Browsing by Subject "Biomedical"
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Item Remote-Controlled Self-Assembly of Three-Dimensional Micro Structures for Ultra-Sensitive Sensors and Three-Dimensional Metamaterials(2018-10) Liu, ChaoSelf-assembly has been widely used to fabricate micro-scale three-dimensional (3D) structures for various applications like sensors, drug delivery systems, and advanced robotics (e.g., micro-actuators, micro-machines). Self-assembly is always driven by external sources (e.g., heat, solvent, pH), which makes the assembly process hard to control and leads to extremely low yield. Direct contact of heat or chemicals is usually required to trigger a self-assembly process, which limits the applications of self-assembly and decreases the manipulative capability of the process. To address the issues of the traditional direct triggered self-assembly, my Ph.D. work involved in developing novel remote-controlled self-assembly techniques with microwave and induction energies, combining the self-assembly technique with advanced metamaterial (MM) designs, and exploring their potential applications as 3D sensors and devices. The goal of the work is to achieve advanced remotely controlled self-assembly to improve the yield and manipulative capability of the assembly process and discover new aspects of the assembly technique (e.g., biocompatible assembly, multiple and sequential assembly) and its applications (e.g., 3D sensors, 3D MM devices). For remotely controlled self-assembly, electromagnetic waves can be remotely applied to the metal thin films within the microstructures. Eddy current can be created inside the thin films and generate heat to melt the polymeric hinges. The molten hinges generate surface tension force to transform the two-dimensional (2D) net into 3D microstructures. Induction heating can trigger self-assembly without harming live organs or tissues, which is suitable for biomedical applications. Remote-controlled self-assembly also allows multiple and sequential self-assembly. The movements of each part of structure can be precisely controlled by adjusting the energy sources in a remote location, increasing manipulative capability of the 3D assembly process. The achievement of sequential self-assembly and multiple folding angles in a single structure is essential for building complex microstructures and micro-actuators. One important application for remote-controlled 3D self-assembled structure is to build 3D MM devices. Split ring resonators (SRRs) and closed ring resonators (CRRs) can be patterned on each face of the self-assembled structures to achieve 3D MMs with fully anisotropic and isotropic behaviors. However, the quality factor (Q-factor) of conventional MMs is low (typically under 10), results in low sensitivity and selectivity. To increase Q-factor of the MMs, we developed novel nanopillar-based MMs driven by displacement current. The nanopillar-based MMs contain thousands of metallic nanopillars with nanoscale dielectric gaps between them. Forming the MMs with nanopillars and nano gaps decreases the Ohmic energy loss in the resonator and increases the energy storage in the dielectric nano gaps, thus an enhanced Q-factor up to 14000 can be achieved. The ultra-high Q nanopillar-based MM can be patterned on each face of the self-assembled 3D structures to realize ultra-high Q 3D MM structures. Novel ultra-sensitive THz MMs and 3D MMs combined with remote-controlled self-assembly opens a new area of creating diverse sensors and devices for 3D optoelectronic, 3D MMs, and ultra-high sensitive biomedical sensors. This thesis will be roughly divided into two parts. We begin with part one by introducing the novel remotely controlled self-assembly using electromagnetic energies that I have developed over my Ph.D. program as well as its unique properties and benefits over traditional self-assemblies. The second part involves my design and theory of ultra-high Q nanopillar-based MM and the 3D MM devices by combining the nanopillar-based MM with self-assembly technique.Item Supervised and knowlege-based methods for disambiguating terms in biomedical text using the UMLS and MetaMap.(2009-09) McInnes, Bridget ThomsonWord Sense Disambiguation is the task of automatically identifying the appropriate sense (or concept) of an ambiguous word, for example, the term 'cold' could refer to the temperature or a virus depending on the context in which it is used. Not being able to identify the intended concept of an ambiguous word negatively impacts the accuracy of biomedical applications such as medical coding and indexing which are becoming essential in the biomedical and clinical world with the push towards electronic medical records and the growing amount of information that is available to biomedical researchers and clinicians. This dissertation focuses on disambiguating ambiguous words in biomedical text. This dissertation presents two methods, K-CUI and A-CUI, that can disambiguate ambiguous terms in any biomedical text using information from the Unified Medical Language System (UMLS). K-CUI explores the use of Concept Unique Identifiers (CUIs) as assigned by MetaMap, as features for a supervised learning method for word sense disambiguation. It also investigates four techniques to reduce the noise in the feature set by restricting which CUIs to include. The first technique is windowing, whose results show that in biomedical text indicative CUIs are highly localized. The second is a frequency cutoff, whose results show that when a dataset contains a high majority concept, the features that only occur a few times are essential in disambiguating the minority concepts. The third is a MetaMap Indexing cutoff, whose results show that word concepts are correlated with the topical information describing an instance. The fourth is a semantic similarity cutoff, whose results show in biomedical text, indicative features have a high semantic similarity with at least one of the possible concepts of the ambiguous word. A-CUI is a knowledge-based method that uses information from the UMLS and MetaMap mapped text to represent the context of the possible concepts of an ambiguous word. It investigates three types of contextual representations. The first uses the concept's definition in the UMLS, whose results show that the context used with the words the definition can be used to represent its context of the concept. The second uses the preferred and associated terms from the UMLS, whose results show that the terms themselves do not provide enough contextual information to disambiguate between the possible concepts of a target word. The third uses the words surrounding the concept in MetaMap mapped text, whose results show that the information provided by MetaMap is distinct enough to distinguish between the possible concepts for disambiguation purposes. K-CUI and A-CUI are evaluated using the NLM-WSD dataset which consists of Medline abstracts. Previous work in this area have also evaluated their methods using the same dataset and in some cases tailored their methods to work only on Medline abstracts. Identifying the correct concept of an ambiguous term in Medline abstracts is a significant problem but the advantage of K-CUI and A-CUI though is that they are portable systems that can disambiguate terms in any biomedical text, unlike previous methods that are limited to only Medline abstracts. There has also been previous work that determines the correct concept of a target word by first identifying the target words semantic type which is a broad categorization of a concept. After the semantic type of the ambiguous words is identified, then the correct concept is identified based on its semantic type. The assumption is that each possible concept of a target word has a unique semantic type. If the possible concepts have the same semantic type this method cannot distinguish between them; A-CUI and K-CUI do not have the limitation. Also, identifying the semantic type of a target word is a simpler problem than identifying the concept because semantic types are a coarser grained categorization than CUIs which makes them easier to assign.