Encarnación Castillo

As the number of edge devices continues to grow exponentially, ensuring their security and trustworthiness has become increasingly critical. In edge computing environments, the deployment of heterogeneous devices across distributed and often untrusted locations poses significant challenges for secure software maintenance, identity verification, and compliance with cybersecurity standards. These challenges introduce vulnerabilities that can be exploited by attackers to compromise sensitive data or disrupt system operations. To address this concern, this work presents an automated mechanism based on the Automatic Certificate Management Environment (ACME) protocol and its device attestation extension for provisioning cryptographic certificates to devices equipped with a Trusted Platform Module (TPM). In particular, we propose a Public Key Infrastructure (PKI) framework that enables secure communication by verifying device identity and ensuring the confidentiality, integrity, and authenticity of exchanged data. Our approach provides strong assurance that devices are operating in a trusted state, with cryptographic keys securely stored and bound to measured system integrity. We validate the solution through a practical implementation and demonstrate its effectiveness in Vehicular Ad Hoc Networks (VANETs), where vehicles can automatically obtain ephemeral certificates to establish trusted, privacy-preserving communication. This capability not only strengthens security in connected and autonomous vehicles but also lays the groundwork for scalable, standards-compliant security infrastructures in connected transportation systems.

This study presents a complete system for remote single-lead electrocardiogram (ECG) monitoring using a wearable device equipped with dry laser-induced graphene (LIG) electrodes. First, we evaluated the feasibility of LIG electrodes for chest ECG acquisition across different volunteers and over extended recording periods. After that, a simple algorithm for R-peak detection was developed and validated using the acquired signals. This algorithm implemented using open-source libraries in Python, consists of two optimized stages. In the first stage, the ECG signal is preprocessed using the discrete wavelet transform (DWT) to remove the baseline wandering and denoise the signal. Secondly, a clustering-based algorithm classifies the different peaks in the ECG signal into two clusters based on their amplitude range. It is demonstrated that, despite the inter-individual variability in ECG waveforms, the proposed algorithm effectively identifies the R-peaks with high accuracy without requiring the specific cardiac profile for each individual patient, thus ensuring a reliable and robust heart rate (HR) monitoring. Finally, we showcase the potential of integrating novel wearable ECG monitoring systems with advanced sensors and signal processing techniques by a near real-time remote ECG monitoring setup.

Hand gesture recognition (HGR) is a convenient and natural form of human–computer interaction. It is suitable for various applications. Much research has already focused on wearable device-based HGR. By contrast, this paper gives an overview focused on device-free HGR. That means we evaluate HGR systems that do not require the user to wear something like a data glove or hold a device. HGR systems are explored regarding technology, hardware, and algorithms. The interconnectedness of timing and power requirements with hardware, pre-processing algorithm, classification, and technology and how they permit more or less granularity, accuracy, and number of gestures is clearly demonstrated. Sensor modalities evaluated are WIFI, vision, radar, mobile networks, and ultrasound. The pre-processing technologies stereo vision, multiple-input multiple-output (MIMO), spectrogram, phased array, range-doppler-map, range-angle-map, doppler-angle-map, and multilateration are explored. Classification approaches with and without ML are studied. Among those with ML, assessed algorithms range from simple tree structures to transformers. All applications are evaluated taking into account their level of integration. This encompasses determining whether the application presented is suitable for edge integration, their real-time capability, whether continuous learning is implemented, which robustness was achieved, whether ML is applied, and the accuracy level. Our survey aims to provide a thorough understanding of the current state of the art in device-free HGR on edge devices and in general. Finally, on the basis of present-day challenges and opportunities in this field, we outline which further research we suggest for HGR improvement. Our goal is to promote the development of efficient and accurate gesture recognition systems.

As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand gesture recognition (HGR) systems in terms of robustness and accuracy. This research presents a novel machine learning (ML)-based end-to-end solution for hand gesture recognition with low-cost micro-electromechanical (MEMS) system ultrasonic transducers. In contrast to prior methods, our ML model processes the raw echo samples directly instead of using pre-processed data. Consequently, the processing flow presented in this work leaves it to the ML model to extract the important information from the echo data. The success of this approach is demonstrated as follows. Four MEMS ultrasonic transducers are placed in three different geometrical arrangements. For each arrangement, different types of ML models are optimized and benchmarked on datasets acquired with the presented custom hardware (HW): convolutional neural networks (CNNs), gated recurrent units (GRUs), long short-term memory (LSTM), vision transformer (ViT), and cross-attention multi-scale vision transformer (CrossViT). The three last-mentioned ML models reached more than 88% accuracy. The most important innovation described in this research paper is that we were able to demonstrate that little pre-processing is necessary to obtain high accuracy in ultrasonic HGR for several arrangements of cost-effective and low-power MEMS ultrasonic transducer arrays. Even the computationally intensive Fourier transform can be omitted. The presented approach is further compared to HGR systems using other sensor types such as vision, WiFi, radar, and state-of-the-art ultrasound-based HGR systems. Direct processing of the sensor signals by a compact model makes ultrasonic hand gesture recognition a true low-cost and power-efficient input method.

Spiking Neural Networks (SNNs) have emerged as a prominent paradigm for brain-inspired computing, capable of processing temporal information and event-driven data in an efficient and biologically plausible manner. However, their revolutionary and complex nature is one of the key reasons why SNNs are not yet a widely used approach in contrast to traditional Artificial Neural Networks (ANNs). In this paper, we present a comprehensive SNN framework that offers user-friendly implementation. It has been designed so that it is compatible with other well-known software tools for data science, being easy to integrate with them. We showcase the versatility of the framework by applying it to various well-known benchmarking datasets, including image processing of handwritten numbers, time-series forecasting and an advance use case for speech recognition, achieving competitive results compared to traditional ANNs. Our SNN framework aims to bridge the gap between neuroscience and artificial intelligence, empowering researchers and practitioners with an accessible tool to explore the potential of neuro-inspired computing in advancing the field of AI.

Non-invasive fetal electrocardiography (NI-ECG) is based on the acquisition of signals from electrodes on the mother’s abdominal surface. This abdominal ECG (aECG) signal consists of the maternal ECG (mECG) along with the fetal ECG (fECG) and other noises and artifacts. These records allow the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper proposes a procedure based on principal component analysis (PCA) to obtain a single-channel master abdominal ECG record that can be used as input to fetal heart rate extraction techniques. The new procedure requires three main processing stages: PCA-based analysis for fECG-component extraction, polarity test, and curve fitting. To show the advantages of the proposal, this PCA-based method has been used as the feeding stage to a previously developed clustering-based method for single-channel aECG fetal heart rate monitoring. The results obtained for a set of real abdominal ECG recordings from annotated public aECG databases, the Abdominal and Direct Fetal ECG Database and the Challenge 2013 Training Set A, show improved efficiency in fetal heart rate extraction and illustrate the benefits derived from the use of such a master abdominal ECG channel. This allows us to achieve proper fetal heart rate monitoring without the need for manual inspection and selection of channels to be processed, while also allowing us to analyze records that would have been discarded otherwise.