In wireless communication systems, the Ricean K-factor estimation is required in several applications such as link budget calculations. While the SNR estimation can be used to measure the link quality. In this paper, the Ricean K-factor estimation for M-ary Phase Shift Keying (M-PSK) modulated signals with additive noise is studied. To this end, the Fourth-Order Cross-Moments (FOCM) of the received signal are considered. First, the Signal-to-Noise Ratio (SNR) is computed by estimating the powers of the desired components of the signal and the noise. Then, the K-factor is estimated using the kurtosis of the Ricean channel. Simulation results show that our approach outperforms the most recent developed estimator in the literature.

In this paper, the effects of a metal plate to antenna parameters (input impedance, return loss and radiation patterns) is presented when the RFID tag antenna is horizontally placed near the metal plate. The metal plate has finite size and rectangular shape. The new technique based on the moment method and RWG basis functions developed by Ghannay et al. has been used to simulate the dependency of antenna parameters on the size of the plate and the distance between the antenna and the plate.

Skin lesions classification is a crucial endeavor in dermatology, as precise diagnosis is critical for improving patient outcomes and lowering the incidence of skin cancer. The PAD-UFES-20 dataset is very relevant for this task since it contains 2,298 imbalanced skin lesions smartphone images. Thus, data sampling techniques such as Random Over Sampling (ROS) and SMOTE were applied to overcome this issue. This work is based on two scenarios. The first scenario aims to classify the whole six classes figuring in this dataset, using various powerful proposed methods based on Deep Learning (DL) models. The best performance was achieved when employing EfficientNetB0 with data ROS, as it reached an accuracy of 92.01%, F1-score of 92.01%, and AUC of 97.12%. The objective of the second scenario is to make a triage between two skin lesions: Melanoma and Nevus using DL techniques. The results of the use of an EfiicientNetB1 with data ROS demonstrated a high level of accuracy, F1-score, and AUC, with a measurement of 98.98%, 99.22%, and 99.95%, respectively. The study’s high-performance shows that these methods might have the potential to be a useful tool for clinicians in the examination and diagnosis of skin lesions, thereby enhancing the healthcare quality.

In this study, the authors propose a moment‐based estimator of the signal‐to‐noise ratio (SNR) over time‐variant Rayleigh fading single‐input multiple‐output channels. The correlated time‐variant channel is modelled with the well‐known Jakes’ model. The authors’ approach uses the fourth‐order cross‐moments of the received signal to estimate the SNR with the presence of an additive white Gaussian noise which is uncorrelated between antenna elements. The SNR is deduced by estimating, respectively, the powers of the useful signals and the noise. The proposed SNR estimator is a non‐data‐aided (NDA) method since it does not require a training sequence. The performances of this algorithm are investigated in terms of normalised mean square error over a wide range of scenarios. Simulation results show that the proposed algorithm outperforms the NDA maximum‐likelihood‐based estimators and the moment‐based estimators.

This paper introduces a database-based design methodology aimed at optimizing a 26 GHz MIMO antenna system through machine learning (ML) techniques. The procedure is divided into two primary phases. Initially, a rectangular microstrip patch antenna is designed and enhanced using analytical models alongside ML algorithms that are trained on a detailed dataset of geometric parameters. This yields effective impedance matching (S11 < −45 dB) and a high gain (~6.64 dBi), which serve as the foundation for the MIMO structure. In the second phase, split ring resonator (SRR) configurations are integrated between the antenna elements to reduce mutual coupling. A specialized dataset, featuring varied dimensions of SRR, quantities of unit cells, and spatial placements, is utilized to train Random Forest models that forecast arrangements achieving optimal isolation (S21 < −40 dB) while maintaining low reflection losses. Additionally, a secondary dataset is constructed to investigate the best strategies for SRR placement, ensuring an optimal balance between isolation and return loss. The ultimate MIMO design is validated via comprehensive full-wave electromagnetic simulations and experimental measurements. The proposed system exhibits noteworthy performance enhancements, including an envelope correlation coefficient (ECC) < 0.005, diversity gain (DG) ≈ 9.99 dB, channel capacity loss (CCL) < 0.3 bits/s/Hz, total active reflection coefficient (TARC) < −30 dB, radiation efficiency exceeding 80%, and a maximum gain increase up to 10.22 dB. The close correlation between predicted and measured outcomes validates the effectiveness of the ML-driven approach in expediting antenna optimization for 5G and future applications.