U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in ...extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.
Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly ...promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery. This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.
A strong emphasis on safety in commercial and military aviation is as old and as significant as the field of aviation itself. With the growing role of autonomy in aviation, the future of flight ...comprises of two general directions: manned and unmanned. Manned aircraft is the more established area, in which a human flight crew serves as the main driving force in ensuring an aircraft’s safety and success. Within this time-tested concept, the most significant bottleneck of safety lies within a crew managing tasks of high mental workload. In recent years, autonomy has aided in easing cognitive workload. From there, the challenge lies within applying a seamless blend of human and autonomous control based on the needs of one’s mental load. Meanwhile, the field of unmanned aerial vehicles (UAVs) poses its own unique challenges of integrating into a shared airspace and transitioning from remote human-centric control to fully autonomous control. In such a case, minimizing discrepancies between predicted UAV behavior and actual outcomes is an ongoing task to ensure a safe and reliable flight. While manned and unmanned flight safety may seem distinctly different in these regards, this dissertation proposes an overarching common theme that lies within the ability to effectively model inputs and outputs through machine learning to predict potential safety hazards and thereby improve the overall flight experience. This process is conducted by 1) evaluating different machine learning techniques on assessing cognitive workload, 2) predicting trajectories for autonomous UAVs, and 3) developing adaptive systems that dynamically select appropriate algorithms to ensure optimal prediction accuracy at any given time. The first phase of the research involves the manned side of flight safety and does so by examining effects of different machine learning techniques used for assessing cognitive workload. This begins by comparing the different algorithms on four different datasets involving cognitive activity based on physiological and subjective data. From there, two new algorithms are developed that dynamically select a machine learning technique based on the attributes of the given physiological data: one that statically chooses a method and another that dynamically changes methods over time based on which is projected to provide optimal accuracy and efficiency. By being able to accurately classify an activity with a certain amount of expected cognitive load, this can be applied in aircraft to assist pilots in early detection of mental overload and underload. The second phase then invokes unmanned flight safety by aiming to enhance prediction of autonomous UAV data. This is done by fusing navigational coordinates with radar data (e.g. how close a UAV is to another vehicle or an obstacle) using Dempster-Shafer Evidence Theory. The algorithm is then compared against other mechanisms for UAV data prediction with encouraging results. Finally, an additional algorithm is developed that dynamically chooses methods at different points in time based on which is expected to produce the best accuracy. Thus, by improving accurate predictions of future UAV data, unmanned flight safety can be enhanced by minimizing discrepancies between expectations and outcomes in an increasingly unpredictable shared airspace.
Wireless sensor networks are a collection of small, disposable, low-power devices that monitor vital sensory data for a variety of civil, military, and navigational applications. For instance, some ...cities have a network of emergency phones scattered across walkways so that citizens in distress can immediately reach emergency services. Using effective localization techniques that are both highly accurate and of low computational cost, 911 services can dispatch police, fire, or medical services to a caller's location as quickly as humanly possible. Hence, from the standpoint of locating a node in a network, every percent of accuracy achieved and every second of time saved can be the difference between life and death. This thesis presents two novel algorithms for wireless sensor network localization through the incorporation of Dempster-Shafer Evidence Theory. The first technique follows a verbose methodology for node positioning that fuses multiple types of signal measurements, such as received signal strength and angle of arrival, and utilizes the expected value property of DS Theory to geo-locate a node with a moderate accuracy of 78-87%, thereby providing an introductory approach to the previously untapped fusion of WSN localization and DS Theory. The second approach consists of a low cost, highly accurate data fusion technique that incorporates the plausibility property of DS Theory to establish a high level of accuracy. Due to this unique approach to data fusion and predictive data modeling, this second algorithm achieves an optimal accuracy range of 83-98% in a flexible multitude of simulation scenarios at a fraction of the runtime required under prior established localization techniques. Overall, these two algorithms provide a ground-breaking new application of Dempster-Shafer Theory as well as fast, accurate, and informative new approaches to wireless sensor network localization that can improve a wide range of vital applications.
An emerging topic in human-computer interaction research involves optimal collaboration between humans and machines to achieve a particular goal. One approach to such a goal involves sliding-scale ...autonomy, in which a machine dynamically adjusts between different levels of autonomy based on a variety of measurements. In this paper, we propose a system to predict skill level and workload for aircraft pilots using machine learning algorithms. Our proposed system uses the pilot's heart rate variability and flight control data, including pilot inputs such as throttle and aileron, and flight sensor data such as latitude and longitude. We conduct a user study on 15 pilots, each flying the same 5 pre-defined routes on a flight simulator. Our results indicate that the flight control data alone are sufficient to provide a near-perfect classification of a pilot's skill level into expert or novice. On the other hand, predicting mental workload is much more difficult, and a combination of flight control and heart rate data is required to obtain an accurate estimate of mental workload. Our findings provide the first step towards a sliding-scale autonomous system for aviation.
Heparan sulfate proteoglycans are essential for biological processes regulated by fibroblast growth factors (FGFs). Heparan sulfate (HS) regulates the activity of FGFs by acting as a coreceptor at ...the cell surface, enhancing FGF-FGFR affinity, and being a storage reservoir for FGFs in the extracellular matrix (ECM). Here we demonstrate a critical role for heparanase during mouse submandibular gland (SMG) branching morphogenesis. Heparanase, an endoglycosidase, colocalized with perlecan in the basement membrane and in epithelial clefts of SMGs. Inhibition of heparanase activity in organ culture decreased branching morphogenesis, and this inhibition was rescued specifically by FGF10 and not by other FGFs. By contrast, exogenous heparanase increased SMG branching and MAPK signaling and, surprisingly, when isolated epithelia were cultured in a three-dimensional ECM with FGF10, it increased the number of lateral branches and end buds. In a solid-phase binding assay, an FGF10-FGFR2b complex was released from the ECM by heparanase. In addition, surface plasmon resonance (SPR) analysis showed that FGF10 and the FGF10-FGFR2b complex bound to purified perlecan HS and could be released by heparanase. We used the FGF10-FGFR2b complex as a probe for HS in SMGs, and it colocalized with perlecan in the basement membrane and partly colocalized with syndecan 1 in the epithelium, and binding was reduced by treatment with heparanase. In summary, our results show heparanase releases FGF10 from perlecan HS in the basement membrane, increasing MAPK signaling, epithelial clefting, and lateral branch formation, which results in increased branching morphogenesis.
A numerical model is developed for a phase-locked array of quantum cascade lasers. The population density is derived from rate equations. The temperature distribution for stationary generation is ...produced by ohmic heating. It is shown that the results of above-threshold operation modeling by the semi-vectorial beam propagation method are in good agreement with the modal analysis provided by the vectorial-COMSOL solver supplemented with the Rigrod's model estimations. The wall-plug efficiency and the limits of the single-mode lasing are found. Thermal lensing is shown to be the main reason limiting the single-spatial-mode power under CW-operating conditions.
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