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1000 tulosta hakusanalla Pavan Kumar Addanki

Imaging in Endodontics

Imaging in Endodontics

Bhavna Gaikwad; Sakshi Rathod; Pavan Kothari

Lap Lambert Academic Publishing
2026
pokkari
Even with the best intentions and refined technique, images acquire during conventional intra-oral radiographs reveal information in two-dimensions only (height and width) limiting the valuable information in the third dimension i.e. depth of the lesion. The inherent problems of positioning image receptors in the ideal position in relation to the anatomical area of interest, anatomic noise, under or over- estimation of actual healing or failure of endodontic treatment limits the assessment of true nature of the endodontic lesions. In such situations, advanced diagnostic aids like CBCT, CT, micro CT arevery helpful in visualizing the lesion three dimensionally. Especially CBCT technology is improving at rapid pace because of improved diagnostic data provided and therefore appropriate decision making for the management of complex endodontic problems. So, endodontic cases should be judged individually and CBCT should be considered in situation where information from conventional imaging systems does not yield an adequate amount of information to allow appropriate management of endodontic probelm.
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Fracture Mechanics Testing Methods for Polymers, Adhesives and Composites

Fracture Mechanics Testing Methods for Polymers, Adhesives and Composites

D.R. Moore; J.G. Williams; A Pavan

Elsevier Science Ltd
2001
sidottu
This book is an overview of ESIS Technical Committee 4's activities since the mid-1980s. A wide range of tests is described and the numerous authors is a reflection of the wide and enthusiastic support we have had. With the establishment of the Technical Committee 4, two major areas were identified as appropriate for the activity. Firstly there was an urgent need for standard, fracture mechanics based, test methods to be designed for polymers and composites. A good deal of academic work had been done, but the usefulness to industry was limited by the lack of agreed standards. Secondly there was a perceived need to explore the use of such data in the design of plastic parts. Some modest efforts were made in early meetings to explore this, but little progress was made. In contrast things moved along briskly in the standards work and this has dominated the activity for the last fourteen years. The design issue remains a future goal.
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The Surgical Review

The Surgical Review

Paige M. Porrett; Jeffrey A. Drebin; Pavan Atluri; Giorgos C. Karakousis; Robert E. Roses

Lippincott Williams and Wilkins
2015
pokkari
Make the most of your study time preparing for the American Board of Surgery In-Training Examination (ABSITE), written boards, and certification! The 4th Edition of The Surgical Review is a concise yet comprehensive review tool that keeps you up to date with surgical practice and current exams. Short, to-the-point chapters highlight all of the basic science and clinical science you need to know to perform at your best and improve your test scores. Every chapter is co-authored by a resident and an attending physician at the Hospital of the University of Pennsylvania, and the table of contents is organized to match the current exam.Key FeaturesMore than 300 full-color illustrations complement the text, improve retention of key concepts, and facilitate quick review.Key Points located at the beginning of each chapter for a rapid overview or last-minute reference now target both junior residents and senior residents, with specific information for each.Thoroughly updated throughout , including new information on endovascular therapies.Now with the print edition, enjoy the bundled interactive eBook edition, which can be downloaded to your tablet and smartphone or accessed online and includes features like:Complete content with enhanced navigationPowerful search tools and smart navigation cross-links that pull results from content in the book, your notes, and even the webCross-linked pages, references, and more for easy navigationHighlighting tool for easier reference of key content throughout the textAbility to take and share notes with friends and colleaguesQuick reference tabbing to save your favorite content for future use
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Image Understanding using Sparse Representations

Image Understanding using Sparse Representations

Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Pavan Turaga; Andreas Spanias

Springer International Publishing AG
2014
nidottu
Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.
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