A comparative analysis of neural and statistical classifiers for dimensionality reduction-based face recognition systems.

Human face recognition has received a wide range of attention since 1990s. Recent approaches focus on a combination of dimensionality reduction-based feature extraction algorithms and various types of classifiers. This thesis provides an in depth comparative analysis of neural and statistical classifiers by combining them with existing dimensionality reduction-based algorithms. A set of unified face recognition systems were established for evaluating alternate combinations in terms of recognition performance, processing time, and conditions to achieve certain performance levels. A preprocessing system and four dimensionality reduction-based methods based on Principal Component Analysis (PCA), Two-dimensional PCA, Fisher\u27s Linear Discriminant and Laplacianfaces were utilized and implemented. Classification was achieved by using various types of classifiers including Euclidean Distance, MLP neural network, K-nearest-neighborhood classifier and Fuzzy K-Nearest Neighbor classifier. The statistical model is relatively simple and requires less computation complexity and storage. Experimental results were shown after the algorithms were tested on two databases of known individuals, Yale and AR database. After comparing these algorithms in every aspect, the results of the simulations showed that considering recognition rates, generalization ability, classification performance, the power of noise immunity and processing time, the best results were obtained with the Laplacianfaces, using either Fuzzy K-NN.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .X86. Source: Masters Abstracts International, Volume: 45-01, page: 0428. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. © 2004 IEEE.In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the sec ond filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (NSF) under Award EEC-9986821, by an ARO MURI on Demining under Grant DAAG55-97-1-0013, and by the NSF under Award 0208548

Chinese Foreign Direct Investment and Chinese Enterprises\u27 International Strategy

修士論文要

Matching dependence-related queries in the system dependence graph.

In software maintenance and evolution, it is common that develop-ers want to apply a change to a number of similar places. Due to the size and complexity of the code base, it is challenging for develop-ers to locate all the places that need the change. A main challenge in locating the places that need the change is that, these places share certain common dependence conditions but existing code searching techniques can hardly handle dependence relations satisfactorily. In this paper, we propose a technique that enables developers to make queries involving dependence conditions and textual condi-tions on the system dependence graph of the program. We carried out an empirical evaluation on four searching tasks taken from the development history of two real-world projects. The results of our evaluation indicate that, compared with code-clone detection, our technique is able to locate many required code elements that code-clone detection cannot locate, and compared with text search, our technique is able to effectively reduce false positives without losing any required code elements

Baicalin Downregulates RLRs Signaling Pathway to Control Influenza A Virus Infection and Improve the Prognosis

The objective of this study is to investigate the effects of baicalin on controlling the pulmonary infection and improving the prognosis in influenza A virus (IAV) infection. PCR and western blot were used to measure the changes of some key factors in RLRs signaling pathway. MSD electrochemiluminescence was used to measure the expression of pulmonary inflammatory cytokines including IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70, and KC/GRO. Flow cytometry was used to detect the proportion of Th1, Th2, Th17, and Treg. The results showed that IAV infection led to low body weight and high viral load and high expression of RIG-I, IRF3, IRF7, and NF-κB mRNA, as well as RIG-I and NF-κB p65 protein. However, baicalin reduced the rate of body weight loss, inhibited virus replication, and downregulated the key factors of the RLRs signaling pathway. Besides, baicalin reduced the high expression inflammatory cytokines in lung and decreased the ratios of Th1/Th2 and Th17/Treg to arouse a brief but not overviolent inflammatory response. Therefore, baicalin activated a balanced host inflammatory response to limit immunopathologic injury, which was helpful to the improvement of clinical and survival outcomes

Phenotypic analysis of images of zebrafish treated with Alzheimer's γ-secretase inhibitors

<p>Abstract</p> <p>Background</p> <p>Several γ-secretase inhibitors (GSI) are in clinical trials for the treatment of Alzheimer's disease (AD). This enzyme mediates the proteolytic cleavage of amyloid precursor protein (APP) to generate amyloid β protein, Aβ, the pathogenic protein in AD. The γ-secretase also cleaves Notch to generate Notch Intracellular domain (NICD), the signaling molecule that is implicated in tumorigenesis.</p> <p>Results</p> <p>We have developed a method to examine live zebrafish that were each treated with γ-secretase inhibitors (GSI), DAPT {N- [N-(3,5-Difluorophenacetyl-L-alanyl)]-S-phenylglycine <it>t</it>-Butyl Ester}, Gleevec, or fragments of Gleevec. These compounds were first tested in a cell-based assay and the effective concentrations of these compounds that blocked Aβ generation were quantitated. The mortality of zebrafish, as a result of exposure to different doses of compound, was assessed, and any apoptotic processes were examined by TUNEL staining. We then used conventional and automatic microscopes to acquire images of zebrafish and applied algorithms to automate image composition and processing. Zebrafish were treated in 96- or 384-well plates, and the phenotypes were analyzed at 2, 3 and 5 days post fertilization (dpf). We identified that AD95, a fragment of Gleevec, effectively blocks Aβ production and causes specific phenotypes that were different from those treated with DAPT. Finally, we validated the specificity of two Notch phenotypes (pigmentation and the curvature of tail/trunk) induced by DAPT in a dose-dependent manner. These phenotypes were examined in embryos treated with GSIs or AD95 at increasing concentrations. The expression levels of Notch target gene <it>her6 </it>were also measured by <it>in situ </it>hybridization and the co-relationship between the levels of Notch inhibition by DAPT and AD95 and the severity of phenotypes were determined.</p> <p>Conclusion</p> <p>The results reported here of the effects on zebrafish suggest that this newly developed method may be used to screen novel GSIs and other leads for a variety of therapeutic indications.</p

Bioluminescence imaging reveals inhibition of tumor cell proliferation by Alzheimer's amyloid β protein

Background: Cancer and Alzheimer's disease (AD) are two seemingly distinct diseases and rarely occur simultaneously in patients. To explore molecular determinants differentiating pathogenic routes towards AD or cancer, we investigate the role of amyloid β protein (Aβ) on multiple tumor cell lines that are stably expressing luciferase (human glioblastoma U87; human breast adenocarcinoma MDA-MB231; and mouse melanoma B16F). Results: Quantification of the photons emitted from the MDA-MB231 or B16F cells revealed a significant inhibition of cell proliferation by the conditioning media (CM) derived from amyloid precursor protein (APP) over-expressing cells. The inhibition of U87 cells was observed only after the media was conditioned for longer than 2 days with APP over-expressing cells. Conclusion: Our results suggest that Aβ plays an inhibitory role in tumor cell proliferation; this effect could depend on the type of tumor cells and amount of Aβ