2017 |
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Hernández-Morera, P., Travieso-González, C. M., Castaño-González, I., Mompeó-Corredera, B., & Ortega-Santana, F. (2017). Segmentation of elastic fibres in images of vessel wall sections stained with Weigert’s resorcin–fuchsin. Computer Methods and Programs in Biomedicine, 142, 43–54.
Abstract: AbstractBackground and objective The elastic fibres are an essential component of the extracellular matrix in blood vessel walls that allows a long-range of deformability and passive recoil without energy input. The quantitative determination of elastic fibres will provide information on the state of the vascular wall and to determine the role and behaviour of this key structural element in different physiological and pathological vascular processes. Methods We present a segmentation method to identify and quantify elastic fibres based on a local threshold technique and some morphological characteristics measured on the segmented objects that facilitate the discrimination between elastic fibres and other image components. The morphological characteristics analysed are the thickness and the length of an object. Results The segmentation method was evaluated using an image database of vein sections stained with Weigert’s resorcin–fuchsin. The performance results are based on a ground truth generated manually resulting in values of sensitivity greater than 80% with the exception in two samples, and specificity values above 90% for all samples. Medical specialists carried out a visual evaluation where the observations indicate a general agreement on the segmentation results’ visual quality, and the consistency between the methodology proposed and the subjective observation of the doctors for the evaluation of pathological changes in vessel wall. Conclusions The proposed methodology provides more objective measurements than the qualitative methods traditionally used in the histological analysis, with a significant potential for this method to be used as a diagnostic aid for many other vascular pathological conditions and in similar tissues such as skin and mucous membranes.
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Lazcano, R., Madroñal, D., Salvador, R., Desnos, K., Pelcat, M., Guerra, R., et al. (2017). Porting a PCA-based Hyperspectral Image Dimensionality Reduction Algorithm for Brain Cancer Detection on a Manycore Architecture. Journal of Systems Architecture, .
Abstract: This paper presents a study of the parallelism of a Principal Component Analysis (PCA) algorithm and its adaptation to a manycore MPPA (Massively Parallel Processor Array) architecture, which gathers 256 cores distributed among 16 clusters. This study focuses on porting hyperspectral image processing into manycore platforms by optimizing their processing to fulfill real-time constraints, fixed by the image capture rate of the hyperspectral sensor. Real-time is a challenging objective for hyperspectral image processing, as hyperspectral images consist of extremely large volumes of data and this problem is often solved by reducing image size before starting the processing itself. To tackle the challenge, this paper proposes an analysis of the intrinsic parallelism of the different stages of the PCA algorithm with the objective of exploiting the parallelization possibilities offered by an MPPA manycore architecture. Furthermore, the impact on internal communication when increasing the level of parallelism is also analyzed.
Experimenting with medical images obtained from two different surgical use cases, an average speedup of 20 is achieved. Internal communications are shown to rapidly become the bottleneck that reduces the achievable speedup offered by the PCA parallelization. As a result of this study, PCA processing time is reduced to less than 6 seconds, a time compatible with the targeted brain surgery application requiring 1 frame-per-minute.
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Marenzi, E., Carrus, A., Danese, G., Leporati, F., & Callicò, G. M. (2017). Efficient Parallelization of Motion Estimation for Super-Resolution. In 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (pp. 274–277). St. Petersburg, Russia, Russia.
Abstract: This paper presents an efficient parallelization of the Motion Estimation procedure, one of the core parts of Super Resolution techniques. The algorithm considered is the basic version of Block Matching Super Resolution, with a single low-resolution camera and fixed Macro Block dimensions. Two are the implementations provided, with OpenMP and in CUDA on an NVIDIA Kepler GPU. Tests have been conducted on five image sequences and the results show a considerable improvement of the CUDA solution in all cases. Consequently, it can be stated that GPUs can efficiently accelerate computational times assuring the same image quality.
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Quintana-Diaz, G., Mena-Rodriguez, P., Perez-Alvarez, I., Jimenez, E., Dorta-Naranjo, B. - P., Zazo, S., et al. (2017). Underwater Electromagnetic Sensor Networks-Part I: Link Characterization. Sensors (Basel), 17(1).
Abstract: Underwater Wireless Sensor Networks (UWSNs) using electromagnetic (EM) technology in marine shallow waters are examined, not just for environmental monitoring but for further interesting applications. Particularly, the use of EM waves is reconsidered in shallow waters due to the benefits offered in this context, where acoustic and optical technologies have serious disadvantages. Sea water scenario is a harsh environment for radiocommunications, and there is no standard model for the underwater EM channel. The high conductivity of sea water, the effect of seabed and the surface make the behaviour of the channel hard to predict. This justifies the need of link characterization as the first step to approach the development of EM underwater sensor networks. To obtain a reliable link model, measurements and simulations are required. The measuring setup for this purpose is explained and described, as well as the procedures used. Several antennas have been designed and tested in low frequency bands. Agreement between attenuation measurements and simulations at different distances was analysed and made possible the validation of simulation setups and the design of different communications layers of the system. This leads to the second step of this work, where data and routing protocols for the sensor network are examined.
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Rodríguez, R., González, B., García, J., Vega, A., & Núñez, A. (2017). DC characteristics with substrate temperature for GaN on Si MOS-HEMTs. In 2017 Spanish Conference on Electron Devices (CDE) (pp. 1–4).
Abstract: DC characteristics of AlGaN/GaN on Si MOS-HEMTs are measured and numerically simulated, with substrate temperature up to 140°C, varying the gate width and gate length. Different gate recess depths are simulated in ATLAS in order to further investigate and optimize the device performance. Thermal boundary conditions and device thermal resistance are included in the structure for accurate simulation of the heating response. In addition, the relationship of the threshold voltage and saturation velocity with the substrate temperature and gate width has been studied and set in order to ease the modelling of these devices.
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Santana, J. M., Wendel, J., Trujillo, A., Suárez, J. P., Simons, A., & Koch, A. (2017). Multimodal location based services—semantic 3D city data as virtual and augmented reality. (pp. 329–353). Lecture Notes in Geoinformation and Cartography.
Abstract: The visualization of cross-domain spatial data sets has become an important task within the analysis of energy models. The representation of these models is especially important in urban areas, in which the under-standing of patterns of energy production and demand is key for an efficient city planning. Location Based Services (LBS) provide a valuable addition towards the analysis and visualization of those data sets as the user can explore the output of different models and simulations in the real environment at the location of interest. Towards this aim, the present research explores mobile alternatives to the visual analysis of temporal data series and 3D building models. Based on the fields of numerical simulation, GIS and computer graphics, this work presents a novel mobile service that allows exploring urban models at different Level of Details (LoDs) using well-known standards such as CityGML. Ultimately, the project enables researchers, city planners and technicians to explore urban energy datasets in an interactive and immersive manner as Virtual Globes, Virtual Reality and Augmented Reality. Using models of the city of Karlsruhe, the final service has been implemented and tested on the iOS platform providing an empirical insight on the performance of the system. In addition, this research provides a holistic approach by developing one application that is capable of seamlessly change the visualization mode. © Springer International Publishing AG 2017.
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2016 |
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Blaanco, L. J., Travieso, C. M., Quinteiro, J. M., Hernandez, P. V., Dutta, M. K., & Singh, A. (2016). A bark recognition algorithm for plant classification using a least square support vector machine. In 2016 Ninth International Conference on Contemporary Computing (IC3) (pp. 1–5).
Abstract: In this paper, a bark recognition algorithm for plant classification is presented. A Least-Square Support Vector Machine (LSSVM) with image and data processing techniques is used to implement a general purpose automated classifier. Using a data base of 40 sections of photographs taken of each of the 23 species, we applied an algorithm to homogenize the illumination of the images. After applying it, we obtained a 256-elements array from the Local Binary Pattern (LBP) histogram. Each element of the array was introduced in the LSSVM for classification. The success rate of the resultant recognizer is 82.38%.
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Cerezo, J. M., León, S., Vega, E., & Vega, A. (2016). CQMIH PLC simulator host-link communications protocol experience. In 2016 Technologies Applied to Electronics Teaching (TAEE) (pp. 1–6).
Abstract: In this paper it is described the experience gained in the development and use of CQMIH PLC simulator which uses OMRON Host-Link protocol through RS-232 serial port and can works with analogic and digital I/O. The purpose of this simulator is to simplify the task of debugging code when developing communication applications in an Industrial Computing course.
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Cervero, T., Otero, A., López, S., de la Torre, E., Callicó, G. M., Riesgo, T., et al. (2016). A scalable H.264/AVC deblocking filter architecture. Journal of Real-Time Image Processing, 12(1), 81–105.
Abstract: The deblocking filter (DF) is one of the most complex functional cores of the H.264/AVC and SVC codecs. Its computational cost is heavily dependent on the video profile and the selected scalability level. With the goal of providing faster and better solutions, developers are focused on designing hardware architectures. Thus, it is possible taking advantage of multitasking, reusability and parallelization techniques. In this context, this work proposes a scalable DF architecture that is able to adapt its structure and performance to different video configurations, due to its modular and regular structure. The scalability feature avoids redesigning the whole architecture in case of the environmental demands or the configuration settings change. These facts mean savings in terms of design productivity and silicon area by adapting the necessary logical resources to each condition. Furthermore, regarding the data dependences involved in the H.264/AVC DF algorithm, the proposed architecture relies on an improved version of a traditional wavefront parallelization strategy, also proposed by the authors. This solution reduces the amount of clock cycles needed to filter a video frame as compared to traditional strategies. Implementation results, in an FPGA Virtex-5, demonstrate the performance benefits of this flexible solution as compared to some rigid state-of-the-art deblocking filter approaches. © 2013, Springer-Verlag Berlin Heidelberg.
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D. Madroñal, R. Lazcano, H. Fabelo, S. Ortega, G. M. Callicó, E. Juárez, et al. (2016). Hyperspectral image classification using a parallel implementation of the linear SVM on a Massively Parallel Processor Array (MPPA) platform. In 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP) (pp. 154–160). 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP).
Abstract: In this paper, a study of the parallel exploitation of a Support Vector Machine (SVM) classifier with a linear kernel running on a Massively Parallel Processor Array platform is exposed. This system joins 256 cores working in parallel and grouped in 16 different clusters. The main objective of the research has been to develop an optimal implementation of the SVM classifier on a MPPA platform whilst the architectural bottlenecks of the hyperspectral image classifier are analyzed. Experimenting with medical images, the parallelization of the SVM classification has been conducted using three strategies: i) single- and multi-core processing, ii) single- and multi-cluster analysis and iii) single- and double-buffer execution. As a result, an average core processing speedup of 11.8 has been achieved when parallelizing the SVM classification process in a single cluster. On the contrary, since data communication accounts for 34.7% of the total execution time in the sequential case, the total speedup is upper-bounded to 2.9. Using a double-buffer methodology, a total speedup of 2.84 has been achieved on a single cluster. At last, the feasibility of a portable version of a linear SVM has been demonstrated.
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