Mobile Measurement Platform Research Articles

Modeling Urban Temperature Using Measurements from Mobile and Stationary Monitoring Stations

Heat waves are occurring more frequently worldwide as global warming continues, and urban heat islands can threaten conventional life in cities. Measuring, analyzing, and simulating weather data at fine spatial and temporal scales are essential to prevent and reduce the damage caused by extreme heat waves. In urban environments, handling complex micrometeorological situations using current meteorological stations and global simulation models (e.g., weather research forecasting models) is challenging. In this study, the thermal environments of urban areas were measured using a mobile meteorological measurement platform. Both mobile and stationary datasets were incorporated into the meteorological modeling process to simulate the spatial and temporal distribution of temperature. Additionally, various mobile observation implementation scenarios for temperature modeling were examined. We compared simulation combinations with the temperature field generated from the total dataset to obtain a better sampling campaign and properly incorporate mobile data scenarios. When collecting mobile data, it is important to consider spatial features to improve the efficiency of sampling programs. This can substantially reduce the cost of mobile data collection, together with the sensor error bound.

Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment.

Air pollution monitoring using mobile ground-based measurement platforms can provide high quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller scale air quality sensors, it is important to address the need for assessing these measurements in a scalable manner. We explore collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches - a standard collocation assessment technique where the mobile platform is parked near the fixed regulatory site for a period of time and an expanded approach using measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed reference site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, Colorado, USA and in the San Francisco Bay Area, California, USA. Our one-month Denver dataset includes both parked collocation periods near the fixed regulatory sites as well as general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, as the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (year-long) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40-hour running median converges to within ±4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be used during large-scale monitoring campaigns using small sensors to identify potential measurement biases.

Mobile Measurements of Atmospheric Methane at Eight Large Landfills: An Assessment of Temporal and Spatial Variability

Municipal solid waste landfills are major contributors to anthropogenic emissions of methane (CH4), which is the major component of natural gas, a potent greenhouse gas, and a precursor for the formation of tropospheric ozone. The development of sensitive, selective, and fast-response instrumentation allows the deployment of mobile measurement platforms for CH4 measurements at landfills. The objectives of this study are to use mobile monitoring to measure ambient levels of CH4 at eight large operating landfills in southeast Michigan, USA; to characterize diurnal, daily and spatial variation in CH4 levels; and to demonstrate the influence of meteorological factors. Elevated CH4 levels were typically found along the downwind side or corner of the landfill. Levels peaked in the morning, reaching 38 ppm, and dropped to near-baseline levels during midday. Repeat visits showed that concentrations were highly variable. Some variation was attributable to the landfill size, but both mechanistically-based dilution-type models and multivariate models identified that wind speed, boundary layer height, barometric pressure changes, and landfill temperature were key determinants of CH4 levels. Collectively, these four factors explained most (r2 = 0.89) of the variation in the maximum CH4 levels at the landfill visited most frequently. The study demonstrates the ability to assess spatial and temporal variation in CH4 levels at landfills using mobile monitoring along perimeter roads. Such monitoring can identify the location of leaks and the best locations for long-term emission monitoring using fixed site monitors.

Fusion of binocular vision, 2D lidar and IMU for outdoor localization and indoor planar mapping

Emergent fields such as Internet of Things applications, driverless cars, and indoor mobile robots have brought about an increasing demand for simultaneous localization and mapping (SLAM) technology. In this study, we design a SLAM scheme called BVLI-SLAM based on binocular vision, 2D lidar, and an inertial measurement unit (IMU) sensor. The pose estimation provided by vision and the IMU can provide better initial values for the 2D lidar mapping algorithm and improve the mapping effect. Lidar can also assist vision to provide better plane and yaw angle constraints in weak texture areas and obtain higher precision 6-degree of freedom pose. BVLI-SLAM uses graph optimization to fuse the data of the IMU, binocular camera, and laser. The IMU pre-integration combines the visual reprojection error and the laser matching error to form an error equation, which is processed by a sliding window-based bundle adjustment optimization to calculate the pose in real time. Outdoor experiments based on KITTI datasets and indoor experiments based on the trolley mobile measurement platform show that BVLI-SLAM has different degrees of improvement in mapping effect, positioning accuracy, and robustness compared with VINS-Fusion and Cartographer, and can solve the problem of positioning and plane mapping in indoor complex scenes.

A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset.

Mean Wind Velocity Distribution at Near-ground Level Obtained Through Field Measurements

Mean wind velocity profiles enable researchers to conduct efficient and accurate analyses of a wide variety of urban microclimatic problems, such as pollution dispersion, heat island effect and pedestrian wind comfort. However, due to the heterogeneity in the complex urban context, the wind profile at near-ground level within the real urban context remains insufficiently investigated. The present study aims to measure the near-ground level mean wind profiles in an industrial park. To this practical end, a sonic anemometer was equipped to a height adjustable bar on a mobile measurement platform. Through adjusting the sonic anemometer’s height, the mean wind velocities at different heights of the near-ground level were measured. Results indicate that the mean wind velocities’ variation along the near-ground heights measured at the site exposed to open and flat upwind areas can be reasonably captured by the power-law profile, while the other two conventional profiles, i.e. the log-law and the exponential profiles, show different levels of inaccuracy.

The Potential of a Smartphone as an Urban Weather Station—An Exploratory Analysis

The ongoing urbanization requires enhanced understanding of the local meteorological and climatological conditions within the urban environment for multiple applications, concerning energy demand, human health, and spatial planning. Identifying areas with harmful meteorological conditions enables citizens and local authorities to take actions to optimize quality of life for urban dwellers. At the moment cities have (in general) limited networks of meteorological monitoring stations. To overcome this lack of observations, the use of non-traditional data sources is rapidly increasing. However, the use of such data sources without enough prior verification has become a controversial topic in the scientific community. This study aims to verify and assess one of the main non-traditional data sources, i.e. smartphones. The goal is to research the potential of smartphones (using the Samsung Galaxy S4 as an example phone model) to correctly sense air temperature, relative humidity, and solar radiation, and to determine to what extent environmental conditions negatively affect their performance. The smartphone readings were evaluated against observations from reference instrumentation at a weather station and a mobile measurement platform. We test the response time of the smartphone thermometer and hygrometer, and the light sensor’s cosine response. In a lab setting, we find that a smartphone can provide reliable temperature information when it is not exposed to direct solar radiation. The smartphone’s hygrometer performs better at low relative humidity levels while it can over-saturate at higher levels. The light sensor records show substantial correlation with global radiation observations, and short response times. Measurements along an urban transect of 10 km show the smartphone’s ability to react to fast changes of temperature in the field, both in time and space. However, a bias correction (dependent on wind speed and radiation) is required to represent the reference temperature. Finally we show that after such a bias correction, a smartphone record can successfully capture spatial variability over a transect as well.

Guidelines for an Unmanned Aerial Vehicle-Based Measurement Instrument Design

Although Unmanned Aerial Vehicles (UAVs) are used as mobile measurement platforms in several applications, no guidelines are provided to include the measurement uncertainty assessment in the design steps of the UAV platform. In this article, the steps required for the design of a UAV-based measurement instrument according to the application measurement target and the uncertainty assessment are delineated. In order to highlight the necessity of the proposed design guidelines, the case study of 3D reconstruction of archaeological sites is discussed.

The INFRA-EAR: a low-cost mobile multidisciplinary measurement platform for monitoring geophysical parameters

Abstract. Geophysical studies and real-time monitoring of natural hazards, such as volcanic eruptions or severe weather events, benefit from the joint analysis of multiple geophysical parameters. However, typical geophysical measurement platforms still provide logging solutions for a single parameter, due to different community standards and the higher cost per added sensor. In this work, the Infrasound and Environmental Atmospheric data Recorder (INFRA-EAR) is presented, which has been designed as a low-cost mobile multidisciplinary measurement platform for geophysical monitoring. In particular, the platform monitors infrasound but concurrently measures barometric pressure, accelerations, and wind flow and uses the Global Positioning System (GPS) to position the platform. Due to its digital design, the sensor platform can be readily integrated with existing geophysical data infrastructures and be embedded in geophysical data analysis. The small dimensions and low cost per unit allow for unconventional, experimental designs, for example, high-density spatial sampling or deployment on moving measurement platforms. Moreover, such deployments can complement existing high-fidelity geophysical sensor networks. The platform is designed using digital micro-electromechanical system (MEMS) sensors embedded on a printed circuit board (PCB). The MEMS sensors on the PCB are a GPS, a three-component accelerometer, a barometric pressure sensor, an anemometer, and a differential pressure sensor. A programmable microcontroller unit controls the sampling frequency of the sensors and data storage. A waterproof casing is used to protect the mobile platform against the weather. The casing is created with a stereolithography (SLA) Formlabs 3D printer using durable resin. Thanks to low power consumption (9 Wh over 25 d), the system can be powered by a battery or solar panel. Besides the description of the platform design, we discuss the calibration and performance of the individual sensors.

Mobile air quality measurements using bicycle to obtain spatial distribution and high temporal resolution in and around the city center of Stuttgart

Cities often have a problem regarding the air quality because various emissions are produced within these cities and local emission sources among others relate directly to the air quality. As an addition to stationary monitoring stations, mobile measurements can provide valuable information regarding spatial distribution of pollutants. In this research, a case study is presented to illustrate the design and implementation of mobile measurement platform with a bicycle. Different compact measuring devices were selected and installed on the bicycle for mobile measurements along a designated route in Stuttgart, Germany. The measured pollutants included Particulate Matter (PM), Ultrafine Particles (UFP), Black Carbon (BC), nitrogen oxides (NO, NO2 and NOX) and ozone (O3). Meteorological parameters such as air temperature, relative humidity, wind speed, wind direction, solar radiation and air pressure were also measured. These measured parameters were allocated to the location using a GPS device. The measurements were carried out during February 2018. The measurement route covered part of the city center with high traffic roads as well as side roads and a park. The results show a very high spatial variability and the pollutant concentration was influenced by different factors such as local traffic, measurement location, meteorological conditions, etc. For NO, NO2 and UFP, the concentrations in the park (which can be considered as urban background level) were only around 30%–50% of the measured concentrations directly on the roadside. For PM10, the differences between the concentrations on the road and in urban background were less pronounced but still clearly measurable. The mobile measurement platform with the bicycle provided an opportunity to have a spatial distribution of a larger study area and proved to be highly adaptive and flexible method.

Acoustic Roughness Measurement of Railhead Surface Using an Optimal Sensor Batch Algorithm

Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three displacement sensors to reduce measurement deviation and increase the accuracy of existing systems. This paper proposes enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors. As a result, when the two sensor-based measurement algorithm was applied, the spectrum level at λ = 0.314 m, the wavelength amplification associated with wheel diameter, was reduced to at least 6 dB in comparison with that of the three sensors based algorithm. We also verified the accuracy of the proposed batch algorithm through a field test on an operating rail track with a corrugated rail surface.

THE CONCEPT OF A FLYING ELECTROMAGNETIC FIELD MEASURING PLATFORM

Nowadays, humans are surrounded by more and more devices that artificially generate an electromagnetic field. According to law, supervision of the level of the electromagnetic field requires specific measurements. Measurement performed by traditional methods have several limitations, which come from the infrastructure and time taken to perform the measurement. New methods of measurement are being developed in order to execute the research relatively quickly and repeatedly without any limitations. One of the methods is to use a flying mobile measurement platform.

Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment.

Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated by using the Velodyne VLP-16 LiDAR system, so as to extract the diameter at breast height (DBH) of individual trees. The Velodyne VLP-16 LiDAR system and inertial measurement units (IMU) were used to construct a mobile measurement platform for generating 3D point cloud maps for forest areas. The 3D point cloud map in the forest area was processed offline, and the ground point cloud was removed by the random sample consensus (RANSAC) algorithm. The trees in the experimental area were segmented by the European clustering algorithm, and the DBH component of the tree point cloud was extracted and projected onto a 2D plane, fitting the DBH of the trees using the RANSAC algorithm in the plane. A three-dimensional point cloud map of 71 trees was generated in the experimental area, and estimated the DBH. The mean and variance of the absolute error were 0.43 cm and 0.50, respectively. The relative error of the whole was 2.27%, the corresponding variance was 15.09, and the root mean square error (RMSE) was 0.70 cm. The experimental results were good and met the requirements of forestry mapping, and the application value and significance were presented.

Measuring Acoustic Roughness of a Longitudinal Railhead Profile Using a Multi-Sensor Integration Technique.

It is necessary to measure accurately the rolling noise generated by the friction between wheels and rails in railway transport systems. Although many systems have recently been developed to measure the surface roughness of wheels and rails, there exist large deviations in measurements between each system whose measuring mechanism is based on a single sensor. To correct the structural problems in existing systems, we developed an automatic mobile measurement platform, named the Automatic Rail Checker (ARCer), which measures the acoustic roughness of a longitudinal railhead profile maintaining a constant speed. In addition, a new chord offset synchronization algorithm has been developed. This uses three displacement sensors to improve the measuring accuracy of the acoustic roughness of a longitudinal railhead profile, thereby minimizing the limitations of mobile platform measurement systems and measurement deviation. We then verified the accuracy of the measurement system and the algorithm through field tests on rails with different surface wear conditions.

Application Research on Energy Self supply Ocean Observing Platform

The mobile ocean observation platform is the carrier of equipment of various marine information acquisition and communications and is the energy providers ocean observation platform. It is an important platform to realize digital ocean and is paid attention to in marine observation. Study on a mobile observation platform based on marine renewable energy, the platform can convert all renewable energy into electricity supply platform equipment, effectively solve the energy supply problem in marine isolated platform, is an all-weather, long endurance marine mobile self-subjective measurement platform, can effectively reduce the operation cost, improve operation efficiency.

An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements

People living near roadways are exposed to high concentrations of ultrafine particles (UFP, diameter < 100 nm). This can result in adverse health effects such as respiratory illness and cardiovascular diseases. However, accurately characterizing the UFP number concentration requires expensive sets of instruments. The development of an UFP surrogate with cheap and convenient measures is needed. In this study, we used a mobile measurement platform with a Fast Mobility Particle Sizer (FMPS) and sound level meter to investigate the spatiotemporal relations of noise and UFP and identify the hotspots of UFP. UFP concentration levels were significantly influenced by temporal and spatial variations (p < 0.001). We proposed a Generalized Additive Models to predict UFP number concentration in the study area. The model uses noise and meteorological covariates to predict the UFP number concentrations at an industrial site in Taichung, Taiwan. During the one year sampling campaign from fall 2013 to summer 2014, mobile measurements were performed at least one week for each season, both on weekdays and weekends. The proposed model can explain 80% of deviance and has coefficient of determination (R2) of 0.77. Moreover, the developed UFP model was able to adequately predict UFP concentrations, and can provide people with a convenient way to determine UFP levels. Finally, the results from this study could help facilitate the future development of noise mobile measurement.

Urban outdoor water use and response to drought assessed through mobile energy balance and vegetation greenness measurements

Urban vegetation provides many highly valued ecosystem services but also requires extensive urban water resources. Increasingly, cities are experiencing water limitations and managing outdoor urban water use is an important concern. Quantifying the water lost via evapotranspiration (ET) is critical for urban water management and conservation, especially in arid or semi-arid regions. In this study, we deployed a mobile energy balance platform to measure evaporative fraction throughout Riverside, California, a warm, semi-arid, city. We observed the relationship between evaporative fraction and satellite derived vegetation index across 29 sites, which was then used to map whole-city ET for a representative mid-summer period. Resulting ET distributions were strongly associated with both neighborhood population density and income. By comparing 2014 and 2015 summer-period water uses, our results show 7.8% reductions in evapotranspiration, which were also correlated with neighborhood demographic characteristics. Our findings suggest a mobile energy balance measurement platform coupled with satellite imagery could serve as an effective tool in assessing the outdoor water use at neighborhood to whole city scales.

Physiological measurement platform using wireless network with Android application

Abstract Currently, many people suffer from arrhythmia or hypoxia, which are abnormal health conditions. Arrhythmia occurs when a person has an irregular or abnormal heart rate, while hypoxia is realized when there is a deficiency in oxygen reaching the tissues. When a person suffers from arrhythmia, there is the possibility that the person has cardiovascular disease. A low oxygen level eventually leads to organ failure, which can result in death. To prevent such conditions, a mobile physiological measurement platform has been proposed in this paper. This system will continuously monitor the heart rate and the oxygen level of a patient. The proposed system is mainly beneficial because the medical staff or the caregiver can provide care to patients without being in close proximity. In this way, multiple numbers of patients can be treated by the physician at the same time. In this paper, two main physiological signals: the electrocardiogram (ECG) and the photoplethysmogram (PPG) are recorded, to measure the heart rate (in beats per minute) and the peripheral capillary oxygen saturation level or SpO 2 (in percentage) of the patient. This is done by using a convenient graphical user interface (GUI) in the Matrix Laboratory (MATLAB). Pre-processing of the bio-medical signals is done in the GUI and the calculated results are saved as text files in the current directory of MATLAB. We further propose an Android application, which will display the physiological parameters after the text files have been accessed via a wireless network. The heart rate and the oxygen level can both be monitored via this application. In case the results show an abnormal reading, the physician is notified immediately via text messaging.

Analysis on strain sensing characteristic of long period fibre grating based on deep-grooved process

In this paper, the strain sensing characteristic of long period fibre grating (LPFG) based on deep-grooved process was investigated. The core sensitivity of LPFG was fabricated by a high frequency focused and improved CO2 laser irradiation system in ways of periodic and spaced heating on the ordinary single-mode fibre. Combined with the mobile platform and optical measurement platform with high precision, this paper built an optical fibre strain sensor measurement system based on the deep-grooved modulation and it was focused on the deep-grooved LPFG. According to use finite element analysis software to model and static simulation for the grooved fibre grating, the strain distributed contours and the degree of the micro-bend at the grooved structure section can be obtained. The principal research and experimental testing demonstrated that the grooved fibre grating would produce micro-bend structure with the axial strain increasing, which formed some types of corrugated structure, and resonant wavelength could blue shift by according to the elasto-optical effect. Such a measurement system of deep-grooved LPFG strain sensor had a high sensitivity and the average strain sensitivity of resonant wavelength was up to –10.28 pm/με, which was increased by 22.85 times than strain sensor based on the without depth of grooves. It offers practical value in engineering applications.

Searching for collisions between mobile robot and environment

This article is focused on the search for potential collision of a moving object (as a mobile robot is) with the environment. Application of the proposed and presented method requires existence of the three-dimensional model of the environmental space. In this case, the models are generated automatically with the help of mobile measurement platform developed by authors and are available to other applications. Models of moving objects are loaded by the proposed application from external files. The method is based on data fusion, using data from a laser scanner, global positioning system, and inertial navigation system; however, it can also be used for data obtained by other ways. Its correctness was experimentally proved by several experimental tests. Output is in the form of calculated coordinates of the point in which a moving object would potentially collide with the environment if no avoiding action is taken. Such an object may represent an oversized load transferred by a truck, train, ship, the transport means itself or even a person.