Sun, 8 May, 15:00 - 15:45 UTC
In this demo, we present a low-rate, high-dynamic-range analog to digital conversion (ADC). While sampling signals through an ADC, typically, it is assumed that the signal’s dynamic range is within the dynamic range of the ADC. However, in many applications, such as radar and ultrasound imaging, the dynamic range of the received signal could be beyond that of the ADC which results in clipping and leads to inaccurate reconstruction. A modulo preprocessing can be used to avoid clipping and sampling signals beyond the dynamic range of the ADC. The modulo step folds the signal to the dynamic range of the ADC, and the folded signal is sampled. During reconstruction, the true samples are recovered from the folded ones by using an unfolding algorithm. Typically, the unfolding algorithms operate at a much higher rate compared to the rate without a modulo operation. This results in a large number of bits per second (NMBPS) post sampling and quantization which may not be suitable in many applications as a large amount of storage or transmission bandwidth is required.
In this demo, we propose a dedicated hardware prototype that can handle high frequency and high amplitude input signal. Further, we propose a new algorithm called Beyond Bandwidth Residual Recovery, so that unwrapping can be performed robustly at a low sampling rate. In particular, the proposed algorithm uses the time-domain separation and Fourier-domain separation properties of the given finite energy bandlimited signal. Moreover, through simulation and hardware results we show that the proposed algorithm can operate low sampling rate in comparison with the existing methods. In this way, the overall NMBPS is much lower than the existing methods.
We present the hardware demonstration together with an interactive graphical user interface (GUI) during the on-site conference. In addition, we also submit a poster and a video presentation of the entire demo which can be used during the virtual conference.
Related Papers: Gupta, C., Kamath, P., & Wyse, L. (2021). Signal representations for synthesizing audio textures with generative adversarial networks. arXiv preprint arXiv:2103.07390. Wyse, L., Kamath, P., & Gupta, C. (2021). An Integrated System Architecture for Generative Audio Modeling.
Interactive webpage for all attendees: https://animatedsound.com/icassp2022/Trumpinet.60.76/ https://animatedsound.com/icassp2022/oreilly_grid2/