Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples.

Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples.

Broadcast Propagation Time in SpaceFibre Networks with Various Types of Spatial Redundancy.

Various methods of spatial redundancy can be used in local networks based on the SpaceFibre standard for fault mitigation of network hardware and physical communication channels. Usually, a network developer chooses the method of spatial redundancy according to the number of failures that have to be mitigated, the time required for restoring the normal operation of the network, required overheads and hardware costs. The use of different spatial redundancy mechanisms can cause changes in the structure of the links between network nodes, in case of failure and subsequent mitigation.

Positioning Information Privacy in Intelligent Transportation Systems: An Overview and Future Perspective.

Today, the Intelligent Transportation Systems (ITS) are already in deep integration phase all over the world. One of the most significant enablers for ITS are vehicle positioning and tracking techniques. Worldwide integration of ITS employing Dedicated Short Range Communications (DSRC) and European standard for vehicular communication, known as ETSI ITS-G5, brings a variety of options to improve the positioning in areas where GPS connectivity is lacking precision.