Littering quantification is an important step for improving cleanliness of
cities. When human interpretation is too cumbersome or in some cases
impossible, an objective index of cleanliness could reduce the littering by
awareness actions. In this paper, we present a fully automated computer vision
application for littering quantification based on images taken from the streets
and sidewalks. We have employed a deep learning based framework to localize and
classify different types of wastes. Since there was no waste dataset available,
we built our acquisition system mounted on a vehicle. Collected images
containing different types of wastes. These images are then annotated for
training and benchmarking the developed system. Our results on real case
scenarios show accurate detection of littering on variant backgrounds
A novel microwave barrel reactor (MBR) was constructed and used in lipase catalyzed biolubricant synthesis. The MBR is thought as a versatile process tool for biotransformation and green chemistry that overcomes current size limitations in microwave reactors. A lipase mediated biotransformation in the MBR was compared to a state of the art jacketed reactor with external heat exchanger. Oleic acid and trimethylolpropane converted quantitatively (96%) into biolubricants using microwave induction. The heat dissipation in the MBR was analyzed by thermal imaging and inside thermometry. Conversion rates, rate constants and pseudo reaction orders were in line with conventional processing and no microwave effect was detected. The MBR is a versatile new reactor for non solvent, minimal and common solvent processing in the microwave field. While the subject of investigations was biolubricant synthesis in the MBR, the technology described is of wider potential interest in the field of biomass processing and sustainable chemical manufacture. † Electronic supplementary information (ESI) available. See
The behavioral approach to robot navigation, characterized by a representation of the environment that is topological and robot-environmental interactions that are reactive, is preferable to the pure geometrical navigation because it is far more robust to unpredictable changes of the environment. Nevertheless, there is still a need to obtain geometrical maps. This paper considers a geometrical map reconstruction that relies on the topological knowledge and uses redundant odometric measurements taken while the robot moves along the paths of the topological map. Five methods are presented and compared in experiments involving a Nomad200 mobile robot operating in a real environment.
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