This study introduced a capacitive sensing interactive game platform aimed at promoting emotional stability, which we have named the “Sunrise and Sunset” game. This game primarily consists of two pieces of regular t...
This study introduced a capacitive sensing interactive game platform aimed at promoting emotional stability, which we have named the “Sunrise and Sunset” game. This game primarily consists of two pieces of regular textile fabric enveloping conductive silver fabric. A microcontroller was employed to extract the sensed capacitive values, and a game named “Sunrise and Sunset” is designed to complement the slow raising and lowering of both hands. The development of this gaming platform has the potential to offer a novel method of emotional management, particularly in high-stress living environments. It can serve as an effective relaxation tool, aiding individuals in emotional balance, anxiety reduction, and stress alleviation. Simultaneously, this platform can contribute to the promotion of mental well-being, providing an engaging and beneficial means for people to manage their emotions and moods.
Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent ...
Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA’s API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan.
We present a deep-learning method based on Wiener filters and U-Nets that performs image reconstruction in systems with spatially-varying aberrations. We train on simulated microscopy measurements and test on experime...
详细信息
We present implantable silicon photonic probes for selective plane illumination imaging in vivo. The small form factor of the probes minimizes tissue displacement and heat dissipation while providing planar illuminati...
详细信息
We propose a biohybrid sensor based on optical measurements of transmembrane proteins using micro-ring resonators. The scalability and sensitivity of this geometry could enable optical recording of many individual cel...
详细信息
Electronic technologies are growing very rapidly, especially those related to automation and robotics. Robotics technology is currently being developed and implemented in various fields including military, search and ...
详细信息
Miniaturized microscopes for monitoring neural activity are an indispensable tool for neuroscience research. We present a novel MEMS based miniature microscope with patterned optogenetic stimulation capabilities enabl...
详细信息
Among the main causes of road accidents, one of the most significant is related to driver distraction while driving, which is responsible for 18% of car accidents worldwide. This situation has demanded the development...
Among the main causes of road accidents, one of the most significant is related to driver distraction while driving, which is responsible for 18% of car accidents worldwide. This situation has demanded the development of mechanisms to automatically detect this dangerous behavior while driving. One of the computational solutions that has been considered viable to detect situations like this is the use of Convolutional Neural Networks (CNN), but some complex issues arise when deploying CNN models in microcontroller-based embedded devices with constrained processing and memory capabilities. In this context, this paper proposes a driver distraction detection system that achieves high accuracy (99.3%) and low latency (72ms) while requiring minimal computational resources (Peak- RAM of 164 KB and Flash of 52.7 KB). This solution exploiting Tiny Machine Learning (TinyML) algorithms was developed with the support of the Edge Impulse platform, used to perform the entire ML pipeline, from data pre-processing and ML model creation to deployment into an Arduino Portenta H7 board. By designing a driver assistance system that can be integrated into vehicles, it is expected that an affordable solution based on embedded machine learning is provided, tackling a real-world problem by potentially reducing accidents caused by driver distractions.
To enhance the variational quantum eigensolver (VQE), the CAFQA method can utilize classical computational capabilities to identify a better initial state than the Hartree-Fock method. Previous research has demonstrat...
详细信息
暂无评论