scholarly journals Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3617 ◽  
Author(s):  
Hoochang Lee ◽  
Jiseock Kang ◽  
Sungjung Kim ◽  
Yunseok Im ◽  
Seungsung Yoo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Sensor Nodes ◽  
Lookup Table ◽  
Calibration Model ◽  
Statistical Parameters ◽  
Sensor Platform ◽  
Calibration Algorithm ◽  
Term Evaluation ◽  
Spatio Temporal

Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.

scholarly journals Particulate Matter Profiles along the Rack Railway Route Using Low-Cost Sensor

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 126
Author(s):  
Abdul Samad ◽  
Anas Maali ◽  
Bernd Laquai ◽  
Ulrich Vogt
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Fine Particulate Matter ◽  
Temporal And Spatial Distribution ◽  
Fine Particulate ◽  
Sensor Platform ◽  
Federal Highway ◽  
Temporal And Spatial ◽  
Mobile Measurements ◽  
Monitoring Platform

Air pollution due to Particulate Matter (PM) is an increasing concern of global extent. It has been the focus of many research projects worldwide and the latest low-cost technology is offering an ease and cheap way to monitor PM concentration. In this research, a low-cost PM monitoring platform was built with the objectives of evaluating its feasibility and its performance in mobile measurements, as well as characterizing the concentration profiles of PM along the measurement route. The rack railway in Stuttgart was utilized as means of transportation for this low-cost monitoring system with which the temporal and spatial distribution of the PM10, PM2.5 and PM1 concentration along the route was attained. The measurements were conducted for around two months from mid of January until mid of March 2019, during the operation hours of the rack railway. The results showed that the PM concentrations were dominated by fine particulate matter (PM2.5 and PM1) along the route of the rack railway. Higher PM concentrations were measured near the federal highway and high traffic area as compared to the residential area. An overestimation of PM concentration using low-cost sensor platform was observed during high relative humidity conditions as compared to the professional aerosol spectrometers.

scholarly journals Calib-Net: Calibrating the Low-Cost IMU via Deep Convolutional Neural Network

2022 ◽  
Vol 8 ◽  
Author(s):  
Ruihao Li ◽  
Chunlian Fu ◽  
Wei Yi ◽  
Xiaodong Yi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Deep Convolutional Neural Network ◽  
Measurement Unit ◽  
Calibration Model ◽  
Spatio Temporal ◽  
Public Datasets ◽  
Short Time

The low-cost Inertial Measurement Unit (IMU) can provide orientation information and is widely used in our daily life. However, IMUs with bad calibration will provide inaccurate angular velocity and lead to rapid drift of integral orientation in a short time. In this paper, we present the Calib-Net which can achieve the accurate calibration of low-cost IMU via a simple deep convolutional neural network. Following a carefully designed mathematical calibration model, Calib-Net can output compensation components for gyroscope measurements dynamically. Dilation convolution is adopted in Calib-Net for spatio-temporal feature extraction of IMU measurements. We evaluate our proposed system on public datasets quantitively and qualitatively. The experimental results demonstrate that our Calib-Net achieves better calibration performance than other methods, what is more, and the estimated orientation with our Calib-Net is even comparable with the results from visual inertial odometry (VIO) systems.

scholarly journals Investigation of the Spatio-Temporal Behaviour of Submarine Groundwater Discharge Using a Low-Cost Multi-Sensor-Platform

2021 ◽  
Vol 9 (8) ◽  
pp. 802
Author(s):  
Christoph Tholen ◽  
Iain Parnum ◽  
Robin Rofallski ◽  
Lars Nolle ◽  
Oliver Zielinski
Keyword(s):  
Spatial Distribution ◽  
Submarine Groundwater Discharge ◽  
Minimum Distance ◽  
Low Cost ◽  
Environmental Parameters ◽  
Environmental Sensors ◽  
Temporal Behaviour ◽  
Sensor Platform ◽  
Data Points ◽  
Spatio Temporal

Submarine groundwater discharge (SGD) is an important pathway of nutrients into coastal areas. During the last decades, interest of researchers in SGDs has grown continuously. However, methods applied for SGD research usually focus on the aquifer or on the mixing processes on larger scales. The distribution of discharged water within the water column is not well investigated. Small remotely operated vehicles (ROV) equipped with environmental sensors can be used to investigate the spatial distribution of environmental parameters in the water column. Herein, a low-cost multi-sensor platform designed to investigate the spatial distribution of water quality properties is presented. The platform is based on an off-the-shelf underwater vehicle carrying various environmental sensors and a short-baseline localisation system. This contribution presents the results of SGD investigations in the area of Woodman Point (Western Australia). Various potential SGD plumes were detected using a skiff equipped with a recreational echo sounder. It was demonstrated that this inexpensive equipment could be used to detect and investigate SGDs in coastal areas. In addition, the low-cost multi-sensor platform was deployed to investigate the spatial distribution of environmental parameters including temperature (T), electric conductivity (EC), dissolved oxygen (DO), oxidation-reduction potential (ORP), pH, and dissolved organic matter fluorescence (FDOM). Three ROV surveys were conducted from different skiff locations. Analyses of the spatial distribution of the environmental parameters allowed the identification of nine potential SGD plumes. At the same locations, plumes were identified during the sonar surveys. In addition, fuzzy logic was used for the fusion of salinity, DO, and FDOM readings in order to enhance SGD detection capability of the designed multi-sensor system. The fuzzy logic approach identified 293 data points as potential within a SGD plume. Average minimum-distance between these points and the identified SGD plumes was 0.5 m and 0.42 m smaller than the minimum-distance average of the remaining data points of survey one and three respectively. It was shown that low-cost ROVs, equipped with environmental sensors, could be an important tool for the investigation of the spatio-temporal behaviour of SGD sites. This method allows continuous mapping of environmental parameters with a high spatial and temporal resolution. However, to obtain deeper insights into the influence of SGDs on the nearshore areas, this method should be combined with other well-established methods for SGD investigation, such as pore water sampling, remote sensing, or groundwater monitoring.

Low cost HAB platform to measure particulate matter in the troposphere

2016 ◽  
Author(s):  
Mark J. Potosnak ◽  
Bernhard Beck-Winchatz ◽  
Paul Ritter ◽  
Emily Dawson
Keyword(s):  
Particulate Matter ◽  
Low Cost

scholarly journals Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions

2021 ◽  
Vol 11 (5) ◽  
pp. 2093
Author(s):  
Noé Perrotin ◽  
Nicolas Gardan ◽  
Arnaud Lesprillier ◽  
Clément Le Goff ◽  
Jean-Marc Seigneur ◽  
...  
Keyword(s):  
Heart Rate ◽  
Low Cost ◽  
Step Length ◽  
Measurement Unit ◽  
Ecological Conditions ◽  
Sports Science ◽  
Ground Contact Time ◽  
Spatio Temporal ◽  
Performance Results ◽  
Trail Running

The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10km. The three sensors used were (i) a Stryd sensor (Stryd Inc. Boulder CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.

Taking a Deep Dive Into The Batteryless Internet of Things With Shepherd

2021 ◽  
Vol 24 (3) ◽  
pp. 5-8
Author(s):  
Kai Geissdoerfer ◽  
Mikołaj Chwalisz ◽  
Marco Zimmerling
Keyword(s):  
Energy Storage ◽  
Internet Of Things ◽  
Sensor Nodes ◽  
Energy Availability ◽  
Deep Dive ◽  
Temporal Fluctuations ◽  
Temporal Characteristics ◽  
Challenges And Opportunities ◽  
Spatio Temporal

Collaboration of batteryless devices is essential to their success in replacing traditional battery-based systems. Without significant energy storage, spatio-temporal fluctuations of ambient energy availability become critical for the correct functioning of these systems. We present Shepherd, a testbed for the batteryless Internet of Things (IoT) that can record and reproduce spatio-temporal characteristics of real energy environments to obtain insights into the challenges and opportunities of operating groups of batteryless sensor nodes.

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