Smart and green cities are hot topics in current research because people are becoming more conscious about their impact on the environment and the sustainability of their cities as the population increases. Many researchers are searching for mechanisms that can reduce power consumption and pollution in the city environment. This paper addresses the issue of public lighting and how it can be improved in order to achieve a more energy efficient city. This work is focused on making the process of turning the streetlights on and off more intelligent so that they consume less power and cause less light pollution. The proposed solution is comprised of a radar device and an expert system implemented on a low-cost platform based on a DSP. By analyzing the radar echo in both the frequency and time domains, the system is able to detect and identify objects moving in front of it. This information is used to decide whether or not the streetlight should be turned on. Experimental results show that the proposed system can provide hit rates over 80%, promising a good performance. In addition, the proposed solution could be useful in kind of other applications such as intelligent security and surveillance systems and home automation.
In this work, a two-stage architecture is used to analyze the information collected from several sensors. The first stage makes classifications from partial information of the entire target (i.e. from different points of view or from different kind of measures) using a simple artificial neural network as a classifier. In addition, the second stage aggregates all the estimations given by the ensemble in order to obtain the final classification. Four different ensembles methods are compared in the second stage: artificial neural network, plurality majority, basic weighted majority, and stochastic weighted majority. However, not only reliability is an important factor but also adaptation is critical when the ensemble is working in changing environments. Therefore, the artificial neural network and the plurality majority algorithm are compared against our two proposed adaptive algorithms. Unlike artificial neural network, majority methods do not require previous training. The effects of improving the first stage and how the system behaves when different perturbations are presented have been measured. Results have been obtained from two applications: a realistic one and another simpler one, with more training examples for a more accurate comparison. These results show that artificial neural network is the most accurate proposal, whereas the most innovative proposed stochastic weighted voting is the most adaptive one.
This work proposes three different methods to automatically characterize heterogeneous MPSoCs com-posed of a variable number of masters (in the form of processors) and hardware accelerators (HWaccs). These hardware accelerators are given as Behavioral IPs (BIPs) mapped as loosely coupled accelerators on a shared bus system ( i.e. AHB, AXI). BIPs have a distinct advantage over traditional RT-level based IPs given VHDL or Verilog: The ability to generate micro-architectures with different area vs. perfor-mance trade-offs from the same description. This is usually done by specifying different synthesis direc-tives in the form of pragmas. This in turn implies that using different mixes of the accelerators' micro-architectures lead to SoCs with unique area vs. performance trade-offs. Two of the three methods proposed are based on cycle-accurate simulations of the complete MPSoC, while the third method accelerates this exploration by performing it on a Configurable SoC FPGA. Exten-sive experimental results compare these three methods and highlight their strengths and weaknesses. * Corresponding author.ten used as HWAccs on these heterogeneous MPSoCs. The International Technology Roadmap for Semiconductors (ITRS) already suggested in 2013 that by 2020 a 10x productivity increase for designing complex SoCs was needed [1] . Two main factors were predicted to help to achieve this goal. The first is the re-use of components. ITRS estimates that around 90% of the SoCs will be composed of re-used components. Secondly, the use of new design methodologies to raise the level of abstraction i.e . HLS. The use of HLS has led to a new market for 3PBIPs. One of the main advantages of BIPs is that they are much easier to re-use than traditional RT-level IPs. Moreover, micro-architectures of different area vs. performance trade-offs can be easily obtained by synthesizing the behavioral description with different synthesis options. This is typically done by setting different synthesis options in the form of pragmas (comments) inserted directly into the source code or through global synthesis options. For example, these options can control how to synthesize arrays (register or RAM), if a function should be inlined or not and if loops should be fully unrolled, partially unrolled, not unrolled or pipelined.FPGA vendors have also embraced this new paradigm and have released their own Programmable or Configurable SoCs (CSoCs), e.g . Altera's Cyclone V SoC and Xilinx's Zynq FPGA. These CSoCs contain multiple embedded cores mainly in the form of ARM Cortex A9 and reconfigurable fabric onto which to map the
The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.
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