ABSTRACT
COVID-19 digital contact tracing applications for smartphones have become popular worldwide to reduce the effects of the pandemic. We considered that contact information between smartphones used in these applications can be used for the indoor localization of pedestrians. In this paper, we propose two indoor pedestrian localization methods based on contact information obtained from Bluetooth low energy (BLE) beacons installed in pedestrian's smartphones. Proposed method 1 is multilateration, and proposed method 2 solves a nonlinear optimization problem to further improve the accuracy of method 1. These two proposed methods comprise three steps: (1) the smartphones and anchor nodes recognize the proximity relationship with neighbor nodes using BLE signals transmitted from other smartphones and anchor nodes. The recognized proximity relationship is sent to a server. (2) The server estimates the distance between each node (smartphone or anchor node) from the proximity relationship. (3) The positions of smartphones are estimated based on the distance between nodes estimated by the server. We verified the localization accuracy of the proposed methods through simulation experiments. In an indoor area of 15 m × 30 m, the average localization error of the proposed method 2 was 0.74 m when the pedestrian density was 0.5 /m2. © 2022 IEEE.
ABSTRACT
In recent years, due to the COVID-19 pandemic and the widespread use of technology, the Internet and food and beverage websites are often used for take-out and food and beverage reservations, and information such as reviews and photos on these platforms has a significant impact on revenue. In this study, to develop an appetite-enhancing application, we focus on food images that strongly influences appetite and analyze what image features stimulate appetite. Then, based on the results of the analysis, we generate appetizing images using GAN (Generative Adversarial Network). © 2022 SPIE.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 and spread globally to cause severe damage to public health and economics worldwide as coronavirus disease 2019 (COVID-19). A deep ultraviolet light-emitting diode (DUV-LED) would be a promising candidate for the inactivation technology of SARS-CoV-2 to mitigate the spread of COVID-19. The DUV-LED has the potential to provide energy-efficient and contact-less virus inactivation technology with no residual substances after treatment, little effect on targets and the applicability of various viruses, including virus variants. This paper introduces an overview of the virus inactivation technology using DUV-LED and discusses the effect of the DUV-LED irradiation on the SARS-CoV-2.
ABSTRACT
It is important for drivers who are responsible for the lives of passengers to remain in a calm state of mind and body suitable for driving. The objective of this study is to grasp the driver’s state by using physiological data that can be continuously and relatively easily measured. To provide support in a timely manner, changes in the driver’s state must be detected with high time resolution and reliability. The spread of Covid-19 across the world has emphasized the importance of technology to remotely monitor physiological conditions in real time and provide support. This study attempted to improve the accuracy of estimation by using the appropriate indices for individuals. Specifically, we tried to estimate the tension state of drivers using both respiration-wave and ECG data. The results demonstrated that different individuals have different valid indicators and it is possible to improve the accuracy of estimation by using the appropriate indices for each individual. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.