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C r kothari.
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Customers find the book very important for research students. They also appreciate the colourful pages and well-explained content.
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Customers find the content of the book very important and helpful for research students. They also say the topics are covered systematically by both authors and complete the subject logically. Customers also say it's a good book for beginners and an excellent book on RM.
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Last updated : July 18, 2024
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Most professors will require the use of academic (AKA peer-reviewed) sources for student writing. This is because these sources, written for academic audiences of specific fields, are helpful for developing your argument on many topics of interest in the academic realm, from history to biology. While popular sources like news articles also often discuss topics of interest within academic fields, peer-reviewed sources offer a depth of research and expertise that you cannot find in popular sources. Therefore, knowing how to (1) identify popular vs. academic sources, (2) differentiate between primary and secondary sources, and (3) find academic sources is a vital step in writing research. Below are definitions of the two ways scholars categorize types of sources based on when they were created (i.e. time and place) and how (i.e. methodology):
Finding appropriate academic sources from the hundreds of different journal publications can be daunting. Therefore, it is important to find databases –digital collections of articles–relevant to your topic to narrow your search. Albertson’s Library has access to several different databases, which can be located by clicking the “Articles and Databases” tab on the website’s homepage, and navigating to “Databases A-Z” to refine your search. Popular databases include: Academic Search Premier and Proquest Central (non-specific databases which include a wide variety of articles), JSTOR (humanities and social sciences, from literature to history), Web of Science (formal sciences and natural sciences such as biology and chemistry), and Google Scholar (a web search engine that searches scholarly literature and academic sources). If you are unable to access articles from other databases, make sure you’re signed in to Alberton’s Library through Boise State!
Databases include many different types of sources besides academic journals, however, including book reviews and other periodicals. Using the search bar , you can limit search results to those containing specific keywords or phrases like “writing center” or “transfer theory.” Utilizing keywords in your search–names of key concepts, authors, or ideas–rather than questions is the most effective way to find articles in databases. When searching for a specific work by title, placing the title in quotation marks will ensure your search includes only results in that specific word order. In the example below, search terms including the author (“Virginia Woolf”) and subject (“feminism”) are entered into the popular database EBSCOhost:
Many databases have a bar on the left of the screen where you can further refine your results. For example, if you are only interested in finding complete scholarly articles, or peer-reviewed ones, you can toggle these different options to further limit your search. These options are located under the “Refine Results” bar in EBSCOhost, divided into different sections, with a display of currently selected search filters and filter options to refine your search based on your specific needs, as seen in the figure below:
Search results can also be limited by subject : If you search “Romeo and Juliet” on Academic Search Premier to find literary analysis articles for your English class, you’ll find a lot of other sources that include this search term, such as ones about theater production or ballets based on Shakespeare’s play. However, if you’re writing a literary paper on the text of the play itself, you might limit your search results to “fiction” to see only articles that discuss the play within the field of literature. Alternatively, for a theater class discussing the play, you might limit your search results to “drama.”
Charles a. bouman.
The following is a brief reflection from the author of Foundations of Computational Imaging: A Model-based Approach , which was published by SIAM in 2022. This innovative book defines a common foundation for the mathematical and statistical methods that are associated with computational imaging and addresses a variety of research techniques with applications in multiple disciplines, including applied mathematics, physics, chemistry, optics, and signal processing.
Over the past 20 years, computational imaging has emerged as a multidisciplinary field that focuses on the creation of useful images from raw sensor data. Foundations of Computational Imaging presents an essential collection of theoretical materials that can serve as a common language for researchers and practitioners in this new field.
It is commonly believed that images come directly from sensors. For example, people often think that the output of the complementary metal oxide semiconductor (CMOS) sensor in their cell phones is a ready-to-use photograph; this is not true. Instead, the CMOS chip only produces raw data. This data is then processed via a series of complex mathematical algorithms that are implemented on high-performance computational hardware and produce the image we see on the screen. Such computational imaging procedures are required for any imaging system, ranging from cell phone cameras to tomographic imaging systems for medical diagnoses and even the Event Horizon Telescope that generated the first image of the supermassive black hole at the center of the galaxy Messier 87 (which is featured on the front cover of the book).
Any modern imaging system must solve an inverse problem that aims to estimate the desired true image from the available sensor data (see Figure 1). Even if a sensor is able to collect measurements for every pixel in an image, the image must still be denoised, deblurred, and corrected for distortions in geometry and color. And frequently the image must first be reconstructed from indirect, sparse, and nonlinearly distorted sensor measurements that make the formation of the final image computationally challenging and ill posed. In order to design the highest-performing imaging system possible, we must co-design the sensor and computational imaging algorithms/hardware to produce \(\hat{X}\): the best estimate of the desired image.
Foundations of Computational Imaging collects a set of algorithms that are commonly used in the implementation of computational imaging systems and arranges them as a single readable reference. The imaging approach is exemplified by the following maximum a posteriori (MAP) estimate:
\[\hat{X}=\arg \min_{x}\{-\log p (Y|x)-\log p(x)\}. \tag1\]
The term \(p(Y|x)\) is called the forward model because it describes the statistical dependency of the measurements \(Y\) with respect to the unknown image \(x\). The term \(p(x)\) is the prior model , which describes the unknown image that will be reconstructed. Minimizing the sum of these two terms yields a reconstruction that fits the measurements and has a high probability of being the solution. The MAP estimate requires that the solution be both “regular” and consistent with the observed data. In practice, we typically compute the estimate with an iterative algorithm that balances the prior cost function with the forward model cost function. This algorithmic structure—known as model-based iterative reconstruction (MBIR)—is a central theme of computational imaging and appears extensively throughout the text.
Figure 2 illustrates the goal of Bayesian reconstruction algorithms like MBIR. If the measurements are sparse, then the set of reconstructions that fit the observed data intuitively forms a “thin manifold.” However, many of these solutions may be completely unrealistic for the application in question. After the prior manifold characterizes the set of images that are probable to have occurred, the MAP estimate finds a reconstruction that is close to both manifolds. Because the prior manifold is essentially a probability density on all naturally occurring images, it is often quite difficult to model. Consequently, the statistical modeling of images and the efficient computation of resulting MBIR reconstructions are major themes of Foundations of Computational Imaging .
Chapters 2, 3, and 6 introduce the basic theoretical machinery for image modeling, including autoregressive and Markov random field (MRF) models in both one and two dimensions. The text also generalizes the multidimensional MRF model to the non-Gaussian case to better represent natural images.
Chapters 5 and 7 then explain the basic approaches to high-dimensional quadratic and non-quadratic optimization for MBIR reconstruction. These sections present the strengths and weaknesses of optimization approaches such as gradient descent, coordinate descent, and line search, and outline strategies to achieve the best reconstruction quality with limited computation.
Chapter 8 explores majorization and surrogate functions. We can consider these approaches—which have become mainstays of non-quadratic convex optimization—as a generalization of Newton’s method and interpret them as a form of iterative quadratic reweighting. In practice, surrogate functions can dramatically reduce computation without adversely affecting image quality.
Chapter 9 investigates the machinery of constrained optimization, such as the augmented Lagrangian and the alternating direction method of multipliers (ADMM) — essential tools for the efficient solution of large, complex image reconstruction problems. We can use ADMM to break problems into smaller, more modular pieces, which is an important step for practical implementation. Chapter 10 builds upon this foundation by introducing plug-and-play and advanced prior modeling, which provide machinery for the integration of state-of-the-art machine learning algorithms into MBIR reconstruction.
Moving on, chapter 12 offers a systematic, theoretical, and intuitive treatment of the expectation-maximization (EM) algorithm. This algorithm is very useful in a variety of imaging applications because it enables the estimation of model parameters \(\theta\) even when the reconstruction \(\hat{X}\) is unknown. Foundations of Computational Imaging assesses the EM algorithm from both an intuitive, theoretical perspective and a more pragmatic, algorithmic point of view.
Chapters 13, 14, and 15 collectively develop the theory of ergodic Markov chains, Hastings-Metropolis samplers, and Gibbs samplers. These approaches frequently contribute to the simulation of stochastic systems that can be formulated to obey the statistics of a Gibbs distribution. Next, chapter 17 develops standard probabilistic machinery that helps to model the Poisson processes that are associated with photon counting detectors. And finally, the four appendices briefly review important topics—like convexity, proximal operators, and Gibbs distributions—that appear throughout the book.
Ultimately, Foundations of Computational Imaging can serve as a valuable asset to coursework that centers on model-based or computational imaging, advanced numerical analysis, data science, numerical optimization, and approximation theory. It is also a handy reference for researchers and practitioners who work in medical, scientific, commercial, and industrial imaging.
Enjoy this passage? Visit the SIAM Bookstore to learn more about Foundations of Computational Imaging: A Model-based Approach and browse other SIAM titles.
The 2024 SIAM Conference on Imaging Science will take place from May 28-31 in Atlanta, Ga. Peruse the online program and consider attending the meeting to learn more about computational imaging and additional related areas of study. Bouman’s book and other SIAM texts will be available for purchase at conference discounts.
Showalter professor, purdue university.
Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University and a member of the National Academy of Inventors. His research inspired the first commercial model-based iterative reconstruction system for medical X-ray computed tomography.
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Protein desulfurization and deselenization †.
* Corresponding authors
a Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 – UMR 9017, Center for Infection and Immunity of Lille, F-59000 Lille, France E-mail: [email protected]
b Centrale Lille, F-59000 Lille, France
Methods enabling the dechalcogenation of thiols or selenols have been investigated and developed for a long time in fields of research as diverse as the study of prebiotic chemistry, the engineering of fuel processing techniques, the study of biomolecule structures and function or the chemical synthesis of biomolecules. The dechalcogenation of thiol or selenol amino acids is nowadays a particularly flourishing area of research for being a pillar of modern chemical protein synthesis, when used in combination with thiol or selenol-based chemoselective peptide ligation chemistries. This review offers a comprehensive and scholarly overview of the field, emphasizing emerging trends and providing a detailed and critical mechanistic discussion of the dechalcogenation methods developed so far. Taking advantage of recently published reports, it also clarifies some unexpected desulfurization reactions that were observed in the past and for which no explanation was provided at the time. Additionally, the review includes a discussion on principal desulfurization methods within the framework of newly introduced green chemistry metrics and toolkits, providing a well-rounded exploration of the subject.
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The Fifth Edition of the bestseller Research Methodology has reimagined, redesigned, and fully renovated how a textbook can help students achieve success in their methods course or research project. Eight steps: Foolproof for any beginner researcher, the book breaks the process of designing and doing a research project into achievable stages.
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Research Methodology and Strategy Provides comprehensive coverage of the entire research process, methodologies, strategies and their applications, ideal for final-year undergraduate, Masters and PhD students, academics, researchers and professionals. Research Methodology and Strategy: Theory and Practice is designed to help readers understand the research process and equip them with the ...
The textbook adopts a coherent and student-friendly format, and offers a precise introduction to psychological research methodology that includes consideration of a broad range of qualitative and quantitative methods to help students identify and evaluate the best approach for their research needs.
The Fifth Edition of the bestseller Research Methodology has reimagined, redesigned, and fully renovated how a textbook can help students achieve success in their methods course or research project. Eight steps: Foolproof for any beginner researcher, the book breaks the process of designing and doing a research project into achievable stages ...
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RESEARCH METHODOLOGY A Step-by-Step Guide for Beginners Fifth Edition Ranjit Kumar University of Western Australia Eight steps, two pathways, one book. The fifth edition of the bestseller Research Methodology has reimagined, redesigned and fully renovated how a textbook can help students achieve success in their methods course or research project.
The book guides you through your project from beginning to end by offering practical advice on: formulating a research question. ethical considerations. carrying out a literature review. choosing a research design. selecting a sample. collecting and analysing qualitative and quantitative data. writing a research report.
Research Methodology is a remarkable book which I highly recommend to both PhD students and working scientists. It is clear, well written, and thought provoking." (Martin A. Uman, Journal of Research Practice, Vol. 13 (1), August, 2017) Authors and Affiliations.
The Fifth Edition of the bestseller Research Methodology has reimagined, redesigned, and fully renovated how a textbook can help students achieve success in their methods course or research project. Eight steps: Foolproof for any beginner researcher, the book breaks the process of designing and doing a research project into achievable stages ...
This book aims to introduce these methodologies, methods and tools of academic research to PhD students in doctoral programmes in management during their first year. ... As a research methodology ...
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ABSTRACT. Research Methods: The Basics is an accessible, user-friendly introduction to the different aspects of research theory, methods and practice. This second edition provides an expanded resource suitable for students and practitioners in a wide range of disciplines including the natural sciences, social sciences and humanities. Structured ...
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This book is designed to introduce doctoral and postgraduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioural research, and can serve as a standalone text or as a supplement to research readings in any ...
Eight steps, two pathways, one book. The Fifth Edition of the bestseller Research Methodology has reimagined, redesigned, and fully renovated how a textbook can help students achieve success in their methods course or research project. Eight steps: Foolproof for any beginner researcher, the book breaks the process of designing and doing a ...
book deals with the variety of research methods used in management and social sciences, with particular emphasis on the pharmacy course curriculum. A Handbook of Research Methodology is recommended for use in undergraduate and postgraduate courses focusing on research methodologies in various disciplines. Authors Sample Copy.
9. The Research Methods Knowledge Base, 3 rd Edition. This is a great textbook, regardless of the field that you are in. It offers up comprehensive coverage of both qualitative vs quantitative research methods. The language in the book is equally accessible to both novices as well as professional dissertation writers.
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Research Methodology, Methods and Techniques is authored by eminent authors, Dr. CR Kothari and Dr. Gaurav Garg and is published by one of the leading publishers, NEW AGE International Publishers. This latest Multicolour Edition of the book is intended to serve as a textbook for students at undergraduate and postgraduate level.
Therefore, knowing how to (1) identify popular vs. academic sources, (2) differentiate between primary and secondary sources, and (3) find academic sources is a vital step in writing research. Below are definitions of the two ways scholars categorize types of sources based on when they were created (i.e. time and place) and how (i.e. methodology):
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Attention to research methods in language attitudes is very welcome in a globalising world, and at a time of intense migration. This book presents an almost overwhelming breadth and diversity in it...
The following is a brief reflection from the author of Foundations of Computational Imaging: A Model-based Approach, which was published by SIAM in 2022.This innovative book defines a common foundation for the mathematical and statistical methods that are associated with computational imaging and addresses a variety of research techniques with applications in multiple disciplines, including ...
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From selecting the right research design to conducting thorough literature reviews, this comprehensive guide covers every aspect of the research process. Whether you're a seasoned researcher looking to refine your skills or a novice eager to learn the ropes, this book caters to all levels of expertise.
Methods enabling the dechalcogenation of thiols or selenols have been investigated and developed for a long time in fields of research as diverse as the study of prebiotic chemistry, the engineering of fuel processing techniques, the study of biomolecule structures and function or the chemical synthesis of biomolec