Raul Zamorano-Illanes

Rapid growth in the number of sensing elements demands novel communication approaches and the utilization of computational prowess for modern intelligent systems and futuristic technological diligence with the Internet of things (IoT). There are advanced trends in applications related to cyber-physical systems, where interaction with physical sensing elements and digital computational resources is predominant. This article explores an application of a digital twin (DT) in vehicle fleet management utilizing optical wireless communication technology. A digital microservice architecture for receiving sensor data, storage, and real-time visualization is implemented, incorporating the open-source Eclipse Ditto framework as a DT-based IoT middleware solution. The pilot experimental setup for the functional implementation of the proof of concept of the proposed system architecture is analysed. We have used 10 m long visible-light communication links to transmit custom string information, replicating sensor data from a vehicle equipped with light-emitting diodes (LEDs) and sending it to the receiver node on the edge server. The bit error rates of 4 × 10^-10 and 1 × 10^-6 were measured for a maximum link distance of up to 15 m in the case of vehicle to infrastructure links and infrastructure to vehicle, respectively. The research demonstrates an integration between the computational processing scenario and a physical communication interface for vehicular applications within a DT framework. The fleet management application is proposed and implemented as a prototype for a fleet of ten vehicles transmitting sensor information to the DT application for telematics and alert handling.

In this paper, a rolling shutter optical camera communication system is investigated by analysing the internal working principle of a receiver based on complementary metal oxide semiconductor image sensors. Based on the thorough analysis of system parameters such as the clock signal, integration and read‐out times at the sensor level, and the calculation of the bandwidth and the time span of the image frame containing the information signal, it was demonstrated that the communication functionalities can be enhanced. A new model was proposed and validated by developing an experimental testbed and demonstrating matching between the data packet duration and the predicted time span. This enables the optimisation of the samples obtained by the image postprocessing, using different sensor clock signals and evaluating the bit error rate performances. The experimental results demonstrated that the internal design of the camera and its packet structures have impacts on the read‐out times. For camera settings of pixel clock frequency and exposure time of 300 MHz and 61 s, respectively, a data rate of 14.5 kbps at a bit error rate below the forward error correction limit has been achieved.

This paper presents a method for performing traffic light detection using computer vision. Reliable traffic light detection and classification is crucial for automated driving in urban environments. By using big data and artificial intelligence, a complex dataset belonging to an urban area in China is preprocessed to determine the level of vehicular congestion, and then different machine learning algorithms are applied to a dataset of traffic light images in order to validate them in the urban environment to be studied, this process is explained step by step. The models obtained in this work can be applied in optical camera communication (OCC) systems, and also in intelligent transportation systems (ITS), using tracking channels for visible light communication (VLC). The two optical channels, VLC and OCC, are simulated in terms of the quality of information received in order to apply the previously generated datasets. In this work, a traffic light feature dataset has been generated from images and two traffic light classification models present in images and video frames have been generated from their features, obtaining a maximum accuracy of 94.52 %.

This article presents a low cost solution based on visible light communication to report information from low rate sensors in indoor environments using the MIMO QAM channel and polar codes, encoding in Arduino Nano and decoding in a PYNO-Z1 FPGA.

This paper presents a methodology for the generation of detection and classification models of light emitters in digital video frames for indoor and outdoor visible light communication applications, including the generation of a user interface for the generation of Datasets of light emitter characteristics. Showing that is possible to analyze the frames of video by applying a classification model, allowing the development of new models to decrease the processing time per frame.

This work proposes a model for inertia identification of an electrical power system. A non-linear regression is used from a mathematical model that relates power and frequency variation, obtained from a synchrophasor network. Fitting of the non-linear regressions processed with Big Data, from the PMU and the different real generation contingencies, obtained from the national system operator, are presented.

In this paper we will present a new method based on the non-symmetry of the wavelength distributions of exoplanet transits. By the clustering of wavelengths with similar behaviour, it is possible to cancel terrestrial atmospheric turbulence at ground-based observatories. Two types of benchmarks for training the NARX neural network are presented. Complexity and performance studies using transient spectroscopy data from the WASP-19b system are also included.

In this work, we propose the application of Multiresolution Analysis (MRA) for reducing the noise in the signal generated by the FSK (Frequency Shift Keying) modulation used transmitting information through a VLC (Visible Light Communication) channel. In order to apply MRA, the Wavelets of Daubechies and Symlet are used. The effectiveness of this technique depends on the choice of the Wavelet, the decomposition levels of the signal to be analyzed and the threshold level used to eliminate the noise of the detail part. Finally, there is a significant noise reduction in the analyzed signal. To measure this reduction, we use the Mean Square Error (RMS).