Enrique Pelaez

Affordable and secure electronic voting for university elections: The SAVE case study

Traditional electronic voting systems are designed with national elections in mind. However, there are other types of institutions, such as Universities, that are required by law to conduct mid- to large-scale elections. The nature of these institutions and the different regulations that govern their election process made impractical the use of traditional voting systems for their elections. This work presents the SAVE system, an electronic voting system designed to be used in University elections preserving all the requirements that a secure voting system have while being affordable and flexible enough to adapt to the different regulations. The design and implementation of this system are discussed in detail and the results of its 10 years of continuous are presented as a case study.

Human-machine musical composition in real-time based on emotions through a fuzzy logic approach

This work proposes a method for representing human emotions, in the context of musical composition, which then will be used to artificially generate musical melodies through Fuzzy Logic. A real-time prototype system, for Human-Machine Musical Compositions, was also implemented to test this approach, using the emotional intentions captured from a human musician and later used to artificially compose and perform melodies accompanying a human artist while playing the chords. The proposed method was tested with listeners in an experiment with the purpose of verifying if the musical pieces, artificially created, produced emotions in them and if those emotions matched with the emotional intentions captured from the human composer.

Characterizing influential leaders of Ecuador on Twitter using computational intelligence

The popularity of social networks, such as Twitter, have provided users around the world the ability to share information, express opinions or sentiments about any topic. Twitter has become the preferred social network platform used by researchers for measuring popularity or influence of users in social networks. This study seeks to extend analysis of influential users in the spatial context of Ecuador, applying computational intelligence techniques in order to identify influential users and for those users calculate its ranking. The results show that a careful selection and normalization of features found in the Twitter users profile, allows us to detect influential users with high degree of accuracy, and then calculate the ranking only over those users. This approach provide a quicker method compared to previous techniques for determining the ranking by filtering non-influential users.

Fingertip detection approach on depth image sequences for interactive projection system

This study presents a vision-based approach for fingertip tracking on multi-touch tabletop which combines infrared and depth image processing. This approach intends to tackle two main issues on tabletop interaction: improve the performance for real-time applications and increase fingertip detection accuracy. A prototype using this fingertip tracking method was implemented with a depth and infrared camera. This approach processes the users arm, hands and fingertips images using depth-space constraints, as well as clustering. Fingertip positions are accurately corrected using additional infrared information. Quantitative results show high accuracy of fingertip detection, with lower error rates compared to previous studies. Also, increased capabilities for real-time multi-user interaction are further demonstrated through a set of response time tests.

Clustering of EEG occipital signals using k-means

Recent studies show that it is feasible to use electrical signals from Electro-encephalography (EEG) to control devices or prostheses, these signals are provided by the body and can be measured on the scalp to determine the intent of the person when it is observing a visual stimulus frequency range detectable by the human eye. This group of signals are very susceptible to noise due to voltage levels that are able to acquire. Therefore, in this work we propose a statistical analysis of the distribution of normal EEG signals in order to determine the need of a pre-processing to remove noise components from electrical grids or other possible sources. This preprocessing includes the design and use of a filter that will eliminate any signal component that is not in the operating frequency range of the EEG occipital area of the brain. Finally, we will proceed to use the k-means algorithm to cluster with signals according to their frequency and temporal characteristics.