During the last two decades Red Palm Weevil (RPW, Rynchophorus Ferrugineus) has become one of the most dangerous threats to palm trees in many parts of the World. Its early detection is difficult, since palm trees do not show visual evidence of infection until it is too late for them to recover. For this reason the development of efficient early detection mechanisms is a critical element of RPW pest management systems. One of the early detection mechanisms proposed in the literature is based on acoustic monitoring, as the activity of RPW larvae inside the palm trunk is audible for human operators under acceptable environmental noise levels (rural areas, night periods, etc.). In this work we propose the design of an autonomous bioacoustic sensor that can be installed in every palm tree under study and is able to analyze the captured audio signal during large periods of time. The results of the audio analysis would be reported wirelessly to a control station, to be subsequently processed and conveniently stored. That control station is to be accessible via the Internet. It is programmed to send warning messages when predefined alarm thresholds are reached, thereby allowing supervisors to check on-line the status and evolution of the palm tree orchards. We have developed a bioacoustic sensor prototype and performed an extensive set of experiments to measure its detection capability, achieving average detection rates over 90%.

The high efficiency video coding (HEVC) is the newest video coding standard from the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group, which significantly increases the computing demands to encode video to reach the limits on compression efficiency. Our interest is centered on applying parallel processing techniques to HEVC encoder to significantly reduce the computational time without disturbing the coding performance behavior. We propose a parallelization approach to the HEVC encoder which is well suited to multicore architectures. Our proposal uses OpenMP programming paradigm working at slice parallelization level. We encode several slices of each frame at the same time using all available processing cores. The results show that speed-ups up to 9.8 can be obtained for the All Intra mode and up to 8.7 for Low-Delay B, Low-Delay P and Random Access modes for 12 processes with a negligible loss in coding performance.

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Nowadays, more and more vehicles are equipped with communication capabilities, not only providing connectivity with onboard devices, but also with off-board communication infrastructures. From road safety (i.e., multimedia e-call) to infotainment (i.e., video on demand services), there are a lot of applications and services that may be deployed in vehicular networks, where video streaming is the key factor. As it is well known, these networks suffer from high interference levels and low available network resources, and it is a great challenge to deploy video delivery applications which provide good quality video services. We focus our work on supplying error resilience capabilities to video streams in order to fight against the high packet loss rates found in vehicular networks. So, we propose the combination of source coding and channel coding techniques. The former ones are applied in the video encoding process by means of intra-refresh coding modes and tile-based frame partitioning techniques. The latter one is based on the use of forward error correction mechanisms in order to recover as many lost packets as possible. We have carried out an extensive evaluation process to measure the error resilience capabilities of both approaches in both (a) a simple packet error probabilistic model, and (b) a realistic vehicular network simulation framework. Results show that forward error correction mechanisms are mandatory to guarantee video delivery with an acceptable quality level , and we highly recommend the use of the proposed mechanisms to increase even more the final video quality.

The 3D-DWT is a mathematical tool of increasing importance in those applications that require an efficient processing of huge amounts of volumetric info. Other applications like professional video editing, video surveillance applications, multi-spectral satellite imaging, HQ video delivery, etc, would rather use 3D-DWT encoders to reconstruct a frame as fast as possible. In this article, we introduce a fast GPU-based encoder which uses 3D-DWT transform and lower trees. Also, we present an exhaustive analysis of the use of GPU memory. Our proposal shows good trade off between R/D, coding delay (as fast as MPEG-2 for High definition) and memory requirements (up to 6 times less memory than x264).