Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field.

Due to their excellent strength-to-weight ratio, composite laminates are gradually being substituted for traditional metallic materials in a variety of industries. However, due to their orthotropic nature, composite laminates are prone to several different types of damage, with delamination being the most prevalent and serious. Therefore, deep learning-based methods that use sensor data to conduct autonomous health monitoring have drawn much interest in structural health monitoring (SHM). However, the direct application of these models is restricted by a lack of training data, necessitating the use of transfer learning. The commonly used transfer learning models are computationally expensive; therefore, the present research proposes lightweight transfer learning (LTL) models for the SHM of composites. The use of an EfficientNet–based LTL model only requires the fine-tuning of target vibration data rather than training from scratch. Wavelet-transformed vibrational data from various classes of composite laminates are utilized to confirm the effectiveness of the proposed method. Moreover, various assessment measures are applied to assess model performance on unseen test datasets. The outcomes of the validation show that the pre-trained EfficientNet–based LTL model could successfully perform the SHM of composite laminates, achieving high values regarding accuracy, precision, recall, and F1-score.

One of the aims of successful total knee arthroplasty (TKA) is to restore the natural range of motion of the infected joint. The operated leg motion highly depends on the coordinate systems that have been used to prepare the bone surfaces for an implant. Assigning a perfect coordinate system to the knee joint is a considerable challenge. Various commercially available knee arthroplasty devices use different methods to assign the coordinate system at the distal femur. Transepicondylar axis (TEA) and Whiteside’s line are commonly used anatomical axes for defining a femoral coordinate system (FCS). However, choosing a perfect TEA for FCS is trickier, even for experienced surgeons, and a small error in marking Whiteside’s line leads to a misaligned knee joint. This work proposes a modified Whiteside’s line method for the selection of TEA. The Whiteside’s line, along with the knee center and femur head center, define two independent central planes. Multiple prominent points on the lateral and medial sides of epicondyles are marked. Based on the lengths of perpendicular distances between the multiple points and central planes, the most prominent epicondyle points are chosen to define an optimal TEA. Compared to conventional techniques, the modified Whiteside’s line defines a repeatable TEA

Total knee arthroplasty (TKA) is a surgical technique to replace damaged knee joints with artificial implants. Recently, the imageless TKA has brought a revolutionary improvement to the accuracy of implant placement and ease of surgical process. Based on key anatomical points on the knee, the software guides the surgeon during the TKA procedure. However, the number of revision surgeries is increasing due to malalignment caused by registration error, resulting in imbalanced contact stresses that lead to failure of the TKA. Conventional stress analysis methods involve time-consuming and computationally demanding finite element analysis (FEA). In this work, a machine-learning-based approach estimates the contact pressure on the TKA implants. The machine learning regression model has been trained using FEA data. The optimal preprocessing technique was confirmed by the data without preprocessing, data divided by model size, and data divided by model size and optimal angle. Extreme gradient boosting, random forest, and extra trees regression models were trained to determine the optimal approach. The proposed method estimates the contact stress instantly within 10 percent of the maximum error. This has resulted in a significant reduction in computational costs. The efficiency and reliability of the proposed work have been validated against the published literature.