Ultrasonic signals are increasingly being used for predicting material behavior, both in an engineering context (detecting anomalies in a variety of structures) and a biological context (examining human bones, body parts and unborn fetuses). Featuring contributions from authors who are specialists in their subject area, this book presents new developments in ultrasonic research in both these areas, including ultrasonic NDE and other areas which go beyond traditional imaging techniques of internal defects. As such, both those in the biological and physical science communities will find this an informative and stimulating read.

"Index to newspapers" in each no., beginning with Mar. 1908.

A 2022 Choice Reviews Outstanding Academic Title As a timely portrait of international perceptions and media coverage of the United States, this comprehensive collection reveals the global effects of the tumultuous environments and controversial views promoted during the Donald J. Trump presidency. More than thirty accomplished and prominent media, communication, and journalism scholars represent twenty countries with methodically researched assessments of their respective country’s major national newspapers, social media, or comprehensive public opinion surveys. Together, these analyses offer a unique cross-cultural approach that helps students and scholars understand the image of the USA and President Trump through the eyes of politicians, media personalities, and ordinary people across the globe.

Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation