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Numerous applications of Virtual Reality (VR) and Augmented Reality (AR) continue to emerge. However, many of the current mechanisms to provide input in those environments still require the user to perform actions (e.g., press a number of buttons, tilt a stick) that are not natural or intuitive. It would be desirable to enable users of 3D virtual environments to use natural hand gestures to interact with the environments. The implementation of a glove capable of tracking the movement and configuration of a user’s hand has been pursued by multiple groups in the past. One of the most recent approaches consists of tracking the motion of the hand and fingers using miniature sensor modules with magnetic and inertial sensors. Unfortunately, the limited quality of the signals from those sensors and the frequent deviation from the assumptions made in the design of their operations have prevented the implementation of a tracking glove able to achieve high performance and large-scale acceptance. This paper describes our development of a proof-of-concept glove that incorporates motion sensors and a signal processing algorithm designed to maintain high tracking performance even in locations that are challenging to these sensors, (e.g., where the geomagnetic field is distorted by nearby ferromagnetic objects). We describe the integration of the required components, the rationale and outline of the tracking algorithms and the virtual reality environment in which the tracking results drive the movements of the model of a hand. We also describe the protocol that will be used to evaluate the performance of the glove. 

Numerous applications of Virtual Reality (VR) and Augmented Reality (AR) continue to emerge. However, many of the current mechanisms to provide input in those environments still require the user to perform actions (e.g., press a number of buttons, tilt a stick) that are not natural or intuitive. It would be desirable to enable users of 3D virtual environments to use natural hand gestures to interact with the environments. The implementation of a glove capable of tracking the movement and configuration of a user’s hand has been pursued by multiple groups in the past. One of the most recent approaches consists of tracking the motion of the hand and fingers using miniature sensor modules with magnetic and inertial sensors. Unfortunately, the limited quality of the signals from those sensors and the frequent deviation from the assumptions made in the design of their operations have prevented the implementation of a tracking glove able to achieve high performance and large-scale acceptance. This paper describes our development of a proof-of-concept glove that incorporates motion sensors and a signal processing algorithm designed to maintain high tracking performance even in locations that are challenging to these sensors, (e.g., where the geomagnetic field is distorted by nearby ferromagnetic objects). We describe the integration of the required components, the rationale and outline of the tracking algorithms and the virtual reality environment in which the tracking results drive the movements of the model of a hand. We also describe the protocol that will be used to evaluate the performance of the glove. 

Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation. 

The goal of this study is to introduce practical state-of-the-art considerations for designing a highly efficient power generation, transmission, and conversion chain system, PGTCC, to power up the front-end’s electronics that acquire satisfactory EEG signal acquisition in a portable wireless and battery-free EEG cap. Several solutions, strategies, and unique configurations have been presented to reach this goal, including, a highly efficient and compact resonance-inductive link, multi-resonator power transfer, use of magnetized materials to improve power transmission efficiency, closed-loop power transmission, and a highly efficient power conversion chain. The proposed design has the potential to significantly improve the total efficiency and supply stable power for the front-end units that include the EEG signal, filtering, noise cancelation amplification units, processing units, and transceivers. 

This work presents a fully battery-less and wireless (untethered) EEG readout cap. The powering system is equipped with a highly efficient and compact power transmitter mosaicked by an array of 8×11 Tx resonators in a certain pattern operating at the lowest ISM band of 6.78 MHz. The front-end’s power receiver block Rx includes multi-resonators mounted all-around an EEG cap that can be worn by a subject. Furthermore, considering the subject's head which could assume different positions, a well-designed positioning system and an intelligent feeding setup are developed to balance the efficiency drop due to misalignment and to involve the most associated resonators with the powering scenario with the potential of switching off the extraneous resonators that are not engaged with Transmit and Receive (Tx-Rx) power.