Abstract
Object localization and tracking is core to many practical applications, like human-computer interaction, security and surveillance, robot competitions and Industry 4.0. Such task may be computationally demanding, especially for traditional embedded systems, that usually have tight processing and storage constraints. This calls for the investigation of alternatives, including emergent heterogeneous embedded systems, like the Parallella line of single-board-computers (SBCs). The work presented in this paper explores the use of a Parallella board with a 16-core Epiphany co-processor, to perform real-time tracking of objects in frames captured by a Kinect sensor, based on color segmentation. We addressed several processing strategies, trying to assess which one performed better. We also ran the same code (where applicable) in several models of the Raspberry Pi platform, for comparison. We conclude that effectively exploring the Epiphany co-processor is not trivial, requiring considerable programming effort and suitable applications (CPU-demanding and highly parallelizable), to the extent that simpler development approaches, on more recent SBCs may be more effective. © 2017.

Abstract
The usage of small-sized unmanned aerial systems (UAS) has increased in the last years, in many different areas, being agriculture and forestry those who benefit the most from this relatively new remote sensing platform. Leaf area index, canopy and plant volume are among the parameters that can be determined using the very high resolution aerial data obtained by sensors coupled in unmanned aerial vehicles (UAV). This remote sensing technology affords the possibility of monitoring the vegetative development, identifying different types of issues, enabling the application of the most appropriated treatments in the affected areas. In this paper, a methodology allowing to perform multi-temporal UAS-based data analysis obtained by different sensors is proposed. A case study in vineyards and chestnuts is used to prove the benefits of continuous crop monitoring in its management and productivity of agroforestry parcels/activities. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
The autonomy of robotic underwater vehicles is dependent on the ability to perform long-term and long-range missions without need of human intervention. While current state-of-the-art underwater navigation techniques are able to provide sufficient levels of precision in positioning, they require the use of support vessels or acoustic beacons. This can pose limitations on the size of the survey area, but also on the whole cost of the operations. Terrain Based Navigation is a sensor-based navigation technique that bounds the error growth of dead reckoning using a map with terrain information, provided that there is enough terrain variability. An obvious advantage of Terrain Based Navigation is the fact that no external aiding signals or devices are required. Because of this unique feature, terrain navigation has the potential to dramatically improve the autonomy of Autonomous Underwater Vehicles (AUVs). This paper consists on a comprehensive survey on the recent developments for Terrain Based Navigation methods proposed for AUVs. The survey includes a brief introduction to the original Terrain Based Navigation formulations, as well as a description of the algorithms, and a list of the different implementation alternatives found in the literature. Additionally, and due to the relevance, Bathymetric SLAM techniques will also be discussed.

Abstract

Abstract
This paper describes the design and implementation of a coarse-grained reconfigurable array (CGRA) for low-power biological signal processing. It uses an use-case-driven approach which explores the application domain and gathers common requirements. The selected CGRA core architecture is implemented using a standard-cell flow (in a generic 90nm CMOS process), so that the CGRA can be totally or partially turned off by power gating. The selected CGRA design is evaluated for two use-cases using layout information and accurate node activity information. The resulting accelerator is capable of performing various signal processing tasks very efficiently, achieving an average power consumption of 19.9 pJ/cycle (or 1.99mW at 100 MHz). Static power consumption for less intensive tasks can be reduced by using only some sections of the CGRA while powering-off others.

Abstract
The combustion of coal wastes resulting from mining is of particular environmental concern, and the importance of proper management involving real-time assessment of their status and identification of probable evolution scenarios is recognized. Continuous monitoring of the combustion temperature and emission levels of certain gases allows for the possibility of planning corrective actions to minimize their negative impact on the surroundings. Optical fiber technology is well suited to this purpose and here we describe the main attributes and results obtained from a fiber optic sensing system projected to gather data on distributed temperature and gas emissions in these harsh environments.