Early-state damage detection, characterization, and evolution using high-resolution computed tomography

Safely using materials in high performance applications requires adequately understanding the mechanisms which control the nucleation and evolution of damage. Most of a material\u27s operational life is spent in a state with noncritical damage, and, for example in metals only a small portion of its life falls within the classical Paris Law regime of crack growth. Developing proper structural health and prognosis models requires understanding the behavior of damage in these early stages within the material\u27s life, and this early-stage damage occurs on length scales at which the material may be considered ``granular\u27\u27 in the sense that the discrete regions which comprise the whole are large enough to require special consideration. Material performance depends upon the characteristics of the granules themselves as well as the interfaces between granules. As a result, properly studying early-stage damage in complex, granular materials requires a means to characterize changes in the granules and interfaces. The granular-scale can range from tenths of microns in ceramics, to single microns in fiber-reinforced composites, to tens of millimeters in concrete. The difficulty of direct-study is often overcome by exhaustive testing of macro-scale damage caused by gross material loads and abuse. Such testing, for example optical or electron microscopy, destructive and further, is costly when used to study the evolution of damage within a material and often limits the study to a few snapshots. New developments in high-resolution computed tomography (HRCT) provide the necessary spatial resolution to directly image the granule length-scale of many materials. Successful application of HRCT with fiber-reinforced composites, however, requires extending the HRCT performance beyond current limits. This dissertation will discuss improvements made in the field of CT reconstruction which enable resolutions to be pushed to the point of being able to image the fiber-scale damage structures and the application of this new capability to the study of early-stage damage

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Assessment of Structure from Motion for Reconnaissance Augmentation and Bandwidth Usage Reduction

Modern militaries rely upon remote image sensors for real-time intelligence. A typical remote system consists of an unmanned aerial vehicle, or UAV, with an attached camera. A video stream is sent from the UAV, through a bandwidth-constrained satellite connection, to an intelligence processing unit. In this research, an upgrade to this method of collection is proposed. A set of synthetic images of a scene captured by a UAV in a virtual environment is sent to a pipeline of computer vision algorithms, collectively known as Structure from Motion. The output of Structure from Motion, a three-dimensional model, is then assessed in a 3D virtual world as a possible replacement for the images from which it was created. This study shows Structure from Motion results from a modifiable spiral flight path and compares the geoaccuracy of each result. A flattening of height is observed, and an automated compensation for this flattening is performed. Each reconstruction is also compressed, and the size of the compression is compared with the compressed size of the images from which it was created. A reduction of 49-60% of required space is shown

Accurate Simulation of Low-Intensity Transcranial Ultrasound Propagation for Neurostimulation

Neural stimulation with low-intensity ultrasound is a potentially transformative technology with applications in therapy and research. To develop, it will require ultrasound to be tightly focused on brain structures with accurate spatial targeting and fine control over the ultrasound amplitude at the target. However, the skull is an impediment to the effective focusing of ultrasound. Simulations of ultrasound propagation through acoustic property maps derived from medical images can be used to derive focusing drive signals for multi-element arrays. Focusing effectiveness is dependent on the fidelity of the numerical simulations. In combination with MRI based treatment verification, model based focusing has been used to focus high-intensity ultrasound onto the brain for ablation. This thesis presents a thorough and systematic study of the simulation parameters required to achieve effective transcranial focusing. The literature on ultrasonic neurostimulation, transcranial ultrasonic focusing, and the derivation of property maps from medical images is reviewed. The sampling criteria required to ensure numerical accuracy for the k-space pseudospectral time domain simulation scheme is established through testing of individual sources of numerical error, and convergence testing of a simulated time-reversal protocol. With numerical accuracy assured, the importance of acoustic property maps is examined through simulations to determine the sensitivity of intracranial fields to the properties of the skull layer. These results are corroborated by matching experimental measurements of ultrasound propagation through skull bone phantoms with spatially registered simulations. Finally, the impact of image related homogenisation and loss of internal bone structure is determined using simulations through co-registered clinical CT and micro-CT data of the skull

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Forum Bildverarbeitung 2022

Bildverarbeitung verknüpft das Fachgebiet die Sensorik von Kameras – bildgebender Sensorik – mit der Verarbeitung der Sensordaten – den Bildern. Daraus resultiert der besondere Reiz dieser Disziplin. Der vorliegende Tagungsband des „Forums Bildverarbeitung“, das am 24. und 25.11.2022 in Karlsruhe als Veranstaltung des Karlsruher Instituts für Technologie und des Fraunhofer-Instituts für Optronik, Systemtechnik und Bildauswertung stattfand, enthält die Aufsätze der eingegangenen Beiträge