The growing number of unmanned aerial vehicles (UAVs), typically referred to as drones, poses new challenges on how to manage their operations in various internet of things (IoT) use cases such as surveillance and monitoring, weather prediction, agriculture, etc. The latter includes a massive number of devices that sometimes produce invalid messages due to lack of energy or system shutdown and needs to be autonomously monitored with drones in rural areas. In this paper, we develop a blockchain-based platform for managing drone IoT operations while maintaining trust and security. The test-bed consists of IoT devices, a drone and blockchain-enabled gateways through which drones are controlled to replace malfunctioning devices. The latter are detected using Z-score observation algorithm which launches a smart contract and sends the drone with clear operation order. The results obtained in realistic agriculture use case highlight the utility of our proposition in decreasing signaling and operation time, improving the percentage of successful maintenance operations and providing trust and security when managing drones in an autonomous manner.
Crest line extraction has always been a challenging task in image processing and its applications. It is possible to detect ridges and valleys in images using second order filters. In order to estimate crest lines of variable widths, a multiscale analysis of the image is required. In this paper we propose a new ridge/valley detection method in images based on the difference of rotating Gaussian semi filters adapted in a multi-scale process. Due to the directional filters, we obtain a new ridge/valley anisotropic detector enabling very precise detection of ridge/valley of varied widths. Moreover, as the detector filters compute the two directions of crest lines, even highly bended crest lines are correctly extracted. Numerical comparisons with other oriented Gaussian filters and results on real images validate the interest of this method.
Most of embedded cameras use a single sensor to capture images through a color filter. They produce special images with only one color component per pixel. Missing data are usually estimated through a demosaicking process, but this takes undesirable computation time and may generate undesirable color artifacts. Many embedded systems under real-time constraints could use this type of camera for purposes like edge detection. In this paper, a new edge detection method is proposed for the computation of partial derivative images. This algorithm is tested on a large set of synthetic images where edge ground truth is unquestionable. The exploitation of the raw data of images allows not only to drastically reduce computational time, but also to get edge detection results even more precise than using certain demosaicking-based methods.
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