Large-Scale Crowd Analysis through the Use of Passive Radio Sensing Networks.

The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in which we installed a passive Radio Frequency (RF) sensor network in different environments containing thousands of human individuals and discuss the accuracy with which the resulting measurements can be used to estimate the sizes of these crowds. Depending on the selected training approach, a median crowd estimation error of 184 people could be obtained for a large scale environment which contained 3227 people at its peak. Additionally, we look into the potential benefits of dividing one of our experimental environments into multiple subregions and open up a potentially interesting new topic of research regarding the estimation of crowd flows. Finally, we investigate the combination of our measurements with another sources of crowd-related data: sales data from drink stands within the environment. In doing so, we aim to integrate the concept of an automatic RF-based crowd estimation system into the broader domain of crowd analysis.

A novel active-passive sampling approach for measuring time-averaged concentrations of pollutants in water.

Passive sampling with in situ devices offers several advantages over traditional sampling methods (i.e., discrete spot sampling), however, data interpretation from conventional passive samplers is hampered by difficulties in estimating the thickness of the diffusion layer at the sampler/medium interface (δ), often leading to inaccurate determinations of target analyte concentrations. In this study, the performance of a novel device combining active and passive sampling was investigated in the laboratory. The active-passive sampling (APS) device is comprised of a diffusion cell fitted with a pump and a flowmeter. Three receiving phases traditionally used in passive sampling devices (i.e., chelex resin, Oasis HLB, and silicone rubber), were incorporated in the diffusion cell and allowed the simultaneous accumulation of cationic metals, polar, and non-polar organic compounds, respectively. The flow within the diffusion cell was accurately controlled and monitored, and, combined with diffusion coefficients measurements, enabled the average δ to be estimated. Strong agreement between APS and time-averaged total concentrations measured in discrete water samples was found for most of the substances investigated. Accuracies for metals ranged between 87 and 116%, except Cu and Pb (∼50%), whilst accuracies between 64 and 101%, and 92 and 151% were achieved for polar and non-polar organic compounds, respectively. These results indicate that, via a well-defined in situ preconcentration step, the proposed APS approach shows promise for monitoring the concentration of a range of pollutants in water.

Realistic Indoor Radio Propagation for Sub-GHz Communication.

This research article proposes a novel ray-launching propagation loss model that is able to use an environment model that contains the real geometry. This environment model is made by applying a Simultaneous Localization and Mapping (SLAM) algorithm. As a solution to the rising demands of Internet of Things applications for indoor environments, this deterministic radio propagation loss model is able to simulate an accurate coverage map that can be used for localization applications or network optimizations. Since this propagation loss model uses a 2D environment model that was captured by a moving robot, an automated validation model is developed so that a wireless sensor network can be used for validating the propagation loss model. We validated the propagation loss model by evaluated two environment models towards the lowest Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the Mean Error (ME). Furthermore, the correlation between the number of rays and the RMSE is analyzed and the correlation between the number of reflections versus the RMSE is also analyzed. Finally, the performance of the radio propagation loss model is analyzed.