scholarly journals Skylight Light Intensity Data Logging System

1997 ◽  
Vol 20 (1) ◽  
pp. 7-17
Author(s):  
A. Udina ◽  
Lc Jain

The design of a skylight intensity data logging system is presented. The proposed system is able to accept 32 inputs in two 16 input blocks with capabilities for further expansion. A typical application would be to simultaneously monitor real time change in light intensity in two skylight units, over a chosen periods of minutes, hours or days.

Author(s):  
Florian Agbuya ◽  
Gerard Francesco Apolinario ◽  
Dianne Marie Ramos ◽  
JD Mark Villanueva ◽  
Princess Zafe ◽  
...  

Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.


2020 ◽  
Vol 32 (3) ◽  
pp. 373-398
Author(s):  
Anja Thiel ◽  
Aaron J. Dinkin

AbstractWe examine the loss of the Northern Cities Shift raising of trap in Ogdensburg, a small city in rural northern New York. Although data from 2008 showed robust trap-raising among young people in Ogdensburg, in data collected in 2016 no speakers clear the 700-Hz threshold for NCS participation in F1 of trap—a seemingly very rapid real-time change. We find apparent-time change in style-shifting: although older people raise trap more in wordlist reading than in spontaneous speech, younger people do the opposite. We infer that increasing negative evaluation of the feature led Ogdensburg speakers to collectively abandon raising trap between 2008 and 2016. This indicates a role for communal change in the transition of a dialect feature from an indicator to a marker.


2017 ◽  
Vol 5 (2) ◽  
pp. 293-310 ◽  
Author(s):  
Ryan A. Kromer ◽  
Antonio Abellán ◽  
D. Jean Hutchinson ◽  
Matt Lato ◽  
Marie-Aurelie Chanut ◽  
...  

Abstract. We present an automated terrestrial laser scanning (ATLS) system with automatic near-real-time change detection processing. The ATLS system was tested on the Séchilienne landslide in France for a 6-week period with data collected at 30 min intervals. The purpose of developing the system was to fill the gap of high-temporal-resolution TLS monitoring studies of earth surface processes and to offer a cost-effective, light, portable alternative to ground-based interferometric synthetic aperture radar (GB-InSAR) deformation monitoring. During the study, we detected the flux of talus, displacement of the landslide and pre-failure deformation of discrete rockfall events. Additionally, we found the ATLS system to be an effective tool in monitoring landslide and rockfall processes despite missing points due to poor atmospheric conditions or rainfall. Furthermore, such a system has the potential to help us better understand a wide variety of slope processes at high levels of temporal detail.


2018 ◽  
Vol 210 ◽  
pp. 03008
Author(s):  
Aparajita Das ◽  
Manash Pratim Sarma ◽  
Kandarpa Kumar Sarma ◽  
Nikos Mastorakis

This paper describes the design of an operative prototype based on Internet of Things (IoT) concepts for real time monitoring of various environmental conditions using certain commonly available and low cost sensors. The various environmental conditions such as temperature, humidity, air pollution, sun light intensity and rain are continuously monitored, processed and controlled by an Arduino Uno microcontroller board with the help of several sensors. Captured data are broadcasted through internet with an ESP8266 Wi-Fi module. The projected system delivers sensors data to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real time data using graphical information of the environment.


Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6781
Author(s):  
Tomasz Nowak ◽  
Krzysztof Ćwian ◽  
Piotr Skrzypczyński

This article aims at demonstrating the feasibility of modern deep learning techniques for the real-time detection of non-stationary objects in point clouds obtained from 3-D light detecting and ranging (LiDAR) sensors. The motion segmentation task is considered in the application context of automotive Simultaneous Localization and Mapping (SLAM), where we often need to distinguish between the static parts of the environment with respect to which we localize the vehicle, and non-stationary objects that should not be included in the map for localization. Non-stationary objects do not provide repeatable readouts, because they can be in motion, like vehicles and pedestrians, or because they do not have a rigid, stable surface, like trees and lawns. The proposed approach exploits images synthesized from the received intensity data yielded by the modern LiDARs along with the usual range measurements. We demonstrate that non-stationary objects can be detected using neural network models trained with 2-D grayscale images in the supervised or unsupervised training process. This concept makes it possible to alleviate the lack of large datasets of 3-D laser scans with point-wise annotations for non-stationary objects. The point clouds are filtered using the corresponding intensity images with labeled pixels. Finally, we demonstrate that the detection of non-stationary objects using our approach improves the localization results and map consistency in a laser-based SLAM system.


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