Piva, R.G.G. , Bechelli, R.P. and Belardi, A.A., 2018. A New Biomarker in Diagnostic in Spirometry Exams with the Application of Wavelets. In Journal of Advances in Applied & Computational Mathematics (Volume 5, 2018), pp. 22-28. Available at:

Vasconcelos, L. de M., Bechelli, R.P. & Castro, M.C.F., 2017. Utilização do eletro-oculograma para diminuir o sinal de ruído na medição de eeg. In VII Simpósio de Iniciação Científica, Didática e de Ações Sociais da FEI. Sao Bernardo do Campo, Sao Paulo, Brazil. Available at:

Barelli, R.G. et al., 2016. Stimshield – shield para arduíno uno® com dois canais de estimulação elétrica neuromuscular. In XXV Congresso Brasileiro de Engenharia Biomédica (CBEB 2016). Foz do Iguacu, pp. 412–415. Available at:

Neves, L.C. et al., 2016. A Brain Computer Interface Using Emotiv-Epoc on an Arm Orthosis Control. Anais do XXV CBEB - Congresso Brasileiro de Engenharia Biomédica, (Figure 2), pp.1510–1513. Available at:

Bechelli, R.P., 2016. Content based Image Retrieval Databases Classification with Brain Event Related Potential. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (DOCTORAL CONSORTIUM 2016) - BIOSIGNALS. Roma: SCITEPRESS – Science and Technology Publications, Lda., pp. 3–8. Available at:

Capati, F., Bechelli, R.P. & Castro, M.C.F., 2016. Hybrid SSVEP/P300 BCI Keyboard Controlled by Visual Evoked Potential. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS. Roma: SCITEPRESS – Science and Technology Publications, Lda., pp. 214–218. Available at:

Rezende, R.H. & Bechelli, R.P., 2015. Acervos digitais e a socialização do conhecimento arqueológico: o caso do Labeca. In XX Congresso da SBEC Público e Privado na Antiguidade. Mariana.

Bechelli, R.P. & Giacomini, R., 2009. The Corner Effect Influence on Drain Current in Low-Dopped Rounded Corners Triple-Gate Devices. IX Microelectronics Students Forum - SBMicro, 9(1). Available at:

Bechelli, R.P. & Giacomini, R., 2006. Charge distribution in triple-gate devices at threshold voltage. … Technology and Devices (SBMicro, pp.1–2. Available at: [Accessed April 12, 2014].

PhD Thesis

Interface Cérebro–Computador para classificação de banco de imagens de acervos museológicos, 2018.

Abstract: In the last decades, the volume of digitally stored information has grown at rates never seen before and much of this information is generated by the extensive storage of images, videos and digitized documents. One of the most robust and low-cost systems for the acquisition of brain signals, applied in Brain Computer Interface (BCI) systems, are the Electroencephalogram (EEG) devices. The processing of the EEG signals, through artificial intelligence, using a set of wavelets for a spatial and temporal analysis of brain signals, presents an alternative for Content Based Image Retrieval (CBIR) applications as classification and ranking to a set of images. This work proposes a methodology to classify and represent a relation between brain signals and sets of images presented to different groups of volunteers, using a method of capturing brain signals via EEG, as indicative of a higher level comprehension about the image or group of images. Through a set of controlled environment tests, via EEG signals, using a set of evoked potential brain responses in a group of volunteers, target images were identified among a series of images. The work presents significant results of classification of brain signals for application in image databases, a comparison of application between different types of user groups (specialists and non-specialists) and different databases of scientific research images.

keywords: Brain Computer Interface, Content Based Image Retrieval, EEG, Evoked Related Potential, Machine Learning

Master Degree Dissertation

Estudo de Efeitos de Canto em Transistores de Porta Tripla, 2008.

Abstract: This work presents a study of corner effects in tridimensional SOI MOSFET transistors with depleted or neutral second interface using tridimensional numerical simulation. Tridimensional triple gate devices with rounded and sharp corners where simulated with height and width of the silicon island ranging from \(30 nm\) to \(70 nm\) and with channel doping concentration from \(1x10^{16} cm^{−3}\) to \(1x10^{19} cm^{−3}\). Based on simulated results the \(I \times V\) curves were extracted to define and compare these transistors. A methodology to evaluate the corner effects with different dimensions and doping concentrations was developed based on a range of electron concentration along different cut lines at threshold voltage, low drain voltage of \(50 mV\) and two corner profiles: sharp and rounded. The proposed model identify the existence of volume inversion at polarized devices. This work evaluate different corner radius to compare the influence of this parameter over the simulated devices. This work defines a method to describe tridimensional triple gate devices with rounded and sharp corners for simulation script language, which facilitates the parameter and grid variation. It was concluded that triple gate devices have higher total current density at the corners and depends direct on corner radius. Higher doped transistors have intense drain current over the corner. In transistors with doped channels higher than \(3x10^{18} cm^{−3}\) a second peak was identified at second derivative \(Id \times VG\) curve, indicating that there is a different inversion at the corner compared to the rest of the channel. This work shows that corner effects causes drain current influence even if no second peak is identified.

Keywords: semiconductor, transistors, SOI, MOSFET, corner effects, triple gate, volume inversion, rounded corners, tridimensional numerical simulation.