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Underwater imaging is challenging because of the significant attenuation of light due to absorption and scattering of light in water. Using polarization properties of light is one of the options for improving image quality. We present results of imaging of a polarized target in open ocean (Curacao) and coastal (NY Bight) waters. The target in the shape of a square is divided into several smaller squares, each of which is covered with a polarizing film with different polarization orientations or transmission coefficients was placed on a mirror and imaged under water by a green-band full-Stokes polarimetric video camera at the full range of azimuth angles against the Sun. The values of the Stokes vector components from the images are compared with the modeled image of the target using radiative transfer code for the atmosphere-ocean system combined with the simple imaging model. It is shown that even in clear water the impact of the water body on the polarized underwater image is very significant and retrieval of target polarization characteristics from the image is extremely challenging.

This paper provides a synergetic approach between numerical modeling and remote sensing of bio-optical water properties. The work demonstrates that appropriate data-assimilation schemes make numerical modeling a suitable and reliable tool for filling the gaps arising due to satellite imagery unavailability and/or cloud covering. In this research we apply the Princeton Ocean Model to the Sea of Azov, assimilating bio-optical indexes ( index 34 and b bp (555)) from MODIS L2 products. These data identify the presence of suspended matter (mineral suspended matter from river discharges or resuspending as a result of a strong wind), and suspended matter of biological origin. The ad hoc assimilation/correction scheme allows for prediction (and reanalysis) of transport and diffusion of the bio-optical tracers. Results focus on the ability of the method to provide spatial maps that overcome the general issues related to Ocean Color imagery (e.g., cloud cover) and on the comparison between the assimilating and the non-assimilating runs. Methods of joined information analysis are discussed and the quality of model forecasts is estimated depending on the intervals of the satellite data assimilation. Hydrodynamic modeling of the Sea of Azov was carried out for the period of 2013–2014 applying meteorological data of the regional weather forecasting system SKIRON/Eta . The analysis of data coherence helps to detect negative changes to the sea waters, predict them and forecast typical areas and territories subject to anthropogenic impact. The successive data-assimilation algorithm is proved to improve the forecast of suspended matter transfer.

Complex mission specifications can be often specifiedthrough temporal logics, such as Linear Temporal Logic and itssyntactically co-safe fragment, scLTL. Finding trajectories thatsatisfy such specifications becomes hard if the robot is to fulfilthe mission in an initially unknown environment, where neitherlocations of regions or objects of interest in the environmentnor the obstacle space are known a priori. We propose an algorithmthat, while exploring the environment, learns importantsemantic dependencies in the form of a semantic abstraction,and uses it to bias the growth of an Rapidly-exploring randomgraph towards faster mission completion. Our approach leadsto finding trajectories that are much shorter than those foundby the sequential approach, which first explores and then plans.Simulations comparing our solution to the sequential approach,carried out in 100 randomized office-like environments, showmore than 50% reduction in the trajectory length. QC 20210803

Recent developments in environmental studies are related to worldwide ecological problems associated with anthropogenic impacts on the biosphere. Pollution is an undesirable product of human activity. Industrial, agricultural, forestry, and transportation all generate substances and by-products that are considered pollutants. Remote sensing technologies are an effective tool in numerous environmental investigations relevant to ecosystems preservation, biodiversity conservation and other problems of global importance. In agriculture, remote sensing is used for assessing plant growth, condition, and for identification of stress situations. This paper is devoted to the study of the impact of heavy metal contamination on species performance and the possibility to detect pollution stress from measurements of plant spectral characteristics. A main goal is to study the relationships between the stress factor and plant spectral features, and to assess the ability of various spectral indicators to detect plant heavy metal-induced stress. Multispectral measurements were performed over spring barley and pea plots subjected to Ni and Cd pollution. Significant correlations were observed between plant bioparameters and different spectral features. Meaningful statistical relationships were established between the heavy metal pollution amounts, plant bioparameters and spectral properties that allow detection and quantification of the stress factor affect on plant performance.

While the smart city gains global attention as a popular umbrella term for digitally enabled sustainable city development, city ad- ministrations are faced with the managerial challenge that comes with a strategic digital transformation. Smart city projects form the frontline of smart city strategies. In these smart projects, cities find a way to implement the principles of the smart city. Many of these are high-visibility projects with substantial budget implications, and therefore require scrutiny by means of a formal selection and evaluation process. In this research-in-progress paper we propose the outline for a project-level smart value assessment instrument. The instrument should serve at the same time as a tool for smart city managers to assess and plan upfront how a project will con- tribute to reach the city’s smart city ambition, as well as a post factum evaluation. The conceptual instrument has been developed in action-design research mode in collaboration with practitioners in the city of Brisbane, Australia and is demonstrated by mapping four smart city initiatives in different international contexts.

The standard deviation of the Horizontal Wind Speed as a proxy of wind turbulence is used to compare the apparent wind turbulence measured by an off-shore floating Doppler lidar to the one measured by a fixed lidar on a metmast. We use statistical analysis based on clustering the horizontal wind speed measured by the floating lidar as well as buoy angular amplitude and period under the approximation of harmonic motion. Three scenarios with different wave and wind conditions are discussed from the IJmuiden's test campaign (North Sea.). This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigacion (Spanish National Science Foundation). Peer Reviewed

Smart home barrier system is proposed and designed to keep people safe at home, especially for kids and pets. In this paper, working prototype of Internet of Things (IoT) enabled smart automatic door or barrier system is presented, where two models namely (i) monitoring model and (ii) actuated model can be switched to control access to the kitchen remotely by an online platform Ubidots. In experimental scenario, weight detection for monitoring model can measure from 0kg to 200kg with high precision. IoT environment enables door to be controlled remotely in real-time.

In this study, we developed a human attention model for smooth human-robot interaction. The model consists of the saliency map generation module and manipulation map generation module. The manipulation map describes top-down factors, such as human face, hands and gaze in the input image. To evaluate the proposed model, we applied the model to a magic video, and measured human gaze points during watching the video. Based on the experimental results, the proposed model can better explain human attention than the saliency map. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics General Terms Human Factors

The positional accuracy of navigation receiver is an important factor for various applications. Based on the application, the achieved positional accuracy levels vary from meters to millimeters. IRNSS-GPS-SBAS (IGS) receiver is capable of receiving all the seven IRNSS satellite signals in L5 (1176.45MHz) and S1 (2492.028MHz) band in addition to twelve GPS satellite signals and two SBAS (GAGAN) satellite signals in L1 (1575.42 MHz) frequency band. In this paper the position accuracy is evaluated in terms of 2DRMS, 3DRMS CEP, SEP, Geometric Dilution of Precision (GDOP) and Doppler shift in various modes namely Hybrid (IRNSS L5 and GPS L1), IRNSS dual frequency (L5 and S1), IRNSS L5 only and IRNSS S1 only mode. In Hybrid mode, it is found that all the positional accuracy values are better than the corresponding other mode values.