Edward David Moreno

The exponential growth of artificial intelligence (AI) applications in edge devices, embedded systems, and data centers has intensified the demand for efficient and scalable processor architectures. This paper provides a comprehensive comparative analysis of RISC-V and ARM architectures in AI applications, examining their architectural features, performance characteristics, energy efficiency, and ecosystem maturity. We analyze both architectures across multiple AI domains, including edge AI, datacenter inference, computer vision, natural language processing, and embedded systems. Our investigation encompasses recent developments from 2020 to 2025, including RISC-V Vector Extensions (RVV 1.0), ARMScalable Vector Extensions (SVE/SVE2), and commercial implementations from leading vendors. Performance analysis reveals that ARM maintains a 15× advantage in raw inference speed for large models, while consuming higher power. In contrast, RISC-V demonstrates 3-5× superior performance-per-watt for inference workloads. Both architectures offer distinct advantages: ARM provides a mature ecosystem and proven scalability from mobile to data center, while RISC-V offers customization flexibility and eliminates licensing costs. Through systematic evaluation of 40+ research papers and real-world implementations, we identify optimal deployment scenarios for each architecture and discuss future directions for AI-optimized processor designs.

Singing voice synthesis involves the artificial generation of a song given its lyrics and musical notes through computational methods. This paper describes the development and evaluation process of PATRICIA, a proof-of-concept prototype of a system that performs real-time singing voice synthesis (SVS) for the Brazilian Portuguese language. It is the first singing synthesizer specifically designed for this idiom. A literature review was conducted to study the latest developments in real-time singing synthesis and to give directions for PATRICIA design and implementation. The implemented architecture is described, and, for the validation process, a series of video recordings of the system working was made as a musical demonstration. This demonstration was subjected to an evaluation by musical educators. A performance analysis with two CPU usage indicators in two different devices was also conducted. The results of the validation are discussed, and future enhancements are pointed out in order to overcome the system’s current limitations.

Este estudo tem como objetivo revisar na literatura relacionada a sistemas de informação destinados ao acompanhamento de pacientes pediátricos em procedimentos cirúrgicos, identificando as principais inovações tecnológicas, limitações e oportunidades de desenvolvimento. Foram utilizadas bases como Google Patents, Espacenet, INPI, SciELO e PubMed. Os critérios de inclusão consideraram tecnologias aplicáveis ao contexto pediátrico e procedimentos cirúrgicos, excluindo aquelas voltadas exclusivamente para adultos ou sem detalhamento técnico. A análise qualitativa focou em aplicações clínicas, grau de inovação e integração com sistemas existentes. Os resultados destacam ferramentas voltadas para a gestão hospitalar, monitoramento clínico e predição de riscos, com aplicações que incluem planejamento cirúrgico, monitoramento pós-operatório e suporte à decisão clínica. Tecnologias baseadas em inteligência artificial e aprendizado de máquina, como sistemas preditivos de complicações e plataformas para regulação hospitalar, demonstraram impacto significativo na eficiência do atendimento. No entanto, desafios como a fragmentação dos sistemas, a falta de interoperabilidade e a proteção de dados sensíveis ainda limitam sua aplicação prática. Além disso, a carência de soluções adaptadas às especificidades psicossociais dos pacientes pediátricos e de seus cuidadores aponta a necessidade de abordagens mais humanizadas. Este estudo conclui que, apesar dos avanços significativos, há lacunas críticas a serem abordadas, incluindo integração de sistemas, treinamento de profissionais e personalização do cuidado. O estudo contribui para orientar futuras pesquisas e políticas públicas, promovendo um cuidado pediátrico mais seguro, eficiente e humanizado.

Este estudo explora o desenvolvimento e a aplicação de sistemas de informação em saúde e segurança ocupacional (SSO) no Brasil, destacando sua relevância em contextos de serviços de saúde. A pesquisa abrangeu um levantamento bibliográfico e análise de patentes em bases renomadas para identificar lacunas e oportunidades tecnológicas. Foram analisados 15 artigos e 9 patentes, categorizados em legislação, inovação e gestão de dados. Os achados evidenciam a importância de sistemas integrados e adaptáveis que incorporem tecnologias emergentes, como big data e inteligência artificial, enquanto superam desafios como baixa interoperabilidade e infraestrutura limitada em pequenas empresas. Conclui-se que a implementação de soluções acessíveis e escaláveis é essencial para um ambiente de trabalho seguro e eficiente.

COVID-19 is highly transmissible and pathogenic, representing a potential threat to health conditions in correctional environments worldwide. This threat could worsen in prison systems in developing countries due to high crowding levels, lack of individual hygiene materials, inadequate primary sanitary conditions, and difficulties in using health services. Attempts to provide a landscape of academic research findings on COVID-19 data usage from prison systems in developing countries have been limited. This scenario motivated us to carry out this systematic mapping study (SMS) to provide a comprehensive overview of the impacts that may prevent the effective use of COVID-19 data, critical issues, and gaps in the current academic research. Following the steps of a systematic mapping, we examined 79 studies returned from a search string and eight studies returned from the use of the snowballing technique and selected 37 from related journals and conferences published between 2020 and 2021. The results point to possible problems in the use of COVID-19, preventing its effective use in information systems.

Estratégias de Inovação: Uma Análise em Startups de Tecnologia da Informação

Estrategias, tipos y grado de innovación: estudio en startups de tecnología de la información en el Noreste de Brasil

In this paper we present and use the ITW-Index – this name was given in reference to the term Technology Watch, which is a technique of observation, analysis and identification of possible opportunities and threats, linked to the method of technological forecasting called Monitoring and Intelligence Systems. The purpose of TW-Index is to provide the user the capacity of monitoring and identifying whether certain terms which are used for defining a technology or research are currently been searched and used in the Internet. For this, we used as a basis the Google Trends. So, in this paper we present three contributions: (i) the concept of the TW-index, (ii) options for evaluating and getting a value for the TW-index applied in two different examples, and (iii) we measure and explain the means of TW-index when it is applied in two ideas for creating new business into the IdeaLAB program at University of Minho, Portugal.

In literature about the problem of power quality, the authors of many papers use simulated data, since it is hard to find benchmarks with real disturbances. For this reason, some authors work with disturbance emulations using electrical devices (e.g. motors, solenoids), that is more close to real anomalies. Moreover, to make a fair comparison with different approaches, for example in the classification process, could be interesting to have a benchmark which could be used for all researchers, however, this was not available yet. In this context, this paper has as a contribution: the developing of a benchmark of simulated electrical disturbances, providing it for the community. It has been named as SED-Bench, and it contains nine (9) isolated disturbances (interruption, sag, swell, oscillation, impulse, flicker, notching, dc-level, and harmonics), and eight (8) doubled disturbances, four (4) tripled disturbances and the pure sine (without disturbance). Every signal is available in different datasets with fundamental frequency of 50Hz or 60Hz and SNR values of 20dB, 30dB, 50dB, 60dB, 70dB or without noise. Finally, the developed benchmark is evaluated using an in vitro experiment that uses the Random Forest algorithm to classify part of the data available on the proposed benchmark. In this scenario, we achieve the maximum of 98.89% of accuracy for disturbances with 70dB of SNR.