Multisensor Avionics Architecture for BVLOS Drone Services

This ADACORSA demonstrator focuses on the implementation of a failoperational avionics architecture combining Commercial Off-The-Shelf (COTS) elements from the automotive, the aerospace and the artificial intelligence world. A collaborative sensor setup (Time-of-Flight camera and FMCW RADAR from Infineon Technologies, stereo camera, LiDAR, IMU and GPS) allows to test heterogeneous sensor fusion solutions. A Tricore Architecture on AURIXTM Microcontroller supports the execution of safety supervision tasks as well as data fusion. A powerful embedded computer platform (NVIDIA Jetson Nano) accelerates AI algorithms performance and data processing. Furthermore, an FPGA enables power optimization of Artificial Neural Networks. Finally, a Pixhawk open-source flight controller ensures stabilization during normal flight operation and provides computer vision software modules allowing further processing of the captured, filtered and optimized environmental data. This paper shows various hardware and software implementations highlighting their emerging application within BVLOS drone services.EU-funded project ADACORSAECSEL Joint Undertaking (JU) under grant agreement No 876019European Union’s Horizon 2020German Federal Ministry of Education and Researc

Few-Shot User-Adaptable Radar-Based Breath Signal Sensing

Vital signs estimation provides valuable information about an individual’s overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.ITEA3 Unleash Potentials in Simulation (UPSIM) project (N°19006) German Federal Ministry of Education and Research (BMBF)Austrian Research Promotion Agency (FFG)Rijksdienst voor Ondernemend Nederland (Rvo)Innovation Fund Denmark (IFD

Context-adaptable radar-based people counting via few-shot learning

This work has received funding from the ECSEL Joint Under-taking (JU) under grant agreement No. 876925 (ANDANTE). The JU receives support from the European Union's Horizon 2020 research and innovation programme and France, Belgium, Germany, Netherlands, Portugal, Spain, Switzerland. Funding for open access publishing: Universidad de Granada/CBUA.In many industrial or healthcare contexts, keeping track of the number of people is essential. Radar systems, with their low overall cost and power consumption, enable privacy-friendly monitoring in many use cases. Yet, radar data are hard to interpret and incompatible with most computer vision strategies. Many current deep learning-based systems achieve high monitoring performance but are strongly context-dependent. In this work, we show how context generalization approaches can let the monitoring system fit unseen radar scenarios without adaptation steps. We collect data via a 60 GHz frequency-modulated continuous wave in three office rooms with up to three people and preprocess them in the frequency domain. Then, using meta learning, specifically the Weighting-Injection Net, we generate relationship scores between the few training datasets and query data. We further present an optimization-based approach coupled with weighting networks that can increase the training stability when only very few training examples are available. Finally, we use pool-based sampling active learning to fine-tune the model in new scenarios, labeling only the most uncertain data. Without adaptation needs, we achieve over 80% and 70% accuracy by testing the meta learning algorithms in new radar positions and a new office, respectively.ECSEL Joint Under-taking (JU) 876925Horizon 2020Universidad de Granada/CBU

Few-Shot User-Definable Radar-Based Hand Gesture Recognition at the Edge

This work was supported in part by ITEA3 Unleash Potentials in Simulation (UPSIM) by the German Federal Ministry of Education and Research (BMBF) under Project 19006, in part by the Austrian Research Promotion Agency (FFG), in part by the Rijksdienst voor Ondernemend Nederland (Rvo), and in part by the Innovation Fund Denmark (IFD).Technological advances and scalability are leading Human-Computer Interaction (HCI) to evolve towards intuitive forms, such as through gesture recognition. Among the various interaction strategies, radar-based recognition is emerging as a touchless, privacy-secure, and versatile solution in different environmental conditions. Classical radar-based gesture HCI solutions involve deep learning but require training on large and varied datasets to achieve robust prediction. Innovative self-learning algorithms can help tackling this problem by recognizing patterns and adapt from similar contexts. Yet, such approaches are often computationally expensive and hardly integrable into hardware-constrained solutions. In this paper, we present a gesture recognition algorithm which is easily adaptable to new users and contexts. We exploit an optimization-based meta-learning approach to enable gesture recognition in learning sequences. This method targets at learning the best possible initialization of the model parameters, simplifying training on new contexts when small amounts of data are available. The reduction in computational cost is achieved by processing the radar sensed data of gestures in the form of time maps, to minimize the input data size. This approach enables the adaptation of simple convolutional neural network (CNN) to new hand poses, thus easing the integration of the model into a hardware-constrained platform. Moreover, the use of a Variational Autoencoders (VAE) to reduce the gestures' dimensionality leads to a model size decrease of an order of magnitude and to half of the required adaptation time. The proposed framework, deployed on the Intel(R) Neural Compute Stick 2 (NCS 2), leads to an average accuracy of around 84% for unseen gestures when only one example per class is utilized at training time. The accuracy increases up to 92.6% and 94.2% when three and five samples per class are used.Federal Ministry of Education & Research (BMBF) 19006Austrian Research Promotion Agency (FFG)Rijksdienst voor Ondernemend Nederland (Rvo)Innovation Fund Denmark (IFD

Rule-Based Design for Low-Cost Double-Node Upset Tolerant Self-Recoverable D-Latch

This paper presents a low-cost, self-recoverable, double-node upset tolerant latch aiming at nourishing the lack of these devices in the state of the art, especially featuring self-recoverability while maintaining a low-cost pro le. Thus, this D-latch may be useful for high reliability and high-performance safety-critical applications as it can detect and recover faults happening during holding time in harsh radiation environments. The proposed D-latch design is based on a low-cost single event double-node upset tolerant latch and a rule-based double-node upset (DNU) tolerant latch which provides it with the self-recoverability against DNU, but paired with a low transistor count and high performance. Simulation waveforms support the achievements and demonstrate that this new D-latch is fully self-recoverable against double-node upset. In addition, the minimum improvement of the delay-power-area product of the proposed rule-based design for the low-cost DNU tolerant self-recoverable latch (RB-LDNUR) is 59%, compared with the latest DNU self-recoverable latch on the literature.Spanish Government MCIN/AEI/10.13039/501100011033/FEDER PID2020-117344RB-I00Regional Government P20_00265 P20_00633 B-RNM-680-UGR2

A High-Efficiency Isolated Wide Voltage Range DC-DC Converter Using WBG Devices

The recent release of the standard USB-PD 3.1 speci es variable output voltages from 5 V to 48 V featuring a step forward towards a universal adaptor but rising new challenges for the converter topologies used up to now. In such applications, a rst AC-DC stage is followed by a DC-DC stage. In this paper, emerging WBG technologies are applied to the asymmetrical half-bridge yback topology, demonstrating the potential of such combination as a wide voltage range DC-DC stage. Its suitability for high-density and high-ef ciency USB-PD Extended Power Range (EPR) and battery charger applications is discussed. The impact of different switching technologies, silicon and wide band gap, is analyzed. A general method to dimension the converter is presented and an iterative process is used to evaluate the theoretical ef ciency under different conditions and switching devices. Finally, the advantages of the presented converter using Gallium Nitride (GaN) devices are demonstrated in a 240 W DC-DC prototype. It achieves a full load ef ciency of 98%, and it is able to deliver an output voltage from 5 V to 48 V with input voltage range from 120 V to 420 V, as well an outstanding power density of 112 W/inch3 uncased.Infineon Technologies AG through the Spanish Regional Project P20_00265 BRNM-680-UGR20Spanish Ministry of Science MCIN/AEI PID2020-117344RB-I0

Integration of Hardware Security Modules and Permissioned Blockchain in Industrial IoT Networks

Hardware Security Modules (HSM) serve as a hardware based root of trust that offers physical protection while adding a new security layer in the system architecture. When combined with decentralized access technologies as Blockchain, HSM offers robustness and complete reliability enabling secured end-toend mechanisms for authenticity, authorization and integrity. This work proposes an ef cient integration of HSM and Blockchain technologies focusing on, mainly, public-key cryptography algorithms and standards, that result crucial in order to achieve a successful combination of the mentioned technologies to improve the overall security in Industrial IoT systems. To prove the suitability of the proposal and the interaction of an IoT node and a Blockchain network using HSM a proof of concept is developed. Results of time performance analysis of the prototype reveal how promising the combination of HSMs in Blockchain environments is.Infineon Technologies AGEuropean Union's Horizon 2020 Research and Innovation Program through the Cyber Security 4.0: Protecting the Industrial Internet of Things (C4IIoT) 833828FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades B-TIC-588-UGR2

Resonant Hybrid Flyback, a New Topology for High Density Power Adaptors

In this article, an innovative power adaptor based on the asymmetrical pulse width modulation (PWM) flyback topology will be presented. Its benefits compared to other state-of-the-art topologies, such as the active clamp flyback, are analyzed in detail. It will also describe the control methods to achieve high efficiency and power density using zero-voltage switching (ZVS) and zero-current switching (ZCS) techniques over the full range of the input voltage and the output load, providing comprehensive guidelines for the practical design. Finally, we demonstrate the convenience of the proposed design methods with a 65 W adaptor prototype achieving a peak efficiency of close to 95% and a minimum efficiency of 93.4% at full load over the range of the input voltage, as well as a world-class power density of 22 W/inch3 cased.This research is financed by Infineon Tecnologias AG

treNch: Ultra-Low Power Wireless Communication Protocol for IoT and Energy Harvesting

Although the number of Internet of Things devices increases every year, efforts to decrease hardware energy demands and to improve efficiencies of the energy-harvesting stages have reached an ultra-low power level. However, no current standard of wireless communication protocol (WCP) can fully address those scenarios. Our focus in this paper is to introduce treNch, a novel WCP implementing the cross-layer principle to use the power input for adapting its operation in a dynamic manner that goes from pure best-effort to nearly real time. Together with the energy-management algorithm, it operates with asynchronous transmissions, synchronous and optional receptions, short frame sizes and a light architecture that gives control to the nodes. These features make treNch an optimal option for wireless sensor networks with ultra-low power demands and severe energy fluctuations. We demonstrate through a comparison with different modes of Bluetooth Low Energy (BLE) a decrease of the power consumption in 1 to 2 orders of magnitude for different scenarios at equal quality of service. Moreover, we propose some security optimizations, such as shorter over-the-air counters, to reduce the packet overhead without decreasing the security level. Finally, we discuss other features aside of the energy needs, such as latency, reliability or topology, brought again against BLE.ECSEL Joint Undertaking through CONNECT project 737434Federal Ministry of Education & Research (BMBF)European Union's Horizon 2020 research and innovation programSpanish Ministry of Education, Culture and Sport (MECD)/FEDER-EU FPU18/01376BBVA FoundationUniversity of Granad

Adaptative ECT System Based on Reconfigurable Electronics

In this work we present a novel scheme for the design of electrical capacitance tomography systems that is based on the use of reconfigurable electronics. The objective of this strategy is to generate an adaptable and portable prototype for the processing electronics, i.e., an instrument suitable to be easily transported and applied to different ECT sensors and scenarios with no need of hardware redesign. In order to show the benefits of this approach, a prototype of the processing electronics for the readings of the inter-electrode capacitance values has been implemented using a Programmable System on Chip (PSoC) that allows configuring both analog and digital blocks included in the design. The result is a compact and portable instrument that can work with any ECT sensor up to 8 electrodes. The measurements are sent through a wireless Bluetooth link to an external smart-device such as smartphone, where the permittivity distribution is reconstructed using a custom-developed Android application.Junta de Andalucía (University Professor and Researcher Training Program – FPDI grant)EI BIOTiC under project MPTIC1