Abstract
In this study, we present an efficient Haar discrete wavelet transform architecture using Radix- $$2^{r}$$ multiplier and 4:2 compressor. The functionality of this architecture is verified using real-time electrooculogram signal. This architecture is useful for saccade identification and blink detection, as well as for recognizing human eye activity while reading. The standard Haar wavelet structure includes an adder, subtracter, and multiplier. The Haar structure is synthesized the use of the Cadence RTL compiler. The synthesis outcomes display that the proposed structure has much less latency and lower power consumption.
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