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News Article | May 1, 2017
Site: www.prweb.com

LearnHowToBecome.org, a leading resource provider for higher education and career information, has announced its list of the best online Christian colleges in the nation for 2017. The top 50 schools were acknowledged, with Saint Mary-of-the-Woods College, Buena Vista University, Judson College, Amridge University and Chaminade University of Honolulu taking the top five spots. A full list of the winning schools is included below. “As demand for quality online education grows, religious-based schools are offering more flexible online programs than ever before,” said Wes Ricketts, senior vice president of LearnHowToBecome.org. “These schools go above and beyond with their online curriculum, offering the best combination of value and quality that translates into student success.” To be included on the “Best Online Christian Colleges” list, schools must be regionally accredited, not-for-profit and have an active Christian affiliation. Each college is also measured on such data points as the diversity of degree programs offered, academic and career counseling services, variety of student resources and post-college alumni earnings. Complete details on each college, their individual scores and the data and methodology used to determine the LearnHowToBecome.org “Best Online Christian Colleges” list, visit: The Best Online Christian Colleges in the U.S. for 2017 include: Amridge University Baker University Belhaven University Bethel University Bethesda University Briar Cliff University Buena Vista University Canisius College Carlow University Chaminade University of Honolulu Clarks Summit University Concordia University-Nebraska Concordia University-Wisconsin DeSales University Duquesne University Graceland University-Lamoni Gwynedd Mercy University Iowa Wesleyan College Judson College King University LeTourneau University Malone University Marian University McKendree University Messenger College Mississippi College Newman University Niagara University North Greenville University Ohio Christian University Oral Roberts University Ottawa University-Ottawa Presentation College Quincy University Saint Joseph's College of Maine Saint Leo University Saint Mary-of-the-Woods College Siena Heights University Southwestern Adventist University Southwestern College Spring Arbor University University of Detroit Mercy University of Saint Francis-Fort Wayne University of Saint Mary University of St. Francis University of the Cumberlands University of the Incarnate Word Viterbo University Wayland Baptist University William Woods University ### About Us: LearnHowtoBecome.org was founded in 2013 to provide data and expert driven information about employment opportunities and the education needed to land the perfect career. Our materials cover a wide range of professions, industries and degree programs, and are designed for people who want to choose, change or advance their careers. We also provide helpful resources and guides that address social issues, financial aid and other special interest in higher education. Information from LearnHowtoBecome.org has proudly been featured by more than 700 educational institutions.


Biswas S.,University Iowa | Poddar S.,University Iowa | Dasgupta S.,University Iowa | Mudumbai R.,University Iowa | Jacob M.,University Iowa
Conference Record - Asilomar Conference on Signals, Systems and Computers | Year: 2015

We consider the recovery of a low rank and jointly sparse matrix from under sampled measurements of its columns. This problem is highly relevant in the recovery of dynamic MRI data with high spatio-temporal resolution, where each column of the matrix corresponds to a frame in the image time series; the matrix is highly low-rank since the frames are highly correlated. Similarly the non-zero locations of the matrix in appropriate transform/frame domains (e.g. wavelet, gradient) are roughly the same in different frame. The superset of the support can be safely assumed to be jointly sparse. Unlike the classical multiple measurement vector (MMV) setup that measures all the snapshots using the same matrix, we consider each snapshot to be measured using a different measurement matrix. We show that this approach reduces the total number of measurements, especially when the rank of the matrix is much smaller than than its sparsity. Our experiments in the context of dynamic imaging shows that this approach is very useful in realizing free breathing cardiac MRI. © 2014 IEEE.


Haug J.,University Iowa | Goguri S.,University Iowa | Mudumbai R.,University Iowa | Dasgupta S.,University Iowa
Chinese Control Conference, CCC | Year: 2015

We present a class of nonlinear observers that track oscillator states from observations of the complex envelope. We first identify core state variables that are observable and provide a general observer structure for estimating these state variables, that results in globally uniformly asymptotically stable observers. We provide a specialization that induces the 2-norm of the state estimation error to monotonically decline to zero, and has an intuitively appealing structure that has parallels to full order observers of Linear Time Invariant systems. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.


Biswas S.,University Iowa | Achanta H.K.,University Iowa | Jacob M.,University Iowa | Dasgupta S.,University Iowa | Mudumbai R.,University Iowa
IEEE Signal Processing Letters | Year: 2015

We provide a two-step approach to recover a jointly k-sparse matrix , (at most k rows of are nonzero), with rank r << k from its under sampled measurements. Unlike the classical recovery algorithms that use the same measurement matrix for every column of , the proposed algorithm comprises two stages, in each of which the measurement is taken by a different measurement matrix.The first stage uses a standard algorithm, to recover any r columns (e.g.The first r) of .The second uses a new set of measurements and the subspace estimate provided by these columns to recover the rest. We derive conditions on the second measurement matrix to guarantee perfect subspace aware recovery for two cases: First a worst-case setting that applies to all matrices.The second a generic case that works for almost all matrices. We demonstrate both theoretically and through simulations that when r << k our approach needs far fewer measurements. It compares favorably with recent results using dense linear combinations, that do not use column-wise measurements. © 1994-2012 IEEE.

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