Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result.

Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of $65\%$, $98\%$, and $78\%$ in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV.

This paper presents a comparative study of the fatigue life of solder joints in a board-level Ball Grid Array (BGA) assembly. It specifically contrasts the commonly used SAC305 alloy with the advanced Innolot-based solder alloy, recognized for its superior tensile strength. Through Finite Element Analysis (FEA), we simulate and predict the reliability of these solder joints under harmonic and random vibration conditions. Following the JEDEC (Joint Electronic Device Engineering Council) standards, two different board-level BGA assemblies are used for the analysis. In both assemblies, the dimensions of the substrate, molding compound, and solder balls remain identical; only the board dimensions are changed to observe how they affect stress in the solder joints. The results indicate that using Innolot raises the volume-averaged stress levels by more than 25% on larger boards and about 5% on smaller boards compared to SAC305. This increase in stress levels is due to the Innolot alloy having a less significant stiffening effect than SAC305, which results in higher stress levels under the same excitation conditions. While the stress in the Innolot-based solder joint is greater than that in the SAC305 joint under identical excitation conditions, the fatigue life of the Innolot joint is significantly higher than that of the SAC305 joint. The results show that the Innolot-based alloy exceeds the performance of SAC305, and that it is suitable for use as a solder alloy in extreme vibration conditions.

Marine vessels rely heavily on propeller shaft systems to adjust the engine torque and propeller thrust. However, these systems are subjected to various dynamic excitations during operation, such as transverse, longitudinal, and torsional excitations. These excitations can arise from factors like non-uniform stern flow fields, misaligned components, and the whirling motion of the shafts, which can affect the integrity and reliability of the vehicle. To analyze the dynamic response of the propulsion shaft system and ensure its reliability, numerical/analytical models are currently in practice. The finite element method (FEM) is a popular choice, but uncertainties in bearings and connectors stiffness lead to inaccuracies in the Finite Element model, resulting in significant differences between the experimental and theoretical models. This paper proposes the response surface optimization (RSO) technique to estimate unknown bearing stiffness in the propulsion shaft system. The experimental model of the propeller shaft system is constructed using steady-state response with step sine excitation. The RSO technique is then used to update the natural frequencies and vibration amplitude of the FE (Finite Element) model. The updated model shows less than a 10% difference in natural frequencies and vibration amplitude compared to the experimental model, demonstrating that the proposed technique is an efficient tool for marine shaft dynamic analysis.