Communications & Signal Processing Research Group

Welcome to the Communications, Networking, Image and Signal Processing Research Group, School of Engineering, Department of Computing, Electronics, and Mechatronics at the Universidad de las Americas Puebla, Mexico.  The group currently has about 5 members who have research interests in several communications, networking, signal, and image processing areas. We currently focus on the following research areas:

• Cyber Security

• Wavelet-Based Signal and Image Processing

• Learning Algorithms for Wavelet-Neural Networks

• Multiresolution Analysis

• Biometrics for Network Security

• Anomaly and Intrusion Detection in Communication Networks

• Machine Learning

If you are interested in joining us as a postgraduate student or research fellow, please contact Prof. Alarcon-Aquino, Email: vicente dot alarcon at udlap dot mx

Research Projects

1. Jan 2023 - Dec 2024 Bio-inspired detection of anomalies and attacks on the Internet, Project Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

2. Jan 2021 - Dec 2021 Integración Eficiente y Sustentable de las Cadenas de Suministro en el Estado de Puebla, Proyecto apoyado por CONACYT, FONDO FORDECYT-PRONACES (2021), MEXICO. 

3. Jan 2020 - Dec 2022 Internet Security: Pentesting, Blockchains, Biometric Systems, Cryptography, Anomaly Detection, and Wavelets, Project Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

4. Jan 2017 - Dec 2019 Anomaly Detection in EEG Biomedical Signals Using Entropy and Wavelets, Project Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

5. Jan 2017 - Dec 2019 Security Algorithms in IoT, Project Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

6. Jan 2017 - Dec 2019 Security in Automotive Protocols: Attacks and Vulnerabilities, Project Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

7. May 2014 - Dec 2017 National Supercomputer Laboratory of Southeast Mexico, Project Supported by CONACYT, No. 232717-271805-252875-280199,  $53,500,000.00 MN, MEXICO.

8. Jan 2012 - Dec 2014 Approximation Systems for Non-stationary Time Series Forecasting Using Recurrent Connectionist Models and Multiresolution analysis, Project Supported by CONACYT, No. 155250, CB-2010, $347,671.00 MN, MEXICO.

9. Jan 2012 - Dec 2013 Multiresolution Processing of EEG Biomedical Signals for Detecting Anomalies, Project Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

10. Jan 2012 - July 2015  Sensores Electro-Ópticos en Tecnología de Óptica Integrada y Filtraje Óptico, Supported by CONACYT, No. 154438, CB-2010, $1,115,000.00 MN, MEXICO.

11. March 2011 - Dec 2011.  Technological innovation in communication between car and cell phone aimed at new customer services, Project Supported by CONACYT under grant No.56228: VOLKSWAGEN de México, S. A. de C. V. - UDLAP ($3,141,600.00 MN, UDLAP: $1,496,600.00 MN), MEXICO.

12. June 2010 - May 2014  S2EuNet: Security, Services, Networking and Performance of Next Generation IP‐based Multimedia Wireless Networks, Seventh Framework Programme (FP7) European Project, €673,200.00 [pdf]

13. July 2009 - August 2011  Scientific and Technological Development of Telecommunication Systems, Automation, and Image Processing Applied to Forest Fire Detection, Project Supported by FOMIX CONACYT-Puebla State Government, No. 109417, $1,260,000.00 MN, MEXICO

14. Sept 2009 - August 2010   Telecommunications Network Strengthening in Services of Voice, Data and Video, Project Supported by FOMIX CONACYT-Puebla State Government, No. 109115,   $6,816,599.00 MN, MEXICO.

15. Jan 2009 - Dec 2011  Security Algorithms for Communication Networks based on Biometric Systems, Cryptography, and Wavelets, Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

16. Jan 2007 - Dec 2008  Security Algorithms and Models for Detecting Anomalies and Intrusions in Communication Networks, Supported by the Vice-Presidency for Research, Graduate Studies, and Outreach. Universidad de las Américas Puebla, MEXICO.

17. Jan 2005 - Dec 2006   Analysis and Comparison of algorithms for packet routing in MPLS and ATM Networks, Supported by the Vice-presidency for Research, Graduate Studies, and Outreach, Universidad de las Americas Puebla, MEXICO

18. Jan 2003 - Dec 2004  Signal Noise Reduction and Image Compression Using Wavelet Transforms Applied to Mobile Networks, Supported by the Institute of Research and Graduate Programs, Universidad de las Americas Puebla, MEXICO.

19. Sept 2001 - Dec 2001  NexTV (New Media Consumption for Extended Interactive Television Environment), Supported by European Commission IST 5th Framework, Imperial College London, Department of Electrical and Electronic Engineering, London, UK.

20. Jan 1997 - Dec 1998  Virtual University Using High-Speed Networks, Supported by the Institute of Research and Graduate Programs, Universidad de las Américas Puebla, MEXICO.

Multiresolution Analysis for Transiting Exoplanet Identification Using Machine Learning

Collaborators: M. A. Jara-Maldonado (UDLAP, Mexico), V. Alarcon-Aquino (UDLAP, Mexico), R. Rosas-Romero (UDLAP, Mexico) 

An exoplanet (short for the extrasolar planet) is a planet that orbits a star different than our Sun. The task of identifying exoplanets requires looking for transits in light flux time series data. Transits are similar phenomena to eclipses, but instead of the moon being the one that dims the light received from the star, it is the exoplanet that crosses between the star and the observer. Manually searching for transits in light flux-related data (called light curves) consumes huge amounts of time and effort from astronomers, because of the size of the databases that have been generated by satellites and ground-based observatories. For this reason, the application of machine learning techniques to exoplanet identification has been developing in the last few years.

1. M. A. Jara-Maldonado, V. Alarcon-Aquino, R. Rosas-Romero, O. Starostenko, J. M. Ramirez-Cortes, Transiting Exoplanet Discovery Using Machine Learning Techniques: A Survey, in Earth Science Informatics, 2020 [pdf]JCR_SCIE® 

2. M. A. Jara-Maldonado, V. Alarcon-Aquino, R. Rosas-Romero, A New Machine Learning Model Based on the Broad Learning System and Wavelets, in Engineering Applications of Artificial Intelligence June 2022 [pdf]JCR_SCIE®

3. M. Jara-Maldonado, V. Alarcon-Aquino, R. Rosas-Romero, A Multiresolution Machine Learning Technique to Identify Exoplanets, Springer Nature Switzerland AG 2020, L. Martinez-Villaseñor et al. (Eds.): Advances in Soft Computing. MICAI 2020 , LNCS 12468, pp. 50–64, 2020 [pdf]

4. M. Jara-Maldonado, V. Alarcon-Aquino, R. Rosas-Romero, A Multiresolution Machine Learning Technique to Identify Exoplanets, 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Mexico City, Mexico, October 2020.

5. M. Jara-Maldonado, V. Alarcon-Aquino, R. Rosas-Romero, A Multiresolution Analysis Technique to Improve Exoplanet Identification, Exoplanets III, Heidelberg University, July 2020 [abstract]

Anomaly Detection Model based on Immune Danger Theory applied to Computer Networks

Collaborators: David Limon-Cantu (Ph.D. Student), V. Alarcon-Aquino (UDLAP, Mexico) 

Currently, David is centering his research in Evolutionary Computing Algorithms inspired by the Human Immune System and the Danger Theory (DT) model. The Dendritic Cell Algorithm (DCA) is a population-based binary classifier designed to detect anomalies and is inspired by the behavior of Dendritic Cells (DC), which are able to sense Danger Signals (DS) emitted by abnormal behavior of the tissues and cells of the body. The objective of Cantu's research is to enhance this algorithm and incorporate state-of-the-art techniques in order to increase its precision, adaptability, and scalability using computer network communications to influence security applications in the anomaly detection field, which aims to ultimately help detect unknown or dynamic threats in computer networks.

1. D. Limon-Cantu, V. Alarcon-Aquino, Multiresolution Dendritic Cell Algorithm for Network Anomaly Detection, in PeerJ Computer Science, 2021 [pdf] JCR_SCIE

Object Recognition using Multiresolution Analysis in Infrared Imaging for Defense Systems

Collaborators: Daniel Treviño-Sanchez (PhD Student), V. Alarcon-Aquino (UDLAP, Mexico) 

Daniel is currently working on recognizing vehicles, drones, and people (among other things) using infrared images. This is a sensitive topic for national defense systems, especially achieving this goal in total darkness. Since improving national defense systems against new threats has already become a major concern that requires state-of-the-art technology to fill some gaps and address vulnerabilities. Some key components for such tasks are Infrared technology (IR), which enables vision in many adverse circumstances.  This research is focused on Multiresolution techniques to extract the most relevant features with good accuracy and low computational cost from those images, and by combining the use of some advanced methods to enhance edges,  identify shapes and classify objects, and recognize those objects.

1. D. Trevino-Sanchez, V. Alarcon-Aquino. Hybrid pooling with wavelets for convolutional neural networks. Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4327-4336, 2022. [pdf] JCR_SCIE

Models and Algorithms for Video Compression Using Wavelet Transforms and Fovea

Collaborators: J. C. Galan-Hernandez (UDLAP, Mexico), V. Alarcon-Aquino (UDLAP, Mexico), T. Stathaki (Imperial College London, UK) 

Digital image and video stores amounts of data that can be used for solving several computer vision problems. However, this is a challenging goal primarily because a large amount of data makes the task computational very intensive. The subject of video coding is of paramount importance for video processing since it can be of great impact on data transmission and manipulation. Video transmission can be greatly improved when a video compression algorithm is used reducing the communication overhead. With efficient coding, distortions can be assessed and controlled, bandwidth can be managed efficiently, and transmission time reduced. The structure of the human eye can be exploited for compression. The human eye experiments a form of aliasing from the fixation point to the edges of the image. This aliasing is exploited in fovea compression. Fovea compression can be applied over images with ROIs, the use of fovea around ROIs improves the image quality for the human eye. This research focuses on image and video compression using wavelet transforms and the fovea of the human eye for increasing the quality of the compressed image and video.

1. J. C. Galan-Hernandez, V. Alarcon-Aquino, J. M. Ramirez-Cortes, O. Starostenko, Region-of-Interest Coding based on Fovea and Hierarchical Trees, in Information Technology and Control, Vol. 42, No. 4, pp. 343-352, December 2013. [pdf] JCR_SCIE®

2. J. C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, J. M. Ramirez-Cortes, DWT Foveation-Based Multiresolution Compression Algorithm, in Special Issue Advances in Intelligent and Information Technologies, Journal Research in Computing Science, Vol. 50, pp. 197-206, October 2010.  

3. J. C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, J. M. Ramirez-Cortes, Fovea Window for Wavelet-Based Compression, Innovations and Advances in Computer, Information, Systems Sciences, and Engineering, LNEE, Vol. 152, Pages 661-672, 2013, Elleithy, Khaled; Sobh, Tarek (Eds.), ISBN 978-1-4614-3534-1  [pdf]

4. J. C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, and J. M. Ramirez-Cortes, Foveated ROI Compression with Hierarchical Trees for Real-Time Video Processing, LNCS 6718 Springer-Verlag, Pages 240-249, 2011 [pdf]

5. J. C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, J. M. Ramirez-Cortes, Fovea Window for Wavelet-Based Compression, International Conference on Systems, Computing Sciences and Software Engineering (SCSS 11) of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering, Springer CISSE 2011, December 2011 

6. J. C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, and J. M. Ramirez-Cortes, Foveated ROI Compression with Hierachical Trees for Real-Time Video Processing, In the Proceedings of the 3rd Mexican Conference on Pattern Recognition, MCPR 2011, Cancun, Mexico June 2011 

7. J. C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, J. M. Ramirez-Cortes, Wavelet-Based Foveated Compression Algorithm for Real-Time Video Processing, In Proceedings of the IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA, September 2010

Epilepsy Seizures Classification in EEG Signals Using Wavelet-Based Neural Networks

Collaborators: E. Juarez-Guerra (PhD Student), V. Alarcon-Aquino (Universidad de las Americas Puebla, Mexico), P. Gomez-Gil (INAOE)  

The brain is one of the most important organs of the human body, controlling the coordination of the human muscles and nerves. The transient and unexpected electrical disturbances of the brain result in an acute disease called Epileptic seizures. These seizures are seen as a sudden abnormal function of the body, often with loss of consciousness, an increase in muscular activity, or an abnormal sensation. The human brain is obviously a complex system and exhibits rich spatiotemporal dynamics. Among the noninvasive techniques for probing human brain dynamics, electroencephalography provides a direct measure of cortical activity with millisecond temporal resolution. Electroencephalogram (EEG) signals involve a great deal of information about the function of the brain. But evaluation and classification of these signals are limited and since there is no definite criterion evaluated by the experts, visual analysis of EEG signals in the time domain may be insufficient. Routine clinical diagnosis needs the analysis of EEG signals. An important issue in epileptology is the question of whether epileptic seizures can be anticipated prior to their occurrence. The question of whether information extracted from the EEG of epilepsy patients can be used for the classification and prediction of seizures has recently attracted much attention. In this research, we focus on a study of biomedical signals to analyze, detect, classify and predict abnormalities such as Epileptic seizures, brain tumors, etc. using signal processing techniques such as wavelet neural networks and empirical mode decomposition.

1. E. Juarez-Guerra, P- Gomez-Gil, V. Alarcon-Aquino, Biomedical Signal Processing Using Wavelet-Based Neural Networks, in Special Issue Advances in Pattern Recognition, Journal Research in Computing Science, Vol. 61, pp. 23-32, June 2013. 

2. P. Gomez-Gil, E. Juarez-Guerra, V. Alarcon-Aquino, J. M. Ramírez-Cortes, J. Rangel-Magdaleno, Identification of Epilepsy seizures using Multi-resolution analysis and Artificial Neural Networks", Recent Advances on Hybrid Approaches for Designing Intelligent Systems, Series 7092, pp. 337-351, Springer International Publishing, Switzerland, 2014 [pdf]

3. E. Juarez-Guerra, V. Alarcon-Aquino and P. Gomez-Gil. Epilepsy Seizure Detection in EEG Signals Using Wavelet Transforms and Neural Networks, in theProceedings of the Virtual International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2013).  Dec. 12-14, 2013.

Digital Processing of EEG Signals oriented to the Development of BCIs

Collaborators: G. Rosas-Cholula, J. M. Ramirez-Cortes (INAOE), V. Alarcon-Aquino (Universidad de las Americas Puebla) 

Brain-Computer Interfaces (BCIs) are systems that allow people to control computer applications using their brain signals. In this research, we focus on the detection of a P-300 rhythm for potential applications on BCI using an Adaptive Neuro-Fuzzy algorithm (ANFIS). P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered into the ANFIS system. 

1. G. Rosas-Cholula, JM. Ramirez-Cortes, V. Alarcon-Aquino, P. Gomez-Gil, JJ. Rangel-Magdaleno, C. Reyes-Garcia. Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition. Sensors. 2013; 13(8):10561-10583. [pdf] JCR_SCIE®

2. Juan Manuel Ramirez-Cortes, Vicente Alarcon-Aquino, Gerardo Rosas-Cholula, Pilar Gomez-Gil and Jorge Escamilla-Ambrosio, Anfis-Based P300 Rhythm Detection Using Wavelet Feature Extraction on Blind Source Separated EEG Signals, Intelligent Automation and Systems Engineering, LNEE, Vol. 103, 2011, Sio-Iong Ao, Mahyar Amouzegar and Burghard B. Rieger (Eds.), ISBN:978-1-4614-0373-9, pp. 353-365 [pdf] 

3. G. Rosas-Cholula, JM. Ramirez-Cortes, J. Rangel-Magdaleno, P. Gomez-Gil, V. Alarcon-Aquino, Head movement artifact removal in EEG signals using Empirical Mode Decomposition and Pearson Correlation, The 2013 International Conference on Artificial Intelligence (ICAI 2013). July 22-25, 2013. Las Vegas, Nevada USA

4. G. Rosas-Cholula, J. M. Ramirez-Cortes, J. Escamilla-Ambrosio, V. Alarcon-Aquino, On the development of a simple EEG-based mouse using Empirical Mode Decomposition and DWT: A BCI application, In Proceedings of the 15th International Graphonomics Society Conference, IGS 2011, June 12-15 2011 

5. J. M. Ramírez-Cortes, V. Alarcon-Aquino, G. Rosas-Cholula, P. Gomez-Gil, J. Escamilla-Ambrosio, P-300 rhythm detection using ANFIS algorithm and wavelet feature extraction in EEG signals, World Congress on Engineering and Computer Science WCECS 2010, International Conference on Signal Processing and Imaging Engineering 2010 San Francisco, USA, 20-22 October 2010 

6. G. Rosas-Cholula, J. M. Ramirez-Cortes, V. Alarcon-Aquino, J. Martinez, P. Gomez, On signal P-300 detection for BCI applications based on wavelet analysis and ICA preprocessing, In Proceedings of the IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA, September 2010  

Security in Communications Networks Using Biometric Systems

Collaborators: H. A. Garcia-Baleon (PhD Student at Imperial College London, UK), V. Alarcon-Aquino (Universidad de las Americas Puebla) 

Combining biometrics and cryptography is becoming a matter of interest for some researchers due to the fact that this combination can bring together the better of the two worlds. The former guarantees the identification of individuals based on measuring their personal unique features with a high degree of assurance, while the latter mainly assures a high degree of trust in the transactions of information over non-secure communications networks. In this research, we focus on a bimodal biometric system for a cryptographic key generation that works with speech and electrocardiogram (ECG) signals using wavelet transforms. This work is based on the uniqueness and quasi-stationary behavior of ECG and speech signals with respect to an individual. The architecture of the proposed system considers three security factors, namely, user password, biometric samples, and a token. The stages that comprise the architecture are one-time enrollment and key derivation. The system architecture is able to verify the identity of individuals offline avoiding the use of a centralized database for storing the biometric information. The system also implements an error-correction layer using the Hadamard code. The performance of the system is assessed using ECG signals from the MIT-BIH database and speech signals from a speech database created for testing purposes. The random cryptographic key released by the system may be used in several encryption algorithms.

1. V. Alarcon-Aquino, P. Gomez-Gil, J. M. Ramirez-Cortes, O. Starostenko, H. A. Garcia-Baleon, Cancelable Biometrics for Bimodal Cryptosystems, in Latin American Applied Research: An International Journal, Vol. 43, No. 4, October 2013 [pdf] JCR_SCIE®

2. V. Alarcon-Aquino, H. A. Garcia-Baleon, J. M. Ramirez-Cortes, P. Gomez-Gil, O. Starostenko, Biometric Cryptosystem based on Keystroke Dynamics and K- Medoids, IETE Journal of Research, Vol. 57, No. 4, July-August 2011, pp. 367-376 [pdf] JCR_SCIE®

3. H. A. Garcia-Baleon, V. Alarcon-Aquino, O. Starostenko, K-Medoids-Based Random Biometric Pattern for Cryptographic Key Generation, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications , LNCS 5856 Springer, 2009, Volume 5856/2009; Pages 85-94 [pdf]

4. H. A. Garcia-Baleon, V. Alarcon-Aquino, O. Starostenko, K-Medoids-Based Random Biometric Pattern for Cryptographic Key Generation, 14th Iberoamerican Congress on Pattern Recongnition CIARP 2009, November 2009; pp. 85-94 

5. H. A. Garcia-Baleon, V. Alarcon-Aquino, O. Starostenko, J. F. Ramirez-Cruz,  Bimodal Biometric System for Cryptographic Key Generation Using Wavelet Transforms. In Proceedings of the IEEE Mexican International Conference on Computer Science, ENC 2009, September 2009. 

6. H. A. Garcia-Baleon, V. Alarcon-Aquino, Cryptographic Key Generation from Biometric Data Using Wavelets. In Proceedings of the IEEE Electronics, Robotics and Automotive Mechanics Conference, CERMA 2009, September 2009. 

7. H. A. Garcia-Baleon, V. Alarcon-Aquino, O. Starostenko, A Wavelet-Based 128-bit Key Generator Using Electrocardiogram Signals, In Proceedings of the Midwest Symposium on Circuits and Systems MWSCAS 2009, Cancún, Mexico, August 2009.

8. H. A. Garcia-Baleon, V. Alarcon-Aquino, A Power-Line Communication Modem based on OFDM, In Proceedings of the 19th IEEE International Conference on Electronics, Communications, and Computers, CONIELECOMP 2009, Puebla Mexico, March 2009 

Anomaly Detection and Prediction in Communication Networks Using Wavelet Transforms

Collaborators: V. Alarcon-Aquino (UDLAP, Mexico), J. A. Barria (Imperial College London, UK)

It is important for service providers to monitor their systems in order to detect network anomalies and performance degradations in advance of network/service disruptions. In this regard, several anomaly detection schemes have already been proposed in the literature. These schemes are in most cases based on parametric models and thresholding techniques. The underlying aim of this research is to develop models and algorithms based on wavelet transforms for analysing the statistical behaviour of network metrics in order to detect and predict network anomalies and performance degradations in communication networks. A novel wavelet-based algorithm is proposed for detecting network anomalies in communication networks. The wavelet-based algorithm is then used to detect events in different network metrics of a Dial Internet Protocol service and corporate Proxy servers. A sensor fusion scheme, which combines local decisions made from dispersed wavelet-based sensors, is also investigated. This sensor fusion scheme incorporates the spatial dependencies among the monitored network metrics and hence reduces the number of false alarms generated by each network metric. Furthermore, a novel learning algorithm is investigated for time series prediction based on finite impulse response (FIR) neural networks and the multiresolution analysis framework of wavelet theory. A gradient descent method is used to adapt the gain of the non-linear functions in FIR networks at each level of resolution. The multi-resolution learning algorithm is compared with previously reported algorithms using a benchmark time series. The algorithm is also applied to network traffic prediction in an Ethernet environment. The results show that the generalisation ability of the FIR network is improved by the multiresolution learning algorithm. A method is also investigated for predicting and monitoring communication network metrics. The proposed method learns to predict the normal behaviour of the monitored network metric and together with an online decision-making algorithm detects and classify deviations from the normal operating region. The proposed method is used to predict and monitor events incorporate Proxy servers and Local/Wide area network traces. Experimental results show that the proposed method is able to identify moderate and severe abnormal network behaviours in advance of reported network faults and thereby providing a useful method for proactively managing communication networks.

1. V. Alarcon-Aquino, J. A. Barria, Anomaly Detection in Communication Networks Using Wavelets, IEE-Proceedings-Communications, Vol.148, No.6; Dec. 2001; p.355-362. [pdf] JCR_SCIE®

2. V. Alarcon-Aquino, J. A. Barria, Multiresolution FIR Neural Network Based Learning Algorithm Applied to Network Traffic Prediction, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Review, Vol. 36, Issue No. 2, March 2006. pp. 208-220 [pdf] JCR_SCIE®

3. V. Alarcon-Aquino, J. A. Barria, Change Detection in Time Series Using The Maximal Overlap Discrete Wavelet Transform, Latin American Applied Research: An International Journal, Vol. 39, No. 2 April 2009 [pdf]  JCR_SCIE®

4. V. Alarcon-Aquino, J. A. Barria, Multi-Sensor Fusion System Using Wavelet Based Detection Algorithm Applied to Network Monitoring. In Proceedings of London Communications Symposium, IEE LCS 2002 ISBN: 0-9538863-2-8, London, UK., 9-10 September, 2002.[pdf]