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1. Time Series Forecasting for Structures Subjected to Nonstationary Inputs

   https://doi.org/10.1115/SMASIS2021-68338  

   Chowdhury, Puja ; Conrad, Philip ; Bakos, Jason D. ; Downey, Austin ( October 2021 , ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems)

   In this paper, a method for real-time forecasting of the dynamics of structures experiencing nonstationary inputs is described. This is presented as time series predictions across different timescales. The target applications include hypersonic vehicles, space launch systems, real-time prognostics, and monitoring of high-rate and energetic systems. This work presents numerical analysis and experimental results for the real-time implementation of a Fast Fourier Transform (FFT)-based approach for time series forecasting. For this preliminary study, a testbench structure that consists of a cantilever beam subjected to nonstationary inputs is used to generate experimental data. First, the data is de-trended, then the time series data is transferred into the frequency domain, and measures for frequency, amplitude, and phase are obtained. Thereafter, select frequency components are collected, transformed back to the time domain, recombined, and then the trend in the data is restored. Finally, the recombined signals are propagated into the future to the selected prediction horizon. This preliminary time series forecasting work is done offline using pre-recorded experimental data, and the FFT-based approach is implemented in a rolling window configuration. Here learning windows of 0.1, 0.5, and 1 s are considered with different computation times simulated. Results demonstrate that the proposed FFT-based approach can maintain a constant prediction horizon at 1 s with sufficient accuracy for the considered system. The performance of the system is quantified using a variety of metrics. Computational speed and prediction accuracy as a function of training time and learning window lengths are examined in this work. The algorithm configuration with the shortest learning window (0.1 s) is shown to converge faster following the nonstationary when compared to algorithm configuration with longer learning windows.

    

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2. Spectroscopy of the 85 Rb 4 D3/2 state for hyperfine-structure determination

   https://doi.org/10.1088/1367-2630/acf405  

   Duspayev, A. ; Raithel, G. ( September 2023 , New Journal of Physics)

   We report a measurement of the hyperfine-structure constants of the⁸⁵Rb 4$D_{3/2}$state using two-photon optical spectroscopy of the 5$S_{1/2}\to$4$D_{3/2}$transition. The spectra are acquired by measuring the transmission of the low-power 795 nm lower-stage laser beam through a cold-atom sample as a function of laser frequency, with the frequency of the upper-stage, 1476 nm laser fixed. All 4 hyperfine components of the$4D_{3/2}$state are well-resolved in the experimental data. The dominant systematic is the light shift from the 1476 nm laser, which is addressed by extrapolating line positions measured for a set of 1476 nm laser powers to zero laser power. The analysis of our experimental data yields both the magnetic-dipole and electric-quadrupole constants for the⁸⁵Rb 4$D_{3/2}$level, without using earlier hyperfine measurements of other atomic levels. The respective results,$A=$7.419(35) MHz and$B=$4.19(19) MHz, are discussed in context with previous works. Our investigation may be useful for optical atomic clocks for precision metrology and emerging atom-based quantum technologies, all-infrared excitation of Rb Rydberg levels, and molecular physics.

    

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3. Measurements of one-point statistics in 21-cm intensity maps via foreground avoidance strategy

   https://doi.org/10.1093/mnras/stac2826  

   Kittiwisit, Piyanat ; Bowman, Judd D. ; Murray, Steven G. ; Gehlot, Bharat K. ; Jacobs, Daniel C. ; Beardsley, Adam P. ( October 2022 , Monthly Notices of the Royal Astronomical Society)

   Measurements of the one-point probability distribution function and higher-order moments (variance, skewness, and kurtosis) of the high-redshift 21-cm fluctuations are among the most direct statistical probes of the non-Gaussian nature of structure formation and evolution during re-ionization. However, contamination from astrophysical foregrounds and instrument systematics pose significant challenges in measuring these statistics in real observations. In this work, we use forward modelling to investigate the feasibility of measuring 21-cm one-point statistics through a foreground avoidance strategy. Leveraging the characteristic wedge-shape of the foregrounds in k-space, we apply a wedge-cut filtre that removes the foreground contaminated modes from a mock data set based on the Hydrogen Epoch of Re-ionization Array (HERA) instrument, and measure the one-point statistics from the image-space representation of the remaining non-contaminated modes. We experiment with varying degrees of wedge-cutting over different frequency bandwidths and find that the centre of the band is the least susceptible to bias from wedge-cutting. Based on this finding, we introduce a rolling filtre method that allows reconstruction of an optimal wedge-cut 21-cm intensity map over the full bandwidth using outputs from wedge-cutting over multiple sub-bands. We perform Monte Carlo simulations to show that HERA should be able to measure the rise in skewness and kurtosis near the end of re-ionization with the rolling wedge-cut method if foreground leakage from the Fourier transform window function can be controlled.

    

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4. Evaluation of Rolling-Type Isolation Systems for Seismic Hazard Mitigation

   https://doi.org/11244/300332  

   Calhoun, Skylar Josef ( August 2018 , The University of Oklahoma Libraries)

   null (Ed.)

   Nonstructural components within mission-critical facilities such as hospitals and telecommunication facilities are vital to a community's resilience when subjected to a seismic event. Building contents like medical and computer equipment are critical for the response and recovery process following an earthquake. A solution to protecting these systems from seismic hazards is base isolation. Base isolation systems are designed to decouple an entire building structure from destructive ground motions. For other buildings not fitted with base isolation, a practical and economical solution to protect vital building contents from earthquake-induced floor motion is to isolate individual equipment using, for example, rolling-type isolation systems (RISs). RISs are a relatively new innovation for protecting equipment. These systems function as a pendulum-like mechanism to convert horizontal motion into vertical motion. An accompanying change in potential energy creates a restoring force related to the slope of the rolling surface. This study seeks to evaluate the seismic hazard mitigation performance of RISs, as well as propose and test a novel double RIS. A physics-based mathematical model was developed for a single RIS via Lagrange's equation adhering to the kinetic constraint of rolling without slipping. The mathematical model for the single RIS was used to predict the response and characteristics of these systems. A physical model was fabricated with additive manufacturing and tested against multiple earthquakes on a shake table. The system featured a single-degree-of-freedom (SDOF) structure to represent a piece of equipment. The results showed that the RIS effectively reduced accelerations felt by the SDOF compared to a fixed-base SDOF system. The single RIS experienced the most substantial accelerations from the Mendocino record, which contains low-frequency content in the range of the RIS's natural period (1-2 seconds). Earthquakes with these long-period components have the potential to cause impacts within the isolation bearing that would degrade its performance. To accommodate large displacements, a double RIS is proposed. The double RIS has twice the displacement capacity of a single RIS without increasing the size of the bearing components. The mathematical model for the single RIS was extended to the double RIS following a similar procedure. Two approaches were used to evaluate the double RIS's performance: stochastic and deterministic. The stochastic response of the double RIS under stationary white noise excitation was evaluated for relevant system parameters, namely mass ratio and tuning frequency. Both broadband and filtered (Kanai-Tajimi) white noise excitation were considered. The response variances of the double RIS were normalized by a baseline single RIS for a comparative study, from which design parameter maps were drawn. A deterministic analysis was conducted to further evaluate the double RIS in the case of nonstationary excitation. The telecommunication equipment qualification waveform, VERTEQ-II, was used for these numerical simulations. Peak transient responses were compared to the single RIS responses, and optimal design regions were determined. General design guidelines based on the stochastic and deterministic analyses are given. The results aim to provide a framework usable in the preliminary design stage of a double RIS to mitigate seismic responses. 

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5. I am an Earphone and I can Hear my Users Face: Facial Landmark Tracking using Smart Earphones

   https://doi.org/10.1145/3614438  

   Zhang, Shijia ; Lu, Taiting ; Zhou, Hao ; Liu, Yilin ; Liu, Runze ; Gowda, Mahanth ( August 2023 , ACM Transactions on Internet of Things)

   This paper presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user-interfaces, AR/VR applications, affective computing, accessibility, etc. While conventional vision-based solutions break down under poor lighting, occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions, while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form-factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response (CFR) of the inner ear, ultimately resulting in tracking of the facial motion. A transformer based machine learning (ML) model is designed to exploit spectral and temporal relationships in the ultrasonic CFR data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer based ML model runs on smartphone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form-factors. 

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