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Real-time gap-free dynamic waveform spectral analysis with nanosecond resolutions through analog signal processing.

ABSTRACT: Real-time tracking of a waveform frequency content is essential for detection and analysis of fast rare events in communications, radar, radio astronomy, spectroscopy, sensing etc. This requires a method that can provide real-time spectrum analysis (RT-SA) of high-speed waveforms in a continuous and gap-free fashion. Digital signal processing is inefficient to perform RT-SA over instantaneous frequency bandwidths above the sub-GHz range and/or to track spectral changes faster than a few microseconds. Analog dispersion-induced frequency-to-time mapping enables RT-SA of short isolated pulse-like signals but cannot be extended to continuous waveforms. Here, we propose a universal analog processing approach for time-mapping a gap-free spectrogram -the prime method for dynamic frequency analysis- of an incoming arbitrary waveform, based on a simple sampling and dispersive delay scheme. In experiments, the spectrograms of GHz-bandwidth microwave signals are captured at a speed of ~5×109 Fourier transforms per second, allowing to intercept nanosecond-duration frequency transients in real time. This method opens new opportunities for dynamic frequency analysis and processing of high-speed waveforms.

SUBMITTER: Konatham SR

PROVIDER: S-EPMC7335167 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Real-time gap-free dynamic waveform spectral analysis with nanosecond resolutions through analog signal processing.

Konatham Saikrishna Reddy SR Maram Reza R Romero Cortés Luis L Chang Jun Ho JH Rusch Leslie L LaRochelle Sophie S Guillet de Chatellus Hugues H Azaña José J

Nature communications 20200703 1

Real-time tracking of a waveform frequency content is essential for detection and analysis of fast rare events in communications, radar, radio astronomy, spectroscopy, sensing etc. This requires a method that can provide real-time spectrum analysis (RT-SA) of high-speed waveforms in a continuous and gap-free fashion. Digital signal processing is inefficient to perform RT-SA over instantaneous frequency bandwidths above the sub-GHz range and/or to track spectral changes faster than a few microsec ...[more]

PMID: 32620871

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Project description:Purpose The goal of this study was to determine how various aspects of cognition predict speech recognition ability across different levels of speech vocoding within a single group of listeners. Method We tested the ability of young adults (N = 32) with normal hearing to recognize Perceptually Robust English Sentence Test Open-set (PRESTO) sentences that were degraded with a vocoder to produce different levels of spectral resolution (16, eight, and four carrier channels). Participants also completed tests of cognition (fluid intelligence, short-term memory, and attention), which were used as predictors of sentence recognition. Sentence recognition was compared across vocoder conditions, predictors were correlated with individual differences in sentence recognition, and the relationships between predictors were characterized. Results PRESTO sentence recognition performance declined with a decreasing number of vocoder channels, with no evident floor or ceiling performance in any condition. Individual ability to recognize PRESTO sentences was consistent relative to the group across vocoder conditions. Short-term memory, as measured with serial recall, was a moderate predictor of sentence recognition (ρ = 0.65). Serial recall performance was constant across vocoder conditions when measured with a digit span task. Fluid intelligence was marginally correlated with serial recall, but not sentence recognition. Attentional measures had no discernible relationship to sentence recognition and a marginal relationship with serial recall. Conclusions Verbal serial recall is a substantial predictor of vocoded sentence recognition, and this predictive relationship is independent of spectral resolution. In populations that show variable speech recognition outcomes, such as listeners with cochlear implants, it should be possible to account for the independent effects of spectral resolution and verbal serial recall in their speech recognition ability. Supplemental Material https://doi.org/10.23641/asha.12021051.

Dataset Information

Real-time gap-free dynamic waveform spectral analysis with nanosecond resolutions through analog signal processing.

Publications

Real-time gap-free dynamic waveform spectral analysis with nanosecond resolutions through analog signal processing.

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