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Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision problem. Gathering and labelling the amount of data needed for these data-hungry models in the real world may become unfeasible and error-prone, while synthetic data give us the possibility of generating huge amounts of data with pixel-perfect annotations. However, most synthetic datasets lack from enough realism in their rendered images. In that context UnrealROX generation tool was presented in 2019, allowing to generate highly realistic data, at high resolutions and framerates, with an efficient pipeline based on Unreal Engine, a cutting-edge videogame engine. UnrealROX enabled robotic vision researchers to generate realistic and visually plausible data with full ground truth for a wide variety of problems such as class and instance semantic segmentation, object detection, depth estimation, visual grasping, and navigation. Nevertheless, its workflow was very tied to generate image sequences from a robotic on-board camera, making hard to generate data for other purposes. In this work, we present UnrealROX+, an improved version of UnrealROX where its decoupled and easy-to-use data acquisition system allows to quickly design and generate data in a much more flexible and customizable way. Moreover, it is packaged as an Unreal plug-in, which makes it more comfortable to use with already existing Unreal projects, and it also includes new features such as generating albedo or a Python API for interacting with the virtual environment from Deep Learning frameworks., Accepted at International Joint Conference on Neural Networks (IJCNN) 2021

In the last years, the care of dependent people, either by disease, accident, disability, or age, is one of the current priority research topics in developed countries. Moreover, such care is intended to be at patients home, in order to minimize the cost of therapies. Patients rehabilitation will be fulfilled when their integration in society is achieved, either in the family or in a work environment. To address this challenge, we propose the development and evaluation of an assistant for people with acquired brain injury or dependents. This assistant is twofold: in the patient’s home is based on the design and use of an intelligent environment with abilities to monitor and active learning, combined with an autonomous social robot for interactive assistance and stimulation. On the other hand, it is complemented with an outdoor assistant, to help patients under disorientation or complex situations. This involves the integration of several existing technologies and provides solutions to a variety of technological challenges. Deep leaning-based techniques are proposed as core technology to solve these problems. This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by the University of Alicante project GRE16-19 and the Valencian Government GV/2018/022. This work has also been supported by Spanish grants for PhD studies FPU15/04516 and ACIF/2017/243. Experiments were made possible by a generous hardware donation from NVIDIA.

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We firstly define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions., Submitted to TPAMI

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.

Interaction in virtual reality (VR) environments (e.g. grasping and manipulating virtual objects) is essential to ensure a pleasant and immersive experience. In this work, we propose a visually realistic, flexible and robust grasping system that enables real-time interactions in virtual environments. Resulting grasps are visually realistic because hand is automatically fitted to the object shape from a position and orientation determined by the user using the VR handheld controllers (e.g. Oculus Touch motion controllers). Our approach is flexible because it can be adapted to different hand meshes (e.g. human or robotic hands) and it is also easily customizable. Moreover, it enables interaction with different objects regardless their geometries. In order to validate our proposal, an exhaustive qualitative and quantitative performance analysis has been carried out. On one hand, qualitative evaluation was used in the assessment of abstract aspects, such as motor control, finger movement realism, and interaction realism. On the other hand, for the quantitative evaluation a novel metric has been proposed to visually analyze the performed grips. Performance analysis results indicate that previous experience with our grasping system is not a prerequisite for an enjoyable, natural and intuitive VR interaction experience. This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. This work has also been supported by three Spanish national grants for PhD studies (FPU15/04516, FPU17/00166, and ACIF/2018/197), by the University of Alicante project GRE16- 19, and by the Valencian Government project GV/2018/022. Experiments were made possible by a generous hardware donation from NVIDIA.

Object grasping in domestic environments using social robots has an enormous potential to help dependent people with a certain degree of disability. In this chapter, the authors make use of the well-known Pepper social robot to carry out such task. They provide an integrated solution using ROS to recognize and grasp simple objects. That system was deployed on an accelerator platform (Jetson TX1) to be able to perform object recognition in real time using RGB-D sensors attached to the robot. By using the system, the authors prove that the Pepper robot shows a great potential for such domestic assistance tasks.

Electro-optical measurements, i.e., optical waveguides and plasmonic based electrochemical impedance spectroscopy (P-EIS), are based on the sensitive dependence of refractive index of electro-optical sensors on surface charge density, modulated by an AC electrical field applied to the sensor surface. Recently, P-EIS has emerged as a new analytical tool that can resolve local impedance with high, optical spatial resolution, without using microelectrodes. This study describes a high speed image acquisition and processing system for electro-optical measurements, based on a high speed complementary metal-oxide semiconductor (CMOS) sensor and a field-programmable gate array (FPGA) board. The FPGA is used to configure CMOS parameters, as well as to receive and locally process the acquired images by performing Fourier analysis for each pixel, deriving the real and imaginary parts of the Fourier coefficients for the AC field frequencies. An AC field generator, for single or multi-sine signals, is synchronized with the high speed acquisition system for phase measurements. The system was successfully used for real-time angle-resolved electro-plasmonic measurements from 30 Hz up to 10 kHz, providing results consistent to ones obtained by a conventional electrical impedance approach. The system was able to detect amplitude variations with a relative variation of ±1%, even for rather low sampling rates per period (i.e., 8 samples per period). The PC (personal computer) acquisition and control software allows synchronized acquisition for multiple FPGA boards, making it also suitable for simultaneous angle-resolved P-EIS imaging.

In this work, a Schaeffer language recognition system is proposed in order to help autistic children overcome communicative disorders. Using Schaeffer language as a speech and language therapy, improves children communication skills and at the same time the understanding of language productions. Nevertheless, the teaching process of children in performing gestures properly is not straightforward. For this purpose, this system will teach children with autism disorder the correct way to communicate using gestures in combination with speech reproduction. The main purpose is to accelerate the learning process and increase children interest by using a technological approach. Several recurrent neural network‐based approaches have been tested, such as vanilla recurrent neural networks, long short‐term memory networks,and gated recurrent unit‐based models. In order to select the most suitable model, an extensive comparison has been conducted reporting a 93.13% classification success rate over a subset of 25 Schaeffer gestures by using an long short‐term memory‐based approach. Our dataset consists on pose‐based features such as angles and euclidean distances extracted from the raw skeletal data provided by a Kinect v2 sensor. This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. This work has also been supported by a Spanish national grant for PhD studies FPU15/04516 and the grant "Ayudas para Estudios de Máster e Iniciación a la Investigación" from the University of Alicante. Experiments were made possible by a generous hardware donation from NVIDIA (Tesla K40).

In this work, we carry out a study of the effect of adverse conditions, which characterize real-world scenes, on the accuracy of a Convolutional Neural Network applied to 3D object class recognition. Firstly, we discuss possible ways of representing 3D data to feed the network. In addition, we propose a set of representations to be tested. Those representations consist of a grid-like structure (fixed and adaptive) and a measure for the occupancy of each cell of the grid (binary and normalized point density). After that, we propose and implement a Convolutional Neural Network for 3D object recognition using Caffe. At last, we carry out an in-depth study of the performance of the network over a 3D CAD model dataset, the Princeton ModelNet project, synthetically simulating occlusions and noise models featured by common RGB-D sensors. The results show that the volumetric representations for 3D data play a key role on the recognition process and Convolutional Neural Network can be considerably robust to noise and occlusions if a proper representation is chosen. This work has been supported by the Spanish Government DPI2013-40534-R grant for the SIRMAVED project, also supported with FEDER funds. This work has also been funded by the grant “Ayudas para Estudios de Máster e Iniciación a la Investigación” from the University of Alicante.

Intentional oil pollution damages marine ecosystems. Therefore, society and governments require maritime surveillance for early oil spill detection. The fast response in the detection process helps to identify the offenders in the vast majority of cases. Nowadays, it is a human operator whom is trained for carrying out oil spill detection. Operators usually use image processing techniques and data analysis from optical, thermal or radar acquired from aerial vehicles or spatial satellites. The current trend is to automate the oil spill detection process so that this can filter candidate oil spill from an aircraft as a decision support system for human operators. In this work, a robust and automated system for candidate oil spill based on Recurrent Neural Network (RNN) is presented. The aim is to provide a faster identification of the candidate oil spills from SLAR scanned sequences. So far, the majority of the research works about oil spill detection are focused on the classification b etween real oil spills and look-alikes, and they use SAR or optical images but not SLAR. Furthermore, the overall decision is usually taken by an operator in the research works of state-of-art, mainly due to the wide variety of types of look-alikes which cause false positives in the detection process when traditional NN are used. This work provides a RRN-based approach for candidate oil spill detection using SLAR data in contrast with the traditional Multilayer Perceptron Neural Network (MPNN). The system is tested with temporary data acquired from a SLAR sensor mounted on an aircraft. It achieves a success rate in detecting of 97%. This work was supported by the Spanish Ministry of Economy and Competitiveness through the research project ONTIME (RTC-2014-1863-8).

Space agencies all over the world are interested today in very small satellites because of their advantages compared to heavier satellites. This chapter starts with the general characteristics, Earth orbits, eclipses and current missions of very small satellites. It continues with a brief summary of the component parts of the electrical power system: the array of solar cells, batteries for space applications, 3 power architectures and 19 maximum power point tracker’s algorithms. Authors’ attention is mainly focused on designing, simulation and practical demonstration of a prototype with a flexible hybrid proposed architecture of the power conditioning unit for very small satellites, whose component blocks are the battery charge unit (BCU) including the dc–dc converter, the digital controller, the BCU sensors circuitry and the BCU prototype) and the battery charge/discharge monitor unit (BCDMU) including the microcontroller, the BCDMU sensors circuitry, the battery switch, the battery heater and the telemetry system.

The need for high power density has been the trend of the power conversion industry for many years. The key to obtain a high power density is to increase system’s efficiency. In the case of space applications, there is the same need for high power density but with the very important note that the reliability of the system must not be compromised. In order to increase system’s efficiency while maintaining reliability, the latest soft switching converter topologies, high power density packaging, improved heat extraction and planar magnetics are used. Other key parameters are: the digital loop control and the digital energy management through microcontrollers, and digital signal controllers.

In this paper the possibility of using a version of the Cuk converter in telecommunication equipment and telephone lines that require special power needs is put under analysis. The classical Cuk converter is introduced and analyzed. The mathematical equations for this DC-DC converter are deducted and the circuit is successfully simulated in PSpice after choosing correct values for the components. The self-lift Cuk converter is also introduced and modeled in the same manner and its improvements are emphasized from our point of view. Using the results obtained from simulation of the circuits the advantages of the second one are illustrated by comparison.