Publications

@article{Souza2025,

title = {Optimizing power grids: A valley-filling heuristic for energy-efficient electric vehicle charging},

author = {Guilherme Gloriano de Souza and Ricardo Ribeiro dos Santos and Ruben Barros Godoy},

editor = {Barry Kweh},

doi = {10.1371/journal.pone.0316677},

issn = {1932-6203},

year  = {2025},

date = {2025-01-07},

journal = {PLoS ONE},

volume = {20},

number = {1},

publisher = {Public Library of Science (PLoS)},

abstract = {The expansion of electric vehicles (EVs) challenges electricity grids by increasing charging demand, thereby making Demand-Side Management (DSM) strategies essential to maintaining balance between supply and demand. Among these strategies, the Valley-Filling approach has emerged as a promising method to optimize renewable energy utilization and alleviate grid stress. This study introduces a novel heuristic, Load Conservation Valley-Filling (LCVF), which builds on the Classical and Optimistic Valley-Filling approaches by incorporating dynamic load conservation principles, enabling better alignment of EV charging with grid capacity. We conducted a comprehensive analysis of the heuristic across five EV charging scenarios. In both the Original and Flexible scenarios, LCVF reduced energy demand by up to 10.65%, demonstrating its adaptability and effectiveness. Notably, in the 24-hour Availability scenario, LCVF achieved a reduction of over 20% in energy demand compared to CVF. These findings indicate that LCVF could play a crucial role in enhancing real-world EV charging infrastructure, boosting energy efficiency and grid stability. By integrating DSM strategies like LCVF, energy grids can better accommodate renewable energy sources, promoting more sustainable operations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Costa2025,

title = {A neighborhood search-based heuristic for the dynamic vehicle routing problem},

author = {Wilton Gustavo Gomes da Costa and Ricardo Santos and Willy Alves de Oliveira Soler and Bianca De Almeida Dantas},

doi = {10.18265/2447-9187a2024id8297},

issn = {2447-9187},

year  = {2025},

date = {2025-01-15},

journal = {Rev. Principia},

volume = {62},

publisher = {Instituto Federal de Educacao, Ciencia e Tecnologia da Paraiba},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {MontadoDB: A Comprehensive Dataset of Pasture Parameters in the Southern Region of Portugal},

author = {Samuel Rodrigues and Guilherme Defalque and João Serrano and Ricardo Santos},

url = {https://www.sciencedirect.com/science/article/pii/S2352340925007516},

doi = {https://doi.org/10.1016/j.dib.2025.112029},

issn = {2352-3409},

year  = {2025},

date = {2025-09-05},

urldate = {2025-09-05},

journal = {Data in Brief},

number = {112029},

abstract = { This article presents MontadoDB, a dataset comprising samples of pasture quality parameters, weather data, and satellite images from a set of paddocks in the Alentejo region (southern Portugal), the Beira Interior region (Portugal), and Extremadura (Spain). These regions are part of the Montado system, an agro-silvo-pastoral system featuring low-density trees, pastures, and livestock grazing. The pasture parameters were collected along three vegetative cycles from 2018 to 2021 in 8 different paddocks. After the collecting procedure, pasture samples were submitted to laboratory analysis to determine the reference values of pasture moisture content (PMC, in %), crude protein (CP, in g/100g), and neutral detergent fiber (NDF, in g/100g), using standard analytical methods. For each pasture sample collected, GPS coordinates were recorded. Using these coordinates, the Google Earth Engine (GEE) platform was utilized to collect satellite multispectral data and weather information. The MontaDB dataset is publicly available, enabling research focused on the analysis and design of machine learning models for predicting pasture parameters. It can also work as a reference dataset for further experiments based on pasture samples, thereby enhancing research on advanced algorithms that rely on large-scale datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Defalque2024,

title = {Machine learning models for dry matter and biomass estimates on cattle grazing systems},

author = {Guilherme Defalque and Ricardo Santos and Davi Bungenstab and Diego Echeverria and Alexandre Dias and Cristiane Defalque},

doi = {10.1016/j.compag.2023.108520},

issn = {0168-1699},

year  = {2024},

date = {2024-01-00},

journal = {Computers and Electronics in Agriculture},

volume = {216},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Defalque2024b,

title = {A dataset for pasture parameter estimation based on satellite remote sensing and weather variables},

author = {Guilherme Defalque and Pedro Arfux and Marcio Pache and Gumercindo Franco and Ricardo Santos},

doi = {10.1016/j.dib.2024.110206},

issn = {2352-3409},

year  = {2024},

date = {2024-04-00},

journal = {Data in Brief},

volume = {53},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Defalque2024d,

title = {A dataset on Nelore Cattle supplementation in the midwest region of brazil for integrated analysis of supplementation, satellite remote sensing, and weather variables},

author = {Guilherme Defalque and Pedro Arfux and Marcio Pache and Alexandre Dias and Ricardo Santos},

doi = {10.1016/j.dib.2024.110520},

issn = {2352-3409},

year  = {2024},

date = {2024-06-00},

journal = {Data in Brief},

volume = {54},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ferraz2024,

title = {DuMato: An efficient warp-centric subgraph enumeration system for GPU},

author = {Samuel Ferraz and Vinicius Dias and Carlos H.C. Teixeira and Srinivasan Parthasarathy and George Teodoro and Wagner Meira},

doi = {10.1016/j.jpdc.2024.104903},

issn = {0743-7315},

year  = {2024},

date = {2024-09-00},

journal = {Journal of Parallel and Distributed Computing},

volume = {191},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Quinelato2021,

title = {Projeto de um Estimador de Perdas por Sujidade em Módulos Fotovoltaicos Utilizando Modelo Preditivo},

author = {David D. D. Quinelato and Edson A. Batista and Ricardo R. Dos Santos and Eduardo Allatta and Moacyr A. G. Brito},

doi = {10.18618/rep.2021.1.0046},

issn = {1984-557X},

year  = {2024},

date = {2024-06-29},

journal = {Eletrônica de Potência},

volume = {26},

number = {1},

pages = {84--93},

publisher = {Associacao Brasileira de Eletronica de Potencia SOBRAEP},

abstract = {Um problema crucial da geração de energia solar fotovoltaica (FV) é a susceptibilidade dos módulos às sujidades que culminam em perdas na geração de energia ou mesmo em danos físicos. Nesse contexto, uma solução é apresentada neste artigo, a qual propõe o desenvolvimento de tecnologia, que baseada na modelagem matemática das células fotovoltaicas e nos conceitos de modelo preditivo (MPC), possibilite a estimação de perdas por sujidades em sistemas de geração FV. Para realizar a estimação de perdas, foi desenvolvido um dispositivo que recebe informações da unidade fotovoltaica, as processa com base em MPC e, envia para um servidor os dados relativos às perdas encontradas na unidade de geração a fim de auxiliar em tomadas de decisão. Os resultados demonstram reais aplicabilidades do sistema para estimar as perdas por sujidade ou por incompatibilidade na planta fotovoltaica.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Defalque2023b,

title = {A review on beef cattle supplementation technologies},

author = {Guilherme Defalque and Ricardo Santos and Marcio Pache and Cristiane Defalque},

doi = {10.1016/j.inpa.2023.10.003},

issn = {2214-3173},

year  = {2023},

date = {2023-11-00},

journal = {Information Processing in Agriculture},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Catelan2022,

title = {Evaluation and characterization of approximate arithmetic circuits},

author = {Daniela Catelan and Ricardo Santos and Liana Duenha},

doi = {10.1002/cpe.6865},

issn = {1532-0634},

year  = {2023},

date = {2023-08-00},

journal = {Concurrency and Computation},

volume = {35},

number = {17},

publisher = {Wiley},

abstract = {SummaryWith the end of Dennard's scale, processor designers have been looking for new technical alternatives so that approximate computing (AC) has managed to attract the attention of researchers by offering techniques ranging from the application level to the circuit level. When applying approximate circuits into hardware design, the program user may speed up the application. At the same time, a designer may save area and power dissipation at the cost of less accuracy on the results of operations. This article discusses the compromise between controlled accuracy versus physical efficiency by evaluating a set of arithmetic circuits designs. The article also presents new mixed organization approximate designs. To evaluate the approximate design benefits, we performed a comprehensive set of experiments from tailor‐made approximate arithmetic circuits on field‐programmable gate array and application specific integrated circuits technologies. Our results reveal that our proposed mixed approximate designs have promising results on error rates, area usage, and power dissipation compared to the related work. Another interesting result is that one should care about the prototyping platform where the approximate hardware will be synthesized. Tailor‐made approximate cells may take advantage on intrinsic design details than standard cells or even general prototyping platforms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sonohata2022,

title = {Performance predictors for graphics processing units applied to dark‐silicon‐aware design space exploration},

author = {Rhayssa Sonohata and Danillo Christi A. Arigoni and Eraldo Rezende Fernandes and Ricardo Ribeiro dos Santos and Liana Dessandre Duenha},

doi = {10.1002/cpe.6877},

issn = {1532-0634},

year  = {2023},

date = {2023-08-00},

journal = {Concurrency and Computation},

volume = {35},

number = {17},

publisher = {Wiley},

abstract = {AbstractThe limitations on the scalability of computer systems imposed by the dark‐silicon effects are so severe that they support the extensive use of heterogeneity such as the GP‐GPU for general purpose processing. Performance simulators of GP‐GPU heterogeneous systems aim to provide performance accuracy at the cost of execution time. In this work, we handle time‐consuming simulations of design space exploration systems based on GPUs. We have developed performance predictors based on machine learning (ML) algorithms and evaluated them in accuracy and throughput (number of predictions per second). We measure model accuracy through the mean absolute percentage error (MAPE) and the model efficiency through a throughput metric (millions of predictions per second). Our experiments revealed that decision trees predictors are the most promising regarding accuracy and efficiency. We applied the best predictors into the MultiExplorer, a dark silicon‐aware design space exploration tool that allows designers to explore the architecture and microarchitecture of multicore/manycore system design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paranaiba2022,

title = {Design Automation for Emerging Technologies},

author = {Omar Paranaiba and Poliana Oliveira and Renan Marks and Gabriel Novy and Maria Vieira and Laysson Oliveira Luz and Pedro Arthur Silva and Ricardo Ferreira and Jhonattan Ramirez and José Augusto Nacif and Guilherme Caporali},

doi = {10.29292/jics.v17i3.652},

issn = {1872-0234},

year  = {2022},

date = {2022-12-31},

journal = {JICS},

volume = {17},

number = {3},

pages = {1--11},

publisher = {Journal of Integrated Circuits and Systems},

abstract = {After the continuous development of CMOS technology driven by transistor miniaturization and Moore’s law, the scientific community is witnessing the exploration of emerging paradigms to find new ways to develop computational systems. This paper presents critical concepts for understanding some of these new nanocomputing technologies, specifically field-coupled, quantum-dot cellular automata, nanomagnetic logic, silicon dangling bounds, photonic crystal logic, and DNA computing. Next, it shows emerging design automation tools for each of these areas and how they can be applied to support the development of new computing systems. The level of maturity and production speed of solutions achieved by conventional silicon technology thanks to very efficient electronic design automation (EDA) is remarkable. However, here we are dealing with technologies still in their infancy. Therefore, improvements in design automation tools are undoubtedly a way to accelerate the growth of new substrate alternatives and modern applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Souza2022,

title = {A Log-Logistic Predictor for Power Generation in Photovoltaic Systems},

author = {Guilherme Souza and Ricardo Santos and Erlandson Saraiva},

doi = {10.3390/en15165973},

issn = {1996-1073},

year  = {2022},

date = {2022-08-00},

journal = {Energies},

volume = {15},

number = {16},

publisher = {MDPI AG},

abstract = {Photovoltaic (PV) systems are dependent on solar irradiation and environmental temperature to achieve their best performance. One of the challenges in the photovoltaic industry is performing maintenance as soon as a system is not working at its full generation capacity. The lack of a proper maintenance schedule affects power generation performance and can also decrease the lifetime of photovoltaic modules. Regarding the impact of environmental variables on the performance of PV systems, research has shown that soiling is the third most common reason for power loss in photovoltaic power plants, after solar irradiance and environmental temperature. This paper proposes a new statistical predictor for forecasting PV power generation by measuring environmental variables and the estimated mass particles (soiling) on the PV system. Our proposal was based on the fit of a nonlinear mixed-effects model, according to a log-logistic function. Two advantages of this approach are that it assumes a nonlinear relationship between the generated power and the environmental conditions (solar irradiance and the presence of suspended particulates) and that random errors may be correlated since the power generation measurements are recorded longitudinally. We evaluated the model using a dataset comprising environmental variables and power samples that were collected from October 2019 to April 2020 in a PV power plant in mid-west Brazil. The fitted model presented a maximum mean squared error (MSE) of 0.0032 and a linear coefficient correlation between the predicted and observed values of 0.9997. The estimated average daily loss due to soiling was 1.4%.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Oliveira2021,

title = {Evaluating analog arithmetic circuit for approximate computing with DNA strand displacement},

author = {Poliana A. C. Oliveira and João V. C. Teixeira and Renan A. Marks and Marcos V. Guterres and Omar P. Vilela Neto},

doi = {10.1007/s10470-021-01828-9},

issn = {1573-1979},

year  = {2021},

date = {2021-09-00},

journal = {Analog Integr Circ Sig Process},

volume = {108},

number = {3},

pages = {485--493},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marks2021,

title = {Design and Test of Digital Logic DNA Systems},

author = {Renan A. Marks and Daniel K. S. Vieira and Marcos V. Guterres and Poliana A. C. Oliveira and Maria C. O. Fonte Boa and Omar P. Vilela Neto},

doi = {10.1109/mdat.2021.3069369},

issn = {2168-2364},

year  = {2021},

date = {2021-08-00},

journal = {IEEE Des. Test},

volume = {38},

number = {4},

pages = {94--101},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Santos2021,

title = {Dirt Loss Estimator for Photovoltaic Modules Using Model Predictive Control},

author = {Ricardo R. Santos and Edson A. Batista and Moacyr A. G. de Brito and David D. D. Quinelato},

doi = {10.3390/electronics10141738},

issn = {2079-9292},

year  = {2021},

date = {2021-07-00},

journal = {Electronics},

volume = {10},

number = {14},

publisher = {MDPI AG},

abstract = {The central problem tackled in this article is the susceptibility of the solar modules to dirt that culminates in losses in energy generation or even physical damage. In this context, a solution is presented to enable the estimates of dirt losses in photovoltaic generation units. The proposed solution is based on the mathematical modeling of the solar cells and predictive modeling concepts. A device was designed and developed to acquire data from the photovoltaic unit; process them based on a predictive model, and send loss estimates in the generation unit to a web server to help in decision-making support. The results demonstrated the real applicability of the system to estimate losses due to dirt or electrical mismatches in photovoltaic plants.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ribeiro2021,

title = {A Statistical Methodology to Estimate Soiling Losses on Photovoltaic Solar Plants},

author = {Kymberlim Ribeiro and Ricardo Santos and Erlandson Saraiva and Ram Rajagopal},

doi = {10.1115/1.4050948},

issn = {1528-8986},

year  = {2021},

date = {2021-12-01},

volume = {143},

number = {6},

publisher = {ASME International},

abstract = {Abstract

               One of the challenges in photovoltaic solar plants is the performance maintenance in the presence of adverse environmental conditions. Soiling on the solar panels is one of those challenges having a high decrease impact on the power generation. This work proposes a statistical methodology that estimates the energy losses due to soiling on photovoltaic solar plants. Using environmental and power generation data, the proposed methodology predicts the energy generation using a regression model; and then evaluates if the differences between the observed energy generation data and the predicted energy data are due to soiling. The experiments to validate the system are based on one-year dataset of environmental and power generation data from a solar plant located in the northeast region of Brazil. The results showed that the daily energy losses estimates ranged from 2.20% up to 12.31% in a period less than a month.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Santos2021b,

title = {An Integrated Approach between Computing and Mathematical Modelling for Cattle Welfare in Grazing Systems},

author = {R. M. O. Santos and E. F. Saraiva and R. R. Santos},

doi = {10.5540/tcam.2021.022.04.00629},

issn = {2676-0029},

year  = {2021},

date = {2021-10-26},

journal = {TCAM},

volume = {22},

number = {4},

pages = {629--643},

publisher = {Brazilian Society for Computational and Applied Mathematics (SBMAC)},

abstract = {In the last years, the agricultural systems based on Crop-Livestock-Forestry integrationhave emerged as a potential solution due to its capacity to maximize land use and reduces the effects of high temperatures on the animals. Within these systems, there exist an interest in technological solutions capable of monitor the animals in real-time. From this monitoring, one of the main interest is to know if an animal is in the sun or in the shade of a tree by using some environmental measures. However, as there is a possibility that the weather is cloudy, real-time monitoring also needs to identify this case. That is, the realtime monitoring also needs to differentiate the shade of a tree from a cloudy weather. The interest in this kind of monitoring is due to the fact that an animal that remains a long time under a shade of a tree provides substantial insights to indicate if this is in thermal stress. This information can be used in decision-making with the goal to reduce the impact of the thermal stress and consequently to provide welfare to the animal and reduces the financial losses. As a solution to identify if an animal is in the sun or in the shade of a tree or if the weather is cloudy, we developed an electronic device, used to capture values of environmental variables, which integrated with a mathematical model predicts the shade state (sun, shade or cloudy) where the animal can be found. We illustrate the performance of the proposed solution in a real data set.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vieira2020,

title = {DNAr: An R Package to Simulate and Analyze CRN and DSD Networks},

author = {Daniel K. S. Vieira and Marcos V. Guterres and Renan A. Marks and Poliana A. C. Oliveira and Maria C. O. Fonte Boa and Omar P. Vilela Neto},

doi = {10.1021/acssynbio.0c00364},

issn = {2161-5063},

year  = {2020},

date = {2020-12-18},

journal = {ACS Synth. Biol.},

volume = {9},

number = {12},

pages = {3416--3421},

publisher = {American Chemical Society (ACS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Santos2020,

title = {A Physiological-Monitoring Electronic Platform for Cattle Grazing Systems},

author = {Ricardo R. Santos and Fabiana V. Alves and Patrik O. Bressan and Ricardo E. Aguiar and Wellington O. Santos and Rafael A. Costa},

doi = {10.4018/ijaeis.2020070101},

issn = {1947-3206},

year  = {2020},

date = {2020-07-00},

volume = {11},

number = {3},

pages = {1--12},

publisher = {IGI Global},

abstract = {
In this work, we present a non-invasive electronic platform for physiological data acquisition on cattle grazing systems. The platform can be used for dairy and beef cattle to continuously monitor physiological variables such as skin temperature, heartbeats, and respiratory frequency. The set of sensors are coupled into a halter so that they are in touch with the animal's forehead. Users can monitor the data acquired by the electronic device using a mobile device (smartphone or tablet) and it visualizes important physiological parameters in the platform cloud system.
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Defalque2020,

title = {Ingestive Behaviour Activities Based on Bioacoustic Signals in Grazing Cattle},

author = {Guilherme Augusto Defalque and Ricardo Santos and Victor Leonardo Yoshimura and Fabiana Villa Alves},

doi = {10.4018/ijaeis.2020100105},

issn = {1947-3206},

year  = {2020},

date = {2020-10-00},

volume = {11},

number = {4},

pages = {69--83},

publisher = {IGI Global},

abstract = {
This work focuses on the development of an autonomous, electronic platform based on bioacoustic techniques to monitor animal sounds related to ingestive activities and sub-activities in beef cattle. The authors have developed an electronic platform that leverage the usage of applications to predict health and welfare conditions based on the bioacoustic signals.  The platform architecture is comprised of electronic subsystems covering the signal acquiring and filtering; sampling, processing, data storage system; and the coupling of the electronic and computing. The platform is coupled to the animal's halter so that the electronic system is in a case on the animal's forehead. Experiments were performed on grazing cattle (Nelore breed) in three field tests. The system was fully able to acquire, process, and store the animal's bioacoustic sounds. The authors used an audio file processing software to identify ingestive activities such as grazing, rumination, and idle periods. The results, based on the signal waveform, achieved high accuracy when compared to the visual monitoring method.
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Felzmann2020,

title = {ADeLe: A description language for approximate hardware},

author = {Isaías B. Felzmann and Matheus M. Susin and Liana Duenha and Rodolfo Azevedo and Lucas F. Wanner},

doi = {10.1016/j.future.2019.07.073},

issn = {0167-739X},

year  = {2020},

date = {2020-01-00},

journal = {Future Generation Computer Systems},

volume = {102},

pages = {245--258},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}