Deep breaths are one of three breathing patterns in rodents characterized by an increased tidal volume. While humans incorporate deep breaths into vocal behavior, it was unknown whether nonhuman mammals use deep breaths for vocal production. We have utilized subglottal pressure recordings in awake, spontaneously behaving male Sprague-Dawley rats in five contexts: sleep, rest, noxious stimulation, exposure to a female in estrus, and exposure to an unknown male. Deep breaths were produced at rates ranging between 17.5 and 90.3 deep breaths per hour. While overall breathing and vocal rates were higher in social and noxious contexts, the rate of deep breaths was only increased during the male’s interaction with a female. Results also inform our understanding of vocal-respiratory integration in rats. The rate of deep breaths that were associated with a vocalization during the exhalation phase increased with vocal activity. The proportion of deep breaths that were associated with a vocalization (on average 22%) was similar to the proportion of sniffing or eupnea breaths that contain a vocalization. Therefore, vocal motor patterns appear to be entrained to the prevailing breathing rhythm, i.e., vocalization uses the available breathing pattern rather than recruiting a specific breathing pattern. Furthermore, the pattern of a deep breath was different when it was associated with a vocalization, suggesting that motor planning occurs. Finally, deep breaths are a source for acoustic variation; for example, call duration and fundamental frequency modulation were both larger in 22-kHz calls produced following a deep inhalation. NEW & NOTEWORTHY The emission of a long, deep, audible breath can express various emotions. The investigation of deep breaths, also known as sighing, in a nonhuman mammal demonstrated the occasional use of deep breaths for vocal production. Similar to the human equivalent, acoustic features of a deep breath vocalization are characteristic.
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Spectral Breathing and Its Mitigation in Digital Fractional-N PLLs
Although digital phase-locked loops (PLLs) offer several advantages over their analog counterparts, they suffer from a major disadvantage that is rarely mentioned in published articles. The disadvantage, known as spectral breathing, is caused by component mismatches among the frequency control elements within a PLL's digitally controlled oscillator (DCO). The mismatches introduce DCO frequency modulation nonlinearity which fluctuates and, therefore, causes erratic variations in the PLL's measured phase noise spectrum as the DCO's free-running frequency drifts. The phenomenon is called spectral breathing because the measured phase noise spectrum tends to slowly swell and contract over time as if taking breaths of air. During these breaths, the PLL's phase noise often becomes severely degraded. This article presents an experimental demonstration of the spectral breathing phenomenon and its solution in a digital fractional-N PLL. The demonstrated solution is a multi-rate dynamic element matching technique and a mismatch-noise cancellation technique that together eliminate spectral breathing.
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- NSF-PAR ID:
- 10251763
- Date Published:
- Journal Name:
- IEEE Journal of Solid-State Circuits
- ISSN:
- 0018-9200
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT To mitigate the effects of Radio Frequency Interference (RFI) on the data analysis pipelines of 21 cm interferometric instruments, numerous inpaint techniques have been developed. In this paper, we examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that is capable of inpainting RFI corrupted data. We train our network on simulated data and show that our network is capable of inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modelling are best suited for inpainting over narrowband RFI. We show that with our fiducial parameters discrete prolate spheroidal sequences (dpss) and clean provide the best performance for intermittent RFI while Gaussian progress regression (gpr) and least squares spectral analysis (lssa) provide the best performance for larger RFI gaps. However, we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that as the noise level of the data comes down, clean and dpss are most capable of reproducing the fine frequency structure in the visibilities.
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Vital signs (e.g., heart and respiratory rate) are indicative for health status assessment. Efforts have been made to extract vital signs using radio frequency (RF) techniques (e.g., Wi-Fi, FMCW, UWB), which offer a non-touch solution for continuous and ubiquitous monitoring without users’ cooperative efforts. While RF-based vital signs monitoring is user-friendly, its robustness faces two challenges. On the one hand, the RF signal is modulated by the periodic chest wall displacement due to heartbeat and breathing in a nonlinear manner. It is inherently hard to identify the fundamental heart and respiratory rates (HR and RR) in the presence of higher order harmonics of them and intermodulation between HR and RR, especially when they have overlapping frequency bands. On the other hand, the inadvertent body movements may disturb and distort the RF signal, overwhelming the vital signals, thus inhibiting the parameter estimation of the physiological movement (i.e., heartbeat and breathing). In this paper, we propose DeepVS, a deep learning approach that addresses the aforementioned challenges from the non-linearity and inadvertent movements for robust RF-based vital signs sensing in a unified manner. DeepVS combines 1D CNN and attention models to exploit local features and temporal correlations. Moreover, it leverages a two-stream scheme to integrate features from both time and frequency domains. Additionally, DeepVS unifies the estimation of HR and RR with a multi-head structure, which only adds limited extra overhead (<1%) to the existing model, compared to doubling the overhead using two separate models for HR and RR respectively. Our experiments demonstrate that DeepVS achieves 80-percentile HR/RR errors of 7.4/4.9 beat/breaths per minute (bpm) on a challenging dataset, as compared to 11.8/7.3 bpm of a non-learning solution. Besides, an ablation study has been conducted to quantify the effectiveness of DeepVS.more » « less
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Abstract On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D1’ regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion.
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Colloidal quantum dots (CQDs) provide wide spectral tunability and high absorption coefficients owing to quantum confinement and large oscillator strengths, which along with solution processability, allow a facile, low-cost, and room-temperature deposition technique for the fabrication of photonic devices. However, many solution-processed CQD photodetector devices demonstrate low specific-detectivity and slow temporal response. To achieve improved photodetector characteristics, limiting carrier recombination and enhancing photogenerated carrier separation are crucial. In this study, we develop and present an alternate vertical-stack photodetector wherein we use a solution-processed quantum dot photoconversion layer coupled to an amorphous selenium (a-Se) wide-bandgap charge transport layer that is capable of exhibiting single-carrier hole impact ionization and is compatible with active-matrix readout circuitry. This a-Se chalcogenide transport layer enables the fabrication of high-performance and reliable solution-processed quantum dot photodetectors, with enhanced charge extraction capabilities, high specific detectivity (D* ∼ 0.5–5 × 1012 Jones), fast 3 dB electrical bandwidth (3 dB BW ∼ 22 MHz), low dark current density (JD ∼ 5–10 pA/cm2), low noise current (in ∼ 20–25 fW/Hz1/2), and high linear dynamic range (LDR ∼ 130–150 dB) across the measured visible electromagnetic spectrum (∼405–656 nm).more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JSSC.2021.3074814
