End-to-End Ultrasonic Hand Gesture Recognition.

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.

Hardware Implementations of a Deep Learning Approach to Optimal Configuration of Reconfigurable Intelligence Surfaces.

Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with ×20 performance increase over the closest FP32, GPU-accelerated option, and almost ×3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications.

Toward Sensor Measurement Reliability in Blockchains.

In this work, a secure architecture to send data from an Internet of Things (IoT) device to a blockchain-based supply chain is presented. As is well known, blockchains can process critical information with high security, but the authenticity and accuracy of the stored and processed information depend primarily on the reliability of the information sources. When this information requires acquisition from uncontrolled environments, as is the normal situation in the real world, it may be, intentionally or unintentionally, erroneous. The entities that provide this external information, called Oracles, are critical to guarantee the quality and veracity of the information generated by them, thus affecting the subsequent blockchain-based applications. In the case of IoT devices, there are no effective single solutions in the literature for achieving a secure implementation of an Oracle that is capable of sending data generated by a sensor to a blockchain. In order to fill this gap, in this paper, we present a holistic solution that enables blockchains to verify a set of security requirements in order to accept information from an IoT Oracle. The proposed solution uses Hardware Security Modules (HSMs) to address the security requirements of integrity and device trustworthiness, as well as a novel Public Key Infrastructure (PKI) based on a blockchain for authenticity, traceability, and data freshness. The solution is then implemented on Ethereum and evaluated regarding the fulfillment of the security requirements and time response. The final design has some flexibility limitations that will be approached in future work.

RESEKRA: Remote Enrollment Using SEaled Keys for Remote Attestation.

This paper presents and implements a novel remote attestation method to ensure the integrity of a device applicable to decentralized infrastructures, such as those found in common edge computing scenarios. Edge computing can be considered as a framework where multiple unsupervised devices communicate with each other with lack of hierarchy, requesting and offering services without a central server to orchestrate them. Because of these characteristics, there are many security threats, and detecting attacks is essential. Many remote attestation systems have been developed to alleviate this problem, but none of them can satisfy the requirements of edge computing: accepting dynamic enrollment and removal of devices to the system, respecting the interrupted activity of devices, and last but not least, providing a decentralized architecture for not trusting in just one Verifier. This security flaw has a negative impact on the development and implementation of edge computing-based technologies because of the impossibility of secure implementation. In this work, we propose a remote attestation system that, through using a Trusted Platform Module (TPM), enables the dynamic enrollment and an efficient and decentralized attestation. We demonstrate and evaluate our work in two use cases, attaining acceptance of intermittent activity by IoT devices, deletion of the dependency of centralized verifiers, and the probation of continuous integrity between unknown devices just by one signature verification.

Wearable System for Biosignal Acquisition and Monitoring Based on Reconfigurable Technologies.

Wearable monitoring devices are now a usual commodity in the market, especially for the monitoring of sports and physical activity. However, specialized wearable devices remain an open field for high-risk professionals, such as military personnel, fire and rescue, law enforcement, etc. In this work, a prototype wearable instrument, based on reconfigurable technologies and capable of monitoring electrocardiogram, oxygen saturation, and motion, is presented. This reconfigurable device allows a wide range of applications in conjunction with mobile devices. As a proof-of-concept, the reconfigurable instrument was been integrated into ad hoc glasses, in order to illustrate the non-invasive monitoring of the user. The performance of the presented prototype was validated against a commercial pulse oximeter, while several alternatives for QRS-complex detection were tested. For this type of scenario, clustering-based classification was found to be a very robust option.

A clustering-based method for single-channel fetal heart rate monitoring.

Non-invasive fetal electrocardiography (ECG) is based on the acquisition of signals from abdominal surface electrodes. The composite abdominal signal consists of the maternal electrocardiogram along with the fetal electrocardiogram and other electrical interferences. These recordings allow for the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper introduces a procedure for fetal heart rate extraction from a single-channel abdominal ECG signal. The procedure is composed of three main stages: a method based on wavelet for signal denoising, a new clustering-based methodology for detecting fetal QRS complexes, and a final stage to correct false positives and false negatives. The novelty of the procedure thus relies on using clustering techniques to classify singularities from the abdominal ECG into three types: maternal QRS complexes, fetal QRS complexes, and noise. The amplitude and time distance of all the local maxima followed by a local minimum were selected as features for the clustering classification. A wide set of real abdominal ECG recordings from two different databases, providing a large range of different characteristics, was used to illustrate the efficiency of the proposed method. The accuracy achieved shows that the proposed technique exhibits a competitve performance when compared to other recent works in the literature and a better performance over threshold-based techniques.

Unified Compact ECC-AES Co-Processor with Group-Key Support for IoT Devices in Wireless Sensor Networks.

Security is a critical challenge for the effective expansion of all new emerging applications in the Internet of Things paradigm. Therefore, it is necessary to define and implement different mechanisms for guaranteeing security and privacy of data interchanged within the multiple wireless sensor networks being part of the Internet of Things. However, in this context, low power and low area are required, limiting the resources available for security and thus hindering the implementation of adequate security protocols. Group keys can save resources and communications bandwidth, but should be combined with public key cryptography to be really secure. In this paper, a compact and unified co-processor for enabling Elliptic Curve Cryptography along to Advanced Encryption Standard with low area requirements and Group-Key support is presented. The designed co-processor allows securing wireless sensor networks with independence of the communications protocols used. With an area occupancy of only 2101 LUTs over Spartan 6 devices from Xilinx, it requires 15% less area while achieving near 490% better performance when compared to cryptoprocessors with similar features in the literature.

Evaluation of a reconfigurable portable instrument for copper determination based on luminescent carbon dots.

A portable reconfigurable platform for copper (Cu(II)) determination based on luminescent carbon dot (Cdots) quenching is described. The electronic setup consists of a light-emitting diode (LED) as the carbon dot optical exciter and a photodiode as a light-to-current converter integrated in the same instrument. Moreover, the overall analog conditioning is simply performed with one integrated solution, a field-programmable analog array (FPAA), which makes it possible to reconfigure the filter and gain stages in real time. This feature provides adaptability to use the platform as an analytical probe for carbon dots coming from different batches with some variations in luminescence characteristics. The calibration functions obtained that fit a modified Stern-Volmer equation were obtained using luminescence signals from Cdots quenching by Cu(II). The analytical applicability of the reconfigurable portable instrument for Cu(II) using Cdots has been successfully demonstrated in tap water analysis.

[Health-Related Quality of Life as an Explanatory Variable of Primary Care Consultations: sex Differences]. / Calidad de vida relacionada con la salud como variable explicativa del consumo de consultas en atención primaria: un análisis por sexo.

BACKGROUND: It is well known the inverse relationship between health-related quality of life (HRQoL) and the use of consultations. However, most studies deal sex as a confounding variable rather than to explicitly investigate sex differences. The study aims to know the influence of HRQoL of the elderly on the use of Primary Care consultations in a sex analysis. METHODS: Throughout 2013, 191 women and 155 men aged 65 years or older were enrolled in the study and assessed with interviews and analysis of medical records. We used the EuroQol-5D to assess the HRQoL and several demographic, clinical and social support variables were also analyzed. Two multiple linear regression models were developed. RESULTS: HRQoL showed a negative correlation with the use of consultations (Spearman's rho=-0,22; p=0,0001) and a crude OR value of 1,85 (95% CI:1,2-2,9). The association remained significant after adjusting for demographic [OR=1,99 (95% CI: 1,2-3,2)], clinical [OR=1,79 (95% CI: 1,1-2,9)] or social support covariates [OR=1,83 (95% CI: 1,1-2,9)]. In regression analysis, the values of standardized coefficient (ß) related to HRQoL were 0,22 (95% CI:-36,7- -6,9) in females and 0,03 (95% CI:-15,6-23,1) in males. CONCLUSIONS: In women ≥ 65 years, HRQoL shows the greatest explanatory power of use of consultations, after adjusting for demographic, clinical and social support covariates. By contrast, its influence on men is negligible.

Calidad de vida relacionada con la salud como variable explicativa del consumo de consultas en atención primaria: un análisis por sexo / Health-Related Quality of Life as an Explanatory Variable of Primary Care Consultations: Sex Differences

Fundamentos: Es conocida la asociación inversa que existe entre la calidad de vida relacionada con la salud (CVRS) y el consumo de consultas. Sin embargo, el sexo es considerado habitualmente como variable de confusión, y son escasos los estudios que han analizado explícitamente las diferencias de sexo en esta cuestión. El objetivo fue conocer la influencia de la CVRS sobre el uso de consultas de Atención Primaria en las personas >65 años, en un análisis diferenciado por sexo. Métodos: La selección de participantes se realizó a lo largo de 2013, 191 mujeres y 155 varones de ≥65 años fueron estudiados mediante entrevista y análisis de historias clínicas. Se utilizó el Cuestionario EuroQol-5D y fueron analizadas variables demográficas, clínicas y de apoyo social. Se elaboraron 2 modelos de regresión múltiple, uno dirigido a las mujeres y otro a los varones. Resultados: La CVRS se correlacionó de forma negativa con el consumo de consultas (Rho-Spearman=-0,22; p=0,0001), con una OR cruda de 1,85 (IC95%: 1,2-2,9). La asociación se mantuvo significativa al ajustar tanto por variables demográficas [OR=1,99 (IC95%: 1,2–3,2)], como por variables clínicas [OR=1,79 (IC95%: 1,1-2,9)] o de apoyo social [OR=1,83 (IC95%: 1,1–2,9)]. En el análisis de regresión, los valores del coeficiente estandarizado (β) asociado a la CVRS fueron 0,22 (IC95%: -36,7– -6,9) en las mujeres, y 0,03 (-15,6–23,1) en los varones. Conclusiones: En las mujeres de ≥65 años la CVRS es la variable con mayor capacidad explicativa de consumo de consultas, tras ajustar por variables demográficas, clínicas y de apoyo social. Por el contrario, en los varones su influencia es prácticamente nula (AU)

Background: It is well known the inverse relationship between health-related quality of life (HRQoL) and the use of consultations. However, most studies deal sex as a confounding variable rather than to explicitly investigate sex differences. The study aims to know the influence of HRQoL of the elderly on the use of Primary Care consultations in a sex analysis. Methods: Throughout 2013, 191 women and 155 men aged 65 years or older were enrolled in the study and assessed with interviews and analysis of medical records. We used the EuroQol-5D to assess the HRQoL and several demographic, clinical and social support variables were also analyzed. Two multiple linear regression models were developed. Results: HRQoL showed a negative correlation with the use of con-sultations (Spearman´s rho=-0,22; p=0,0001) and a crude OR value of 1,85 (95% CI:1,2–2,9). The association remained significant after adjusting for demographic [OR=1,99 (95% CI: 1,2–3,2)], clinical [OR=1,79 (95% CI: 1,1–2,9)] or social support covariates [OR=1,83 (95% CI: 1,1–2,9)]. In regression analysis, the values of standardized coefficient (β) related to HRQoL were 0,22 (95% CI:-36,7– -6,9) in females and 0,03 (95% CI:-15,6–23,1) in males. Conclusions: In women ≥65 years, HRQoL shows the greatest explanatory power of use of consultations, after adjusting for demographic, clinical and social support covariates. By contrast, its influence on men is negligible (AU)

Evaluación antropométrica del estado nutricio de los médicos Hospital Infantil Dr. Robert Reid Cabral / Anthropometric evaluation of the nutricional conditions physicians of the Hospital Infantil Dr. Robert Reid Cabral

Se realizó un estudio descriptivo observacional de 42 médicos seleccionados aleatoriamente del personal del hospital infantil Dr. Robert Reid Cabral, de Santo Domingo, República Dominicana 1993, para determinar si el estado nutricio del médico se relacionaba con el valor proteíno-energético de su dieta, la jornada de trabajo y su nivel socioeconómico. Los resultados confirmaron que el estado nutricio de los médicos encuestados según el índice de masa corporal (IMC), fue normal en 21 (50 por ciento ), hubo 5 desnutridos (11.9 por ciento ), 16 obesos (38.1 por ciento ), 10 con obesidad grado I (23.8 por ciento ), y 6 con obesidad grado II (14.3 por ciento ). La desnutrición apareció en los médicos de ingresos familiares menores de 10 mil pesos y la obesidad apareció por encima, alcanzando mayor grado a medida que el ingreso era más alto. El valor energético de la dieta en 100 por ciento de los desnutridos estuvo por debajo de 2,500 cals/día, mientras que 61.5 por ciento de los médicos con buen estdo nutricional fueron lo más elevados