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Acoustic and Hemodynamic Analysis of Phantoms: Roughness and Flow Dynamics
Full text linkThe purpose of this study was to determine whether different vascular phantoms, with and without a surrogate arm, could serve as effective analogs to the human brachial artery for assessing endothelial dysfunction, an early indicator of cardiovascular disease, particularly atherosclerosis. Atherosclerosis is characterized by plaque buildup and endothelial damage, which precede more severe clinical events and are often detectable through changes in vascular function. A 40” water tower was used to simulate diastolic pressure, enabling water flow through each phantom. Strain gauge pressure transducers were placed at both ends of the phantoms to measure pressure drop, while a microphone captured the sound profile of the flow. The sound was expected to vary between phantoms due to differences in their luminal endothelial linings, which affect Reynolds number and, consequently, friction factor and pressure drop. To analyze the sound data, a Fast Fourier Transform (FFT) was applied to convert time domain signals into the frequency domain. A two factor ANOVA was then conducted to evaluate the effects of two variables: the presence of a surrogate arm and the type of vascular phantom. Results showed that both the type of phantom and the presence of the surrogate arm had statistically significant effects on the measured parameters. These findings suggest that this prototype system holds promise for non-invasive clinical evaluation of endothelial dysfunction and early-stage atherosclerosis, potentially enabling earlier diagnosis and treatment, when intervention is most effective, thereby improving patient outcomes and saving lives
Enhanced Post-Capture Automatic White Balance for sRGB Images
No full textAutomatic white balancing (AWB) aims to correct color casts caused by varying illumination conditions, typically assuming access to RAW sensor data. However, many real-world applications involve only sRGB images that have already been processed by in-camera pipelines. In these cases, traditional AWB algorithms often underperform due to the nonlinear transformations done by these pipelines.
This thesis builds upon a data-driven color correction framework introduced by Afifi et al. that relies on RGB-UV histograms and learned color transforms. A revised automatic white balancing (AWB) framework that improves both color accuracy and runtime efficiency is proposed. A fallback routine is implemented to improve correction quality in failure cases where the original polynomial model performs poorly. A more efficient histogram computation is also performed using subsampling, which drastically reduces the computational cost with minimal impact on visual quality.
The proposed method is evaluated with the Cube+ dataset, where it achieves a lower average color difference error across all tested metrics and fewer extreme correction failures compared to the original method, while also running significantly faster
Coherent Synchronization For Distributed Digital Phased Arrays
No full textDistributed digital phased arrays are rising technologies that help enable applications such as search and rescue operations, wireless communication, radar navigation, and military operations, among many others. Due to their improved angular resolution, digital phased arrays offer superior direction-finding capabilities compared to traditional analog phased array systems. However, this improvement comes at the cost of increased complexity—specifically, the need for precise synchronization of phase, time, and frequency across physically separated nodes. Without synchronization, the distributed phased array\u27s gain and direction-of-arrival (DoA) estimations deteriorate significantly.
There are multiple aspects to implementing and synchronizing a non-stationary distributed digital phased array. This research focuses on one aspect: it implements a decentralized synchronization algorithm that synchronizes the phase, frequency, and time of linear phased arrays to enhance direction-finding estimations, without reliance on external infrastructure such as GPS. For this research, the positions of the array elements are assumed to be known. A Two-Way Time Synchronization (TWTS) structure is simulated, which involves the transmission and reception of a two-tone waveform and calculating the time offsets between nodes to synchronize time. The two-tone waveform is capable of sending all the data needed for synchronization. Next, a phase-frequency consensus algorithm based on Kalman filtering is implemented to estimate and synchronize the phase and frequency states of each node in the array. By implementing this algorithm, we minimize the phase, frequency, and time offsets between nodes, consequently improving DoA estimations.
This work then validates the efficacy and ability of the synchronization algorithm to synchronize a linear array under various environmental and system challenges, demonstrating that the synchronized distributed digital array is capable of providing high-resolution DoA estimates. Furthermore, this research offers a straightforward and cost-effective synchronization solution for facilitating coherent operation in distributed digital phased arrays, with potential applications across a broad range of real-world systems
Compact, Low-Power Underwater Acoustic Modem Characterization and Sensing Network Node Implementation
No full textThere is a growing market for compact, power-efficient underwater communication technology. Due to the limited propagation of electromagnetic waves through water, modulated acoustic waves have emerged as an effective means of wireless communication underwater. In decades past, many acoustic modems had been developed primarily for defense customers in larger systems functioning at high power. In recent years, smaller, relatively inexpensive acoustic modems have become available on the market—but data on their performance in a real-world environment is scarce. Companies such as OWL Integrations are interested in employing such acoustic modems as part of sensing networks across multiple mediums. For such applications, data on maximum distance, directivity, and adjacent channel interference are valuable. Developing a demonstration underwater sensing node with hardware amenable to hosting an RF transceiver is a key step towards the integration of air-sea boundary nodes and subsea sensing nodes into an underwater sensing network. In this work, the performance of the Waterlinked Modem M16 is tested in the harbor environment of Morro Bay, California. The testing revealed a maximum measured range of 521 meters, with some multipath propagation ability observed; a front-to-back difference of 21.8 in relative signal strength on a logarithmically scaled 0-255 scale, comparable to a distance change of 100 meters; and adjacent channel interference sufficient to decrease the signal-to-noise metric by 5 on the same scale. Throughout the tests, the modem consumed less power than its stated maximum values. Ocean pH data was collected and transmitted over the link, and the modem, sensor, and microcontroller boards were all powered from OWL’s QuAD-RC1 power board. The practical testing quantified the modem’s limitations, and the success of the overall integrated testing showed that compact, low-power modems like the Modem M16 are strong candidates for underwater sensing, communication, and control networks
The XenaForm
No full textThe XenaForm explores how complex, expressive structures can emerge from simple, low-tech construction methods using principles of parametric design and structural geometry. By combining folded plate techniques with modular tetrahedral components, the project bridges high-tech digital modeling and hands-on physical fabrication. The result is a lightweight, buildable system that reclaims the engineer’s role as a form-finder
Short Exact Confidence Intervals for the Parameters of the Negative Hypergeometric Distribution
No full textWe study interval estimation for two of the parameters of the negative hypergeometric distribution: (i) the number of successes in a population and (ii) the size of the population. The negative hypergeometric distribution has been relatively overlooked, as the only existing exact procedure that has been applied to it is the analog of the Clopper-Pearson method. To address this gap, we develop several new methods for the negative hypergeometric case, all of which maintain coverage at or above the nominal confidence level. Traditional methods that rely on large sample sizes tend to perform poorly when applied to discrete distributions. In contrast, we construct confidence intervals by reverse-engineering them from an ideal coverage probability function that we establish. We then conduct a comparative analysis of various methods to identify which procedure performs best. Our evaluation criteria include expected and average confidence interval width. We provide a link to a Shiny web app and R package for computing the recommended confidence intervals in practice. Finally, we compare the confidence intervals produced from hypergeometric sampling with those from negative hypergeometric sampling, identifying the scenarios in which each method performs best
