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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.

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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.

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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.

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The recent incorporation of skateboarding as an Olympic discipline has aroused interest in the epidemiological analysis of injuries suffered in this sport with the aim of adopting preventive measures and thus reduce the athlete's time out of training or competition. There is little published on the topic. The few statistical data available refers to amateur practice and analyzes information obtained retrospectively. This situation leads to bias because It does not register relevant data that could be seen directly on the field of play or through video analysis. For example: issues involving the mechanism of injury, type of fall, etc. We propose this survey to be trialed in professional competitions and training in order to collect information from non-amateur practice of skateboarding. It will allow us to make effective decisions on preventive actions. All international federations should have access to it.

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Son múltiples las variables que se deben tener en cuenta a la hora de recomendar cómo realizar actividad física: tipo, frecuencia, intensidad, duración y densidad. Existen varias guías que se dedican a este tema, pero la situación que se vive en la actualidad producto de la pandemia por COVID 19 generó un gran interés por evitar la aparición de injurias luego de un extenso período de desentrenamiento. El propósito de este trabajo es sugerir la Práctica de Ejercicio Físico (PEF) con el objetivo de informar y promover su ejecución adecuada, estimular su progreso y limitar el riesgo de lesiones

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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.

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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.

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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.

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Se realizó un estudio retrospectivo y descriptivo en 110 pacientes fallecidos, portadores de Diabetes Mellitus; con el fin de conocer las causas de mortalidad en estos pacientes, en el Hospital Regional Honorio Delgado Arequipa; en un periodo de 12 años, del 1ro. de junio de 1984 al 30 de junio de 1996. De los 110 pacientes diabéticos, 64 correspondieron al sexo femenino (58.2 por ciento) y 46 al masculino (41.8 por ciento); siendo no significativa la diferencia; los grupos etáreos de mayor mortalidad estuvieron entre los 60 y 69 años; el promedio de edad para ambos sexos es de 68.9 años; la mayoría tenía de ocupación su casa; y el 19.1 por ciento presentaban antecedentes familiares positivos de Diabetes Mellitus. Dentro de 1984 - 1996 las causas de muerte más frecuente en los pacientes diabéticos del Hospital Regional Honorio Delgado de Arequipa fueron: Las infecciones (40.9 por ciento), principalmente Bronconeumonía; seguido de los Transtornos Vasculares (31.8 por ciento), principalmente por Infarto Agudo de Miocardio y Nefropatía Diabética; y luego los Transtornos Metabólicos Agudos (17.3 por ciento), principalmente por Coma Hiperosmolar no Cetósico. Las condiciones asociadas a la Diabetes Mellitus más frecuentes fueron: Anemia e Infección Urinaria.

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