Alcorn State University is a historically black comprehensive land-grant institution in Lorman, Mississippi. It was founded in 1871 by the Reconstruction era legislature to provide higher education for freedmen. It is the first black land grant college established in the United States.The university is counted as a census-designated place and had a resident population of 1,017 at the 2010 census.Medgar Evers, a civil rights activist, graduated from the university in 1948. Students at the college were part of the mid-twentieth century civil rights struggle, working to register residents for voting and struggling to end segregation. Other alumni have been activists, politicians and professionals in Mississippi and other states. The university is a member-school of the Thurgood Marshall College Fund. Wikipedia.
News Article | February 16, 2017
More than 54 colleges & universities including the Historic Black Colleges & Universities (HBCU) and several area middle and high schools will descend on the campus of Second Ebenezer Church February 24, 25 & 26, 2017 for a spectacular 20th Anniversary of College Weekend, announced Bishop Edgar L. Vann II today. In its 20th year, Second Ebenezer's College Weekend has awarded more than $400,000 in scholarships and has reached more than 7,000 high school students and young adults with the promise of higher education. New this year, a career fair sponsored by Ebsource, Second Ebenezer's workforce development program, is being added. Participants are encouraged to bring their resumes and learn about both internships and career opportunities at area employers. A celebration of Black History Month, hundreds of students and their families are anticipated to participate, and all are welcome. Organizer Elder James Johnson said, "This event helps to shape the future for many metro Detroit youth. We are reaching out to all high school students and their parents and encouraging them to plan early. Going to college is a family affair. Economics alone make it out-of-reach for many. From understanding the value of higher education to financing college and preparing for the ACT and SAT exams, we provide all the resources they need to succeed. As always, we will be giving away scholarships and offering on-site admissions," said Johnson. Hosted by Hot 107.5FM Host Kamal Smith & BET "Sunday Best" Finalist DeAgelo Gardner, more than 10 marching bands will compete for a cash prize of $500. Talented student musicians from Pershing High School, Cass Technical High School, Levi Middle School, Loving Academy Marching Band are among the schools that will entertain and compete at the Battle of the Bands! Tickets are $7.00 per person. On Saturday, February 25, from 10am-2pm, high school students, young adults and their families are invited to interface with 54 colleges from around the state and the country, as well as learn about financial aid, test preparation, student banking and a special workshop for parents as well. Admission is free and guests are encouraged to register online at http://www.secondebenezer.org. Participating colleges and universities include, but are not limited to: Alcorn State University, Bethune-Cookman, Central Michigan University, Eastern Michigan University, Grand Valley State, Hampton University, Knoxville College, Michigan State University, Michigan Technical University, Miles College, Morehouse College, Morgan State University, North Carolina A&T, Oakland Community College, Oakland University, Ohio State University, Ohio Technical College, Philander Smith College, Prairie View A&M University, Spelman College, Tennessee State University, University of Detroit Mercy, University of Michigan Dearborn, Wayne State University, Western Michigan University and Xavier University. College Weekend will close on Sunday with a major College Spirit Day Session led by DeAngelo Gardner of BET's "Sunday Best," at 10:00 a.m. Second Ebenezer Church is centrally located at 14601 Dequindre, at I-75 and McNIchols, in Detroit. Telephone 313.867.4700 and visit http://www.secondebenezer.org.
Zheng Y.,Alcorn State University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010
A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition. © 2010 Copyright SPIE - The International Society for Optical Engineering.
Zheng Y.,Alcorn State University
ICALIP 2012 - 2012 International Conference on Audio, Language and Image Processing, Proceedings | Year: 2012
Multispectral images present complimentary information, which enables night vision (NV). Specifically, night vision colorization using multispectral image increases the reliability of interpretation, and thus they are good for visual analysis (human vision). The purpose of NV colorization is to resemble a natural scene in colors, which differs from false coloring. This paper gives an overview of NV colorization techniques proposed in past decade. Two categories of coloring methods, color fusion and color mapping, are discussed and compared in this paper. Color fusion directly combines multispectral NV images into a color-version image by mixing pixel intensities. A channel-based color fusion method will be reviewed. Color mapping usually maps the color properties of a false-colored NV image (source) onto that of a true-color daylight picture (target). Four coloring mapping methods, statistical matching, histogram matching, joint histogram matching, and lookup table (LUT) will be presented and compared. The joint histogram matching is newly introduced in this paper. The experimental NV imagery includes visible (RGB), image intensified, near infrared, long wave infrared. From the experimental results, the following conclusions can be made: (i) The segmentation-based color mapping method produces the most impressive and realistic colors but it requires heavy computations; (ii) Color fusion and LUT-based methods run very fast but their results are less realistic; (iii) The statistical matching method always provides acceptable results (i.e., never fails); and (iv) Histogram matching and joint-histogram matching can generate more impressive colors when the color distributions between source and target are similar. © 2012 IEEE.
Zheng Y.,Alcorn State University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012
Thermal face recognition becomes an active research direction in human identification because it does not rely on illumination condition. Face detection and eyeglasses detection are necessary steps prior to face recognition using thermal images. Infrared light cannot go through glasses and thus glasses will appear as dark areas in a thermal image. One possible solution is to detect eyeglasses and to exclude the eyeglasses areas before face matching. In thermal face detection, a projection profile analysis algorithm is proposed, where region growing and morphology operations are used to segment the body of a subject; then the derivatives of two projections (horizontal and vertical) are calculated and analyzed to locate a minimal rectangle of containing the face area. Of course, the searching region of a pair of eyeglasses is within the detected face area. The eyeglasses detection algorithm should produce either a binary mask if eyeglasses present, or an empty set if no eyeglasses at all. In the proposed eyeglasses detection algorithm, block processing, region growing, and priori knowledge (i.e., low mean and variance within glasses areas, the shapes and locations of eyeglasses) are employed. The results of face detection and eyeglasses detection are quantitatively measured and analyzed using the manually defined ground truths (for both face and eyeglasses). Our experimental results shown that the proposed face detection and eyeglasses detection algorithms performed very well in contrast with the predefined ground truths. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
Zheng Y.,Alcorn State University
Algorithms | Year: 2010
A new breast cancer detection algorithm, named the "Gabor Cancer Detection" (GCD) algorithm, utilizing Gabor features is proposed. Three major steps are involved in the GCD algorithm, preprocessing, segmentation (generating alarm segments), and classification (reducing false alarms). In preprocessing, a digital mammogram is down-sampled, quantized, denoised and enhanced. Nonlinear diffusion is used for noise suppression. In segmentation, a band-pass filter is formed by rotating a 1-D Gaussian filter (off center) in frequency space, termed as "Circular Gaussian Filter" (CGF). A CGF can be uniquely characterized by specifying a central frequency and a frequency band. A mass or calcification is a space-occupying lesion and usually appears as a bright region on a mammogram. The alarm segments (suspicious to be masses/calcifications) can be extracted out using a threshold that is adaptively decided upon the histogram analysis of the CGF-filtered mammogram. In classification, a Gabor filter bank is formed with five bands by four orientations (horizontal, vertical, 45 and 135 degree) in Fourier frequency domain. For each mammographic image, twenty Gabor-filtered images are produced. A set of edge histogram descriptors (EHD) are then extracted from 20 Gabor images for classification. An EHD signature is computed with four orientations of Gabor images along each band and five EHD signatures are then joined together to form an EHD feature vector of 20 dimensions. With the EHD features, the fuzzy C-means clustering technique and k-nearest neighbor (KNN) classifier are used to reduce the number of false alarms. The experimental results tested on the DDSM database (University of South Florida) show the promises of GCD algorithm in breast cancer detection, which achieved TP (true positive rate) = 90% at FPI (false positives per image) = 1.21 in mass detection; and TP = 93% at FPI = 1.19 in calcification detection. © 2010 by the authors.
Zheng Y.,Alcorn State University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013
A face recognition system usually consists of one recognition algorithm by processing single spectral images. For example, face pattern byte (FPB) algorithm was initially created using thermal (LWIR) images, while Elastic Bunch Graphic Matching (EBGM) algorithm was originated with visible (RGB) images. When there are two or more recognition algorithms and/or spectral images available, system performance can be enhanced using information fusion. In this paper, a score fusion with multispectral images is proposed to improve system performance, which is termed as an integrated multispectral face recognition system. Score fusion actually combines several scores from multiple matchers (algorithms) and/or multiple modalities (multispectra). The system performance is measured by the recognition accuracy (AC; the higher the better) and false accept rate (FAR; the lower the better). Specifically, a fusion method will combine the face scores from three matchers (Circular Gaussian Filter, FPB, EBGM) and from two-spectral bands (visible and thermal). We present and compare the system performance using seven fusion methods: linear discriminant analysis (LDA), k-nearest neighbor (KNN), support vector machine (SVM), binomial logistic regression (BLR), Gaussian mixture model (GMM), artificial neural network (ANN), and hidden Markov model (HMM). Our experiments are conducted with the Alcon State University multispectral face dataset that currently consists of two spectral images from 105 subjects. The experimental results show that the KNN score fusion produces the best performance (AC = 98.98%; FAR = 0.35%); and the SVM yields the second best. Compared with the performance of the single best matcher (AC = 91.67%, FAR = 8.33%), the integrated system with score fusion highly improves the accuracy, meanwhile dramatically reduces the FAR. © 2013 Copyright SPIE.
Zheng Y.,Alcorn State University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011
There are strong evidences of that multimodal biometric score fusion can significantly improve human identification performance. Score level fusion usually involves score normalization, score fusion, and fusion decision. There are several types of score fusion methods, direct combination of fusion scores, classifier-based fusion, and density-based fusion. The real applications require achieving greater reliability in determining or verifying person's identity. The goal of this research is to improve the accuracy and robustness of human identification by using multimodal biometrics score fusion. The accuracy means high verification rate if tested on a closed dataset, or a high genuine accept rate under low false accept rate if tested on an open dataset. While the robustness means the fusion performance is stable with variant biometric scores. We propose a hidden Markov model (HMM) for multiple score fusion, where the biometric scores include multimodal scores and multi-matcher scores. The state probability density functions in a HHM model are estimated by Gaussian mixture model. The proposed HMM model for multiple score fusion is accurate for identification, flexible and reliable with biometrics. The proposed HMM method are tested on three NIST-BSSR1 multimodal databases and on three face-score databases. The results show the HMM method is an excellent and reliable score fusion method. © 2011 SPIE.
Agency: NSF | Branch: Standard Grant | Program: | Phase: HIST BLACK COLLEGES AND UNIV | Award Amount: 297.77K | Year: 2016
The Historically Black Colleges and Universities-Undergraduate Program (HBCU-UP) Research Initiation Awards (RIAs) provide support to STEM junior faculty at HBCUs who are starting to build a research program, as well as for mid-career faculty who may have returned to the faculty ranks after holding an administrative post or who need to redirect and rebuild a research program. Faculty members may pursue research at their home institution, at an NSF-funded Center, at a research intensive institution or at a national laboratory. The RIA projects are expected to help further the faculty members research capability and effectiveness, to improve research and teaching at his or her home institution, and to involve undergraduate students in research experiences. With support from the National Science Foundation, Alcorn State University (ASU) will conduct research aimed at understanding cellulosic ethanol production from major feedstocks. This research will be used to enhance teaching and learning at ASU and thus, the PI will gain hands-on training in yeast transcriptomics which the PI expects to utilize as a tool in optimizing production of other bioproducts. Alcorn State University, a land-grant minority institution, recently included a dedicated bioenergy program to diversify its agriculture curriculum and research portfolio. This research will complement these ongoing bioenergy initiatives at Alcorn State University. Presently, Alcorn State does not have researchers engaged in omics research and this will ensure the expansion of Alcorns research portfolio. The research and educational efforts will contribute to the Universitys goal to increase the number of minority students receiving BS degrees in STEM fields. This project will allow the PI to incorporate genomics research in his future projects and students to obtain hands-on research and training. The skills obtained from this research will not only help prepare students for careers in cellulosic ethanol and biofuel research but will be valuable for their graduate education and/or career.
The goal of the proposed study is to characterize the transcriptome profile of S. cerevisiae and use it as a proxy to decode the impact of major feedstocks on overall ethanol production. Specifically, this project aims to conduct yeast transcriptomic profiling from cellulosic fermentation of four major feedstocks namely corn stover, switchgrass, Freedom Giant Miscanthus (FGM) and hybrid poplar. Results from this study will provide a blue-print of S. cerevisiae transcriptomic behavior during fermentation. This study has the potential to make significant contributions to understanding cellulosic ethanol production from the perspective of yeast genomics and provide baseline data for future development or engineering of industrial yeast strains for optimal ethanol production. The proposed RIA research will complement ongoing bioenergy initiatives at Alcorn State University. This project will be conducted in collaboration with Kansas State University.
Agency: NSF | Branch: Standard Grant | Program: | Phase: HIST BLACK COLLEGES AND UNIV | Award Amount: 377.94K | Year: 2016
The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Targeted Infusion Projects supports the development, implementation, and study of evidence-based innovative models and approaches for improving the preparation and success of HBCU undergraduate students so that they may pursue science, technology, engineering or mathematics (STEM) graduate programs and/or careers. The project at Alcorn State University seeks to improve STEM learning by training faculty in active learning methods and developing a cohort of student peer leaders to support the use of the methods. The activities and strategies are evidence-based and a strong plan for formative and summative evaluation is part of the project.
This project has the goals of developing faculty expertise in active learning instructional practices and developing a STEM Learning Resource Center for implementing peer led teaching and learning into the STEM curriculum. These will be done by recruiting and training two cohorts of STEM faculty in best practices through workshops, conferences, and an ongoing faculty learning community. The Learning Resource Center will be led by STEM undergraduate students and supervised by STEM faculty. Along with being a repository for STEM career information, the space will be used for course workshops, project and homework assistance, exam preparation and assistance with using resources such as software and library databases. The evaluation and dissemination of this project should be useful in developing models of student and faculty support for STEM majors at other institutions.
Agency: NSF | Branch: Standard Grant | Program: | Phase: HIST BLACK COLLEGES AND UNIV | Award Amount: 199.89K | Year: 2015
The Historically Black Colleges and Universities-Undergraduate Program (HBCU-UP) Research Initiation Awards (RIAs) provide support to STEM junior faculty at HBCUs who are starting to build a research program, as well as for mid-career faculty who may have returned to the faculty ranks after holding an administrative post or who needs to redirect and rebuild a research program. Faculty members may pursue research at their home institution, at an NSF-funded Center, at a research intensive institution or at a national laboratory. The RIA projects are expected to help further the faculty members research capability and effectiveness, to improve research and teaching at his or her home institution, and to involve undergraduate students in research experiences. With support from the National Science Foundation, Alcorn State University will conduct research to examine the fungal communities associated with energy grasses such as the giant miscanthus. The project will enhance the research capabilities of the principle investigator as well as teaching and learning at Alcorn State University. Undergraduate students will benefit from the collaborations with university and industry partners and the research experiences and training in fungal ecology. This experience will help to build the competency of the undergraduate students and support the nations efforts in building a robust STEM workforce.
The aim of the proposed study is to examine the giant miscanthus fungal communities and the role of root endophytes in enhancing feedstock production. Specific objectives are: 1) to employ next generation sequencing (NGS) tools (locus targeted Illumina MiSeq sequencing) to examine the composition, diversity and richness, and the seasonal and temporal variation of fungal communities of the shoot, root and rhizosphere; and 2) to isolate and identify root colonizing fungal endophytes and to select mutualistic and parasitic/pathogenic fungal endophytes by growth chamber studies. Findings from this study will: 1) provide a blueprint of the fungal biome associated with an important C4 energy grass; 2) identify abiotic drivers of fungal diversity and composition and their spatial structuring; 3) lay the foundation for dissecting the contribution of arbuscular mycorrhizal fungi towards high nitrogen sustainability of giant miscanthus; 4) identify the impact of root fungal endophytes on the host. This study will prepare the foundation for future university-industry partnership to conduct larger collaborative investigations on the microbial symbiosis of giant miscanthus. This project will be conducted in collaboration with Kansas State University.