@inproceedings{ALF:WSCAD:23,
title = {Análise da Execução de Algoritmos de Aprendizado de Máquina em Dispositivos Embarcados},
author = {Lucas Alf and Renato Barreto Hoffmann and Caetano Müller and Dalvan Griebler},
url = {https://doi.org/},
year = {2023},
date = {2023-10-01},
booktitle = {Anais do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD)},
pages = {1-12},
publisher = {SBC},
address = {Porto Alegre, Brasil},
keywords = {Deep learning, IoT},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{MENCAGLI:PDP:22,
title = {Towards Parallel Data Stream Processing on System-on-Chip CPU+GPU Devices},
author = {Gabriele Mencagli and Dalvan Griebler and Marco Danelutto},
url = {https://doi.org/10.1109/PDP55904.2022.00014},
doi = {10.1109/PDP55904.2022.00014},
year = {2022},
date = {2022-04-01},
booktitle = {30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
pages = {34-38},
publisher = {IEEE},
address = {Valladolid, Spain},
series = {PDP'22},
abstract = {Data Stream Processing is a pervasive computing paradigm with a wide spectrum of applications. Traditional streaming systems exploit the processing capabilities provided by homogeneous Clusters and Clouds. Due to the transition to streaming systems suitable for IoT/Edge environments, there has been the urgent need of new streaming frameworks and tools tailored for embedded platforms, often available as System-onChips composed of a small multicore CPU and an integrated onchip GPU. Exploiting this hybrid hardware requires special care in the runtime system design. In this paper, we discuss the support provided by the WindFlow library, showing its design principles and its effectiveness on the NVIDIA Jetson Nano board.},
keywords = {GPGPU, IoT, Stream processing},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{GOMES:IJCSIT:22,
title = {Steam++: An Extensible End-to-end Framework for Developing IoT Data Processing Applications in the Fog},
author = {Márcio Miguel Gomes and Rodrigo Rosa da Righi and Cristiano André da Costa and Dalvan Griebler},
url = {http://dx.doi.org/10.5121/ijcsit.2022.14103},
doi = {10.5121/ijcsit.2022.14103},
year = {2022},
date = {2022-02-01},
journal = {International Journal of Computer Science & Information Technology},
volume = {14},
number = {1},
pages = {31-51},
publisher = {AIRCC},
abstract = {IoT applications usually rely on cloud computing services to perform data analysis such as filtering, aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT needs to deal with unique constraints. Besides the hostile environment such as vibration and electricmagnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions. In this context, we present STEAM++, a lightweight and extensible framework for real-time data stream processing and decision-making in the network edge, targeting hardware-limited devices, besides proposing a micro-benchmark methodology for assessing embedded IoT applications. In real-case experiments in a semiconductor industry, we processed an entire data flow, from values sensing, processing and analysing data, detecting relevant events, and finally, publishing results to a dashboard. On average, the application consumed less than 500kb RAM and 1.0% of CPU usage, processing up to 239 data packets per second and reducing the output data size to 14% of the input raw data size when notifying events.},
keywords = {Cloud computing, IoT, Stream processing},
pubstate = {published},
tppubtype = {article}
}

@article{GOMES:CEE:21,
title = {Simplifying IoT data stream enrichment and analytics in the edge},
author = {Márcio Miguel Gomes and Rodrigo Rosa da Righi and Cristiano André da Costa and Dalvan Griebler},
url = {https://doi.org/10.1016/j.compeleceng.2021.107110},
doi = {10.1016/j.compeleceng.2021.107110},
year = {2021},
date = {2021-06-01},
journal = {Computers & Electrical Engineering},
volume = {92},
pages = {107110},
publisher = {Elsevier},
abstract = {Edge devices are usually limited in resources. They often send data to the cloud, where techniques such as filtering, aggregation, classification, pattern detection, and prediction are performed. This process results in critical issues such as data loss, high response time, and overhead. On the other hand, processing data in the edge is not a simple task due to devices’ heterogeneity, resource limitations, a variety of programming languages and standards. In this context, this work proposes STEAM, a framework for developing data stream processing applications in the edge targeting hardware-limited devices. As the main contribution, STEAM enables the development of applications for different platforms, with standardized functions and class structures that use consolidated IoT data formats and communication protocols. Moreover, the experiments revealed the viability of stream processing in the edge resulting in the reduction of response time without compromising the quality of results.},
keywords = {IoT, Stream processing},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{larcc:DL_Classificacao:SAPS:20,
title = {Algoritmo de Deep Learning para Classificação de Áreas de Lavaoura com VANTs},
author = {Anthony Vanzan and Gabriel Rustick Fim and Greice Aline Welter and Matheus César Sausen and Dalvan Griebler},
url = {https://larcc.setrem.com.br/wp-content/uploads/2020/11/SAPS_2020_Anthony.pdf},
year = {2020},
date = {2020-10-01},
booktitle = {22 Salão de Pesquisa Setrem (SAPS)},
pages = {5},
publisher = {Sociedade Educacional Três de Maio},
address = {Três de Maio, RS, Brazil},
abstract = {O Brasil é um dos maiores produtores e exportadores de milho do globo. A criação e implementação de novas tecnologias partindo da inteligência artificial podem proporcionar melhorias na produção do grão e, consequentemente, uma melhoria econômica no país. Nota-se também que as tecnologias de inteligência artificial estão conquistando espaço no mercado e auxiliando diversas áreas, tendo um avanço considerável de desempenho e produtividade. Nesse sentido, o presente trabalho visa apresentar a implementação e resultados de um modelo de rede neural utilizando a arquitetura LeNet5 para realizar a classificação de imagens, de cultivo do milho. Estas áreas classificadas servirão futuramente para o cálculo de estimativa de produção.},
keywords = {Agriculture, Deep learning, IoT},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{larcc:DL_preprocessamento:SAPS:20,
title = {Desenvolvimento de Um Algoritmo de Pré-processamento Automático de Imagens Retiradas com VANTs},
author = {Gabriel Rustick Fim and Anthony Vanzan and Greice Aline Welter and Matheus César Sausen and Dalvan Griebler},
url = {https://larcc.setrem.com.br/wp-content/uploads/2020/11/SAPS_2020_Gabriel.pdf},
year = {2020},
date = {2020-10-01},
booktitle = {22 Salão de Pesquisa Setrem (SAPS)},
pages = {5},
publisher = {Sociedade Educacional Três de Maio},
address = {Três de Maio, RS, Brazil},
abstract = {Um dos passos mais importantes para a realização do treinamento de uma rede neural é a criação de um dataset que possua imagens que satisfaçam os requisitos de entrada da rede. O presente trabalho tem como objetivo o desenvolvimento de um algoritmo na linguagem de programação Python para realizar o pré-processamento de uma série de imagens retiradas com veículos aéreos não tripulados. Para realizar este pré-processamento duas bibliotecas foram utilizadas, o NumPy e o OpenCV, ambas trabalhando em conjunto para realizar modificações nas imagens, tais como cortá-las em imagens menores e realizar conversões de cores de forma automática. Ao final, foi possível testar no escopo do projeto AGROCOMPUTAÇÃO (uma parceria entre SETREM e TECNICON Sistemas) e concluir que o algoritmo criado foi eficiente. Ele permitiu o pré-processamento de uma grande quantidade de imagens de forma automática.},
keywords = {Agriculture, IoT},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{larcc:DL_Metodologia:SAPS:20,
title = {Metodologia para Captura de Imagens com VANT para a Cultura do Milho},
author = {Matheus César Sausen and Greice Aline Welter and Anthony Vanzan and Gabriel Rustick Fim and Dalvan Griebler},
url = {https://larcc.setrem.com.br/wp-content/uploads/2020/11/SAPS_2020_Matheus.pdf},
year = {2020},
date = {2020-10-01},
booktitle = {22 Salão de Pesquisa Setrem (SAPS)},
pages = {5},
publisher = {Sociedade Educacional Três de Maio},
address = {Três de Maio, RS, Brazil},
abstract = {O setor agropecuário ao longo das décadas vem passando por transformações, que por muitas vezes tem relação direta com o avanço tecnológico. Os Veículos Aéreos não Tripulados (VANTs) vêm sendo utilizados como forma auxiliar para pulverização, detecção de pragas e realizar estimativas sobre diferentes culturas. Grande parte desse processo envolve a extração de imagens que são analisadas por programas especializados. Porém, as imagens não podem ser capturadas na lavoura e da cultura de qualquer forma, pois compromete a eficiência nas análises. Por isso, o objetivo é definir uma metodologia de captura de imagens para obter métricas na cultura do milho (Zea mays). Essa metodologia com representação taxonômica foi elaborada baseando-se em artigos publicados em eventos e revistas internacionais e nacionais. Assim, foi possível oferecer um guia para que agrônomos saibam como capturar as imagens com VANTS.},
keywords = {Agriculture, IoT},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{FISHER:Elasticity-Hospital:SENSORS:19,
title = {Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals},
author = {Gabriel Souto Fischer and Rodrigo Rosa da Righi and Cristiano André da Costa and Guilherme Galante and Dalvan Griebler},
url = {https://doi.org/10.3390/s19173800},
doi = {10.3390/s19173800},
year = {2019},
date = {2019-09-01},
journal = {Sensors},
volume = {19},
number = {17},
pages = {3800},
publisher = {MDPI},
abstract = {Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.},
keywords = {IoT},
pubstate = {published},
tppubtype = {article}
}

@misc{larcc:djalma:TCC:19,
title = {Modelo Conceitual de Monitoramento e Gerenciamento para Smart Datacenters},
author = {Djalma Rafael Teixeira},
url = {https://larcc.setrem.com.br/wp-content/uploads/2020/08/TCC_SETREM__Djalma_.pdf},
year = {2019},
date = {2019-06-01},
address = {Três de Maio, RS, Brazil},
school = {Sociedade Educacional Três de Maio (SETREM)},
abstract = {The demand for smart datacenters has been increasing considerably due to the complexity of managing the current infrastructures, which is due to the increasing need for computing resources within organizations. The present work aims to propose a model of intelligent management and monitoring for datacenters and to test its effectiveness through the partial implementation of the same. A complete survey of the physical infrastructure, logical network and services in the LARCC IT infrastructure was carried out. By means of the results obtained, the classification of the datacenter of the laboratory was made according to the requirements of ANSI TIA 942. Through the analysis and research carried out by related works, a conceptual model for monitoring and management was elaborated intelligent for computer infrastructures, which was divided into five major areas: air conditioning, energy, computing, network and security. We also defined the events that affect these elements, how to monitor them and how to manage them based on the autonomous computing approach. With this, the models were implemented regarding temperature and energy, which uses reactive actions to address and contain consequences of overheating and energy loss. To implement this flow of actions was used the tool Zabbix, and its function of executing remote commands for practical application of the model. It is concluded that the proposed conceptual model is more effective in the containment of critical events that may affect the infrastructure, these results were tested and validated in practice for the elements of temperature and energy.},
howpublished = {Undergraduate Thesis},
keywords = {Cloud computing, IoT},
pubstate = {published},
tppubtype = {misc}
}