Abstract
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 10log10N-2 dB , where N is the number of averages.
Highlights
M ANY ultrasound applications produce signals that are weak and potentially fall below the noise level at the receiver
Without an Analog-digital conversion (ADC), the acquisition system becomes 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; the maximum sampling rate of standard ADCs is usually less than the system clock
We review the theory of binary quantization from previous work and investigate the input signal-to-noise ratio (SNR) range of practical interest for ultrasound applications
Summary
M ANY ultrasound applications produce signals that are weak and potentially fall below the noise level at the receiver. The same result was reported in [21] (a decade before) where binary quantization was employed with time-reversal techniques and pulse compression without degrading the spatial or temporal resolution of an array of sensors These findings have an important implication in the acquisition of signals embedded in noise since no analog-to-digital converters (ADCs) are required; standard ADCs could be replaced by a comparator and a binary latch. For signals with greater SNR, i.e., above the noise threshold, the work has been focused on incorporating some control input before quantization or adding extra quantization levels [27] This approach introduces extra complexity in the acquisition system. We review the theory of binary quantization from previous work (mainly that related to WSNs) and investigate the input SNR range of practical interest for ultrasound applications. Experiments with binary-quantized ultrasound signals are presented, and conclusions are drawn
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