoretically proved and experimentally tested for face recognition. The proposed algorithm was evaluated using the FERET database. Experimental results show that the proposed face recognition system outperforms other related approaches in terms of recognition rate.     

Scheduling deteriorating jobs on a single machine with release times

This paper investigates a single-machine deteriorating job scheduling problem with job release times where its objective is to minimize the makespan. The problem is known to be NP-hard. Therefore, a branch-and-bound algorithm incorporating with several dominance properties and lower bounds is proposed to derive the optimal solution for the problem. In addition, easy-implemented heuristic algorithms are also provided to obtain the near-optimal solution. The computational experiments indicate that the branch-and-bound algorithm can solve most of the medium-job-sized problems within a reasonable time, and the heuristic is quite accurate with an average error percentage of less than 0.3%.     

Iris recognition using combined support vector machine and Hamming distance approach

Iris based authentication system is essentially a pattern recognition technique that makes use of iris patterns, which are statistically unique, for the purpose of personal identification. In this study, a novel method for recognition of iris patterns is considered by using a combination of support vector machine and Hamming distance. The zigzag collarette area of the iris is selected for iris feature extraction because it captures the most important areas of iris complex pattern and higher recognition rate is achieved. The proposed approach also used parabola detection and trimmed median filter for the purpose of eyelid and eyelash detection & removal, respectively. The proposed method is computationally effective as well as reliable with a recognition rate of 99.91% and 99.88% on CASIA and Chek image database respectively.     

Cell cycle phase detection with cell deformation analysis

Medical experts and pathologists studies on cells to examine the effects on cell activities due to external factors. In order to observe the live cell activity over a long period of time, new imaging techniques such as differential interference contrast microscopy have been deployed to overcome the shortcomings of transparent cells under the conventional microscopy. A normal healthy cell regulates the mitosis process (cell splitting) to prevent over growing. Abnormal cell cycle pattern may provide an important clue to disease or mutation, for example, cancerous cells expand uncontrollably with irregular mitosis process. In order to analyze the cell patterns, it is important to identify different stages in a cell cycle, and such process often requires human interactions. In this paper, proposed an automatic cell cycle phase detection process by analyzing the variations in the shapes and sizes of cells in the life cycle. We first adopted the geodesic active contour segmentation algorithm to extract the cells and by adjusting the initial contour for the GAC process, we can speed up the segmentation process almost half of the original processing time. By analyzing the deformation features such as the cell area and strain of the cell contours deforming over long period of time, we established the behaviors and the states of cells. Our results have demonstrated that the strain analysis of the cell deformation can detect the interphase and mitosis phase in cell cycle, and by analyzing the changes in cell area during mitosis phase, cytokinesis can be identified.     

有源电力滤波器的研究现状与展望

有源电力滤波器(Active Power Filter, APF)是一种具备动态谐波抑制和无功补偿功能的新型电力电子装置，其性能优劣与所采用的拓扑结构、谐波电流检测技术和电流追踪控制方法等密切相关。为了推广在高压大容量下APF的控制技术，拓宽其应用范围，综述了国内外APF研究现状，分类整理了APF拓扑结构。归纳总结了APF的谐波检测方法和电流跟踪控制技术，综合对比了各种方法的优缺点，并从多个角度阐述了当前所存在的问题。最后展望了APF相关技术的研究方向。     

一种新型的三电平VIENNA整流器调制策略

对三相三线三电平VIENNA整流器进行研究,构建了整流器电压和电流双环控制环路。针对该整流器降低开关损耗和中点电位控制问题,在分析冗余小矢量对中点电位影响的基础上,首先提出了2种新的对中点电位影响相反的五段式断续调制序列,在此基础上又提出了旨在降低开关损耗的集中式大电流不动作调制原则并引入一角度调节量对不动作区域进行拓展,实现了兼顾中点电位平衡控制的同时最大限度地降低开关损耗。仿真和实验进一步验证了所提调制策略的有效性。