A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.

Visual and optical performance and quality of life after implantation of posterior chamber phakic intraocular lens

Purpose To evaluate visual, optical, and quality of life (QoL) outcomes and intercorrelations after bilateral implantation of posterior chamber phakic intraocular lenses. Methods Twenty eyes with high to moderate myopia of 10 patients that underwent PRL implantation (Phakic Refractive Lens, Carl Zeiss Meditec AG) were examined. Refraction, visual acuity, photopic and low mesopic contrast sensitivity (CS) with and without glare, ocular aberrations, as well as QoL outcomes (National Eye Institute Refractive Error Quality of Life Instrument-42, NEI RQL-42) were evaluated at 12 months postoperatively. Results Significant improvement in uncorrected (UDVA) and best-corrected distance (CDVA) visual acuities were found postoperatively (p < 0.01), with significant reduction in spherical equivalent (p < 0.01). Low mesopic CS without glare was significantly better than measurements with glare for 1.5, 3, and 6 cycles/degree (p < 0.01). No significant correlations between higher order root mean square (RMS) with CDVA (r = −0.26, p = 0.27) and CS (r ≤ 0.45, p ≥ 0.05) were found. Postoperative binocular photopic CS for 12 cycles/degree and 18 cycles/degree correlated significantly with several RQL-42 scales. Glare index correlated significantly with CS measures and scotopic pupil size (r = −0.551, p = 0.04), but not with higher order RMS (r = −0.02, p = 0.94). Postoperative higher order RMS, postoperative primary coma and postoperative spherical aberration was significant higher for 5-mm pupil diameter (p < 0.01) compared with controls. Conclusions Correction of moderate to high myopia by means of PRL implantation had a positive impact on CS and QoL. The aberrometric increase induced by the surgery does not seem to limit CS and QoL. However, perception of glare is still a relevant disturbance in some cases possibly related to the limitation of the optical zone of the PRL.

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.

Empoderamiento y discapacidad, factores moduladores del desarrollo de esta potencialidad

Introducción. El desarrollo del empoderamiento implica la posesión de múltiples habilidades que ayudan a las personas a afrontar la adversidad, por lo que el desarrollo de esta capacidad puede ser prioritario entre estas personas para mejorar su calidad de vida. Objetivos. Los objetivos del estudio analizan la capacidad de empoderamiento de un grupo de jóvenes con discapacidad en función de la tipología y etapa en la que se adquiere la discapacidad. Metodología. Participaron 98 jóvenes con diferentes tipos de discapacidad (física, intelectual, visual y auditiva). Contestaron la versión española adaptada de la Escala de Rogers, Chamberlin, Ellison y Crean (1997) diseñada para medir esta capacidad. Resultados. Los análisis indicaron altos niveles de esta capacidad entre los jóvenes observándose en mayor medida en las personas con discapacidad sobrevenida, así como en la discapacidad motora y visual. Conclusiones. Esto nos sugiere que esta capacidad puede variar y evolucionar, de ahí la importancia de fomentarla en programas de intervención-acción.

Estilos de empoderamiento y factores sociodemográficos en jóvenes con discapacidad

Introducción. El concepto de empoderamiento está cobrando interés en los programas de apoyo hacia la integración psicosocial de las personas con discapacidad. Esta capacidad implica la posesión de múltiples habilidades que ayudan a las personas a afrontar la adversidad, por lo que el desarrollo de esta capacidad puede ser prioritario entre estas personas para mejorar su calidad de vida. Objetivos. Los objetivos del estudio son analizar la capacidad de empoderamiento de un grupo de personas con discapacidad en función de la edad, nivel de estudios, ocupación y redes de apoyo. Metodología. Participaron 98 jóvenes con diferentes tipos de discapacidad (física, intelectual, visual y auditiva). Contestaron la versión española adaptada de la Escala de Rogers, Chamberlin, Ellison y Crean (1997), que es diseñada para medir esta capacidad. Resultados. Los resultados indicaron altos niveles de esta capacidad entre las personas con discapacidad observándose en mayor medida en los participantes con ocupación. Conclusiones. Esto nos sugiere que esta capacidad puede evolucionar y de ahí la importancia de fomentarla en programas de intervención-acción.

RetinaStudio: A bioinspired framework to encode visual information

The retina is a very complex neural structure, which performs spatial, temporal, and chromatic processing on visual information and converts it into a compact ‘digital’ format composed of neural impulses. This paper presents a new compiler-based framework able to describe, simulate and validate custom retina models. The framework is compatible with the most usual neural recording and analysis tools, taking advantage of the interoperability with these kinds of applications. Furthermore it is possible to compile the code to generate accelerated versions of the visual processing models compatible with COTS microprocessors, FPGAs or GPUs. The whole system represents an ongoing work to design and develop a functional visual neuroprosthesis. Several case studies are described to assess the effectiveness and usefulness of the framework.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.