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Image compression is a vital component for domains in which the computational resources are usually scarce such as automotive or telemedicine fields. Also, when discussing real-time systems, the large amount of data that must flow through the system can represent a bottleneck. Therefore, the storage of images, alongside the compression, transmission, and decompression procedures, becomes vital. In recent years, many compression techniques that only preserve the quality of the region of interest of an image have been developed, the other parts being either discarded or compressed with major quality loss. This paper proposes a study of relevant papers from the last decade which are focused on the selection of a region of interest of an image and on the compression techniques that can be applied to that area. To better highlight the novelty of the hybrid methods, classical state-of-the-art approaches are also analyzed. The current work will provide an overview of classical and hybrid compression methods alongside a categorization based on compression ratio and other quality factors such as mean-square error and peak signal-to-noise ratio, structural similarity index measure, and so on. This overview can help researchers to develop a better idea of what compression algorithms are used in certain domains and to find out if the presented performance parameters are of interest for the intended purpose. [ABSTRACT FROM AUTHOR]

An electrical power grid, is an interconnected network for delivering electricity from producers to consumers. Electrical grids vary in size from covering a single building through national grids (which cover whole countries) to transnational grids (which can cross continents). As the rollout of smart meters continues worldwide, there are use-cases where common solutions fail and the network availability of certain meters is very low due to poor communication conditions. This paper proposes a data slicing model for large data files which have to travel securely and reliably throughout the Smart Grid. The manuscript addresses improvements for PRIME PLC network availability by using correct data slicing at the application level along a tuned transmission rate in accordance with the noise levels of the power grid. Successful communications, even at low rates, mean that no manual interaction from energy supplier operators is needed reducing the maintenance costs for both the energy companies as well as for the end user. Experiments on a low power electrical grid setup have been performed in order to evaluate availability improvements through the proposed method as well as the feasibility of remote firmware upgrades. The results have shown that the current approach has similar upgrade time results with a manual firmware upgrade performed through an optical probe. Moreover, the results show that the presented remote firmware upgrade method is reliable and practical. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can't be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics. [ABSTRACT FROM AUTHOR]