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Network-on-Chip (NoC) have been used as an interesting option in design of communication infrastructures for embedded systems, providing a scalable structure and balancing the communication between cores. Because several data packets can be transmitted simultaneously through the network, an efficient routing strategy must be used in order to avoid congestion delays. In this paper, ant colony algorithms were used to find and optimize routes in a mesh-based NoC. The routing optimization is driven by the minimization of total latency in packets transmission. The simulation results show the effectiveness of the ant colony inspired routing by comparing it with general purpose algorithms for deadlock free routing under different traffic patterns.

Synchronous finite state machines are very important for digital sequential designs. Among other important aspects, they represent a powerful way for synchronizing hardware components so that these components may cooperate adequately in the fulfillment of the main objective of the hardware design. In this paper, we propose an evolutionary methodology to solve one of the problems related to the design of finite state machines. We optimally solve the state assignment NP -complete problem using a quantum inspired evolutionary algorithm. This is motivated by the fact that with an optimal state assignment one can physically implement the state machine using a minimal hardware area and response time.

This paper presents an approach in using unmanned aerial vehicles (UAVs) with remote imaging for urban waste monitoring. The system is designed to monitor green areas, public trash cans, and unregulated landfills and to detect possible violations of garbage disposal rules. Public green urban areas, such as parks, green surfaces, sport terrains, and bathing areas, are gathering places for people and therefore prone to unregulated waste disposal. The proposed solution describes the real-time monitoring of the area using drones and the detection of irregularities in a garbage disposal. The cameras mounted on drones are used to take images of public targeted areas at pre-mapped points. Visual data collected by supervisor drones are used for further processing and notification of authorized personnel and institutions.

Environmental parameters required for geo-information modelling are subject to spatial and temporal dynamics. Remote sensing data can contribute to measure those parameters. For that purpose high-accuracy classifications of remote sensing data are required which can be very time-consuming due to the large data volumes involved. In many applications, however, the rapid provision of classified mass data is of higher priority than classification accuracy. One important focus on research and development efforts in the past years has been to optimise the automated interpretation of remote sensing data. Different investigators have shown that this interpretation can both be effective and efficient in JPEG compressed data with acceptable accuracy. This paper presents an operational processing chain for cloud detection in JPEG-compressed quick-look products of LANDSAT 7/ETM+-scenes (compression ratio is 10:1). Two well-developed conventional algorithms are applied to these datasets for cloud detection. Results show that the processing chain developed is stable and produces quality results with substantially compressed mass data.