An experimental study of laser-supported plasmas for laser propulsion: Center director's discretionary fund project DFP-82-33

The rudiments of a rocket thruster, which receives its enthalpy from an energy source which is remotely beamed from a laser, is described. An experimental study, now partially complete, is discussed which will eventually provide a detailed understanding of the physics for assessing the feasibility of using hydrogen plasmas for accepting and converting this energy to enthalpy. A plasma ignition scheme which uses a pulsed CO2 laser was develped and the properites of the ignition spark documented, including breakdown intensities in hydrogen. A complete diagnostic system capable of determining plasma temperature and the plasma absorptivitiy for subsequent steady-state absorption of a high power CO2 laser beam are developed and demonstrative use is discussed for the preliminary case study, a two atmosphere laser supported argon plasma

Establishing Pose Based Features Using Histograms for the Detection of Abnormal Infant Movements

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In this paper, we conducted a pilot study on extracting important information from video sequences to classify the body movement into two categories, normal and abnormal, and compared the results provided by an independent expert reviewer based on GMA. We present two new pose-based features, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for the pose-based analysis and classification of infant body movement from video footage. We extract the 2D skeletal joint locations from 2D RGB images using Cao et al.’s method 1. Using the MINI-RGBD dataset 2, we further segment the body into local regions to extract part specific features. As a result, the pose and the degree of displacement are represented by histograms of normalised data. To demonstrate the effectiveness of the proposed features, we trained several classifiers using combinations of HOJO2D and HOJD2D features and conducted a series of experiments to classify the body movement into categories. The classification algorithms used included k-Nearest Neighbour (kNN, k=1 and k=3), Linear Discriminant Analysis (LDA) and the Ensemble classifier. Encouraging results were attained, with high accuracy (91.67{\%}) obtained using the Ensemble classifier

Automated early prediction of cerebral palsy: interpretable pose-based assessment for the identification of abnormal infant movements

Cerebral Palsy (CP) is currently the most common chronic motor disability occurring in infants, affecting an estimated 1 in every 400 babies born in the UK each year. Techniques which can lead to an early diagnosis of CP have therefore been an active area of research, with some very promising results using tools such as the General Movements Assessment (GMA). By using video recordings of infant motor activity, assessors are able to classify an infant’s neurodevelopmental status based upon specific characteristics of the observed infant movement. However, these assessments are heavily dependent upon the availability of highly skilled assessors. As such, we explore the feasibility of the automated prediction of CP using machine learning techniques to analyse infant motion. We examine the viability of several new pose-based features for the analysis and classification of infant body movement from video footage. We extensively evaluate the effectiveness of the extracted features using several proposed classification frameworks, and also reimplement the leading methods from the literature for direct comparison using shared datasets to establish a new state-of-the-art. We introduce the RVI-38 video dataset, which we use to further inform the design, and establish the robustness of our proposed complementary pose-based motion features. Finally, given the importance of explainable AI for clinical applications, we propose a new classification framework which also incorporates a visualisation module to further aid with interpretability. Our proposed pose-based framework segments extracted features to detect movement abnormalities spatiotemporally, allowing us to identify and highlight body-parts exhibiting abnormal movement characteristics, subsequently providing intuitive feedback to clinicians. We suggest that our novel pose-based methods offer significant benefits over other approaches in both the analysis of infant motion and explainability of the associated data. Our engineered features, which are directly mapped to the assessment criteria in the clinical guidelines, demonstrate state-of-the-art performance across multiple datasets; and our feature extraction methods and associated visualisations significantly improve upon model interpretability

Space Applications Industrial Laser System (SAILS)

A program is underway to develop a YAG laser based materials processing workstation to fly in the cargo bay of the Space Shuttle. This workstation, called Space Applications Industrial Laser System (SAILS), will be capable of cutting and welding steel, aluminum, and Inconel alloys of the type planned for use in constructing the Space Station Freedom. As well as demonstrating the ability of a YAG laser to perform remote (fiber-optic delivered) repair and fabrication operations in space, fundamental data will be collected on these interactions for comparison with terrestrial data and models. The flight system, scheduled to fly in 1996, will be constructed as three modules using standard Get-Away-Special (GAS) canisters. The first module holds the laser head and cooling system, while the second contains a high peak power electrical supply. The third module houses the materials processing workstation and the command and data acquisition subsystems. The laser head and workstation cansisters are linked by a fiber-optic cable to transmit the laser light. The team assembled to carry out this project includes Lumonics Industrial Products (laser), Tennessee Technological University (structural analysis and fabrication), Auburn University Center for Space Power (electrical engineering), University of Waterloo (low-g laser process consulting), and CSTAR/UTSI (data acquisition, control, software, integration, experiment design). This report describes the SAILS program and highlights recent activities undertaken at CSTAR

God\u27s Church Is Just: A Specific Discussion Of Some Cases of Church Discipline

https://digitalcommons.acu.edu/crs_books/1364/thumbnail.jp

A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework’s classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features

On Birthing Dancing Stars: The Need for Bounded Chaos in Information Interaction

While computers causing chaos is acommon social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order—and the systems we create with it—support highly-structured information tasks well, but provide poor support for less-structured tasks.We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offerwhat we term ‘bounded chaos’, and offers research directions that may support the creation of such interface

Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results, however these manual methods can be laborious. The prospect of automating these processes is seen as key in advancing this field of study. In our previous works, we examined the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a new deep learning framework for this classification task. We also propose a visualization framework which identifies body-parts with the greatest contribution towards a classification decision. The inclusion of a visualization framework is an important step towards automation as it helps make the decisions made by the machine learning framework interpretable. We directly compare the proposed framework's classification with several other methods from the literature using two independent datasets. Our experimental results show that the proposed method performs more consistently and more robustly than our previous pose-based techniques as well as other features from related works in this setting. We also find that our visualization framework helps provide greater interpretability, enhancing the likelihood of the adoption of these technologies within the medical domain

Discovering the Unfindable: The Tension Between Findability and Discoverability in a Bookshop Designed for Serendipity

Serendipity is a key aspect of user experience, particularly in the context of information acquisition - where it is known as information encountering. Unexpectedly encountering interesting or useful information can spark new insights while surprising and delighting. However, digital environments have been designed primarily for goal-directed seeking over loosely-directed exploration, searching over discovering. In this paper we examine a novel physical environment - a bookshop designed primarily for serendipity - for cues as to how information encountering might be helped or hindered by digital design. Naturalistic observations and interviews revealed it was almost impossible for participants to find specific books or topics other than by accident. But all unexpectedly encoun-tered interesting books, highlighting a tension between findability and discoverability. While some of the bookshop’s design features enabled information en-countering, others inhibited it. However, encountering was resilient, as it occurred despite participants finding it hard to understand the purpose of even those features that did enable it. Findings suggest the need to consider how transparent or opaque the purpose of design features should be and to balance structure and lack of it when designing digital environments for findability and discoverability