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    A comparative evaluation of modulation strategies for Hexverter–based Modular Multilevel Converters
    (IEEE, 2019-02)
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    Mancilla-David, Fernando
    In this work two different modulation strategies termed: i) nearest level control, and ii) phase disposition-sinusoidal pulse width modulation, are described, simulated and compared when applied to a Hexverter-based modular multilevel converter. In addition, two different voltage balancing algorithms are implemented and evaluated. Furthermore, total harmonic distortion regarding three-phase system voltages are assessed. In the end, synthesized branch voltage spectrum for each modulation strategy is analyzed.
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    Practical Evaluation of an Optimized LES-QB Converter: Implementation and Experimentation
    (IEEE, 2025-11-12)
    Solís-Rodriguez, Jose
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    Elias Valdez-Resendiz, Jesus
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    Guillen, Daniel
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    Developing a Sustainable Biofuel Industry for Aerospace and Aviation: A Roadmap to Market Viability
    (International Astronautical Federation (IAF), 2025)
    Gómez Falcón, Liliana
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    Montiel Viruel, Vanessa
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    Sumbarda Ramos, Emigdia Guadalupe
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    Xingu, Estefania
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    Us, Dilain
    The growing urgency to reduce carbon emissions in the aerospace and aviation industries has created a strong demand for sustainable fuel alternatives. Renewable biofuels offer a viable solution, providing both environmental benefits and economic opportunities. One promising approach is the use of Hydroprocessed Esters and Fatty Acids (HEFA), a refining process capable of producing renewable diesel, commonly known as Hydrotreated Vegetable Oil (HVO), Sustainable Aviation Fuel (SAF), and a bio-based version of Rocket Propellant-1 (bio-RP-1) for space applications. This study presents a roadmap for establishing a biofuel business that generates early revenue while advancing technical feasibility and regulatory approval, focusing on Mexico and Latin America as emerging biofuel hubs. The strategy unfolds over 36 months in four phases. The first stage prioritizes immediate revenue generation through HVO sales to transportation fleets and government agencies while offering consultancy and oil pre-treatment services to industries producing waste oils. Simultaneously, research refines HEFA processing techniques, optimizes feedstock sources—including used cooking oil (UCO), animal fats, and agro-industrial byproducts—and develops a commercially viable business model. The second phase shifts toward SAF production, ensuring compliance with the American Society for Testing and Materials (ASTM) D7566 certification and initiating test programs with airlines. Further expansion includes refining bio-RP-1, adapting HEFA-derived kerosene for high-performance rocket propulsion. This phase emphasizes advanced distillation techniques, aromatic content regulation, and collaborations with aerospace startups for preliminary engine tests. The final stage focuses on scaling biofuel production, obtaining regulatory certifications, and securing longterm agreements with airlines, space agencies, and private launch providers. Revenue streams include HVO sales, SAF exports leveraging sustainability incentives, and bio-RP-1 integration in the clean space propulsion market. This research highlights the financial feasibility of biofuels by aligning commercialization efforts with technological milestones. Immediate income from HVO ensures financial stability while SAF certification progresses, facilitating a smoother transition to full-scale commercialization. The integration of bio-RP-1 into the business model distinguishes this approach, addressing the demand for environmentally friendly fuels in orbital and suborbital launch systems. Ultimately, this roadmap demonstrates that biofuels can establish a profitable and sustainable energy industry, positioning Mexico and Latin America as key contributors to clean aviation and spaceflight. Future research will refine process efficiency, expand viable feedstocks, and secure investment to accelerate the transition from aviation fuels to space-grade biopropellants, ensuring long-term environmental and economic resilience. ©The authors © IAF 2006-2026 all rights reserved.
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    Pro-Game: Single-machine sequencing simulator
    (IEEE, 2025)
    Palma-Mendoza, Jaime Alberto
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    Arana-Solares, Ivan A.
    This work presents a game-based learning simulator that simulates a single-machine sequencing problem. The simulator provides a dynamic and interactive platform for industrial engineering students to apply disciplinary concepts, such as operations research, production planning, and optimization techniques, in the pursuit of finding optimal sequencing solutions. The implementation of this simulator in an industrial engineering course demonstrated its efficacy as an engaging and relevant pedagogical tool. Feedback collected from students revealed that the activity not only increased their interest and motivation but also significantly deepened their understanding of the complexities involved in sequencing problems. This study concludes that the use of such simulators in the classroom can dramatically enhance the learning experience by making abstract concepts more tangible and by providing students with a hands-on approach to mastering complex topics. The findings suggest that incorporating simulation-based activities into the curriculum is a valuable strategy for enhancing student outcomes in industrial engineering education. ©The authors ©IEE.
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    Preface : Proceedings of the 21st International Symposium on Medical Information Processing and Analysis (SIPAIM)
    (IEEE, 2025)
    Rueda, Andrea
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    Romero, Eduardo
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    Revelo, Javier
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    Guevara, Pamela
    This year’s meeting included keynote lectures by five recognized experts who addressed advances in neuroimaging and computational methods for medical applications. The keynote talks covered topics including PET imaging in Alzheimer’s disease and its role in understanding disease progression and supporting the development of new therapies; large-scale global collaboration through the integration of neuroimaging data from around the world; implicit neural models for representing medical images, spanning reconstruction to shape analysis; and a data-driven approach to the management of craniosynostosis. In addition, the conference hosted special clinical sessions in ophthalmology, pathology, and gastroenterology, focusing on the application of artificial intelligence to support clinical decision-making in these domains. The editors would like to thank the authors, reviewers and committee members, without whom the publication of the present volume would not have been possible. Likewise, we would like to thank IEEE for publishing our proceedings in IEEE Xplore. Last but not least, we are grateful to all our sponsors for both financial and logistical contributions: Universidad de Nariño, Children’s National Hospital, the SIPAIM Society, Pontificia Universidad Javeriana, Galileo University, Voxel Healthcare, and for the technical co-sponsorship by the IEEE Engineering in Biology and Medicine Society, IEE Signal Processing and the endorsement by the Medical Image Computing and Computer Assisted Intervention (MICCAI) society. ©The authors ©IEE.
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    A Deep-Learning and Bio-inspired Vision Model-Based Approach for Automatic Coronary Arteries Segmentation
    (IEEE, 2025)
    López-Figueroa, Alberto
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    Gomez-Coronel, Sandra L.
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    Renza, Diego
    The accurate segmentation of coronary arteries from X-ray angiograms is critical for the diagnosis and treatment of cardiovascular diseases. Yet, it remains a challenging task due to low image contrast and complex vessel structures. This paper introduces a novel hybrid methodology that combines a bio-inspired vision model, the Steered Hermite Transform (SHT), with a deep learning architecture for robust vessel segmentation. We leverage the SHT to decompose each angiogram into a rich, multi-resolution set of 15 feature maps that capture local image structures at different scales and orientations. These Hermite coefficients, along with the original image, form a 16-channel input tensor used to train a U-Net. This approach enables the network to learn from an enhanced feature space that explicitly represents vessel-like patterns. Evaluated on a public dataset of 134 coronary angiograms, our model demonstrates outstanding performance, achieving an Area Under the Curve (AUC) of 0.9872. The results confirm that enriching the input of a deep neural network with SHT coefficients significantly improves its ability to identify and segment complex vascular networks accurately. ©The authors ©IEEE.
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    Image Facial Expression Recognition based on Active Muscles and their Notable Triangle Points
    (IEEE, 2025)
    Aguilera-Hernández, Edgar I.
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    Cruz-Aceves, Ivan
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    Hernández-Aguirre, Arturo
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    During emotion experience originated in psychological changes, the effect in face muscles results in a characteristic set of contractions associated to specific emotions. This paper propose an intuitive representation of these interactions with the objective of facial expression recognition through geometric features. In medical research, it has generated insights regarding emotional state, cognitive function, and pain level during clinical procedures leading to an effective patient treatment, assisting diagnosis and monitoring disease progression mainly in neurological conditions. Starting from a facial muscle modeling using triangles, it utilizes an initial 68 landmarks fitting algorithm, and later the computation of triangle notable points to work as anchors of specific muscles. Secondly, the optimization process through stochastic techniques is applied to set the point type combination so that the F1-Score is maximized. Experimental results were performed with conventional classifiers and no fine tuning, accomplishing an accuracy, precision, recall and F1-score of 0.88 for KDEF dataset, while 0.84, 0.86, 0.84, and 0.84 respectively for the JAFFE dataset, proving to be a reliable technique in the expression recognition problem. ©The authors ©IEEE.
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    A Literature Review on Real-Time Crime Detection Using Deep Learning and Edge Computing
    (IEEE, 2025-10-21)
    Silva, Carlos Julio Fierro
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    Varela-Aldás, José
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