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56,482 results on '"Nedjah Nadia"'

257. Fast hardware for modular exponentiation with efficient exponent pre-processing

Author

Nedjah, Nadia and Mourelle, Luiza De Macedo

Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.sysarc.2006.09.008 Byline: Nadia Nedjah (a), Luiza de Macedo Mourelle (b) Keywords: Cryptography; Modular exponentiation; m-ary method Abstract: Modular exponentiation is an important operation in several public-key cryptosystems. It is performed using successive modular multiplications. For the sake of efficiency, one needs to reduce the total number of required modular multiplications. In this paper, we propose two efficient hardware implementations for computing modular exponentiations, which are based on the m-ary method. The m-ary method includes a power pre-computation step. The first design ignores the exponent and pre-computes all possible powers while the second takes advantage of the formation of the exponent to compute only those powers that are really necessary for the rest of the computation. As it can be expected, the implementation of the first architecture, compared with that of the second one, requires more time to complete an exponentiation. In compensation, however, the first should require less hardware area than the second. We provide a comparison of these two implementations using the performance factor, which takes into account both space and time requirements. Author Affiliation: (a) Department of Electronics Engineering and Telecommunication, Faculty of Engineering, State University of Rio de Janeiro, Brazil (b) Department of Systems Engineering and Computation, Faculty of Engineering, State University of Rio de Janeiro, Brazil Article History: Received 2 May 2006; Revised 29 August 2006; Accepted 15 September 2006

Published
2007

258. A Swarm Robotics Approach to Decontamination

Author

Alves, Daniel S. F., primary, Soares, E. Elael M., additional, Strachan, Guilherme C., additional, Carvalho, Guilherme P. S., additional, Xaud, Marco F. S., additional, Couto, Marcos V. B., additional, Mendonça, Rafael M., additional, Freitas, Renan S., additional, Santos, Thiago M., additional, Gonçalves, Vanessa C. F., additional, Mourelle, Luiza M., additional, Nedjah, Nadia, additional, Maculan, Nelson, additional, Lima, Priscila M. V., additional, and França, Felipe M. G., additional

Published
2013
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264. Three hardware architectures for the binary modular exponentiation: sequential, parallel, and systolic

Author

Nedjah, Nadia and Mourelle, Luiza de Macedo

Subjects
Cryptography -- Analysis, Business, Computers and office automation industries, Electronics, Electronics and electrical industries
Abstract

Modular exponentiation is the cornerstone computation in public-key cryptography systems such as RSA cryptosystems. The operation is time consuming for large operands. This paper describes the characteristics of three architectures designed to implement modular exponentiation using the fast binary method: the first field-programmable gate array (FPGA) prototype has a sequential architecture, the second has a parallel architecture, and the third has a systolic array-based architecture. The paper compares the three prototypes as well as Blum and Paar's implementation using the time x area classic factor. All three prototypes implement the modular multiplication using the popular Montgomery algorithm. Index Terms--Cryptography, exponentiation, modular, multiplication.

Published
2006

277. PSO in Building Fuzzy Systems

Author

Nedjah, Nadia, primary, Costa, Sergio Oliveira, additional, de Macedo Mourelle, Luiza, additional, dos Santos Coelho, Leandro, additional, and Mariani, Viviana Cocco, additional

Published
2011
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280. A Novel CNN, Bidirectional Long-Short Term Memory, and Gated Recurrent Unit-Based Hybrid Approach for Human Activity Recognition

Author

Nedjah, Nadia, Peraković, Dragan, Rafsanjani, Marjan, Thakur, Narina, Singh, Sunil, Gupta, Akash, Jain, Kunal, and Jain, Rachna

Abstract

Human activity recognition (HAR) is a crucial and challenging classification task for a range of applications from surveillance to assistance. Existing sensor-based HAR systems have limited training data availability and lack fast and accurate methods for robust and rapid activity recognition. In this paper, a novel hybrid HAR technique based on CNN, bi-directional long short-term memory, and gated recurrent units is proposed that can accurately and quickly recognize new human activities with a limited training set and high accuracy. The experiment was conducted on UCI Machine Learning Repository's MHEALTH dataset to analyze the effectiveness of the proposed method. The confusion matrix and accuracy score are utilized to gauge the performance of the presented model. Experiments indicate that the proposed hybrid approach for human activity recognition integrating CNN, bi-directional LSTM, and gated recurrent outperforms computing complexity and efficiency. The overall findings demonstrate that the proposed hybrid model performs exceptionally well, with enhanced accuracy of 94.68%.

Published
2022
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