This paper compares four methods of feature extraction: Fractional Eigenfaces and Vander Lugt Correlator as global methods, and Gabor Ordinal Measures and Uniform Local Binary Pattern as local ones. We evaluate the four methods on the standard FERET probe data sets in order to study the robustness of these techniques against illumination variation, facial expression variation and aging. The Gabor ordinal measures as a combination of Gabor filters and ordinal measures outperforms the others methods on the four test sets in terms of recognition rate.

The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers, and eases the difficulty of determining weights in case of the weighted least square (WLS). Another method is proposed by incorporating a weight into the general entropy (GE) loss function to estimate the parameters of the WD to obtain a modified loss function (WGE). Furthermore, a Monte Carlo simulation is performed to examine the performance of the proposed OLSMLP method in comparison with approximate Bayesian estimation (BLWGE) by using a weighted GE loss function. The results of the simulation showed that the two proposed methods produced good estimates even for small sample sizes. In addition, the techniques proposed here are typically the preferred options when estimating parameters compared with other available methods, in terms of the mean squared error and requirements related to time.

Agriculture 5.0 is an emerging concept that builds on the integration of cutting-edge technologies to create smarter, more sustainable, and efficient farming systems. Integrating Unmanned Aerial Vehicles (UAVs) is a key enabler of precision agriculture, allowing real-time data collection, analysis, and decision-making to boost farming practices. UAVs provide a crucial function in this ecosystem, contributing to achieving Sustainable Development Goals (SDGs). Nevertheless, these vehicles encounter significant computing power, throughput, real-time delay, and battery capacity challenges. Addressing these challenges requires innovations in network design, power management, and communication technologies. To this end, we propose to deploy 6G’s Massive MIMO (Multiple Input Multiple Output) technology within a three-layered architecture for UAV-based smart agricultural systems. Our study focuses on the power allocation problem in both downlink and uplink transmissions, aiming to maximize throughput while ensuring fairness among different terminals. To achieve this, we deploy a downlink fairness-based algorithm (DFA) and a downlink hybrid algorithm (DHA). The first approach ensures fairness by optimizing the throughput of the weakest IoT terminal in the network, while the second approach balances throughput and fairness among IoT terminals. Additionally, we employ an uplink heuristic power control algorithm (UHPCA) to determine the optimal power allocation. The numerical results indicate that the proposed allocation algorithms achieve a better trade-off between throughput and fairness compared to conventional power allocation strategies in the literature.