Abstract
Processing of sensor signals in modern wireless networks becomes a challenging task because of power limitation at sensor nodes. The data collected by sensors such as pressure, humidity, temperature, speech and electrocardiograms contains only low frequencies. These signals have very negligible variations for such a long time. Therefore, traditional uniform sampling analog-to-digital (A/D) converters make more samples even if no variations in the signal, which may result in the data processing system, becomes computationally complex and needs higher power. In order to overcome this, an adaptive level cross sampling scheme (ALCSS) is presented in this paper. An iterative algorithm is used in this paper in order to reconstruct the original signal. The performance of the proposed ALCCS is compared with the traditional level cross scheme (LCS) in terms of computational complexity.
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Ravuri, V., Terlapu, S.K., Nayak, S.S. (2021). Adaptive Level Cross Sampling for Next-Generation Data-Driven Applications. In: Chowdary, P., Chakravarthy, V., Anguera, J., Satapathy, S., Bhateja, V. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 655. Springer, Singapore. https://doi.org/10.1007/978-981-15-3828-5_65
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DOI: https://doi.org/10.1007/978-981-15-3828-5_65
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