Abolfazl Mehbodniya

TL;DR: This paper proposes an adaptive sparse channel estimation (ASCE) method with zero-attracting LMS/F (ZA-L MS/F) algorithm by introducing an ℓ1-norm sparse constraint into the cost function, and proposes a Monte Carlo method for a regularization parameter selection in RA-LMS/f and RZA- lms/F to achieve better steady-state estimation performance.

TL;DR: Simulation results show that using the green energy can present significant traditional energy savings for the BS when the connection demand is not heavy, and performance of the proposed IC mechanism will be close to the social optimal when the greenenergy production capacity increases.

TL;DR: In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds.

TL;DR: In this article, the problem of ON/OFF scheduling of self-powered SBSs in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs consisting of energy consumption and transmission delay of a network was studied.

TL;DR: In this letter, the Cramer-Rao lower bound (CRLB) for the sojourn time-based velocity estimation is analyzed and it is shown that theSojournTime-Based velocity estimation exhibit a lower CRLB compared to the CRLBs of classical velocity estimation using handover count.