Chaos-based cryptography: a new promise to online security

A team of researchers at IFSC at the University of S. Paulo is working on the development and improvement of a new encryption system based on chaos theory, more efficient than conventional methods which could offer better security for online financial transactions, for instances, with the advantage of operating at high speed even on devices with limited hardware and low processing capacity. (read more)

Biodiversity and Math

Computer Science, Physics, Mathematics and biodiversity, are there connections between these fields ? In this link you can find an article about how the SCG deals and connects these topics.

SCG Admission

Post-doctorate: There are two positions available for post-doctorate researchers at the SCG, if you are interested send a message on the contact page.

Doctorate: There are no positions avaiable for PhD candidates at the moment. Send a message to enter in the PhD candidates queue for the next selection contact page.

Master Degree: There are no positions avaiable for Master Degree at the moment. Send a message to enter in the Master candidates queue for the next selection contact page.

Undergraduate: There are available positions to undergraduate students.

arXiv papers

Download open scientific papers. Check out the SCG's arXiv papers

SCG's Source Code

The following links present the source-codes of the algorithms, methods or systems developed by SCG group research. Please, if some work is usefull for you, do not forget to cite.

CNDescriptors - A complex network-based approach for boundary shape analysis

This page provides the source code for the complex network-based approach for boundary shape analysis. In pattern recognition and image analysis, shape is one of the most important visual attributes to characterize objects. It provides the most relevant information about an object in order to identify and classify tasks.

The method proposed here, has a novel approach to the shape boundary analysis using the Complex Networks Theory. It considers the shape boundary as a set of points and models this set as a graph. The topology of a Small World network model is obtained artificially by transformations on vertices of shape model. The topological features, derived from the dynamics of the network growth, are correlated to physical aspects of the shape. Consequently, these measurements can be used to compose a shape descriptor or signature. These descriptors may be used to identify and distinguish objects.

Traditional shape boundary methods yield the shape descriptors using the contour as continuous closed curves formed by the adjacent consecutive pixels. By modeling the shape boundary as complex network, the method proposed here, on the other hand, does not need adjacent and sequential pixels as the graph model only takes the distance between the boundary elements into account. The page presents the demonstration and source-code of the paper:

Backes, A. R., Casanova D. and Bruno, O. M. "A complex network-based approach for boundary shape analysis". Pattern Recognition, 42(1):54-67, 2009.

CV-LEAFs - A Computer Vision on Histologic Leaf structure

A computational method to access information in leaf transversal cuts is presented in this site. It is a complementary Web Page about the methodology presented in: “Measuring and analyzing color and texture information in anatomical leaf cross-sections: an approach using computer vision to aid plant species identification”, published in Botany, Volume 89, Number 7, Pages 467-479 (link).

Survey of Distance Transforms

This is the companion website to the paper "2D Euclidean distance transforms: a comparative survey", ACM Computing Surveys, Vol 40, Issue 1, Feb 2008. (pdf)(link).

A more complete set of results is posted there, together with source code for the algorithms and the performance benchmark. Further results and errata may also be posted in the future. In the results sections of this page you will find test images and performance plots, along with distance histograms characterizing the distance maps of the test images.

SIP - Scilab Image Processing Toolbox

SIP stands for Scilab Image Processing toolbox. SIP intends to do imaging tasks such as filtering, blurring, edge detection, thresholding, histogram manipulation, segmentation, mathematical morphology, color image processing, etc.

These operations are useful for problem solving in real-world applications ranging from car motion planning to automatic diagnosis of medical images.

SIP is meant to be a complete, useful, and FREE digital image processing toolbox for Scilab.

What SIP can do:

Although SIP is early in its development, it has the following useful features:

  • I/O of image files in many formats, including BMP, JPEG, GIF, PNG, TIFF, XPM, PCX, and more. (Thanks to ImageMagick)
  • Numerous functions with flexible interface and error treatment (see a listing)
  • Documentation with examples for all the functions

License: GPL

Chaos based criptography

This is the companion website to the paper "Fast, parallel and secure cryptography algorithm using Lorenz's attractor", International Journal of Modern Physics C, 21(3):365-382, 2010 (pdf)(link).

A web version of the chaotic cryptography algorithm is fully funcional. The users can encrypt ASCII messages and files directly in the site. The site also claims the Hackers and crypto-anaylists to a challenge: Can you broke this crypto algorithm ?.

The site is wrotten in portuguese, but the crypto system can be easily used for people that does not understand portuguese.