Héctor Migallón

Bio: Héctor Migallón is an academic researcher from Universidad Miguel Hernández de Elche. The author has contributed to research in topics: Parallel algorithm & Encoder. The author has an hindex of 8, co-authored 43 publications receiving 262 citations.

Monitoring Pest Insect Traps by Means of Low-Power Image Sensor Technologies

Abstract: Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for large plantations or large forestry areas, so it would be of great advantage to have an affordable system capable of doing this task automatically in an accurate and a more efficient way. This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application. Our autonomous monitoring system will be able to cover large areas with very low energy consumption. This issue would be the main key point in our study; since the operational live of the overall monitoring system should be extended to months of continuous operation without any kind of maintenance (i.e., battery replacement). The images delivered by image sensors would be time-stamped and processed in the control station to get the number of individuals found at each trap. All the information would be conveniently stored at the control station, and accessible via Internet by means of available network services at control station (WiFi, WiMax, 3G/4G, etc.).

Slice-based parallel approach for HEVC encoder

Abstract: 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 $$9.8$$9.8 can be obtained for the All Intra mode and up to $$8.7$$8.7 for Low-Delay B, Low-Delay P and Random Access modes for $$12$$12 processes with a negligible loss in coding performance.

Parallel strategies for 2D Discrete Wavelet Transform in shared memory systems and GPUs

Abstract: In this work, we analyze the behavior of several parallel algorithms developed to compute the two-dimensional discrete wavelet transform using both OpenMP over a multicore platform and CUDA over a GPU. The proposed parallel algorithms are based on both regular filter-bank convolution and lifting transform with small implementations changes focused on both the memory requirements reduction and the complexity reduction. We compare our implementations against sequential CPU algorithms and other recently proposed algorithms like the SMDWT algorithm over different CPUs and the Wippig&Klauer algorithm over a GTX280 GPU. Finally, we analyze their behavior when algorithms are adapted to each architecture. Significant execution times improvements are achieved on both multicore platforms and GPUs. Depending on the multicore platform used, we achieve speed-ups of 1.9 and 3.4 using two and four processes, respectively, when compared to the sequential CPU algorithm, or we obtain speed-ups of 7.1 and 8.9 using eight and ten processes. Regarding GPUs, the GPU convolution algorithm using the GPU shared memory obtains speed-ups up to 20 when compared to the CPU sequential algorithm.

Insect pest monitoring with camera-equipped traps: strengths and limitations

Abstract: Integrated pest management relies on insect pest monitoring to support the decision of counteracting a given level of infestation and to select the adequate control method. The classic monitoring approach of insect pests is based on placing in single infested areas a series of traps that are checked by human operators on a temporal basis. This strategy requires high labor cost and provides poor spatial and temporal resolution achievable by single operators. The adoption of image sensors to monitor insect pests can result in several practical advantages. The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps. The use of software and image recognition algorithms can support automatic trap usage to identify and/or count insect species from pictures. Considering the high image resolution achievable and the opportunity to exploit data transfer systems through wireless technology, it is possible to have remote control of insect captures, limiting field visits. The availability of real-time and on-line pest monitoring systems from a distant location opens the opportunity for measuring insect population dynamics constantly and simultaneously in a large number of traps with a limited human labor requirement. The actual limitations are the high cost, the low power autonomy and the low picture quality of some prototypes together with the need for further improvements in fully automated pest detection. Limits and benefits resulting from several case studies are examined with a perspective for the future development of technology-driven insect pest monitoring and management.

Energy management system optimization in islanded microgrids: An overview and future trends

Abstract: Islanded microgrids (IMGs) provide a promising solution for reliable and environmentally friendly energy supply to remote areas and off-grid systems. However, the operation management of IMGs is a complex task including the coordination of a variety of distributed energy resources and loads with an intermittent nature in an efficient, stable, reliable, robust, resilient, and self-sufficient manner. In this regard, the energy management system (EMS) of IMGs has been attracting considerable attention during the last years, especially from the economic and emissions point of view. This paper provides an in-depth overview of the EMS optimization problem of IMGs by systematically analyzing the most representative studies. According to the state-of-the-art, the optimization of energy management of IMGs has six main aspects, including framework, time-frame, uncertainty handling approach, optimizer, objective function, and constraints. Each of these aspects is discussed in detail and an up-to-date overview of the existing EMSs for IMGs and future trends is provided. The future trends include the need for improved models, advanced data analytic and forecasting techniques, performance assessment of real-time EMSs in the whole MG’s control hierarchy, fully effective decentralized EMSs, improved communication and cyber security systems, and validations under real conditions. Besides, a comprehensive overview of the widely-used heuristic optimization methods and their application in EMSs of IMGs as well as their advantages and disadvantages are given. It is hoped that this study presents a solid starting point for future researches to improve the EMS of IMGs.

A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System

Abstract: In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.