First Light for the BYU 0.9-m Telescope

Biological systems often have a narrow temperature range of operation, which require highly accurate spatially resolved temperature measurements, often near +/- 0.1 K. However, many temperature sensors cannot meet both accuracy and spatial distribution requirements, often because their accuracy is limited by data fitting and temperature reconstruction models. Machine learning algorithms have the potential to meet this need, but their usage in generating spatial distributions of temperature is severely lacking in the literature. This work presents the first instance of using neural networks to process fluorescent images to map the spatial distribution of temperature. Three standard network architectures were investigated using non-spatially resolved fluorescent thermometry (simply-connected feedforward network) or during image or pixel identification (U-net and convolutional neural network, CNN). Simulated fluorescent images based on experimental data were generated based on known temperature distributions where Gaussian white noise with a standard deviation of +/- 0.1 K was added. The poor results from these standard networks motivated the creation of what is termed a moving CNN, with an RMSE error of +/- 0.23 K, where the elements of the matrix represent the neighboring pixels. Finally, the performance of this MCNN is investigated when trained and applied to three distinctive temperature distributions characteristic within microfluidic devices, where the fluorescent image is simulated at either three or five different wavelengths. The results demonstrate that having a minimum of 10(3.5) data points per temperature and the broadest range of temperatures during training provides temperature predictions nearest to the true temperatures of the images, with a minimum RMSE of +/- 0.15 K. When compared to traditional curve-fitting techniques, this work demonstrates that greater accuracy when spatially mapping temperature from fluorescent images can be achieved when using convolutional neural networks.

The directional characteristics of human speech have many applications in speech acoustics, audio, telecommunications, room acoustical design, and other areas. However, professionals in these fields require carefully conducted, high-resolution, spherical speech directivity measurements taken under distinct circumstances to gain additional insights for their work. Because head orientation and human-body diffraction influence speech radiation, this work explores such effects under various controlled conditions through the changing directivity patterns of a head and torso simulator. The results show that head orientation and body diffraction at low frequencies impact directivities only slightly. However, the effects are more substantial at higher frequencies, particularly above 1 kHz.

We consider how mathematical models enable predictions for conditions that are qualitatively different from the training data. We propose techniques based on information topology to find models that can apply their learning in regimes for which there is no data. The first step is to use the manifold boundary approximation method to construct simple, reduced models of target phenomena in a data-driven way. We consider the set of all such reduced models and use the topological relationships among them to reason about model selection for new, unobserved phenomena. Given minimal models for several target behaviors, we introduce the supremum principle as a criterion for selecting a new, transferable model. The supremal model, i.e., the least upper bound, is the simplest model that reduces to each of the target behaviors. We illustrate how to discover supremal models with several examples; in each case, the supremal model unifies causal mechanisms to transfer successfully to new target domains. We use these examples to motivate a general algorithm that has formal connections to theories of analogical reasoning in cognitive psychology.

Silicon (Si) is one of the most abundant elements on Earth, and it is the most widely used semiconductor. Despite extensive study, some properties of Si, such as its behaviour under dynamic compression, remain elusive. A detailed understanding of Si deformation is crucial for various fields, ranging from planetary science to materials design. Simulations suggest that in Si the shear stress generated during shock compression is released via a high-pressure phase transition, challenging the classical picture of relaxation via defect-mediated plasticity. However, direct evidence supporting either deformation mechanism remains elusive. Here, we use sub-picosecond, highly-monochromatic x-ray diffraction to study (100)-oriented single-crystal Si under laser-driven shock compression. We provide the first unambiguous, time-resolved picture of Si deformation at ultra-high strain rates, demonstrating the predicted shear release via phase transition. Our results resolve the longstanding controversy on silicon deformation and provide direct proof of strain rate-dependent deformation mechanisms in a non-metallic system.

Because the noise source mechanisms and radiation properties associated with high-thrust, tactical jet engines are not fully understood, analysis of full-scale measurements can shed significant insight on such characteristics. One method for examining spectral data is to compare them to empirical models for jet noise spectra. This paper compares measured near-field spectra from a T-7A-installed GE F404-103 engine with analytical similarity spectra for fine-scale mixing noise, large-scale mixing noise, and broadband shock-associated noise. This initial similarity spectra analysis enables the determination of spatial trends in overall level and peak frequency and the relative importance of each type of noise radiation as a function of location. This approach can be used to gain insights on spatial and frequency trends of noise source mechanisms for different engine conditions and for rapid comparisons to other aircraft and jets of other scales and conditions.

Multisource statistically-optimized near-field acoustical holography and hybrid beamforming are two inverse techniques that have been successfully used to reproduce sound fields from limited measurements of military aircraft. These methods solve the inverse problem through different means but arrive at the same conclusion. In this paper, the performance of each method is compared to the same baseline measurement. It is found that while both perform well at mid-range frequencies, holography excels at lower frequencies and beamforming at higher frequencies. The spatial Nyquist frequency imposes a soft limit on the accuracy of field reconstructions and limits the usable frequency range.