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Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification
(Institute of Electrical and Electronics Engineers Inc., 2019)
Despite the well-known success of sub-Nyquist sampling in reducing the hardware and computational costs of spectrum sensing, it still has the shortcoming of requiring a pre-determined spectrum sparsity level. This paper ...
Selection of waveform parameters using machine learning for 5G and beyond
(Institute of Electrical and Electronics Engineers Inc., 2019)
Flexibility is one of the essential requirements for future cellular communications technologies. Providing customized communications solutions for each user and service type cannot be possible without the flexibility in ...
SNF-CVAE: Computational method to predict drug-disease interactions using similarity network fusion and collective variational autoencoder
(Elsevier, 2021)
Drug repositioning is an emerging approach to identify novel therapeutic potentials for approved drugs and discover therapies for previously untreatable diseases. Drug repositioning has also attracted considerable attention ...
Estimating multi-dimensional sparsity level for spectrum sensing
(Institute of Electrical and Electronics Engineers Inc., 2023)
Identifying spectrum opportunities is a crucial element of efficient spectrum utilization for future wireless networks. Spectrum sensing offers a convenient means for revealing such opportunities. Studies showed that usage ...
Automated analysis of the EEG signals for prediction of possible effectiveness of rTMS treatment in alzheimer's patient
(Institute of Electrical and Electronics Engineers Inc., 2022)
Alzheimer's disease (AD) is a progressive, chronic neurodegenerative brain disease that generally infects the elderly. The analysis of electroencephalography (EEG) signals has been commonly used for diagnosis. Repetitive ...