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Jurair R. de P. Junior

Bio: Jurair R. de P. Junior is an academic researcher from Centro Federal de Educação Tecnológica de Minas Gerais. The author has contributed to research in topics: Wireless sensor network & Topology control. The author has an hindex of 3, co-authored 5 publications receiving 20 citations. Previous affiliations of Jurair R. de P. Junior include Centro Federal de Educação Tecnológica Celso Suckow da Fonseca.

Papers
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Journal ArticleDOI
TL;DR: This work presents a robust hierarchical evolutionary technique which employs a heuristic initialization and provides robustness against noise in the Volterra series and improves on the computational complexity of existing methods without harming the identification accuracy.

17 citations

Proceedings ArticleDOI
29 Apr 2021
TL;DR: A frequency-domain heuristic for reducing the visual impact of digital steganography in grayscale images is presented, and a Python language library was made available in the PyPI repository, allowing for both concealment and revelation of messages using the presented digital Steganography methods.
Abstract: Sensitive information being shared on the internet is growing. Because of this, it is increasingly necessary to take security measures whilst this information travels in the network. Digital steganography allows one to send sensitive information in a hidden manner. Although there is a plethora of techniques for such a goal, finding an appropriate one is not always simple. This paper implements and compares spatial-domain digital steganography techniques in both RGB and grayscale images. A frequency-domain heuristic for reducing the visual impact of digital steganography in grayscale images is presented. As another result of this work, a dataset is also available in the Kaggle platform with 18 GB of images, containing secret messages using the techniques under study. In addition, a Python language library was also made available in the PyPI repository, allowing for both concealment and revelation of messages using the presented digital steganography methods.

4 citations

Proceedings ArticleDOI
29 Oct 2019
TL;DR: An optimization methodology based on a Genetic Algorithm is proposed, in order to solve the Sensor Allocation Problem for a WSN, which defines the position of sensor nodes according to their different operation modes while pursuing the optimization of network efficiency with respect to performance parameters.
Abstract: An Internet of Things (IoT) environment usually is composed by sensor nodes connected to the Internet, which constitutes a Wireless Sensor Network (WSN). In this work, the Sensor Allocation Problem (SAP) for a WSN is addressed, which defines the position of sensor nodes according to their different operation modes while pursuing the optimization of network efficiency with respect to performance parameters. An optimization methodology based on a Genetic Algorithm is proposed, in order to solve the SAP. Case studies are performed in order to evaluate the efficiency of the proposed solution method.

4 citations

Proceedings ArticleDOI
01 Jul 2018
TL;DR: The proposed non-linear regressor presents higher precision when compared to the linear model, and requires less information to do so, and the developed solution brings to light the assumed relationship between soil bulk density and some soil chemical properties.
Abstract: Computer models have been an important tool to determine soil bulk density. This soil property is fundamental to estimate soil carbon reserves and consequently to understand the global carbon cycle. The estimation of soil bulk density is not a trivial task since it demands an intensive and often impractical work. The purpose of this paper is to evaluate the performance of a pedotransfer function against an Artificial Neural Networks to estimate soil bulk density for soils at Brazilian biomes. The first one consists of a linear model composed of a Least Square method. The latter employs a robust committee of multilayer perceptron networks and a model selection procedure based on k-fold cross-validation. The data are composed of 3404 soil layers distributed in different Brazilian regions and with different uses. The proposed non-linear regressor presents higher precision when compared to the linear model, and requires less information to do so. Additionally, the developed solution brings to light the assumed relationship between soil bulk density and some soil chemical properties.

4 citations

Proceedings ArticleDOI
29 Oct 2019
TL;DR: This paper addresses this lack of information on public bus services and presents ShareBus, an information system to improve urban mobility and uses the idea of collaboration to obtain and maintain data, either from users or companies, keeping it as much as possible updated.
Abstract: In recent years, the search to make cities smart often has been a strategy designed to mitigate problems generated by urban population growth. To improve the population's quality of life and optimize the use of resources and infrastructure, applications in various fields have been developed. Public bus services are widely deployed in cities around the world because they provide cost-effective public transportation. Most of the time the citizens are not provided information about the buses in real-time (location, route, etc). This paper address this lack of information on public bus services and presents ShareBus, an information system to improve urban mobility. The system architecture and its main functionalities are described. A preliminary implementation of the system is also presented which is evaluated through real tests. The system uses the idea of collaboration to obtain and maintain data, either from users or companies, keeping it as much as possible updated.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a generalized discrete cosine transform with three parameters was proposed and its orthogonality was proved for some new cases, and a new type of discrete W transform was proposed.
Abstract: The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

79 citations

Journal ArticleDOI
01 Jan 2022
TL;DR: In this article , the identification issue of discrete-time nonlinear Volterra systems and uses a tensorial decomposition called PARAFAC to represent the VOLTERRA kernels.
Abstract: The Volterra model can represent a wide range of nonlinear dynamical systems. However, its practical use in nonlinear system identification is limited due to the exponentially growing number of Volterra kernel coefficients as the degree increases. This paper considers the identification issue of discrete-time nonlinear Volterra systems and uses a tensorial decomposition called PARAFAC to represent the Volterra kernels which can provide a significant parametric reduction compared with the conventional Volterra model. Applying the multi-innovation identification theory, the recursive algorithm by combining the l2-norm is proposed for the PARAFAC-Volterra models with the Gaussian noises. In addition, the multi-innovation algorithm combining with the logarithmic p-norms is investigated for the nonlinear Volterra systems with the non-Gaussian noises. Finally, some simulation results illustrate the effectiveness of the proposed identification methods.

41 citations

Journal ArticleDOI
TL;DR: This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil, by extending the SEIR model with an on / off strategy and developing a neural regressor.
Abstract: The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.

29 citations

Journal ArticleDOI
TL;DR: The simulation results confirm that the GGS-KF-based identification approach results in the most accurate estimations compared to the conventional KF and other reported techniques in terms of parameter estimation error, mean-squared error (MSE), fitness percentage (FIT%), mean-Squared deviation (MSD), and cumulative density function (CDF).
Abstract: This paper proposes an efficient global gravitational search (GGS) algorithm-assisted Kalman filter (KF) design, called a GGS-KF technique, for accurate estimation of the Volterra-type nonlinear systems. KF is a well-known estimation technique for the dynamic states of the system. The best estimate is achieved if the system dynamics and noise statistical model parameters are available at the beginning. However, to estimate the real-time problems, these parameters are unstipulated or partly known. Due to this limitation, the performance of the KF degrades or sometimes diverges. In this work, two steps have been proposed for unknown system identification while overcoming the difficulty encountered in KF. The first step is to optimise the parameters of the KF using the GGS algorithm by considering a properly balanced fitness function. The second step is to estimate the unknown coefficients of the system by using the basic KF method with the optimally tuned KF parameters obtained from the first step. The proposed GGS-KF technique is tested on five different Volterra systems with various levels of noisy (10 dB, 15 dB and 20 dB) and noise-free input conditions. The simulation results confirm that the GGS-KF-based identification approach results in the most accurate estimations compared to the conventional KF and other reported techniques in terms of parameter estimation error, mean-squared error (MSE), fitness percentage (FIT%), mean-squared deviation (MSD), and cumulative density function (CDF). To validate the practical applicability of the proposed technique, two benchmark systems have also been identified based on the original data sets.

23 citations

Journal ArticleDOI
TL;DR: In this paper, the authors attempted to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil, by extending the SEIR model with an on/off strategy.
Abstract: The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.

16 citations