Guilherme Perin

Pós-doutorado

Idiomas

Áreas de atuação

Participação em eventos

Produções bibliográficas

• PERIN, GUILHERME ; WU, LICHAO ; PICEK, STJEPAN . The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA. Algorithms , v. 16, p. 127, 2023.

• PICEK, STJEPAN ; PERIN, GUILHERME ; MARIOT, LUCA ; WU, LICHAO ; BATINA, LEJLA . SoK: Deep Learning-based Physical Side-channel Analysis. ACM COMPUTING SURVEYS , v. 55, p. 1-35, 2023.

• PERIN, GUILHERME ; WU, LICHAO ; PICEK, STJEPAN . Exploring Feature Selection Scenarios for Deep Learning-based Side-channel Analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems , v. 4, p. 828-861, 2022.

• WU, LICHAO ; PERIN, GUILHERME ; PICEK, STJEPAN . The Best of Two Worlds: Deep Learning-assisted Template Attack. IACR Transactions on Cryptographic Hardware and Embedded Systems , v. 3, p. 413-437, 2022.

• WU, LICHAO ; PERIN, GUILHERME ; PICEK, STJEPAN . I Choose You: Automated Hyperparameter Tuning for Deep Learning-based Side-channel Analysis. IEEE Transactions on Emerging Topics in Computing , v. 1, p. 1-12, 2022.

• RIJSDIJK, JORAI ; WU, LICHAO ; PERIN, GUILHERME ; PICEK, STJEPAN . Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems , v. 3, p. 677-707, 2021.

• PERIN, GUILHERME ; CHMIELEWSKI, L. ; S. Picek . Strength in Numbers: Improving Generalization with Ensembles in Machine Learning-based Profiled Side-channel Analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems , v. 2020, p. 337, 2020.

• PERIN, GUILHERME ; CHMIELEWSKI, 'UKASZ ; BATINA, LEJLA ; PICEK, STJEPAN . Keep it Unsupervised: Horizontal Attacks Meet Deep Learning. IACR Transactions on Cryptographic Hardware and Embedded Systems , v. 2021, p. 343-372, 2020.

• PERIN, GUILHERME ; IMBERT, LAURENT ; MAURINE, PHILIPPE ; TORRES, LIONEL . Vertical and horizontal correlation attacks on RNS-based exponentiations. Journal of Cryptographic Engineering , v. 5, p. 171-185, 2015.

• PERIN, G. ; A. Kozakevicius . Filtragem Wavelet de Sinais Cardíacos através de Algoritmos Adaptativos. REVISTA DE INFORMÁTICA TEÓRICA E APLICADA: RITA , v. 20, p. 95-111, 2013.

• PERIN, G. ; Mesquita, D.G. ; Martins, J.B. . Montgomery Modular Multiplication on Reconfigurable Hardware: Systolic versus Multiplexed Implementation. INTERNATIONAL JOURNAL OF RECONFIGURABLE COMPUTING (PRINT) , v. 2011, p. 1-10, 2011.

• PERIN, G. ; WU, LICHAO ; S. Picek . Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-Based Side-Channel Analysis. In: Mark Stamp; Corrado Aaron Visaggio; Francesco Mercaldo; Fabio Di Troia. (Org.). Artificial Intelligence for Cybersecurity. 1ed.Chalm: Springer Nature Switzerland, 2022, v. 54, p. 217-242.

• KRCEK, M. ; LI, H. ; PAGUADA, S. ; RIOJA, U. ; PERIN, G. ; CHMIELEWSKI, 'UKASZ . Deep Learning on Side-Channel Analysis. In: Lejla Batina; Thomas Bäck; Ileana Buhan; Stjepan Picek. (Org.). Security and Artificial Intelligence. 1ed.Chalm: Springer Nature Switzerland, 2022, v. 13049, p. 48-71.

• LELLIS, RODRIGO ; SOARES, RAFAEL ; PERIN, GUILHERME . Pruning-based Neural Network Reduction for Faster Profiling Side-Channel Attacks. In: 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2022, Glasgow. 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2022. p. 1.

• WU, LICHAO ; PERIN, G. ; S. Picek . On the Evaluation of Deep Learning-Based Side-Channel Analysis. In: Constructive Side-Channel Analysis and Secure Design (COSADE), 2022, Leuven. Constructive Side-Channel Analysis and Secure Design - 13th International Workshop. New York City: Springer, 2022. v. 13211. p. 49-71.

• KERKHOF, M. ; WU, LICHAO ; PERIN, G. ; S. Picek . Focus is Key to Success: A Focal Loss Function for Deep Learning-Based Side-Channel Analysis. In: Constructive Side-Channel Analysis and Secure Design (COSADE), 2022, Leuven. Constructive Side-Channel Analysis and Secure Design - 13th International Workshop. New York City: Springer, 2022. v. 13211. p. 29-48.

• REZAEEZADE, A. ; S. Picek ; PERIN, G. . To Overfit, or Not to Overfit: Improving the Performance of Deep Learning-Based SCA. In: AFRICACRYPT, 2022, Fes. Progress in Cryptology - AFRICACRYPT 2022: 13th International Conference n Cryptology in Africa,. New York City: Springer, 2022. v. 2022. p. 397-421.

• SWAMINATHAN, S. ; CHMIELEWSKI, 'UKASZ ; PERIN, G. ; S. Picek . Deep Learning-Based Side-Channel Analysis Against AES Inner Rounds. In: Applied Cryptography and Network Security Workshops - ACNS - AIWHS, 2022, Roma. Applied Cryptography and Network Security Workshops - {ACNS} 2022 Satellite Workshops. New York City: Springer, 2022. v. 13285. p. 165-185.

• RIJSDIJK, J. ; WU, L. ; PERIN, G. . Reinforcement Learning-Based Design of Side-Channel Countermeasures. In: Security, Privacy, and Applied Cryptography Engineering (SPACE), 2021, Kolkata. Security, Privacy, and Applied Cryptography Engineering - 11th International Conference. New York City: Springer, 2021. v. 13162. p. 168-187.

• PERIN, G. ; BUHAN, I. ; PICEK, STJEPAN . Learning When to Stop: A Mutual Information Approach to Prevent Overfitting in Profiled Side-Channel Analysis. In: Constructive Side-Channel Analysis and Secure Design (COSADE), 2021, Lugano. Constructive Side-Channel Analysis and Secure Design - 12th International Workshop, COSADE 2021. New York City: Springer, 2021. v. 12910. p. 53-81.

• ARORA, V. ; BUHAN, I. ; PERIN, G. . A Tale of Two Boards: On the Influence of Microarchitecture on Side-Channel Leakage. In: Smart Card Research and Advanced Applications (CARDIS), 2021, Lubeck. Smart Card Research and Advanced Applications (CARDIS) - 20th International Conference. New York City: Springer, 2021. v. 13173. p. 80-96.

• HEUSER, A. ; S. Picek ; PERIN, G. ; GUILLEY, S. . Profiled Side-Channel Analysis in the Efficient Attacker Framework. In: Smart Card Research and Advanced Applications (CARDIS), 2021, Lubeck. Smart Card Research and Advanced Applications (CARDIS) - 20th International Conference. New York City: Springer, 2021. v. 13173. p. 44-63.

• WU, LICHAO ; PERIN, G. . On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis. In: Applied Cryptography and Network Security Workshops (ACNS - AIHWS), 2021, Kamakura. Applied Cryptography and Network Security Workshops - ACNS 2021 Satellite Workshops, AIBlock, AIHWS, AIoTS, CIMSS, Cloud S&P. New York City: Springer, 2021. v. 12809. p. 114-132.

• LI, H. ; KRCEK, M. ; PERIN, GUILHERME . A Comparison of Weight Initializers in Deep Learning-Based Side-Channel Analysis. In: Applied Cryptography and Network Security Workshops - (ACNS), 2020, Rome. Applied Cryptography and Network Security Workshops, 2020. v. 12418. p. 126-143.

• PERIN, G. ; PICEK, STJEPAN . On the Influence of Optimizers in Deep Learning-Based Side-Channel Analysis. In: Selected Areas in Cryptography, 2020, Halifax, NS. Selected Areas in Cryptography (SAC). New York City: Springer, 2020. v. 12804. p. 615-636.

• C. Andrikos ; L. Batina ; CHMIELEWSKI, L. ; LERMAN, L. ; MAVROUDIS, V. ; PAPAGIANNOPOULOS, K. ; PERIN, GUILHERME ; RASSIAS, G. ; SONNINO, A. . Location, Location, Location: Revisiting Modeling and Exploitation for Location-Based Side Channel Leakages. In: ASIACRYPT 2019, 2019, Kobe. International Conference on the Theory and Application of Cryptology and Information Security. Berlim: Springer, 2019. v. 11923. p. 285-314.

• NEGRE, C. ; PERIN, G. . Trade-Off Approaches for Leak Resistant Modular Arithmetic in RNS. In: ACISP 2015, 2015, Brisbane. Information Security and Privacy - 20th Australasian Conference, ACISP 2015. Berlin: Springer, 2015. v. 9144. p. 107-124.

• PERIN, GUILHERME ; CHMIELEWSKI, L. . A Semi-Parametric Approach for Side-Channel Attacks on Protected RSA Implementations. In: Smart Card Research and Advanced Applications - 14th International Conference (CARDIS), 2015, Berlim. Smart Card Research and Advanced Applications - 14th International Conference CARDIS. Berlim: Springer, 2015. v. 9514. p. 34-53.

• PERIN, G. ; IMBERT, L. ; Torres, L. ; Maurine, P. . Attacking Randomized Exponentiations Using Unsupervised Learning. In: Constructive Side-Channel Analysis and Secure Design (COSADE), 2014, Paris. Constructive Side-Channel Analysis and Secure Design - 5th International Workshop (COSADE). Europa: Springer, 2014. v. 8622. p. 144-160.

• PERIN, GUILHERME ; IMBERT, LAURENT ; TORRES, LIONEL ; MAURINE, PHILIPPE . Electromagnetic Analysis on RSA Algorithm Based on RNS. In: 2013 Euromicro Conference on Digital System Design (DSD), 2013, Los Alamitos. 2013 Euromicro Conference on Digital System Design, 2013. p. 345-352.

• PERIN, G. ; IMBERT, L. ; Torres, L. ; Maurine, P. . Practical Analysis of RSA Countermeasures Against Side-Channel Electromagnetic Attacks. In: Smart Card Research and Advanced Applications (CARDIS), 2013, Berlim. Smart Card Research and Advanced Applications - 12th International Conference, (CARDIS) 2013, Berlin, Germany, November 27-29, 2013. Revised Selected Papers. Europa: Springer, 2013. v. 8419. p. 200-215.

• PERIN, G. ; Torres, L. ; Benoit, P. ; Maurine, P. . Amplitude demodulation-based EM analysis of different RSA implementations. In: 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE 2012), 2012, Dresden. 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE). p. 1167.

• PERIN, GUILHERME ; MESQUITA, DANIEL G. ; HERRMANN, FERNANDO L. ; MARTINS, JOAO BAPTISTA . Montgomery modular multiplication on reconfigurable hardware: Fully systolic array vs parallel implementation. In: , 2010, Ipojuca. . p. 61.

• MESQUITA, DANIEL GOMES ; PERIN, GUILHERME ; HERRMANN, FERNANDO LUÍS ; MARTINS, JOÃO BAPTISTA DOS SANTOS . An efficient implementation of montgomery powering ladder in reconfigurable hardware. In: the 23rd symposium, 2010, São Paulo. Proceedings of the 23rd symposium on Integrated circuits and system design - SBCCI '10. New York: ACM Press. p. 121.

• Herrmann, F. Luís ; PERIN, G. ; FREITAS, J. P. ; Martins, J.B. . An UDP/IP network stack in FPGA. In: SForum2009, 2009, Natal-RN. SFORUM 2009, 2009.

• PERIN, G. ; A. Kozakevicius . Algoritmos Adaptativos para Filtragem de Sinal Cardíaco. In: XXXII Congresso Nacional de Matemática Aplicada e Computacional, 2009, Cuiabá - MT. CNMAC2009, 2009.

• HERRMANN, FERNANDO LUIS ; PERIN, GUILHERME ; DE FREITAS, JOSUE PAULO JOSE ; BERTAGNOLLI, RAFAEL ; DOS SANTOS MARTINS, JOAO BAPTISTA . A Gigabit UDP/IP network stack in FPGA. In: 2009 16th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2009), 2009, Yasmine Hammamet. 2009 16th IEEE International Conference on Electronics, Circuits and Systems - (ICECS 2009). p. 836.

• Martins, J.B. ; PERIN, G. ; SANTIN, E. ; TAFFAREL, J. E. . Gerador de Arritmias Cardíacas. In: AUGM - Jornada Internacional de Jovens Pesquisadores, 2006, Campinas - SP. XIV Jornadas de Jovens Pesquisadores, 2006.

• PERIN, G. ; WU, LICHAO ; S. Picek . Exploring Feature Selection Scenarios for Deep Learning-based Side-channel Analysis. 2022. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; CHMIELEWSKI, 'UKASZ ; BATINA, LEJLA ; S. Picek . Keep it Unsupervised: Horizontal Attacks Meet Deep Learning. 2021. (Apresentação de Trabalho/Conferência ou palestra).

• HEUSER, A. ; S. Picek ; PERIN, G. ; GUILLEY, S. . Profiled Side-Channel Analysis in the Efficient Attacker Framework. 2021. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; BUHAN, I. ; S. Picek . Learning When to Stop: A Mutual Information Approach to Prevent Overfitting in Profiled Side-Channel Analysis. 2021. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, GUILHERME ; CHMIELEWSKI, L. ; S. Picek . Strength in Numbers: Improving Generalization with Ensembles in Machine Learning-based Profiled Side-channel Analysis. 2020. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; S. Picek . On the Influence of Optimizers in Deep Learning-Based Side-Channel Analysis. 2020. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; IMBERT, LAURENT ; Torres, L. ; MAURINE, PHILIPPE . Attacking Randomized Exponentiations Using Unsupervised Learning. 2014. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; IMBERT, LAURENT ; Torres, L. ; MAURINE, PHILIPPE . Electromagnetic Analysis on RSA Algorithm Based on RNS. 2013. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; IMBERT, LAURENT ; Torres, L. ; MAURINE, PHILIPPE . Practical Analysis of RSA Countermeasures Against Side-Channel Electromagnetic Attacks. 2013. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; Maurine, P. ; Benoit, P. ; Torres, L. . Amplitude Demodulation-based EM Analysis of Different RSA Implementations. 2012. (Apresentação de Trabalho/Conferência ou palestra).

• PERIN, G. ; Herrmann, F. Luís ; Mesquita, D.G. ; Martins, J.B. . Montgomery Modular Multiplication in Reconfigurable Hardware: Systolic vs Parallel Implementation. 2010. (Apresentação de Trabalho/Congresso).

• SANTIN, E. ; PERIN, G. ; TAFFAREL, J. E. ; Martins, J.B. . Gerador de Arritmias Cardíacas. 2006. (Apresentação de Trabalho/Congresso).

Projetos de pesquisa

• 2022 - Atual

  IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness, Descrição: This project proposes a novel method, IB-RAR, which uses Information Bottleneck (IB) to strengthen adversarial robustness for both adversarial training and non-adversarial-trained methods. We first use the IB theory to build regularizers as learning objectives in the loss function. Then we filter out unnecessary features of intermediate representation according to their mutual information (MI) with labels, as the network trained with IB provides easily distinguishable MI for its features. Experimental results show that IB-RAR can be naturally combined with adversarial training and provides consistently better accuracy on new adversarial examples. The IB-RAR method improves the accuracy by an average of 2.66% against five adversarial attacks for ResNet-18, wide ResNet-28-10, and VGG-16, trained with three adversarial training benchmarks and the CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. In addition, IB-RAR also provides good robustness for undefended methods, such as training with cross-entropy loss only. Finally, without adversarial training, the VGG-16 network trained using IB-RAR on the CIFAR-10 dataset reaches an accuracy of 35.86% against PGD examples, while using all layers reaches 25.61% accuracy.. , Situação: Em andamento; Natureza: Pesquisa. , Alunos envolvidos: Doutorado: (1) . , Integrantes: Guilherme Perin - Coordenador / Stjepan Picek - Integrante / Xiaoyun Xu - Integrante.

• 2022 - Atual

  Profiled Side-Channel Analyis with Generative Models, Descrição: Profiling side-channel attacks can be conducted in a so-called worst-case scenario when it is assumed that an adversary has control of a target that is identical to the target one. This means that this adversary can obtain a dataset from this device under control, by knowing the randomness and key of the evaluated cryptographic algorithm. In this project, we propose a conditional generative adversarial network (CGAN) framework that can perform profiling side-channel analysis when the open and target device are not necessarily identical devices. Results in this research direction will open new perspectives for SCA, demanding stronger countermeasures for cryptographic algorithms.. , Situação: Em andamento; Natureza: Pesquisa. , Alunos envolvidos: Doutorado: (1) . , Integrantes: Guilherme Perin - Coordenador / Stjepan Picek - Integrante.

• 2022 - Atual

  Shift-invariance Robustness of Convolutional Neural Networks in Side-channel Analysis, Descrição: Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets that are protected with masking and/or hiding countermeasures. One hiding countermeasure is commonly investigated: desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This project investigates that claim in more detail and reveals that the situation is not so simple. While CNNs do have certain shift-invariance, it is far from sufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We propose to use data augmentation to improve the shift-invariance and, as an especially powerful version, ensembles of data augmentation. Our results show the proposed techniques work very well and improve the attack significantly, even to an order of magnitude.. , Situação: Em andamento; Natureza: Pesquisa. , Alunos envolvidos: Doutorado: (2) . , Integrantes: Guilherme Perin - Coordenador / Stjepan Picek - Integrante / Marina Krcek - Integrante / WU, LICHAO - Integrante.

• 2021 - 2023

  ExDL-SCA - Layer-wise Explainability of Deep Learning Side-channel Analysis, Descrição: Masked cryptographic implementations can be vulnerable to higher-order attacks. For instance, deep neural networks have proven effective for second-order profiling side-channel attacks even in a black-box setting (no prior knowledge of masks and implementation details). While such attacks have been successful, no explanations were provided for understanding why a variety of deep neural networks can (or cannot) learn high-order leakages and what the limitations are. In other words, we lack the explainability on neural network layers combining (or not) unknown and random secret shares, which is a necessary step to defeat, e.g., Boolean masking countermeasures. This project uses information-theoretic metrics to explain the internal activities of deep neural network layers. We propose a novel methodology for the explainability of deep learning-based profiling side-channel analysis (denoted ExDL-SCA) to understand the processing of secret masks. Inspired by the Information Bottleneck theory, our explainability methodology uses perceived information to explain and detect the different phenomena that occur in deep neural networks, such as fitting, compression, and generalization. We provide experimental results on masked AES datasets showing where, what, and why deep neural networks learn relevant features from input trace sets while compressing irrelevant ones, including noise. This paper opens new perspectives for understanding the role of different neural network layers in profiling side-channel attacks.. , Situação: Concluído; Natureza: Pesquisa. , Alunos envolvidos: Doutorado: (1) . , Integrantes: Guilherme Perin - Coordenador / Stjepan Picek - Integrante / WU, LICHAO - Integrante.

• 2018 - 2019

  Understanding Deep Learning with Mutual Information in the Side-Channel Analysis Context, Descrição: Today, deep neural networks are a common choice for conducting the profiled side-channel analysis. Such techniques commonly do not require pre-processing, and yet, they can break targets protected with countermeasures. Unfortunately, it is not trivial to find neural network hyper-parameters that would result in such top-performing attacks. The hyper-parameter leading the training process is the number of epochs during which the training happens. If the training is too short, the network does not reach its full capacity, while if the training is too long, the network overfits, and is not able to generalize to unseen examples. Finding the right moment to stop the training process is particularly difficult for side-channel analysis as there are no clear connections between machine learning and side-channel metrics that govern the training and attack phases, respectively. In this project, we tackle the problem of determining the correct epoch to stop the training in deep learning-based side-channel analysis. We explore how information is propagated through the hidden layers of a neural network, which allows us to monitor how training is evolving. We demonstrate that the amount of information, or, more precisely, mutual information transferred to the output layer, can be measured and used as a reference metric to determine the epoch at which the network offers optimal generalization. To validate the proposed methodology, we provide extensive experimental results that confirm the effectiveness of our metric for avoiding overfitting in the profiled side-channel analysis.. , Situação: Concluído; Natureza: Pesquisa. , Alunos envolvidos: Mestrado acadêmico: (1) . , Integrantes: Guilherme Perin - Coordenador / Marius Pop - Integrante.

• 2018 - 2018

  Convolutional Neural Networks for Side-Channel Analysis on ECC implementations, Descrição: This research project was done at Riscure from 15 July to 15 October 2018.The research is on applying Convolution Neural Networks (CNN) to attack elliptic curve cryptography (ECC) implementations in a similar setting as template attacks. These attacks have already been done in the literature, thus to add more novelty in this research the relationship of jitter in the traces and parameter selection are evaluated. The CNNs are known to be resistant to jitter and even improve over it, but after a certain amount of jitter the CNN cannot learn anymore on the traces set and perform the attack successfully.. , Situação: Concluído; Natureza: Pesquisa. , Alunos envolvidos: Doutorado: (1) . , Integrantes: Guilherme Perin - Coordenador / Pedro Maat Costa Massolino - Integrante.

• 2017 - 2019

  A Deep Learning Framework for Side-Channel Analysis in Commercial Software, Descrição: Riscure provides Inspector software as a advanced tool to perform side-channel analysis (SCA). The tool contains several SCA methods including SPA, DPA, and template attacks. After recent advances in the deep learning field, we build a deep learning framework for side-channel analysis as a commercial module for Inspector software. The framework contains all necessary functionalities including the construction of neural networks, attack targets and visualization metrics. The project started in 2017 and was concluded in 2019. The framework is used by security market leading companies around the world.. , Situação: Concluído; Natureza: Pesquisa. , Alunos envolvidos: Mestrado acadêmico: (4) Doutorado: (1) . , Integrantes: Guilherme Perin - Coordenador.

• 2010 - 2011

  Circuitos Integrados para Aplicações Seguras, Descrição: Este projeto, financiado pelo CNPq através do processo 290182/2010-7 e integrante do Edital RHAE Inovação de 2010, contemplou o aluno com uma bolsa período de 10 meses no LIRMM (Laboraroire de Informatique, Robotique et Microelectronique de Montpellier), laboratório associando ao CNRS (Centre National de Recherche Scientifique) e à Université de Montpellier 2 (França). O estágio teve como foco a implementação de circuitos digitais, prototipados em FPGA, para execução segura do algoritmo de criptografia de chave pública RSA. O projeto ainda contou com testes práticos em laboratórios, incluindo ataques por canais laterais realizados sobre as implementações.. , Situação: Concluído; Natureza: Pesquisa. , Alunos envolvidos: Mestrado acadêmico: (1) . , Integrantes: Guilherme Perin - Coordenador / Philippe Maurine - Integrante / Pascal Benoit - Integrante / Lionel Torres - Integrante / Cesar Ramos Rodrigues - Integrante., Financiador(es): Conselho Nacional de Desenvolvimento Científico e Tecnológico - Bolsa.

• 2008 - 2009

  Comunicação segura de Voz sobre IP em redes wireless(VoipWIFI), Descrição: Projeto de um sistema integrado de comunicação Voz sobre IP em redes sem fim. Projeto de um circuito integrado referente à uma pilha de comunicação UDP/IP.. , Situação: Em andamento; Natureza: Pesquisa. , Integrantes: Guilherme Perin - Integrante / João Baptista dos Santos Martins - Coordenador / Fernando Luís Herrmann - Integrante., Financiador(es): Conselho Nacional de Desenvolvimento Científico e Tecnológico - Bolsa.

• 2006 - 2008

  Circuito Integrado de um Filtro FIR Adaptativo para uso em Marca-Passo Cardíaco de Demanda, Descrição: Projeto de um filtro FIR adaptivo para aplicação em Marca Passo Cardíaco.... , Situação: Concluído; Natureza: Pesquisa. , Integrantes: Guilherme Perin - Integrante / Edinei Santin - Integrante / João Baptista dos Santos Martins - Coordenador.

• 2006 - 2007

  Redimensionamento dos Transisitores CMOS para otimização do consumo de potência ao nível de portas lógicas, Descrição: .... , Situação: Concluído; Natureza: Pesquisa. , Integrantes: Guilherme Perin - Integrante / João Baptista dos Santos Martins - Coordenador.

Prêmios