This study presents a comprehensive analysis of power consumption in the context of AR applications on the HoloLens 2. As an ablation study, the research systematically dissects various aspects, shedding light on both hardware and software components. The power consumption was examined in diverse scenarios, ranging from the device at idle to running AR applications of increasing complexity. Overall, the study provides insights into the power management aspects associated with Hololens 2 AR devices and applications, offering cues for optimizing energy efficiency and enhancing the user experience. The findings confirm that display-related factors, such as the screen brightness and content complexity, significantly impact power consumption. However, the results reveal that power usage does not scale linearly with the number of objects in an AR scene due to overhead factors and object occlusion. Furthermore, usage scenarios such as lighting conditions and large/minor user motions are analyzed. Moreover, the influence of different development platforms, Unity and Unreal, on power consumption is discussed, highlighting their respective contributions to energy usage. These findings might be useful for researchers, developers, and users of AR technology.

Most of the current image processing methods used in the near-infrared imaging of finger vascular system concentrate on the extraction of internal structures (veins). In this paper, we propose a novel approach which allows to enhance both internal and external features of a finger. The method is based on the Distance Transformation and allows for selective extraction of physiological structures from an observed finger. We evaluate the impact of its parameters on the effectiveness of the already established processing pipeline used for biometric identification. The new method was compared with five state-of-the-art approaches to features extraction (position-gray-profile-curve—PGPGC, maximum curvature points in image profiles—MC, Niblack image adaptive thresholding—NAT, repeated dark line tracking—RDLT, and wide line detector—WD) on the GustoDB database of images obtained in a wide range of NIR wavelengths (730–950 nm). The results indicate a clear superiority of the proposed approach over the remaining alternatives. The method managed to reach over $90\%$ identification accuracy for all analyzed datasets.