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.

Efficient sampling of band-limited signals from sine wave crossings

This correspondence presents an efficient method for reconstructing a band-limited signal in the discrete domain from its crossings with a sine wave. The method makes it possible to design A/D converters that only deliver the crossing timings, which are then used to interpolate the input signal at arbitrary instants. Potentially, it may allow for reductions in power consumption and complexity in these converters. The reconstruction in the discrete domain is based on a recently-proposed modification of the Lagrange interpolator, which is readily implementable with linear complexity and efficiently, given that it re-uses known schemes for variable fractional-delay (VFD) filters. As a spin-off, the method allows one to perform spectral analysis from sine wave crossings with the complexity of the FFT. Finally, the results in the correspondence are validated in several numerical examples.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Self-heating and deicing conductive cement. Experimental study and modeling

This research studies the self-heating produced by the application of an electric current to conductive cement pastes with carbonaceous materials. The main parameters studied were: type and percentage of carbonaceous materials, effect of moisture, electrical resistance, power consumption, maximum temperature reached and its evolution and ice melting kinetics are the main parameters studied. A mathematical model is also proposed, which predicts that the degree of heating is adjustable with the applied voltage. Finally, the results have been applied to ensure that cementitious materials studied are feasible to control ice layers in transportation infrastructures.

Smart Monitoring Embedded Service for Energy-Efficient and Sustainable Management in Data Centers

Information technologies (IT) currently represent 2% of CO2 emissions. In recent years, a wide variety of IT solutions have been proposed, focused on increasing the energy efficiency of network data centers. Monitoring is one of the fundamental pillars of these systems, providing the information necessary for adequate decision making. However, today’s monitoring systems (MSs) are partial, specific and highly coupled solutions. This study proposes a model for monitoring data centers that serves as a basis for energy saving systems, offered as a value-added service embedded in a device with low cost and power consumption. The proposal is general in nature, comprehensive, scalable and focused on heterogeneous environments, and it allows quick adaptation to the needs of changing and dynamic environments. Further, a prototype of the system has been implemented in several devices, which has allowed validation of the proposal in addition to identification of the minimum hardware profile required to support the model.