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Reference-frame dependent differences in spatial navigation network functional connectivity
No full textSpatial navigation deficits are an early indicator of aging-related cognitive decline. Successful navigation depends on integrating allocentric and egocentric reference frames (RFs), processed by the hippocampus and posterior parietal cortex (PPC). The retrosplenial cortex (RSC) shares projections with both regions, facilitating RF integration through multimodal sensory processing. However, the impact of aging and RF preference on network connectivity remains unclear. We measured PPC-RSC functional connectivity using functional magnetic resonance imaging (fMRI) in younger adults during a Y-Maze navigation task. Participants were classified as having an allocentric or egocentric RF preference based on their performance during the Y-Maze probe trials. Voxelwise and region-based functional connectivity analyses were conducted to assess measures of RSC-PPC connectivity during resting-state and task-based conditions. Results revealed distinct RSC-PPC connectivity patterns when comparing navigation preferences across scanning conditions. Participants with an egocentric RF preference demonstrated greater resting-state RSC-PPC connectivity, whereas participants with an allocentric RF preference demonstrated greater task-based RSC-PPC connectivity. These findings indicate context-dependent activation within the spatial navigation network and demonstrate the impact of individual RF preferences and biases on network connectivity patterns.UndergraduateNeuroscienc
Exciton Delocalization and Quantum Dynamics in Push-Pull Semiconductor Polymers
No full textConjugated polymers are a class of potential semiconductor materials that can be employed in stretchable microelectronics, electrophysiology and infrared photodetectors due to their intrinsic stretchability and highly-engineerable molecular levels. Their interesting photophysical behavior and electrical performance largely originates from the bound electron-hole pairs shared between monomer units upon excitation. Such quasiparticles were termed Frenkel excitons. Although the polymer chain backbone planarization enhances the -electron conjugation, polymers chains are disordered, with torsional modes breaking long-range exciton delocalization. This thesis work shows how tuning the polymer solutions concentrations will impact the photophysical responses in electron push-pull conjugated polymers, which are sensitively determined by the chain conformations. The well-characterized sample set allows us to further investigate the intriguing many-body phenomenon as well as their dynamics. By performing fluence-dependent transient absorption and excitation-correlation photoluminescence spectroscopy, we show that the temporal decays cannot be simply explained by a time-independent exciton-exciton annihilation model, which indicates that the exciton diffusion process in DPP-DTT is not isotropic. Instead, we have to employ the one-dimensional exciton diffusion model to describe dynamics, which results in a constant diffusion length of 9 nm. Combining this result and the exciton delocalization behavior we showed in the first work, we suggest that the exciton exciton interactions are most likely enhanced by short-range Coulomb interactions or wave function overlap. To further investigate the nature of the many-body interactions and multi-quantum correlations in this type of materials, we implemented the two-dimension coherent spectroscopy. Interestingly, we observed not only attractive biexcitons but also unbound biexciton pairs, manifested as the dominant spectral features along the diagonal axis. In addition, we ascribe the side peak to a heterogeneous vibronic state. By considering the complete set of Feynman pathways, we were able to explain the imbalanced side peak observed in the one-quantum spectra. In perspective, although solution concentrations are probably one of the most fundamental parameters to consider for polymer sample processing, their impact on the chain conformation and aggregation should be addressed carefully. The gentle alterations in macromolecular conformation in these materials can have a significant impact on both how well the device operates and the quantum phenomena occurring within the material. Our work shed new light on the many-body interactions and correlations in the Frenkel exciton systems and their implications could be considered as figures of merit in explaining different performance of novel optoelectronic devices.Ph.D.Chemistry and Biochemistr
Measurement and Modeling of D2D Propagation Channels at Terahertz and Millimeter-wave bands in Workstation and Workbench Environments.
No full textPropagation channel measurements and modeling are required for proper design and efficient deployment of wireless communication systems. These channel models extract empirical parameters that help in statistical characterization of the propagation environment. Since any communication system is highly dependent on the propagating environment, it becomes necessary to study the channel in its environment of use.
The evolution of wireless communication has led to the connection of billions of devices and emergence of new(er) applications in remote sensing, localization, and ranging. These devices and applications will require significantly higher data throughput and lower latency. To support such high fidelity communication, there is a need for a reliable wireless architecture. However, the development of any such wireless systems requires a thorough study of scenario-specific channel models.
A large portion of the data traffic arising from the devices and applications would originate from indoor communication, especially in workstation and workbench environments. These environments are hotspots for network congestion because of the dense deployment of wireless multimedia devices communicating at these locations, such as laptops to monitors (for extended display), cell phones to projectors (for multimedia presentation), and other lab workbench equipment.
This thesis intends to provide a detailed characterization of a Device-to-Device (D2D) wireless propagation channel at Terahertz (THz) and Millimeter-wave (mm-wave) frequencies in the aforementioned environments. For the comprehensive characterization of the propagating channel, the experiments were performed in two environments:
1. Office Workstation Environment : The office workstation is unique in its structure and layout, with constituents such as computers, monitors, etc. serving as impediments to wireless propagation in this environment. Therefore, for the efficient deployment of D2D wireless systems operating at THz and mm-wave frequencies in this environment, a thorough characterization of Electromagnetic (EM) wave propagation in the aforementioned environment was performed. Large-scale parameters such as pathloss was found to be increasing with increasing frequency, i.e., higher at THz than mm-wave frequencies, while shadowing was larger in mm-wave than THz band. The spatial correlation values (computed after removing Line-of-Sight (LOS)) ranged between 0.13 and 0.34, indicating the sub-channels to be uncorrelated. Furthermore, the difference in capacity between LOS and Obstructed-Line-of-Sight (OLOS) was more pronounced in the THz band than at mm-wave frequencies.
2. Laboratory Workbench Environment : The workbench environment is replete with interacting objects that could act as obstructions and scatterers for wireless signal propagation. The pathloss was found to be severely affected by obstructions (around 66 dB in the experiments). The root-mean-squared (rms) delay spread was observed to decrease with increasing Transmitter (TX)-Receiver (RX) separation distance. The environment of study in this work is densely congested, in contrast to a sparse channel measurement. Therefore, the statistical channel model extracted from the channel parameters provided in this work will aid in realistic simulations for such environments.
There are very few works on scenario-specific channel measurement and modeling in these environments. A comprehensive channel measurement campaign and subsequent channel modeling have been provided in this thesis. Results presented in this study can be used by wireless system engineers working on sixth-generation (6G and beyond) applications to aid D2D wireless system design and implementation.M.S.Electrical and Computer Engineerin
Tuning Sparse Matrix Kernel Performance via Lightweight Signatures
No full textThis dissertation introduces lightweight sparse-matrix pattern and occupancy signatures for automatic input-dependent tiling of sparse kernels such as sparse-dense matrix multiplication (SpMM), sampled dense-dense matrix multiplication (SDDMM), etc. Sparse matrix kernels are typically bandwidth limited and can benefit from optimizations such as tiling to improve cache effectiveness. However, tiling sparse kernels is challenging. Irregular sparse matrices often present intra-matrix variations in the distribution and structure of non-zeros and a one-size-fits all approach to tiling these kernels can result in sub-optimal performance. I present lightweight signatures, Residues, that use down-sampling techniques and bit-vectors to capture this irregularity. Residues capture non-zero occupancy and structure of rectangular regions of the sparse-matrix plane in such a manner that combinations of these allow the evaluation of arbitrarily larger regions of the sparse matrix. I demonstrate how Residues can be used for making intelligent tiling decisions that are both data reuse- and data-movement-aware, tiling, specifically for single sparse matrix kernels like SpMM and SDDMM. These tiling techniques, ResGeMM and RASSM, greedily combine residue entries to analyze different tile shapes and generate tiles with a high cache volume footprint and data-reuse potential to improve performance. The maximum cache resident volume (temporal volume) of sparse kernels varies during execution, and statically determining this is not straightforward. I make the observation that the temporal volume problem for single-sparse-matrix-kernels is input-dependent and can be mapped to the maximum overlapping interval analysis problem. I augment RASSM with the ability to leverage this analysis for improved tiling. This results in higher performance over static-spatial techniques and other state-of-the-art sparse tiling methods. Finally, this dissertation demonstrates the use of signatures in tiling sparse-sparse matrix multiplication (SpGeMM) when hardware accelerators are used for the partial product reduction phase of the algorithm.Ph.D.Computer Scienc
Human-AI Partnerships in Gesture-Controlled Interactive Music Systems
No full textThis dissertation explores the use of artificial intelligence (AI) in interactive music systems designed to create music based on gestural input from users. It presents three AI-based interactive music systems that collaborate with a performer by analyzing their gestures and motion to generate audio changes. The first system, Captune, uses machine learning models of varying depth to automate changes in musical parameters for looping audio. The second system, PoseFX, communicates its decision-making to the user through visualizations and musical output. The third system, GestAlt, uses online machine learning and reinforcement learning to adapt to a user’s hand motion patterns and allows a user to communicate their musical goals to the system.
Each system was evaluated with a study that measured how participants perceived the systems as creatively autonomous partners, how their understanding of the systems affected their relationships with the AI, and how their perceptions evolved as they learned how to perform with the system. Participants reported higher amounts of creativity and expression with a version of Captune with a deeper neural network. Additionally, visualizations supporting their understanding of PoseFX improved the ability of the participants to perform with it in a way that better matched their performance goals. When performing with GestAlt multiple times, their ability to communicate with the agent increased trust over time and the participants developed a sense of shared goals and motion with the system. This dissertation presents findings from these studies as design principles for AI-based interactive music systems to support human-AI collaboration.Ph.D.Music Technolog
Raw data for article: "Supra-resonant wingbeats in insects"
No full textThis data has two parts: (1) mechanical testing of bumblebee thorax, and (2) stretch-hold characterization of bumblebee flight muscle.
See included README for detailed description.Raw data accompanying journal article titled 'Supra-resonant wingbeats in insects
Accurate Predictions of the Adsorption Space and Efficient Sorbent Discovery in Metal-Organic Frameworks
No full textAdsorption-based separations using metal–organic frameworks (MOFs) are promising candidates for replacing common energy-intensive separation processes. The so-called adsorption space formed by the combination of billions of possible molecules and thousands of reported MOFs is vast. It is very challenging to comprehensively evaluate the performance of MOFs for chemical separation through experiments. Molecular simulations and machine learning (ML) have been widely applied to make predictions for adsorption-based separations. Previous ML approaches to these issues were typically limited to smaller molecules and often had poor accuracy in the dilute limit. The present thesis addresses this limitation by first developing accurate ML models predicting Henry’s constants and heats of adsorption. We then developed accurate ML models predicting adsorption isotherms of diverse molecules in large libraries of MOFs. By combining molecular simulation data, ML predictions with Ideal Adsorbed Solution Theory, we tested the ability of these approaches to make predictions of adsorption selectivity and loading for challenging near-azeotropic mixtures. We then focused on exploring MOFs for direct air capture (DAC). We presented Open DAC(ODAC) 2023 database with over 38 million quantum chemistry calculations on thousands of MOFs containing CO2 and/or H2O. We introduced a tool to automatically generate missing-linker defects in MOFs and applied the tool to include more than three thousand defective MOFs to the database. Over two hundreds of promising MOFs were identified and the influence of defects was studied. Machine learning models were developed based on this database to accelerate the development of MOFs for DAC.Ph.D.Chemical and Biomolecular Engineerin
Investigation of the Chemical and Physical Impact of No-Clean Flux Residue on Board-Level Underfill Performance
No full textThis paper investigated the chemical and physical impact of No-Clean Fluxes (NCFs)
residue among the Solder/underfill/Cu-clad interface in electronic packaging. NCFs use
mild activator acid to mitigate the influence in the package, but the NCFs residue can still
cause reliability issues in the long term, such as adhesion failure and corrosion.
Two flux types, alcohol- and rosin-based based were heated with lead-free SAC 305 (96.5
Sn-3 Ag-0.5 Cu) solder through a reflow process and underwent a curing profile with dis-
pensed bisphenol-A expoxy. The residues were generated via a solder reflow process (soak-
ing: 120 to 140°C, peak: 240 to 260°C, under N) and characterized pre-/post-reflow using
FTIR, revealing oxidative degradation (increased C=O, decreased C-O/O-H peaks). Laser
microscopy confirmed solder wettability under reflow conditions.
The collected residues were mixed with epoxy and analyzed using Thermogravimetric
Analysis (TGA)/Differential Scanning Calorimetry (DSC), revealing altered curing kinet-
ics. The cross-sectional SEM/EDS of the samples revealed voids and accumulation of
interfacial residue, which were correlated with adhesion failure during dicing and the DSC
curing curve. The migration of metal salts was induced with Highly Accelerated Stress
Test (HAST) treatment and confirmed by X-ray Photoelectron Spectroscopy (XPS), where
copper diffuses more than tin. These results highlight the critical effect of flux chemistry
in interfacial reliability and provide suggestions on mitigating residue propagation.M.S.Materials Science and Engineerin
Traffic Sign Localization Using SfM and Deep Learning
No full textThis study addresses the challenge of traffic sign inventory management faced by the U.S. Department of Transportation in complying with Manual on Uniform Traffic Control Devices (MUTCD) standards. The study proposes a cost-effective methodology for geo-localizing traffic signs using smartphone-recorded video and GPS data. The approach employs various techniques, including depth estimation deep learning models, and Structure-from-Motion (SfM), to accurately determine the geographic coordinates of roadside traffic signs. The methodology was tested in diverse environments, including challenging mountain roads with curves and urban settings. Structure-from-Motion (SfM) is shown as the most effective approach, demonstrating high accuracy with 90.91% of tested signs (40 out of 44) in Pima County, Arizona, and 88.24% of tested signs (90 out of 102) in Peyton Road, Atlanta, Georgia, achieving a distance error below 4.9 meters. The remaining discrepancies were mainly caused by GPS inaccuracies rather than the limitations. These results show SfM as a promising solution for efficient and accurate traffic sign geo-localization.M.S.Computer Scienc
Multi-omic Investigation of Plastic-Associated Microbes: Bioinformatic Insights into Plastic Biodegradation and Novel Degrading Genes Across Environments
No full textSynthetic plastics and their resulting waste are ubiquitous across the planet, from the Arctic to the tropics. Despite increasing efforts to understand the fate and transport of these plastics, their fate and impact on the environment and public health are still not well understood. To better comprehend the microbial ecology associated with plastic waste and its potential for bioremediation, we conducted a large-scale analysis of all publicly available meta-omic studies investigating plastics in the environment. Importantly, we observed low prevalence of previously reported plastic degrading populations throughout most environments, except for substantial enrichment in riverine systems. This indicates rivers may be the one of the most promising environments for sources of plastic bioremediation. Ocean samples associated with degrading plastics showed clear differentiation from non-degrading polymers, showing enrichment of novel putative biodegrading taxa in the degraded samples. In regards to plastisphere pathogenicity, we observe no association between virulence factors and plastics in any environment. Additionally, we report a co-occurrence network analysis of 10+ million proteins associated with the plastisphere. This analysis revealed a localized sub-region enriched with known and putative plastizymes. These novel putative plastizymes may be useful for deeper investigations of nature’s ability to biodegrade man-made plastics. Finally, the combined data from our meta-analysis was used to construct a publicly available database. These data should allow for integrated exploration of the microbial plastisphere and aid the community in continued research efforts to better understand the fate of plastics in the environment.M.S.Bioengineerin