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250 results on '"Azurdia-Meza, Cesar"'

1. Experimental Assessment of a Forward-Collision Warning System Fusing Deep Learning and Decentralized Radio Sensing

Author

Cardenas, Jorge D., Contreras-Ponce, Omar, Gutierrez, Carlos A., Aguilar-Ponce, Ruth, Castillo-Soria, Francisco R., and Azurdia-Meza, Cesar A.

Subjects
Computer Science - Machine Learning
Abstract

This paper presents the idea of an automatic forward-collision warning system based on a decentralized radio sensing (RS) approach. In this framework, a vehicle in receiving mode employs a continuous waveform (CW) transmitted by a second vehicle as a probe signal to detect oncoming vehicles and warn the driver of a potential forward collision. Such a CW can easily be incorporated as a pilot signal within the data frame of current multicarrier vehicular communication systems. Detection of oncoming vehicles is performed by a deep learning (DL) module that analyzes the features of the Doppler signature imprinted on the CW probe signal by a rapidly approaching vehicle. This decentralized CW RS approach was assessed experimentally using data collected by a series of field trials conducted in a two-lanes high-speed highway. Detection performance was evaluated for two different DL models: a long short-term memory network and a convolutional neural network. The obtained results demonstrate the feasibility of the envisioned forward-collision warning system based on the fusion of DL and decentralized CW RS.

Published
2023

2. Analysis of Spectrum Detection and Decision Using Machine Learning Algorithms in Cognitive Mobile Radio Networks

Author

Játiva, Pablo Palacios, Azurdia-Meza, Cesar, Sánchez, Iván, Zabala-Blanco, David, Cañizares, Milton Román, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Wu, Xi, editor, Wu, Kaishun, editor, and Wang, Cong, editor

Published
2021
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3. A Multi-channel Speech Enhancement Method Based on Subband Affine Projection Algorithm in Combination with Proposed Circular Nested Microphone Array

Author

Firoozabadi, Ali Dehghan, Irarrazaval, Pablo, Adasme, Pablo, Durney, Hugo, Olave, Miguel Sanhueza, Zabala-Blanco, David, Azurdia-Meza, Cesar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Slowik, Adam, editor, Snášel, Václav, editor, El-Deeb, Hisham, editor, and Tolba, Fahmy M., editor

Published
2021
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4. Simultaneous Sound Source Localization by Proposed Cuboids Nested Microphone Array Based on Subband Generalized Eigenvalue Decomposition

Author

Firoozabadi, Ali Dehghan, Irarrazaval, Pablo, Adasme, Pablo, Durney, Hugo, Olave, Miguel Sanhueza, Zabala-Blanco, David, Azurdia-Meza, Cesar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hassanien, Aboul Ella, editor, Slowik, Adam, editor, Snášel, Václav, editor, El-Deeb, Hisham, editor, and Tolba, Fahmy M., editor

Published
2021
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8. Propagation Features of Visible Light Communication in Underground Mining Environments

Author

Palacios Játiva, Pablo, Azurdia-Meza, Cesar A., Román Cañizares, Milton, Zabala-Blanco, David, Saavedra, Carlos, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Botto-Tobar, Miguel, editor, Zambrano Vizuete, Marcelo, editor, Torres-Carrión, Pablo, editor, Montes León, Sergio, editor, Pizarro Vásquez, Guillermo, editor, and Durakovic, Benjamin, editor

Published
2020
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10. Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing.

Author

Urbina Fredes, Sebastián, Dehghan Firoozabadi, Ali, Adasme, Pablo, Zabala-Blanco, David, Palacios Játiva, Pablo, and Azurdia-Meza, Cesar

Subjects
EPILEPSY, SIGNAL processing, ELECTROENCEPHALOGRAPHY, DISCRETE wavelet transforms, BIOMEDICAL signal processing, SIGNAL detection, SUPPORT vector machines
Abstract

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment. [ABSTRACT FROM AUTHOR]

Published
2024
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27. A novel traffic prediction method using machine learning for energy efficiency in service provider networks

Author

Rau, Francisco, Soto, Ismael, Zabala-Blanco, David, Azurdia-Meza, Cesar A, Ijaz, Muhammad, Ekpo, Sunday, Gutierrez, Sebastian, Rau, Francisco, Soto, Ismael, Zabala-Blanco, David, Azurdia-Meza, Cesar A, Ijaz, Muhammad, Ekpo, Sunday, and Gutierrez, Sebastian

Abstract

The paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and Online Sequential Extreme Learning Machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results showed that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed a potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.

Published
2023

39. A novel and adaptive angle diversity-based receiver for 6G underground mining VLC systems

Author

Palacios Játiva, Pablo, Sánchez, Iván, Soto, Ismael, Azurdia-Meza, Cesar A, Zabala-Blanco, David, Ijaz, Muhammad, Dehghan Firoozabadi, Ali, Plets, David, Palacios Játiva, Pablo, Sánchez, Iván, Soto, Ismael, Azurdia-Meza, Cesar A, Zabala-Blanco, David, Ijaz, Muhammad, Dehghan Firoozabadi, Ali, and Plets, David

Abstract

Visible light communication (VLC) is considered an enabling technology for future 6G wireless systems. Among the many applications in which VLC systems are used, one of them is harsh environments such as Underground Mining (UM) tunnels. However, these environments are subject to degrading environmental and intrinsic challenges for optical links. Therefore, current research should focus on solutions to mitigate these problems and improve the performance of Underground Mining Visible Light Communication (UM-VLC) systems. In this context, this article presents a novel solution that involves an improvement to the Angle Diversity Receivers (ADRs) based on the adaptive orientation of the Photo-Diodes (PDs) in terms of the Received Signal Strength Ratio (RSSR) scheme. Specifically, this methodology is implemented in a hemidodecahedral ADR and evaluated in a simulated UM-VLC scenario. The performance of the proposed design is evaluated using metrics such as received power, user data rate, and bit error rate (BER). Furthermore, our approach is compared with state-of-the-art ADRs implemented with fixed PDs and with the Time of Arrival (ToA) reception method. An improvement of at least 60% in terms of the analyzed metrics compared to state-of-the-art solutions is obtained. Therefore, the numerical results demonstrate that the hemidodecahedral ADR, with adaptive orientation PDs, enhances the received optical signal. Furthermore, the proposed scheme improves the performance of the UM-VLC system due to its optimum adaptive angular positioning, which is completed according to the strongest optical received signal power. By improving the performance of the UM-VLC system, this novel method contributes to further consideration of VLC systems as potential and enabling technologies for future 6G deployments.

Published
2022

44. Underground Mine Positioning: A Review

Author

Seguel, Fabian, primary, Palacios-Jativa, Pablo, additional, Azurdia-Meza, Cesar A., additional, Krommenacker, Nicolas, additional, Charpentier, Patrick, additional, and Soto, Ismael, additional

Published
2022
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