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1. Fast and Fourier: extreme mass ratio inspiral waveforms in the frequency domain

   https://doi.org/10.3389/fams.2023.1266739  

   Speri, Lorenzo; Katz, Michael L; Chua, Alvin_J K; Hughes, Scott A; Warburton, Niels; Thompson, Jonathan E; Chapman-Bird, Christian_E A; Gair, Jonathan R (January 2024, Frontiers in Applied Mathematics and Statistics)

   Extreme Mass Ratio Inspirals (EMRIs) are one of the key sources for future space-based gravitational wave interferometers. Measurements of EMRI gravitational waves are expected to determine the characteristics of their sources with sub-percent precision. However, their waveform generation is challenging due to the long duration of the signal and the high harmonic content. Here, we present the first ready-to-use Schwarzschild eccentric EMRI waveform implementation in the frequency domain for use with either graphics processing units (GPUs) or central processing units (CPUs). We present the overall waveform implementation and test the accuracy and performance of the frequency domain waveforms against the time domain implementation. On GPUs, the frequency domain waveform takes in median 0.044 s to generate and is twice as fast to compute as its time domain counterpart when considering massive black hole masses $\ge 2\times 10^6{\text{M}}_{\odot }$and initial eccentricitiese₀> 0.2. On CPUs, the median waveform evaluation time is 5 s, and it is five times faster in the frequency domain than in the time domain. Using a sparser frequency array can further speed up the waveform generation, reaching up to 0.3 s. This enables us to perform, for the first time, EMRI parameter inference with fully relativistic waveforms on CPUs. Future EMRI models, which encompass wider source characteristics (particularly black hole spin and generic orbit geometries), will require significantly more harmonics. Frequency domain models will be essential analysis tools for these astrophysically realistic and important signals. 

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2. Synchro-Waveforms: A Window to the Future of Power Systems Data Analytics

   https://doi.org/10.1109/MPE.2023.3288583  

   Mohsenian-Rad, Hamed; Xu, Wilsun (September 2023, IEEE Power and Energy Magazine)

   WAVEFORMS ARE THE MOST GRANULAR ANDauthentic representation of voltage and current in power systems. With the latest advancements in power system measurement technologies, it is now possible to obtain time-synchronized waveform measurements, i.e., synchrowaveforms, from different locations of a power system. The measurement technology to obtain synchro-waveforms is referred to as a waveform measurement unit (WMU). WMUs can capture the most inconspicuous disturbances that are overlooked by other types of time-synchronized sensors, such as phasor measurement units (PMUs). WMUs also monitor system dynamics at much higher frequencies as well as much lower frequencies than the fundamental components of voltage and current that are commonly monitored by PMUs. Thus, synchro-waveforms introduce a ew frontier to advance power system and equipment monitoring and control, with direct applications in situational awareness, system dynamics tracking, incipient fault detection and identification, condition monitoring, and so on. They also play a critical role in monitoring inverter-based resources (IBR) due to the high-frequency switching characteristics of IBRs. Accordingly, in this article, we provide a high-level overview of this new and emerging technology and its implications, discussing the latest advancements in the new field of synchro waveforms, including basic principles, real-world examples, potentials in data analytics, and innovative applications 

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3. Anesthetics affect peripheral venous pressure waveforms and the cross-talk with arterial pressure

   https://doi.org/10.1007/s10877-020-00632-6  

   Al-Alawi, Ali Z.; Henry, Kaylee R.; Crimmins, Lauren D.; Bonasso, Patrick C.; Hayat, Md Abul; Dassinger, Melvin S.; Burford, Jeffrey M.; Jensen, Hanna K.; Sanford, Joseph; Wu, Jingxian; et al (February 2021, Journal of Clinical Monitoring and Computing)

   null (Ed.)

   Abstract Analysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two pediatric cohorts were studied to test the effect of anesthetic agents on the PVP waveform and cross-talk between peripheral veins and arteries: (1) dehydration setting in a pyloromyotomy using the infused anesthetic propofol and (2) hemorrhage setting during elective surgery for craniosynostosis with the inhaled anesthetic isoflurane. PVP waveforms were collected from 39 patients that received propofol and 9 that received isoflurane. A multiple analysis of variance test determined if anesthetics influence the PVP waveform. A prediction system was built using k-nearest neighbor (k-NN) to distinguish between: (1) PVP waveforms with and without propofol and (2) different minimum alveolar concentration (MAC) groups of isoflurane. 52 porcine, 5 propofol, and 7 isoflurane subjects were used to determine the cross-talk between veins and arteries at the heart and respiratory rate frequency during: (a) during and after bleeding with constant anesthesia, (b) before and after propofol, and (c) at each MAC value. PVP waveforms are influenced by anesthetics, determined by MANOVA: p value  < 0.01, η 2 = 0.478 for hypovolemic, and η 2 = 0.388 for euvolemic conditions. The k-NN prediction models had 82% and 77% accuracy for detecting propofol and MAC, respectively. The cross-talk relationship at each stage was: (a) ρ = 0.95, (b) ρ = 0.96, and (c) could not be evaluated using this cohort. Future research should consider anesthetic agents when analyzing PVP waveforms developing future clinical monitoring technology that uses PVP. 

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4. Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks

   https://doi.org/10.1029/2022WR033895  

   Wang, Zhuangji; Hua, Shan; Timlin, Dennis; Kojima, Yuki; Lu, Songtao; Sun, Wenguang; Fleisher, David; Horton, Robert; Reddy, Vangimalla_R; Tully, Katherine (February 2023, Water Resources Research)

   Abstract Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution. 

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5. Intracortical microstimulation pulse waveform and frequency recruits distinct spatiotemporal patterns of cortical neuron and neuropil activation

   https://doi.org/10.1088/1741-2552/ac5bf5  

   Stieger, Kevin C; Eles, James R; Ludwig, Kip A; Kozai, Takashi D (March 2022, Journal of Neural Engineering)

   Abstract Objective . Neural prosthetics often use intracortical microstimulation (ICMS) for sensory restoration. To restore natural and functional feedback, we must first understand how stimulation parameters influence the recruitment of neural populations. ICMS waveform asymmetry modulates the spatial activation of neurons around an electrode at 10 Hz; however, it is unclear how asymmetry may differentially modulate population activity at frequencies typically employed in the clinic (e.g. 100 Hz). We hypothesized that stimulation waveform asymmetry would differentially modulate preferential activation of certain neural populations, and the differential population activity would be frequency-dependent. Approach . We quantified how asymmetric stimulation waveforms delivered at 10 or 100 Hz for 30 s modulated spatiotemporal activity of cortical layer II/III pyramidal neurons using in vivo two-photon and mesoscale calcium imaging in anesthetized mice. Asymmetry is defined in terms of the ratio of the duration of the leading phase to the duration of the return phase of charge-balanced cathodal- and anodal-first waveforms (i.e. longer leading phase relative to return has larger asymmetry). Main results . Neurons within 40–60 µ m of the electrode display stable stimulation-induced activity indicative of direct activation, which was independent of waveform asymmetry. The stability of 72% of activated neurons and the preferential activation of 20%–90% of neurons depended on waveform asymmetry. Additionally, this asymmetry-dependent activation of different neural populations was associated with differential progression of population activity. Specifically, neural activity tended to increase over time during 10 Hz stimulation for some waveforms, whereas activity remained at the same level throughout stimulation for other waveforms. During 100 Hz stimulation, neural activity decreased over time for all waveforms, but decreased more for the waveforms that resulted in increasing neural activity during 10 Hz stimulation. Significance. These data demonstrate that at frequencies commonly used for sensory restoration, stimulation waveform alters the pattern of activation of different but overlapping populations of excitatory neurons. The impact of these waveform specific responses on the activation of different subtypes of neurons as well as sensory perception merits further investigation. 

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