scholarly journals Adaptive spatio-temporal filter for low-cost camera depth maps

2012 ◽  
Author(s):  
M. Camplani ◽  
L. Salgado
Keyword(s):  
Low Cost ◽  
Depth Maps ◽  
Temporal Filter ◽  
Spatio Temporal

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.

How low-cost air pollution sensors could make homes smarter?

2021 ◽  
Author(s):  
Hamid Omidvarborna ◽  
Prashant Kumar
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Air Pollutants ◽  
Indoor Air Pollution ◽  
Prediction Models ◽  
Disease Risk ◽  
Low Cost ◽  
Mortality And Morbidity ◽  
Spatio Temporal ◽  
Indoor Air Pollutants

<p>The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors.</p>

scholarly journals User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns

2021 ◽  
Vol 18 (23) ◽  
pp. 12544
Author(s):  
Johanna Amalia Robinson ◽  
Rok Novak ◽  
Tjaša Kanduč ◽  
Thomas Maggos ◽  
Demetra Pardali ◽  
...  
Keyword(s):  
Air Pollution ◽  
Design Thinking ◽  
Low Cost ◽  
Personal Exposure ◽  
Final Report ◽  
Draft Report ◽  
Horizon 2020 ◽  
User Centred Design ◽  
Exposure Monitoring ◽  
Spatio Temporal

Using low-cost portable air quality (AQ) monitoring devices is a growing trend in personal exposure studies, enabling a higher spatio-temporal resolution and identifying acute exposure to high concentrations. Comprehension of the results by participants is not guaranteed in exposure studies. However, information on personal exposure is multiplex, which calls for participant involvement in information design to maximise communication output and comprehension. This study describes and proposes a model of a user-centred design (UCD) approach for preparing a final report for participants involved in a multi-sensor personal exposure monitoring study performed in seven cities within the EU Horizon 2020 ICARUS project. Using a combination of human-centred design (HCD), human–information interaction (HII) and design thinking approaches, we iteratively included participants in the framing and design of the final report. User needs were mapped using a survey (n = 82), and feedback on the draft report was obtained from a focus group (n = 5). User requirements were assessed and validated using a post-campaign survey (n = 31). The UCD research was conducted amongst participants in Ljubljana, Slovenia, and the results report was distributed among the participating cities across Europe. The feedback made it clear that the final report was well-received and helped participants better understand the influence of individual behaviours on personal exposure to air pollution.

scholarly journals Multitemporal Chlorophyll Mapping in Pome Fruit Orchards from Remotely Piloted Aircraft Systems

2019 ◽  
Vol 11 (12) ◽  
pp. 1468 ◽  
Author(s):  
Yasmin Vanbrabant ◽  
Laurent Tits ◽  
Stephanie Delalieux ◽  
Klaas Pauly ◽  
Wim Verjans ◽  
...  
Keyword(s):  
Hyperspectral Imagery ◽  
Low Cost ◽  
Pome Fruit ◽  
Retrieval Models ◽  
Tree Vitality ◽  
Aircraft Systems ◽  
Remotely Piloted Aircraft ◽  
Spatio Temporal ◽  
Fruit Orchards ◽  
June July

Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R2 = 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R2 > 0.9 for May, June, July, and August, and R2 = 0.5 for October) and hyperspectral imagery (0.6 < R2 < 0.9) led to satisfactory high and consistent accuracies for all months.

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 Generating Super-Resolved Depth Maps Using Low-Cost Sensors and RGB Images

2014 ◽  
pp. 632-641 ◽  
Author(s):  
Leandro Tavares Aragão dos Santos ◽  
Manuel Eduardo Loaiza Fernandez ◽  
Alberto Barbosa Raposo
Keyword(s):  
Low Cost ◽  
Depth Maps ◽  
Rgb Images

Assessment of single-frequency observations in GNSS Tropospheric Tomography

2020 ◽  
Author(s):  
Robert Weber ◽  
Zohreh Adavi ◽  
Marcus Franz Glaner
Keyword(s):  
Water Vapor ◽  
Low Cost ◽  
Temporal Structure ◽  
Single Frequency ◽  
Dual Frequency ◽  
Alternative Approach ◽  
Spatio Temporal ◽  
Gnss Network ◽  
Vapor Distribution ◽  
Reconstructed Model

&lt;p&gt;Water vapor is one of the most variable components in the Earth&amp;#8217;s atmosphere, which has a significant role in the formation of clouds, rain and snow, air pollution and acid rain. Therefore, increasing the accuracy of estimated water vapor can lead to more accurate predictions of severe weather, upcoming storms, and reducing natural hazards. In recent years, GNSS has turned out to be a valuable tool for remotely sensing the atmosphere. GNSS tomography is one of the most valuable tools to reconstruct the Spatio-temporal structure of the troposphere. However, locating dual-frequency receivers with&amp;#160;a sufficient spatial resolution for GNSS tomography of a few tens of kilometers is not economically feasible. Therefore, in this research, the feasibility of using single-frequency receivers in GNSS tomography as a possible alternative approach has been investigated. The accuracy of the reconstructed model of water-vapor distribution using low-cost receivers is verified using radiosonde measurements in the area of&amp;#160;the EPOSA (Echtzeit Positionierung Austria) GNSS network, which is mostly located in the east part of Austria for the period DoYs 233-246 in 2019.&lt;/p&gt;

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