Ultraviolet Radiation Measurement via Smart Devices

Mei, Bo; Li, Ruinian; Cheng, Wei; Yu, Jiguo; Cheng, Xiuzhen

doi:10.1109/jiot.2017.2717845

Cited by 23 publications

(16 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Currently the accuracy of these devices is much lower than scientific-grade UV sensors in use, either due to poor technical characteristics and calibration, 238 or due to inappropriate measurement principles. 239 In the last few years, the possibility of using smartphones to record radiation spectra has emerged, mostly by coupling various classes of entrance optics to these units with data recording achieved using the smartphone camera. UV spectra recorded with these devices have been based on sensors located outside of the smartphone body.…”

Section: Low-cost Crowd-sourcing Sensors (Smart Phone Applications)mentioning

confidence: 99%

Ozone—climate interactions and effects on solar ultraviolet radiation

Bais

Bernhard

McKenzie

et al. 2019

Photochem Photobiol Sci

141

149

View full text Add to dashboard Cite

show abstract

Section: Low-cost Crowd-sourcing Sensors (Smart Phone Applications)mentioning

confidence: 99%

Ozone—climate interactions and effects on solar ultraviolet radiation

Bais

Bernhard

McKenzie

et al. 2019

Photochem Photobiol Sci

141

149

View full text Add to dashboard Cite

show abstract

“…First, edge computing can work with wearable devices to collect clinical speech data from the patients with Parkinson's disease [92]. Second, mobile phone cameras can be used to detect ultraviolet, and the mobile side results are gathered and amended to edge DCs to achieve more accurate ultraviolet measurement [93].…”

Section: E Healthcarementioning

confidence: 99%

Edge Computing and Networking: A Survey on Infrastructures and Applications

Zhao

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

As a concept to enhance and extend cloud-computing capabilities, edge computing aims to provide Internet-based services in the close proximity to users by placing IT infrastructures at the network edge in forms of tiny datacenters. Taking advantage of the close distance to end user and access networks, edge datacenters can provide low-latency and context-aware services and further improve users' quality of experience. As the network edge is a geographically spread concept, the edge datacenters are usually highly distributed so that they can provide nearby storage and processing capabilities to most of the end users. Furthermore, edge datacenters also co-work with centralized cloud datacenters for service orchestration. Such decentralization and collaboration are expected to introduce significant transformations to both infrastructures and applications. To provide an overview of how edge can be integrated with cloudcomputing and how edge computing can benefit applications, this paper studies the infrastructure and application issues of edge computing and networking in several sub-aspects, including related concepts, infrastructures, resource management and virtualization, performance, and applications.

show abstract

“…Moreover, as the technology related to artificial neural network develops, research cases are being introduced to solve and predict complicated problems by establishing a deep learning model through the characteristic analysis between highly correlated factors [ 13 , 14 ]. Meanwhile, Mei et al calculated the UV intensities through the images collected through smartphones’ CMOS sensors [ 15 ]. Feister et al introduced a method based on the regression equation that calculated the UV information with the element values such as illuminance of solar light and solar zenith angle [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Lim

2021

Sensors

View full text Add to dashboard Cite

Ultraviolet rays are closely related with human health and, recently, optimum exposure to the UV rays has been recommended, with growing importance being placed on correct UV information. However, many countries provide UV information services at a local level, which makes it impossible for individuals to acquire user-based, accurate UV information unless individuals operate UV measurement devices with expertise on the relevant field for interpretation of the measurement results. There is a limit in measuring ultraviolet rays’ information by the users at their respective locations. Research about how to utilize mobile devices such as smartphones to overcome such limitation is also lacking. This paper proposes a mobile deep learning system that calculates UVI based on the illuminance values at the user’s location obtained with mobile devices’ help. The proposed method analyzed the correlation between illuminance and UVI based on the natural light DB collected through the actual measurements, and the deep learning model’s data set was extracted. After the selection of the input variables to calculate the correct UVI, the deep learning model based on the TensorFlow set with the optimum number of layers and number of nodes was designed and implemented, and learning was executed via the data set. After the data set was converted to the mobile deep learning model to operate under the mobile environment, the converted data were loaded on the mobile device. The proposed method enabled providing UV information at the user’s location through a mobile device on which the illuminance sensors were loaded even in the environment without UVI measuring equipment. The comparison of the experiment results with the reference device (spectrometer) proved that the proposed method could provide UV information with an accuracy of 90–95% in the summers, as well as in winters.

show abstract

Ultraviolet Radiation Measurement via Smart Devices

Cited by 23 publications

References 14 publications

Ozone—climate interactions and effects on solar ultraviolet radiation

Ozone—climate interactions and effects on solar ultraviolet radiation

Edge Computing and Networking: A Survey on Infrastructures and Applications

Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s Location

Contact Info

Product

Resources

About