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As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a single joint. To capture the joint structure and manipulation sequence of any object, I introduce the "Object Kinematic State Machines" (OKSMs), a novel representation that models the kinematic constraints and manipulation sequences of multi-DoF objects. I also present Pokenet, a deep neural network architecture that estimates the OKSMs from the sequence of point cloud data of human demonstrations. I conduct experiments on both simulated and real-world datasets to validate my approach. First, I evaluate the modeling of multi-DoF objects on a simulated dataset, comparing against the current state-of-the-art method. I then assess Pokenet's real-world usability on a dataset collected in my lab, comprising 5,500 data points across 4 objects. Results showcase that my method can successfully estimate joint parameters of novel multi-DoF objects with over 25% more accuracy on average than prior methods.
ContributorsGUPTA, ANMOL (Author) / Gopalan, Nakul (Thesis advisor) / Zhang, Yu (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2024
Description
Janus Transition Metal Dichalcogenides (TMDs) are emerging 2D quantum materials with an asymmetric chalcogen configuration that induces an out-of-plane dipole moment. Their synthesis has been a limiting factor in exploring these systems' many-body physics and interactions. This dissertation examines the challenges associated with synthesis and charts the excitonic landscape of Janus crystals by proposing the development of the Selective Epitaxy and Atomic Replacement (SEAR) technique. SEAR utilizes ionized radical precursors to modify TMD monolayers into their Janus counterparts selectively. The synthesis is coupled with optical spectroscopy and monitored in real-time, enabling precise control of reaction kinetics and the structural evolution of Janus TMDs. The results demonstrate the synthesis of Janus TMDs at ambient temperatures, reducing defects and preserving the structural integrity with the hitherto best-reported exciton linewidth emission value, indicating ultra-high optical quality. Cryogenic optical spectroscopy (4K) coupled with a magnetic field on Janus monolayers has allowed the isolation of excitonic transitions and the identification of charged exciton complexes. Further study into macroscopic and microscopic defects reveals that structural asymmetry results in the spontaneous formation of 2D Janus Nanoscrolls from an in-plane strain. The chalcogen arrangement in these structures dictates two types of scrolling dynamics that form Archimedean or inverted C-scrolls. High-resolution scanning transmission electron microscopy of these superlattices shows a preferential orientation of scrolling and formation of Moiré patterns. These materials' thermodynamically favorable defect states are identified and shown to be optically active. The encapsulation of Janus TMDs with hexagonal Boron Nitride (h-BN) has allowed isolation defect transitions. DFT coupled with power-dependent PL spectroscopy at 4K shows the broad defect band to be a convolution of individual defect states with extremely narrow linewidth (2 meV) indicative of a two-state quantum system. The research presents a comprehensive synthesis approach with insights into the structural and morphological stability of 2D Janus layers, establishing a complete structure-property correlation of optical transitions and defect states, broadening the scope for practical applications in quantum information technologies.
ContributorsSayyad, Mohammed Yasir (Author) / Tongay, Sefaattin (Thesis advisor) / Esqueda, Ivan S (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2024
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024
Description
Image denoising, a fundamental task in computer vision, poses significant challenges due to its inherently inverse and ill-posed nature. Despite advancements in traditional methods and supervised learning approaches, particularly in medical imaging such as Medical Resonance Imaging (MRI) scans, the reliance on paired datasets and known noise distributions remains a practical hurdle. Recent progress in noise statistical independence theory and diffusion models has revitalized research interest, offering promising avenues for unsupervised denoising. However, existing methods often yield overly smoothed results or introduce hallucinated structures, limiting their clinical applicability. This thesis tackles the core challenge of progressing towards unsupervised denoising of MRI scans. It aims to retain intricate details without smoothing or introducing artificial structures, thus ensuring the production of high-quality MRI images. The thesis makes a three-fold contribution: Firstly, it presents a detailed analysis of traditional techniques, early machine learning algorithms for denoising, and new statistical-based models, with an extensive evaluation study on self-supervised denoising methods highlighting their limitations. Secondly, it conducts an evaluation study on an emerging class of diffusion-based denoising methods, accompanied by additional empirical findings and discussions on their effectiveness and limitations, proposing solutions to enhance their utility. Lastly, it introduces a novel approach, Unsupervised Multi-stage Ensemble Deep Learning with diffusion models for denoising MRI scans (MEDL). Leveraging diffusion models, this approach operates independently of signal or noise priors and incorporates weighted rescaling of multi-stage reconstructions to balance over-smoothing and hallucination tendencies. Evaluation using benchmark datasets demonstrates an average gain of 1dB and 2% in PSNR and SSIM metrics, respectively, over existing approaches.
ContributorsVora, Sahil (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Zhou, Yuxiang (Committee member) / Arizona State University (Publisher)
Created2024
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
ContributorsMadiraju, NaveenSai (Author) / Liang, Jianming (Thesis advisor) / Wang, Yalin (Thesis advisor) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recently, two-dimensional (2D) materials have emerged as a new class of materials with highly attractive electronic, optical, magnetic, and thermal properties. However, there exists a sub-category of 2D layers wherein constituent metal atoms are arranged in a way that they form weakly coupled chains confined in the 2D landscape. These weakly coupled chains extend along particular lattice directions and host highly attractive properties including high thermal conduction pathways, high-mobility carriers, and polarized excitons. In a sense, these materials offer a bridge between traditional one-dimensional (1D) materials (nanowires and nanotubes) and 2D layered systems. Therefore, they are often referred as pseudo-1D materials, and are anticipated to impact photonics and optoelectronics fields.
This dissertation focuses on the novel growth routes and fundamental investigation of the physical properties of pseudo-1D materials. Example systems are based on transition metal chalcogenide such as rhenium disulfide (ReS2), titanium trisulfide (TiS3), tantalum trisulfide (TaS3), and titanium-niobium trisulfide (Nb(1-x)TixS3) ternary alloys. Advanced growth, spectroscopy, and microscopy techniques with density functional theory (DFT) calculations have offered the opportunity to understand the properties of these materials both experimentally and theoretically. The first controllable growth of ReS2 flakes with well-defined domain architectures has been established by a state-of-art chemical vapor deposition (CVD) method. High-resolution electron microscopy has offered the very first investigation into the structural pseudo-1D nature of these materials at an atomic level such as the chain-like features, grain boundaries, and local defects.
Pressure-dependent Raman spectroscopy and DFT calculations have investigated the origin of the Raman vibrational modes in TiS3 and TaS3, and discovered the unusual pressure response and its effect on Raman anisotropy. Interestingly, the structural and vibrational anisotropy can be retained in the Nb(1-x)TixS3 alloy system with the presence of phase transition at a nominal Ti alloying limit. Results have offered valuable experimental and theoretical insights into the growth routes as well as the structural, optical, and vibrational properties of typical pseudo-1D layered systems. The overall findings hope to shield lights to the understanding of this entire class of materials and benefit the design of 2D electronics and optoelectronics.
This dissertation focuses on the novel growth routes and fundamental investigation of the physical properties of pseudo-1D materials. Example systems are based on transition metal chalcogenide such as rhenium disulfide (ReS2), titanium trisulfide (TiS3), tantalum trisulfide (TaS3), and titanium-niobium trisulfide (Nb(1-x)TixS3) ternary alloys. Advanced growth, spectroscopy, and microscopy techniques with density functional theory (DFT) calculations have offered the opportunity to understand the properties of these materials both experimentally and theoretically. The first controllable growth of ReS2 flakes with well-defined domain architectures has been established by a state-of-art chemical vapor deposition (CVD) method. High-resolution electron microscopy has offered the very first investigation into the structural pseudo-1D nature of these materials at an atomic level such as the chain-like features, grain boundaries, and local defects.
Pressure-dependent Raman spectroscopy and DFT calculations have investigated the origin of the Raman vibrational modes in TiS3 and TaS3, and discovered the unusual pressure response and its effect on Raman anisotropy. Interestingly, the structural and vibrational anisotropy can be retained in the Nb(1-x)TixS3 alloy system with the presence of phase transition at a nominal Ti alloying limit. Results have offered valuable experimental and theoretical insights into the growth routes as well as the structural, optical, and vibrational properties of typical pseudo-1D layered systems. The overall findings hope to shield lights to the understanding of this entire class of materials and benefit the design of 2D electronics and optoelectronics.
ContributorsWu, Kedi (Author) / Tongay, Sefaattin (Thesis advisor) / Zhuang, Houlong (Committee member) / Green, Matthew (Committee member) / Arizona State University (Publisher)
Created2018
Description
There has been a surge in two-dimensional (2D) materials field since the discovery of graphene in 2004. Recently, a new class of layered atomically thin materials that exhibit in-plane structural anisotropy, such as black phosphorous, transition metal trichalcogenides and rhenium dichalcogenides (ReS2), have attracted great attention. The reduced symmetry in these novel 2D materials gives rise to highly anisotropic physical properties that enable unique applications in next-gen electronics and optoelectronics. For example, higher carrier mobility along one preferential crystal direction for anisotropic field effect transistors and anisotropic photon absorption for polarization-sensitive photodetectors.
This dissertation endeavors to address two key challenges towards practical application of anisotropic materials. One is the scalable production of high quality 2D anisotropic thin films, and the other is the controllability over anisotropy present in synthesized crystals. The investigation is focused primarily on rhenium disulfide because of its chemical similarity to conventional 2D transition metal dichalcogenides and yet anisotropic nature. Carefully designed vapor phase deposition has been demonstrated effective for batch synthesis of high quality ReS2 monolayer. Heteroepitaxial growth proves to be a feasible route for controlling anisotropic directions. Scanning/transmission electron microscopy and angle-resolved Raman spectroscopy have been extensively applied to reveal the structure-property relationship in synthesized 2D anisotropic layers and their heterostructures.
This dissertation endeavors to address two key challenges towards practical application of anisotropic materials. One is the scalable production of high quality 2D anisotropic thin films, and the other is the controllability over anisotropy present in synthesized crystals. The investigation is focused primarily on rhenium disulfide because of its chemical similarity to conventional 2D transition metal dichalcogenides and yet anisotropic nature. Carefully designed vapor phase deposition has been demonstrated effective for batch synthesis of high quality ReS2 monolayer. Heteroepitaxial growth proves to be a feasible route for controlling anisotropic directions. Scanning/transmission electron microscopy and angle-resolved Raman spectroscopy have been extensively applied to reveal the structure-property relationship in synthesized 2D anisotropic layers and their heterostructures.
ContributorsChen, Bin, 1968- (Author) / Tongay, Sefaattin (Thesis advisor) / Bertoni, Mariana (Committee member) / Chang, Lan-Yun (Committee member) / Arizona State University (Publisher)
Created2018
Description
Layer structured two dimensional (2D) semiconductors have gained much interest due to their intriguing optical and electronic properties induced by the unique van der Waals bonding between layers. The extraordinary success for graphene and transition metal dichalcogenides (TMDCs) has triggered a constant search for novel 2D semiconductors beyond them. Gallium chalcogenides, belonging to the group III-VI compounds, are a new class of 2D semiconductors that carry a variety of interesting properties including wide spectrum coverage of their bandgaps and thus are promising candidates for next generation electronic and optoelectronic devices. Pushing these materials toward applications requires more controllable synthesis methods and facile routes for engineering their properties on demand.
In this dissertation, vapor phase transport is used to synthesize layer structured gallium chalcogenide nanomaterials with highly controlled structure, morphology and properties, with particular emphasis on GaSe, GaTe and GaSeTe alloys. Multiple routes are used to manipulate the physical properties of these materials including strain engineering, defect engineering and phase engineering. First, 2D GaSe with controlled morphologies is synthesized on Si(111) substrates and the bandgap is significantly reduced from 2 eV to 1.7 eV due to lateral tensile strain. By applying vertical compressive strain using a diamond anvil cell, the band gap can be further reduced to 1.4 eV. Next, pseudo-1D GaTe nanomaterials with a monoclinic structure are synthesized on various substrates. The product exhibits highly anisotropic atomic structure and properties characterized by high-resolution transmission electron microscopy and angle resolved Raman and photoluminescence (PL) spectroscopy. Multiple sharp PL emissions below the bandgap are found due to defects localized at the edges and grain boundaries. Finally, layer structured GaSe1-xTex alloys across the full composition range are synthesized on GaAs(111) substrates. Results show that GaAs(111) substrate plays an essential role in stabilizing the metastable single-phase alloys within the miscibility gaps. A hexagonal to monoclinic phase crossover is observed as the Te content increases. The phase crossover features coexistence of both phases and isotropic to anisotropic structural transition.
Overall, this work provides insights into the controlled synthesis of gallium chalcogenides and opens up new opportunities towards optoelectronic applications that require tunable material properties.
In this dissertation, vapor phase transport is used to synthesize layer structured gallium chalcogenide nanomaterials with highly controlled structure, morphology and properties, with particular emphasis on GaSe, GaTe and GaSeTe alloys. Multiple routes are used to manipulate the physical properties of these materials including strain engineering, defect engineering and phase engineering. First, 2D GaSe with controlled morphologies is synthesized on Si(111) substrates and the bandgap is significantly reduced from 2 eV to 1.7 eV due to lateral tensile strain. By applying vertical compressive strain using a diamond anvil cell, the band gap can be further reduced to 1.4 eV. Next, pseudo-1D GaTe nanomaterials with a monoclinic structure are synthesized on various substrates. The product exhibits highly anisotropic atomic structure and properties characterized by high-resolution transmission electron microscopy and angle resolved Raman and photoluminescence (PL) spectroscopy. Multiple sharp PL emissions below the bandgap are found due to defects localized at the edges and grain boundaries. Finally, layer structured GaSe1-xTex alloys across the full composition range are synthesized on GaAs(111) substrates. Results show that GaAs(111) substrate plays an essential role in stabilizing the metastable single-phase alloys within the miscibility gaps. A hexagonal to monoclinic phase crossover is observed as the Te content increases. The phase crossover features coexistence of both phases and isotropic to anisotropic structural transition.
Overall, this work provides insights into the controlled synthesis of gallium chalcogenides and opens up new opportunities towards optoelectronic applications that require tunable material properties.
ContributorsCai, Hui, Ph.D (Author) / Tongay, Sefaattin (Thesis advisor) / Dwyer, Christian (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Nanoporous materials, with pore sizes less than one nanometer, have been incorporated as filler materials into state-of-the-art polyamide-based thin-film composite membranes to create thin-film nanocomposite (TFN) membranes for reverse osmosis (RO) desalination. However, these TFN membranes have inconsistent changes in desalination performance as a result of filler incorporation. The nano-sized filler’s transport role for enhancing water permeability is unknown: specifically, there is debate around the individual transport contributions of the polymer, nanoporous particle, and polymer/particle interface. Limited studies exist on the pressure-driven water transport mechanism through nanoporous single-crystal nanoparticles. An understanding of the nanoporous particles water transport role in TFN membranes will provide a better physical insight on the improvement of desalination membranes.
This dissertation investigates water permeation through single-crystal molecular sieve zeolite A particles in TFN membranes in four steps. First, the meta-analysis of nanoporous materials (e.g., zeolites, MOFs, and graphene-based materials) in TFN membranes demonstrated non-uniform water-salt permselectivity performance changes with nanoporous fillers. Second, a systematic study was performed investigating different sizes of non-porous (pore-closed) and nanoporous (pore-opened) zeolite particles incorporated into conventionally polymerized TFN membranes; however, the challenges of particle aggregation, non-uniform particle dispersion, and possible particle leaching from the membranes limit analysis. Third, to limit aggregation and improve dispersion on the membrane, a TFN-model membrane synthesis recipe was developed that immobilized the nanoparticles onto the support membranes surface before the polymerization reaction. Fourth, to quantify the possible water transport pathways in these membranes, two different resistance models were employed.
The experimental results show that both TFN and TFN-model membranes with pore-opened particles have higher water permeance compared to those with pore-closed particles. Further analysis using the resistance in parallel and hybrid models yields that water permeability through the zeolite pores is smaller than that of the particle/polymer interface and higher than the water permeability of the pure polymer. Thus, nanoporous particles increase water permeability in TFN membranes primarily through increased water transport at particle/polymer interface. Because solute rejection is not significantly altered in our TFN and TFN-model systems, the results reveal that local changes in the polymer region at the polymer/particle interface yield high water permeability.
This dissertation investigates water permeation through single-crystal molecular sieve zeolite A particles in TFN membranes in four steps. First, the meta-analysis of nanoporous materials (e.g., zeolites, MOFs, and graphene-based materials) in TFN membranes demonstrated non-uniform water-salt permselectivity performance changes with nanoporous fillers. Second, a systematic study was performed investigating different sizes of non-porous (pore-closed) and nanoporous (pore-opened) zeolite particles incorporated into conventionally polymerized TFN membranes; however, the challenges of particle aggregation, non-uniform particle dispersion, and possible particle leaching from the membranes limit analysis. Third, to limit aggregation and improve dispersion on the membrane, a TFN-model membrane synthesis recipe was developed that immobilized the nanoparticles onto the support membranes surface before the polymerization reaction. Fourth, to quantify the possible water transport pathways in these membranes, two different resistance models were employed.
The experimental results show that both TFN and TFN-model membranes with pore-opened particles have higher water permeance compared to those with pore-closed particles. Further analysis using the resistance in parallel and hybrid models yields that water permeability through the zeolite pores is smaller than that of the particle/polymer interface and higher than the water permeability of the pure polymer. Thus, nanoporous particles increase water permeability in TFN membranes primarily through increased water transport at particle/polymer interface. Because solute rejection is not significantly altered in our TFN and TFN-model systems, the results reveal that local changes in the polymer region at the polymer/particle interface yield high water permeability.
ContributorsCay Durgun, Pinar (Author) / Lind, Mary Laura (Thesis advisor) / Lin, Jerry Y. S. (Committee member) / Green, Matthew D. (Committee member) / Seo, Dong K. (Committee member) / Tongay, Sefaattin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy.
ContributorsZhao, Xinlin (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016