News Article | August 8, 2017
"LeeRoy embodies the strong commitment to our company, its culture, and the multitude of employees and customers we serve to provide world class performance even in the face of the strongest storms Mother Nature throws at us," said JF Brossoit, senior vice president of transformation and operations support at CMS Energy and Consumers Energy. "LeeRoy's extensive engineering and operations background will bring a unique perspective to Consumers Energy customers." Wells joined Consumers Energy and has served in many capacities within electric operations since 2006, helping to ensure reliability and safety within our electrical infrastructure. He has worked as an executive director in electric operations since 2015, leading a workforce of over 480 employees in the area of electric low voltage distribution, construction and service restoration. Wells graduated from Michigan Technological University in 2002 with a Bachelor of Science in Electrical Engineering. He also holds a Master's degree in Organizational Leadership and Management from Lourdes College, along with business certifications from George Washington University and the University of Michigan – Stephen M. Ross School of Business. Consumers Energy, Michigan's largest utility, is the principal subsidiary of CMS Energy (NYSE: CMS), providing natural gas and electricity to 6.7 million of the state's 10 million residents in all 68 Lower Peninsula counties. For more information about Consumers Energy, go to www.ConsumersEnergy.com.
Vinsley S.S.,Lourdes College
Journal of Computational and Theoretical Nanoscience | Year: 2016
In recent years brain tumor detection using MRI images is an effective clinical research area since MRI images does not make any tissue damage with its radiation and provides useful information about the tissue we are using MRI images in our proposed work for the brain tumor detection. Our proposed brain tumor detection method consists of four sections, i.e., pre-processing, segmentation, feature extraction and classification. Initially the input image fetched from the MRI database will be subjected to skull stripping in order to remove the unwanted region from the image. Then the skull stripped image is segmented using efficient watershed segmentation algorithm. Afterwards from the segmented image shape, intensity and texture features will be extracted. Then that extracted features is given as the input to the ANN classifier. Here the ANN classifier is optimized by wellknown ABC optimization technique in order to get the enhanced classification accuracy. Thus from the classified abnormal images the tumor and edema region will be separated using modified region growing algorithm. The results will be analyzed to demonstrate the performance of the proposed segmentation and classification technique with other existing techniques. © Copyright 2016 American Scientific Publishers All rights reserved.
Sathianathan V.,Lourdes College
Asian Journal of Information Technology | Year: 2016
The major target of our research is to design and develop wiener filtering based on Short Time Fractional Fourier Transform for chirp signal enhancement. One of the efficient methods to analyze the chirp signal is fractional Fourier Transform (FRFT). Still, it fails in locating the Fractional Fourier Domain (FRFD) frequency contents which is required in some applications. For that reason in our research, we introduce the Short-Time Fractional Fourier Transform (STFRFT) for chirp signal enhancement. Using the advantage of STFRFT, we plan to design the wiener filtering based on STFRFT. In this study, at first the input signal is split in to two signals such as clean chirp signal and the noisy chirp signal based on the size of the hamming window. After that, multiplying the hamming window function with the frame of the signal is then multiplied with the Fractional Fourier Transform (FRFT). Finally, we apply the wiener filter to remove the noise from the signal. In experimental evaluation, we generate the mono signal model to compare the results of our proposed method.We compare our proposed technique with the wiener filter based on STFT, FFT and FRFT. Here, we obtain the maximum SNR of 30.79 db which is high compare to existing approach. © Medwell Journals, 2016.
Nagarajan P.,Anna University |
Vinsley S.S.,Lourdes College
International Journal of Pharmaceutical Sciences Review and Research | Year: 2016
Optic Disc (OD) is considered as one of the main features of a retinal fundus image. Segmenting the OD is a key pre-processing element in many algorithms designed for automatic extraction of anatomical structures. Information about the OD can be used to examine the severity of some diseases such as glaucoma, proliferative diabetic retinopathy, disc edema, etc. An elliptical template based methodology is proposed for the segmentation of optic disc. The detection procedure comprises of two independent methodologies namely optic disc detection and boundary approximation. To improve the accuracy of Optic Disc detection, the candidate regions are first determined by clustering the brightest pixels in red plane of the fundus image. Different image contrast analysis methods are applied within that candidate region to locate the optic disc. Sub image having the optic disc can be separated for boundary detection using histogram. Boundary detection methodology estimates an elliptical approximation of the OD boundary by applying mathematical morphology, edge detection techniques and circular Hough transform along with circular template. Due to the exceptional ellipsity degree of the optic disc, elliptical template is proposed to increase the overlapping rate from 86% achieved with a circular template matching to 95%. © 2016, Global Research Online. All rights reserved.
Vinsley S.S.,Lourdes College
Journal of Computational and Theoretical Nanoscience | Year: 2017
High performance radar requires more and more accurate target models to achieve target detection and repress disturbance. Fractional Fourier Transform (FrFT) is a powerful tool that detects the chirp signals in the noisy environments. Yet, if the interference is inseparable from the signal in any order of FrFT, its performance degrades. In this paper, we propose the Short Time Fractional Fourier Transform (STFrFT) to detach the signal from the interference. This paper also provides an optimum radar signal processor design that relies on STFrFT to detect the known target model from the noisy environment. The performance of the proposed optimum radar processor is evaluated and compared with the processor based on Fast Fourier Transform (FFT), FrFT, Short Time Fourier Transform (STFT) and Short Time Fractional Fourier Domain (STFrFD) filter. It shows best detection results, when the signal and the interference lie close with smaller distance between them. Further, the parameters like, time of arrival and pulse width are detected for the estimated chirp signals. © Copyright 2017 American Scientific Publishers All rights reserved.
Smitha J.C.,Lourdes College
International Review on Computers and Software | Year: 2014
The most necessary part of the living things which standardizes and manages other organs is the brain. The brain may get affected through any disease if the patient is not in a normal condition. Therefore it is significant to examine the condition of the brain. In the region of brain MRI image deformity fragmentation, various research works were made. However these research efforts presentations are needed in the image pre-analysis. During pre-analysis brain via MRI brain images for identifying the deformity, it is essential to examine the acquired patient's image in detail. An error treatment will be specified to the influenced patient if the study may have any error. So there is a necessity to develop precision in the deformity segmentation by achieving the fundamental pre-analysis in the MRI images. A combined approach with MRI brain image abnormality segmentation and denoising process is proposed in this paper. The proposed technique comprised of five stages namely, (i) Preprocessing, (ii) Feature Extraction, (iii) Image Classification, (iv) Segmentation and (v) Tissues Classification. Initially the database images are given to the preprocessing stage, for removing the noise. In preprocessing, the denoising process is performed it increases the segmentation and feature extraction accuracy. After the preprocessing, the image features are extracted to classify the images in the image database into normal and abnormal. After the image classification, the abnormal MRI images abnormal tissues like stroke, trauma and tumor are segmented. For this, the features are extracted from the segmented abnormal tissues. In the proposed technique, three features such as modified entropy, energy and innovative feature are extracted in the feature extraction stage. By using these extracted features, the abnormal tissues are classified by using a well known classification technique called Feed Forward Back Propagation Neural Network (FFBNN). The implementation results show the effectiveness of proposed MRI abnormality tissues segmentation technique in segmenting and classifying the MRI images and the achieved improvement in the segmentation and classification result. Furthermore, the performance of the proposed technique is evaluated by comparing with the existing MRI image segmentation techniques. © 2014 Praise Worthy Prize S.r.l. - All rights reserved.
Smitha J.C.,Lourdes College
Advances in Intelligent Systems and Computing | Year: 2015
A combined approach with MRI brain image denoising and abnormality detection process is proposed in this paper. The proposed technique is comprised of three stages, namely (i) image preprocessing, (ii) feature extraction, and (iii) image classification. Initially, in the preprocessing stage, denoising is performed on the input brain MRI image. The denoising process on the input image increases the accuracy of feature extraction stage. In feature extraction phase, the image features such as mean, variance, and multilevel 2D Haar wavelet decomposition are extracted for classifying the images in the database into normal and abnormal. By using these extracted features, the MRI brain images are classified by the well-known classification technique such as feed forward back propagation neural networks (FFBNN). The implementation of the proposed method shows improvements in classification of MRI images. © Springer India 2015.
Zajac L.,Lourdes College
The ABNF journal : official journal of the Association of Black Nursing Faculty in Higher Education, Inc | Year: 2011
A culturally diverse work force is vital to meeting the health care needs of an increasingly diverse population. The lack of minority faculty has been documented as a barrier to recruitment and retention of culturally diverse nursing students. Literature that addresses the nursing faculty shortage and the shortage of minority nurse educators is investigated. A double-loop approach to recruitment and retention of minority nursing faculty is proposed and includes the strategies of focused faculty searches, emphasizing internal resources of the academic institution, traditional and distance mentoring, nursing department initiatives, welcoming activities, and campus programs.
Matuszek S.,Lourdes College
Holistic nursing practice | Year: 2010
With a soaring trend of the incorporation of complementary therapies into the mainstream of health care, animal-facilitated therapy has become a popular interest for the health care team to integrate into a patient's plan of care. This systematic literature summarizes the current research on the use of animal therapy in several patient populations and provides nursing implications for practice.
de Carvalho A.A.,Lourdes College |
Gomes L.,Catholic University of Brasília |
Loureiro A.M.L.,Catholic University of Brasília
Jornal Brasileiro de Pneumologia | Year: 2010
Objective: To determine the prevalence of smoking among elderly patients admitted to long-term care facilities (LTCFs) and to determine whether the degree of nicotine dependence is associated with sociodemographic variables, affective ties, motivation for smoking cessation and depression. Methods: Cross-sectional, population-based study involving 573 individuals over the age of 60, admitted to 13 LTCFs in the Federal District of Brasília, Brazil. We analyzed the following variables: type of LTCF, gender, age, level of education, monthly income, marital status, retirement status, affective ties, probable depression, motivation for smoking cessation and degree of nicotine dependence. In order to collect these data, the following instruments were used: a sociodemographic questionnaire; the Flanagan Quality of Life Scale; the Mini-Mental State Examination; the Geriatric Depression Scale; the Richmond test; and the Fagerström Test for Nicotine Dependence. Results: The prevalence of smokers in the study sample (573 individuals) was 23.0%. Of the 132 smokers, there were 90 males (25.8%) and 42 females (18.7%). Of these, 116 smokers were included in the study, 70 of whom (60.3%) presented with probable depression. The degree of nicotine dependence was found to be significantly associated with level of education, monthly income, affective ties, motivation for smoking cessation and probable depression, although not with the type of LTCF, gender, age, retirement status or marital status. Conclusions: Among elderly patients admitted to LTCFs in the Federal District of Brasília, the prevalence of smoking is high and the motivation for smoking cessation is low.