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The problem of this work consists in the computational cost reflected in the factorization of the RSA keys, since this cryptographic system bases its security on the factorization of too large integers. The methodology used was with an experimental approach divided into 3 phases and based on a bibliographic review to theoretically support the development with the resources and sources found by various authors. The results obtained are the result of experimental work to provide the necessary information to solve the problem. En el artículo \"The Quadratic Sieve Factoring Algorithm\" el reconocido profesor Carl Pomerance explica los fundamentos matemáticos de la técnica de factorización de Criba Cuadrática, la cual se ha demostrado que hasta ahora es la mejor técnica de factorización para números conformados por hasta 110 dígitos (Pomerance, 1985), con lo que dicha técnica se establece como la mejor opción a implementar, al menos hasta los 90 dígitos, pues de ahí en adelante se utiliza Number Field Sieve (NFS) algoritmo de factorización denominado msieve.

En este artículo se presenta la implementación de una variedad de nodos lógicos (NLs) de sistemas de generación basados en recursos energéticos distribuidos (DER), en una placa electrónica (o SBC) que permite la adquisición y empaquetamiento de señales analógicas de un arreglo fotovoltaico con baterías, con base en el estándar IEC 61850-7-420. Para esto, se usa una placa electrónica SBC (Single Board Computer) integrada con una tarjeta de conversión análogo digital (ADC) que permite la lectura de los valores analógicos del sistema. La SBC se comunica con la tarjeta ADC para empaquetar los datos leídos dentro de los objetos de datos propios del estándar IEC 61850, usando el nodo lógico (NL) respectivo. Se usó una librería con licencia abierta para la creación del servidor IEC 61850 en la placa electrónica, y el driver del fabricante de la tarjeta ADC para comunicar las dos tarjetas efectivamente. Lo que se busca con este trabajo es el desarrollo de nodos lógicos (NLs) para recursos energéticos distribuidos (DER), de tal forma que los fabricantes de tecnologías de generación basadas en fuentes renovables, como la solar y/o la eólica, incorporen los equipos electrónicos inteligentes (IED) y los controladores de acuerdo con la extensión del estándar para estos nodos lógicos. Se presentan las pruebas de comunicación de la implementación realizada y los resultados obtenidos.

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain.

The early detection of handguns and knives from surveillance videos is crucial to enhance people’s safety. Despite the increasing development of Deep Learning (DL) methods for general object detection, weapon detection from surveillance videos still presents open challenges. Among these, the most significant are: (i) the very small size of the weapons with respect to the camera field of view and (ii) the need of a real-time feedback, even when using low-cost edge devices for computation. Complex and recently-developed DL architectures could mitigate the former challenge but do not satisfy the latter one. To tackle such limitation, the proposed work addresses the weapon-detection task from an edge perspective. A double-step DL approach was developed and evaluated against other state-of-the-art methods on a custom indoor surveillance dataset. The approach is based on a first Convolutional Neural Network (CNN) for people detection which guides a second CNN to identify handguns and knives. To evaluate the performance in a real-world indoor environment, the approach was deployed on a NVIDIA Jetson Nano edge device which was connected to an IP camera. The system achieved near real-time performance without relying on expensive hardware. The results in terms of both COCO Average Precision (AP = 79.30) and Frames per Second (FPS = 5.10) on the low-power NVIDIA Jetson Nano pointed out the goodness of the proposed approach compared with the others, encouraging the spread of automated video surveillance systems affordable to everyone.

Personal protective equipment (PPE) is crucial for infection prevention and is effective only when worn correctly and consistently. Health organizations often use education or inspections to mitigate non-compliance, but these are costly and have limited success. This study developed a novel on-device, AI-based computer vision system to monitor healthcare worker PPE adherence in real time. Using a custom-built image dataset of 7142 images of 11 participants wearing various combinations of PPE (mask, gloves, gown), we trained a series of binary classifiers for each PPE item. By utilizing a lightweight MobileNetV3 model, we optimized the system for edge computing on a Raspberry Pi 5 single-board computer, enabling rapid image processing without the need for external servers. Our models achieved high accuracy in identifying individual PPE items (93–97%), with an overall accuracy of 85.58 ± 0.82% when all items were correctly classified. Real-time evaluation with 11 unseen medical staff in a cardiac intensive care unit demonstrated the practical viability of our system, maintaining a high per-item accuracy of 87–89%. This study highlights the potential for AI-driven solutions to significantly improve PPE compliance in healthcare settings, offering a cost-effective, efficient, and reliable tool for enhancing patient safety and mitigating infection risks.

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

Intelligent compaction (IC) is a technology that uses non-contact sensors to monitor and record the compaction level of geomaterials in real-time during road construction. However, current IC devices have several limitations: (i) they are unable to visualize or compare multiple intelligent compaction measurement values (ICMVs) in real-time during compaction; (ii) they are not retrofittable to different conventional rollers that exist in the field; (iii) they do not incorporate corrections for ICMVs reflecting variable field conditions; (iv) they are unable to integrate construction specifications as needed for performance-based compaction; and (v) they do not record all the key roller parameters for further compaction analysis. To address these issues, an innovative retrofittable platform with cutting-edge hardware and software was developed. This platform, called the intelligent compaction analyzer (ICA) platform, is effective at calculating conventional acceleration amplitude-based ICMVs and stiffness-based parameters and at displaying the spatial distributions of these parameters in a color-coded map in real-time during compaction.

This paper presents the implementation of ORB-SLAM3 for visual odometry on a low-power ARM-based system, specifically the Jetson Nano, to track a robot’s movement using RGB-D cameras. Key challenges addressed include the selection of compatible software libraries, camera calibration, and system optimization. The ORB-SLAM3 algorithm was adapted for the ARM architecture and tested using both the EuRoC dataset and real-world scenarios involving a mobile robot. The testing demonstrated that ORB-SLAM3 provides accurate localization, with errors in path estimation ranging from 3 to 11 cm when using the EuRoC dataset. Real-world tests on a mobile robot revealed discrepancies primarily due to encoder drift and environmental factors such as lighting and texture. The paper discusses strategies for mitigating these errors, including enhanced calibration and the potential use of encoder data for tracking when camera performance falters. Future improvements focus on refining the calibration process, adding trajectory correction mechanisms, and integrating visual odometry data more effectively into broader systems.

We have developed a phase retrieval holography system using a single-board computer (SBC) with a graphics processing unit (GPU) for particle size measurement. The system comprises two cameras connected to the SBC with a GPU (Jetson NanoTM, NVIDIA®), a diode-pumped solid-state green laser, and a beam splitter. The GPU enables us to reconstruct holograms in real-time and measure particle size. The system can record the shapes and positions of particles falling in a static flow in a three-dimensional volume as two holograms generating an interference pattern. Two holograms solve the twin image problem that arises because of the lack of phase information using phase retrieval holography. We also present the requirement of this system for experimentally recording and numerically reconstructing holograms of particles. Finally, we compare the particle size distribution obtained by the system to that of conventional two-dimensional image measurement.

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion.