Sensors

Despite recent major advances in autonomous driving, several challenges remain. Even with modern advanced sensors and processing systems, vehicles are still unable to detect all possible obstacles present in complex urban settings and under diverse environmental conditions. Consequently, numerous studies have investigated artificial intelligence methods to improve vehicle perception capabilities. This paper presents a new methodology using a framework named CarAware, which fuses multiple types of sensor data to predict vehicle positions using Deep Reinforcement Learning (DRL). Unlike traditional DRL applications centered on control, this approach focuses on perception. As a case study, the PPO algorithm was used to train and evaluate the effectiveness of this methodology.

Wrist-worn devices enable new paradigms of implicit and continuous user authentication; however, identifying biometric modalities that combine reliability with practical integrability remains challenging. Inner-wrist skin texture represents a relatively unexplored biometric characteristic that may be acquired unobtrusively using commodity hardware. This study evaluates biometric verification based on inner-wrist skin texture using an off-the-shelf capacitive fingerprint sensor and an unmodified, manufacturer-provided fingerprint verification algorithm. Two experiments were conducted. Experiment 1 assessed baseline verification performance under controlled acquisition conditions in a cohort of 33 participants (21 male, 12 female; mean age 30.0 ± 16.9 years, range 10–71 years), yielding 1768 genuine authentication trials. Experiment 2 examined the effect of wrist posture variation under controlled flexion in a separate cohort of 15 participants (11 male, 4 female; mean age 30.9 years, range 18–49 years), with 3900 authentication trials recorded. Across 86,897 impostor comparisons in Experiment 1, no false acceptances were observed, corresponding to a conservative upper bound on the false acceptance rate of 6.7 × 10⁻⁵ at the 99.7% confidence level, while the false rejection rate was approximately 2.93%. In Experiment 2, the overall false rejection rate increased to 3.52%, with no clear monotonic relationship between wrist angle and verification performance within the tested range. The results demonstrate that inner-wrist skin texture can be captured and matched using fingerprint-oriented sensing and matching technology under controlled conditions, providing an experimental baseline for this biometric modality. At the same time, the use of a closed matching algorithm and a sensor designed for fingerprints limits interpretability and generalization. These findings motivate further investigation using dedicated recognition methods, larger sensing areas, and extended evaluation protocols tailored specifically to wrist skin print biometrics.

This study investigates the humidity sensing characteristics of microwave sensors coated with polyvinyl alcohol/carboxymethyl cellulose (PVA/CMC) composites with different weight percentages. The microwave sensor has a band-stop filter characteristic and consists of a microstrip transmission line with an interdigital capacitor-defected ground structure (IDC-DGS). To evaluate performance, PVA/CMC composites were prepared in 100/0 (pure PVA), 90/10, 80/20, 60/40, and 0/100 (pure CMC) weight percentages. The humidity sensing capability of the IDC-DGS-based microwave sensors coated with the PVA/CMC composites with different weight percentages was compared by measuring the variations in the resonant frequency and magnitude level of the transmission coefficient. The relative humidity (RH) was changed from 40% to 90% with increments of 10% at a temperature around 25 °C. The experimental results demonstrate that the humidity sensing capability of the microwave sensor in terms of the variations in the resonant frequency and magnitude level of the transmission coefficient increased as the weight percentage of CMC content increased. Pure CMC shows enhanced humidity sensing performance compared to gelatin and PVA in terms of the percent relative frequency shift and effective relative permittivity.