Authoring Platform for Mobile Citizen Science Apps with Client-side ML

Data collection is an integral part of any citizen science project. Given the wide variety of projects, some level of expertise or, alternatively, some guidance for novice participants can greatly improve the quality of the collected data. A significant portion of citizen science projects depends on visual data, where photos or videos of different subjects are needed. Often these visual data are collected from all over the world, including remote locations. In this article, we introduce an authoring platform for easily creating mobile apps for citizen science projects that are empowered with client-side machine learning (ML) guidance. The apps created with our platform can help participants recognize the correct data and increase the efficiency of the data collection process. We demonstrate the application of our proposed platform with two use cases: a rip current detection app for a planned pilot study and a detection app for biodiversity-related projects

A Novel Technique for the In Vivo Imaging of Autoimmune Diabetes Development in the Pancreas by Two-Photon Microscopy

Type 1 diabetes (T1D) is characterized by the immune-mediated destruction of beta cells in the pancreas. Little is known about the in vivo dynamic interactions between T cells and beta cells or the kinetic behavior of other immune cell subsets in the pancreatic islets. Utilizing multiphoton microscopy we have designed a technique that allows for the real-time visualization of diabetogenic T cells and dendritic cells in pancreatic islets in a live animal, including their interplay with beta cells and the vasculature. Using a custom designed stage, the pancreas was surgically exposed under live conditions so that imaging of islets under intact blood pressure and oxygen supply became possible. We demonstrate here that this approach allows for the tracking of diabetogenic leukocytes as well as vascularization phenotype of islets and accumulation of dendritic cells in islets during diabetes pathogenesis. This technique should be useful in mapping crucial kinetic events in T1D pathogenesis and in testing the impact of immune based interventions on T cell migration, extravasation and islet destruction

Towards a Low-Cost Mobile Subcutaneous Vein Detection Solution Using Near-Infrared Spectroscopy

Excessive venipunctures are both time- and resource-consuming events, which cause anxiety, pain, and distress in patients, or can lead to severe harmful injuries. We propose a low-cost mobile health solution for subcutaneous vein detection using near-infrared spectroscopy, along with an assessment of the current state of the art in this field. The first objective of this study was to get a deeper overview of the research topic, through the initial team discussions and a detailed literature review (using both academic and grey literature). The second objective, that is, identifying the commercial systems employing near-infrared spectroscopy, was conducted using the PubMed database. The goal of the third objective was to identify and evaluate (using the IEEE Xplore database) the research efforts in the field of low-cost near-infrared imaging in general, as a basis for the conceptual model of the upcoming prototype. Although the reviewed commercial devices have demonstrated usefulness and value for peripheral veins visualization, other evaluated clinical outcomes are less conclusive. Previous studies regarding low-cost near-infrared systems demonstrated the general feasibility of developing cost-effective vein detection systems; however, their limitations are restricting their applicability to clinical practice. Finally, based on the current findings, we outline the future research direction

Crowded-Field Astrometry with the Space Interferometry Mission - I. Estimating the Single-Measurement Astrometric Bias Arising from Confusion

The accuracy of position measurements on stellar targets with the future Space Interferometry Mission (SIM) will be limited not only by photon noise and by the properties of the instrument (design, stability, etc.) and the overall measurement program (observing strategy, reduction methods, etc.), but also by the presence of other "confusing" stars in the field of view (FOV). We use a simple "phasor" model as an aid to understanding the main effects of this "confusion bias" in single observations with SIM. This analytic model has been implemented numerically in a computer code and applied to a selection of typical SIM target fields drawn from some of the Key Projects already accepted for the Mission. We expect that less than 1% of all SIM targets will be vulnerable to confusion bias; we show that for the present SIM design, confusion may be a concern if the surface density of field stars exceeds 0.4 star/arcsec^2. We have developed a software tool as an aid to ascertaining the possible presence of confusion bias in single observations of any arbitrary field. Some a priori knowledge of the locations and spectral energy distributions of the few brightest stars in the FOV is helpful in establishing the possible presence of confusion bias, but the information is in general not likely to be available with sufficient accuracy to permit its removal. We discuss several ways of reducing the likelihood of confusion bias in crowded fields. Finally, several limitations of the present semi-analytic approach are reviewed, and their effects on the present results are estimated. The simple model presented here provides a good physical understanding of how confusion arises in a single SIM observation, and has sufficient precision to establish the likelihood of a bias in most cases.Comment: 28 pages, 20 figures, 1 table; to appear in December 2007 issue of PAS

Sparse and low rank approximations for action recognition

Action recognition is crucial area of research in computer vision with wide range of applications in surveillance, patient-monitoring systems, video indexing, Human- Computer Interaction and many more. These applications require automated action recognition. Robust classification methods are sought-after despite influential research in this field over past decade. The data resources have grown tremendously owing to the advances in the digital revolution which cannot be compared to the meagre resources in the past. The main limitation on a system when dealing with video data is the computational burden due to large dimensions and data redundancy. Sparse and low rank approximation methods have evolved recently which aim at concise and meaningful representation of data. This thesis explores the application of sparse and low rank approximation methods in the context of video data classification with the following contributions. 1. An approach for solving the problem of action and gesture classification is proposed within the sparse representation domain, effectively dealing with large feature dimensions, 2. Low rank matrix completion approach is proposed to jointly classify more than one action 3. Deep features are proposed for robust classification of multiple actions within matrix completion framework which can handle data deficiencies. This thesis starts with the applicability of sparse representations based classifi- cation methods to the problem of action and gesture recognition. Random projection is used to reduce the dimensionality of the features. These are referred to as compressed features in this thesis. The dictionary formed with compressed features has proved to be efficient for the classification task achieving comparable results to the state of the art. Next, this thesis addresses the more promising problem of simultaneous classifi- cation of multiple actions. This is treated as matrix completion problem under transduction setting. Matrix completion methods are considered as the generic extension to the sparse representation methods from compressed sensing point of view. The features and corresponding labels of the training and test data are concatenated and placed as columns of a matrix. The unknown test labels would be the missing entries in that matrix. This is solved using rank minimization techniques based on the assumption that the underlying complete matrix would be a low rank one. This approach has achieved results better than the state of the art on datasets with varying complexities. This thesis then extends the matrix completion framework for joint classification of actions to handle the missing features besides missing test labels. In this context, deep features from a convolutional neural network are proposed. A convolutional neural network is trained on the training data and features are extracted from train and test data from the trained network. The performance of the deep features has proved to be promising when compared to the state of the art hand-crafted features

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Computational fluid dynamics analysis of moisture ingress in aircraft structural composite materials

Moisture in composite materials has been proven to be an important issue leading to significant deterioration of commercial aircraft wing structures. Lingering problems associated with this issue which is initiated with defects during manufacturing and finishing include delamination, de-bonding, potential fracture, debris etc. Despite extensive investigation and refinement in structural design, the water ingress problem persists as no general mitigation technique has yet been developed. Developing sustainable solutions to the water ingress problem can be very time-consuming and costly. The increasing use of composites in the aviation industry, in, for example, honeycomb sandwich components highlights the significant need to address the moisture ingress problem and develop deeper insights which can assist in combatting this problem. Experimental testing, although the most dependable approach, can take months, if not years. Numerical simulations provide a powerful and alternative approach to experimental studies for obtaining an insight into the mechanisms and impact of moisture ingress in aircraft composites. The principal advantage is that they can be conducted considerably faster, are less costly than laboratory testing, and furthermore can also utilize the results of laboratory studies to aid in visualizing practical problems. Therefore, the present study applies a computational fluid dynamics (CFD) methodology, specifically ANSYS finite volume software and the three fluid-based solvers, Fluent, CFX and ANSYS fluid structure interaction (FSI), to simulate water ingress in composite aerospace structures. It is demonstrated that ANSYS Fluent is a satisfactory computational solver for fundamental studies, providing reasonably accurate results relatively quickly, especially while simulating two-dimensional components. Three-dimensional components are ideally simulated on CFX, although the accuracy achievable is reduced. The structural-fluid based solver, ANSYS FSI (fluid structure interaction), unfortunately does not fully implement the material studied leading to reduced accuracy. The simulations reveal interesting features associated with different inlet velocities, inlet fastener hole numbers, void number and dimensions. Pressure, velocity, streamline, total deformation and normal stress plots are presented with extensive interpretation. Furthermore, some possible mitigation pathways for water ingress effects including hydrophobic coatings are outlined. KEY WORDS: Aircraft composites, Computational Fluid Dynamics, ANSYS, moisture ingress, Fluent, CFX, (fluid structure interaction) FSI, velocity, pressure, total deformation; elevator, mesh density