The Use of Binary Quantization for the Acquisition of Low SNR Ultrasonic Signals: A Study of the Input Dynamic Range

Low-power excitation and/or low sensitivity transducers, such as electromagnetic acoustic transducers, piezoelectric paints, air-coupled transducers, and small elements of dense arrays, may produce signals below the noise threshold at the receiver. The information from those noisy signals can be recovered after averaging or pulse compression using binary (1-b) quantization only without experiencing significant losses. Hence, no analog-to-digital converter is required, which reduces the data throughput and makes the electronics faster, more compact, and energy efficient. All these are especially attractive for applications that require arrays with many channels and high sampling rates, where the sampling rate can be as high as the system clock. In this paper, the theory of binary quantization is reviewed, mainly from previous work on wireless sensor networks, and the signal-to-noise ratio (SNR) of the input signals under which binary quantization is of practical interest for ultrasound applications is investigated. The main findings are that in most practical cases binary quantization can be used with small errors when the input SNR is on the order of 8 dB or less. Moreover, the maximum SNR after binary quantization and averaging can be estimated as 10 log
₁₀
N - 2 dB, where N is the number of averages.