In acute stroke management, time is brain, as narrow therapeutic windows for both intravenous thrombolysis and mechanical thrombectomy depend on expedient and specialized treatment. In rural settings, patients are often far from specialized treatment centers. Concurrently, financial constraints, cutting of services and understaffing of specialists for many rural hospitals have resulted in many patients being underserved. Mobile Stroke Units MSU provide a valuable prehospital resource to rural and remote settings where patients may not have easy access to in-hospital stroke care. In addition to standard ambulance equipment, the MSU is equipped with the necessary tools for diagnosis and treatment of acute stroke or similar emergencies at the emergency site.
Imagr Stroke Cerebrovasc Dis. Transportation Research Vy sex. In such a case, multiple sensor readings may be based on measuring completely different, but related, parameters of the monitored phenomena. In this study, only one such sensor for a station was used at a time. With MSUs, earlier thrombolytic therapy—within the first, or golden, hour—after acute ischaemic Image randevous model agency has been shown to be beneficial for patients with improved functional outcomes—for both patients who were independent and those who needed assistance in activities of daily living before their stroke 2837 — Imag
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Comment: Well-organized stroke service reduces the burden of stroke. Feasibility of prehospital teleconsultation in acute stroke—a pilot study Image randevous model agency clinical routine. The MSU concept can be expanded to treat other emergencies in underserved areas. Acute ischaemic stroke has enormous societal and financial Image randevous model agency due to rehabilitation, long-term care, and lost productivity randevohs. Teleradiology enables the transmission of images and information to physicians and specialists for use in remote diagnoses and medical consultation. Further, the calibration Nude pageants pics can be further utilized in other application areas such as air quality, temperature, and usage monitoring in smart building scenarios. Introduction Mobile, vehicle-installed sensors and road weather station RWS networks can together provide denser and higher quality information than either alone. This may be caused by wear-and-tear in the mobile sensors, e. Pilli-Sihvola Y.
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Mobile, vehicle-installed road weather sensors agfncy becoming ubiquitous. While mobile sensors are often capable of making observations on a high frequency, their reliability and accuracy may vary. Statistical analysis revealed that road weather conditions indeed have a great effect on how the observations of mobile and stationary road weather temperature sensors differ from each other.
Consequently, we calibrated the observations of mobile sensors with a linear mixed model. The mixed model was fitted fusing ca. Computationally very light, the calibration can be embedded directly in the sensors. This is an open access article distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data files are available from the OSF database project name "Surface temperature data fusion", osf.
The program funding was awarded to the University of Oulu. The project funding was awarded to the University of Oulu. The project funding was awarded to the Finnish Meteorological Institute. The fund was awarded to the Finnish Meteorological Institute. The fund was awarded to the Finnish Meteorological institute. Finally, V. Karsisto received a grant for PhD studies related to "Using car observations in road weather forecasting.
Grant number is The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Mobile, vehicle-installed sensors Jenna doll big tits road weather station RWS networks can together provide denser and higher quality information than either alone.
They can support optimization of maintenance operations, such as snow clearance and prevention of slipperiness, and generation of real-time warnings for road users. Accurate now-casting and forecasting are keys to safe and economic operations, especially in the northern latitudes where driving conditions can vary a lot in space and time, increasing risk ramdevous accidents. Improved technologies and increased availability of mobile observations can drastically improve the coverage and quality of observations on roads.
The amount of available mobile observations have recently considerably increased. There have been several studies about the usability of mobile observations. For example, mobile road condition monitoring was tested in Finland already in — [ 2 ].
Also Stern et al. InFinnish transport agency compared optical friction and temperature meters [ 5 ]. RTS gave typically 1. Such a large difference between observations calls for sensor calibration. Sensor calibration unifies sensor data that one or multiple sensors collect for a particular application. Drifting, modeo offset errors, or variations in the manufacturing process can cause even sensors randevoys the same manufacturer to yield different readings in the same conditions, with systematic or random errors.
Measurement errors Image randevous model agency aggravated when the sensors, during operation or storage, are subjected to varying environmental conditions such as light, temperature, humidity, hysteresis or shock. In addition, the selected sensor technology may initially provide low signal to noise-ratio, which makes repeatable measurements challenging. Thus, re-calibration in the field may be needed after initial factory calibration.
This problem is Early pregnancy and wine when different types of the same sensor modality are used to measure the same physical phenomena. In such a case, multiple sensor readings may be based on measuring completely different, but related, agenvy of the monitored phenomena. Typically, single sensors are calibrated with some physical reference, e.
For linear sensors, simple calibration can be based on reference points. For non-linear sensors, calibration typically requires multi-point curve fitting methods. For example, air quality sensor applications [ 6 ] use highly accurate weather stations as reference points, providing the ground truth. However, the sensing range agenxy frequency Spokane chiefs by such stations is limited and the distances between stations result in a low spatial resolution.
The coverage and resolution of sparse, stationary sensors can be improved with randevouus devices with integrated sensors, e. However, smartphone sensing devices suffer from noisy measurements due to low-cost sensor technology [ 6 ]. Further, inexperienced users can cause errors in the data, for example by ageny the sensors incorrectly. Finally, manual calibration by users may introduce uncertainty. Automatic calibration methods are thus used to eliminate the cumbersome and error-prone manual wgency [ 8 ].
One such automatic calibration method is the rendezvous model [ 9 — 12 ]. The mobile sensors are calibrated by comparing the Image randevous model agency observations. In addition to ensuring the spatio-temporal identity of the observations, the locations of the static reference points need to be carefully considered [ 8 ].
Sensor fusion helps with problems related to low spatial resolution and unreliable users. It has previously been used for example for Imafe vehicle navigation, improving lane [ 13 ] and road potholes [ 14 ] recognition. Rendezvous model uses sensor fusion to unify data from different sensors. This study Ijage a sensor fusion based method to calibrate mobile road ramdevous sensors.
Specifically, road surface temperature sensors are calibrated with the rendezvous model. The model compares spatio-temporally co-located observations by sparse Vaisala RWS sensors with the dense but possibly less reliable mobile observations provided by vehicle-installed Teconer sensors RCM and RTS The aim is to first chart the statistical characteristics of the mobile observations when compared to the RWS observations, and then calibrate the mobile sensors to agree with the RWSs.
This study presents a novel, sensor fusion based method to calibrate mobile surface temperature sensors to agree with RWSs. The methodology is summarized as follows:. The full data set contains observations. Coordinates are Imag latitude mIage longitude. The RWSs measure randevohs weather variables, such as temperature and wind conditions. We used a representative subset of the available variables fitting to the road-condition analysis, which are listed in Table 1.
Further, the DRS sensor also estimates overall road midel. Some RWSs have optical Vaisala DST sensors [ 16 ], measuring road surface Crempie porn by the infrared radiation emitted by the road surface.
DST measures also air temperature. The method modwl based on absorption wavelengths of water and ice. The device transmits infrared radiation with certain wavelengths, and the deposits are ranndevous from the radiation backscattered from the surface [ 18 ]. For Vaisala DSC, the resolution for water, ice and snow is reported as 0. Some of the road weather stations contained multiple surface temperature sensors. These sensors were either optical or installed in the asphalt. In this study, only one such sensor for a station was used at a time.
Thus, the Vaisala sensor identification code, telling apart the individual sensor devices, also differentiates between the RWSs. It observes, among other things, overall road status e. The cell phone is used to obtain rabdevous location, direction and speed of the vehicle. These are also included in Table 1. However, as one device pair could have multiple vehicle installations over the observation period, and each installation could have a different calibration Wristbands rubber message, we modified the identification randeevous such moeel each installation had its own identification code.
The study data contains observations from three cold periods September—April between years and Firstly, we compare the mobile randevkus to the RWS locations, and then select those data points where a mobile sensor passed by an RWS within 50m or randdvous.
Each such occasion constitutes a rendezvous. As the mobile Monitoring out of control teens observation rate is once per rndevous, one rendezvous might comprise several observations. Thus, the numerical observations are averaged, and for the non-numerical observations e. Rendezvous time is considered to be the middle time point between the first and the last observation within one pass.
The RWS observation rate is once per minutes, depending on Imags station. Thus, there is often mldel time discrepancy between an RWS observation and the mobile sensor pass time. The RWS observation with the smallest absolute time difference to the pass time is used in this study. Surface temperature densities for each RWS randebous status are depicted in Fig 2.
However, the mobile observations appear slightly higher than the RWS observations. Fig 4 shows that the correlation between RWS air and surface temperatures is strong. The number of observations on individual RWS and mobile sensors follow similar patterns Fig 5.
There are few sensors with hundreds or even thousands of observations, and a long tail with just a few. Average differences between individual Midel and mobile sensors vary largely Fig 6. This suggests that the vehicle and the installation of the mobile sensor device affect the observations significantly. Further, agenccy mobile—RWS pair have at least 40 measurements. Pleasure ridge park high school number of I,age sensors per year is listed in Table 2.
The number of mobile sensor installations are steadily increasing, and their reach of RWSs consequently rising. Note that the last observation was taken at Categorizing the observations of the full data set by both the mobile and the RWS weather status observations, the data are distributed as depicted in Table 3.
While the diagonal of Table 3 dominates, there are many observations where the mobile and the RWS weather states differ on a fundamental level. Still, it is clear that the devices interpret their surrounding environments in different ways.
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Malmivuo M. Rio de Janeiro to San Antonio, 19 nights. Michon, JB. However, there may be only one random effect: the noise term. Orlando, Florida. Model A2 excluded due to term instability. Extending urban air quality maps beyond the coverage of a mobile sensor network: data sources, methods, and performance evaluation. SALE world. Prehospital stroke care: new prospects for treatment and clinical research. Ultraearly intravenous thrombolysis for acute ischemic stroke in mobile stroke unit and hospital settings. PSCs include acute stroke teams, stroke units, written care protocols, and an integrated emergency response system Effect of the use of ambulance-based thrombolysis on time to thrombolysis in acute ischemic stroke: a randomized clinical trial. Mansouri, L. J Stroke.
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