Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Frequency Estimation From Compressed Measurements Of A Sinusoid In Moving‐Average Colored Noise, Nuha A. S. Alwan, Zahir M. Hussain
Frequency Estimation From Compressed Measurements Of A Sinusoid In Moving‐Average Colored Noise, Nuha A. S. Alwan, Zahir M. Hussain
Research outputs 2014 to 2021
Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disci-plines by studying the effects of compressed measurements of a single sinusoid in moving‐average colored noise on its frequency estimation accuracy. CCS techniques can recover the second‐order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation‐based frequency estimation of single tones in colored noise using higher order lags. Ac-ceptable accuracy is achieved for …
Deep Learning Versus Spectral Techniques For Frequency Estimation Of Single Tones: Reduced Complexity For Software-Defined Radio And Iot Sensor Communications, Hind R. Almayyali, Zahir M. Hussain
Deep Learning Versus Spectral Techniques For Frequency Estimation Of Single Tones: Reduced Complexity For Software-Defined Radio And Iot Sensor Communications, Hind R. Almayyali, Zahir M. Hussain
Research outputs 2014 to 2021
Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. …