IEEE Latin America Transactions最新文献_第9页

Time frequency distribution and deep neural network for automated identification of insomnia using single channel EEG-signals 利用单通道脑电信号的时频分布和深度神经网络自动识别失眠症

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431420

Kamlesh Kumar;Prince Kumar;Ruchit Kumar Patel;Manish Sharma;Varun Bajaj;U Rajendra Acharya

{"title":"Time frequency distribution and deep neural network for automated identification of insomnia using single channel EEG-signals","authors":"Kamlesh Kumar;Prince Kumar;Ruchit Kumar Patel;Manish Sharma;Varun Bajaj;U Rajendra Acharya","doi":"10.1109/TLA.2024.10431420","DOIUrl":"https://doi.org/10.1109/TLA.2024.10431420","url":null,"abstract":"It is essential to have enough sleep for a healthy life; otherwise, it may lead to sleep disorders such as apnea, narcolepsy, insomnia, and periodic leg movements. A polysomnogram (PSG) is typically used to analyze sleep and identify different sleep disorders. This work proposes a novel convolutional neural network (CNN)-based technique for insomnia detection using single-channel electroencephalogram (EEG) signals instead of complex PSG. Morlet wavelet-based continuous wavelet transforms and smoothed pseudo-Wigner-Ville distribution (SPWVD) are explored in the proposed method to obtain scalograms of EEG signals of duration 1s along with convolutional layers for features extraction and image classification. The Morlet transform is found to be a better time-frequency distribution. We have developed Morlet wavelet-based CNN (MWTCNNet) for the classification of healthy and insomniac patients using cyclic alternating pattern (CAP) and sleep disorder research centre (SDRC) databases with C4-A1 single-channel EEG derivation. We have used multiple cohorts/settings of the CAP and SDRC databases to analyse the performance of proposed model. The proposed MWTCNNet achieved an accuracy, sensitivity, and specificity of 98.9%, 99.03%, and 98.66%, respectively, using the CAP database, and 99.03%, 99.20%, and 98.87%, respectively, with the SDRC database. Our proposed model performs better than existing state-of-the-art models and can be tested on a vast, diverse database before being installed for clinical application.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10431420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Table of Contents March 2024 目录 2024 年 3 月

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431417

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Evaluation of Solar Panel Bandwidth for RGB Channels in Visible Light Communication 评估可见光通信中 RGB 信道的太阳能电池板带宽

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431426

Roger Martinez;Francisco Eugenio Lopez Giraldo;Jose Martin Luna Rivera;Juan David Navarro Restrepo;Juan David Rojas Usuga

{"title":"Evaluation of Solar Panel Bandwidth for RGB Channels in Visible Light Communication","authors":"Roger Martinez;Francisco Eugenio Lopez Giraldo;Jose Martin Luna Rivera;Juan David Navarro Restrepo;Juan David Rojas Usuga","doi":"10.1109/TLA.2024.10431426","DOIUrl":"https://doi.org/10.1109/TLA.2024.10431426","url":null,"abstract":"Visible light communication (VLC) is an emerging technology that uses white light-emitting diodes (LEDs) to transmit information and provide illumination simultaneously. Recently, solar panels have been proposed as optical detectors at the receiver to retrieve data from light signals. However, very few studies have addressed the behavior of the solar panel bandwidth at different wavelengths. In this paper, we propose the design of a low-complexity VLC system with a red-green-blue (RGB) LED transmitter and a solar panel receiver whose bandwidth is modified using a parallel load resistor. We define a set of experiments to validate the performance of the VLC system using an RGB LED source and a solar panel as the optical receiver. The VLC systems performance is evaluated across various baud rates (4800, 9600, 19200, 38400, 57600, and 115200 bits/s) at a free space transmission distance of less than 105 cm. Our measurements indicate that the solar panels highest bandwidth is achieved with the red channel, yielding a maximum data rate of 57600 bits/s at a bit error rate (BER) of 5 103. These results are analyzed and discussed to highlight the benefits and limitations of using solar panels for VLC purposes.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10431426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139713629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Novelty detection algorithms to help identify abnormal activities in the daily lives of elderly people 新奇事物检测算法帮助识别老年人日常生活中的异常活动

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431423

Anita Fernandes;Valderi Leithardt;Juan Francisco Santana

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Automatic Modulation Classification for low-power IoT applications 针对低功耗物联网应用的自动调制分类

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431424

Yasmin R. Mondino-Llermanos;Graciela Corral-Briones

{"title":"Automatic Modulation Classification for low-power IoT applications","authors":"Yasmin R. Mondino-Llermanos;Graciela Corral-Briones","doi":"10.1109/TLA.2024.10431424","DOIUrl":"https://doi.org/10.1109/TLA.2024.10431424","url":null,"abstract":"The Internet of Things (IoT) has swiftly become one of the most important technologies in recent years. Radio spectrum access represents a stern challenge for the IoT as a consequence of the increased use of connected devices. This is particularly true for IoT devices operating in the unlicensed band where the huge demand for wireless connectivity will require techniques that use the spectrum efficiently. Avoiding training sequences enables a more efficient spectrum usage and has the additional advantage of reducing the power consumption of IoT devices, but it requires modulation identification mechanisms. This paper presents a simple yet efficient method to classify received signals according to their modulation type. We propose the application of a single hidden layer neural network with a small number of trainable parameters for performing the classification between seven different modulation types. The designed classifier achieves a maximum accuracy of 95% when the signal-to-noise ratio (SNR) of the input data is 12 dB, and in the presence of multi-path fading, sample rate offset and carrier frequency offset.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10431424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparison Study of Depth Map Estimation in Indoor Environments Using pix2pix and CycleGAN 使用 pix2pix 和 CycleGAN 进行室内环境深度图估算的比较研究

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431422

Ricardo Salvino Casado;Emerson Carlos Pedrino

{"title":"A Comparison Study of Depth Map Estimation in Indoor Environments Using pix2pix and CycleGAN","authors":"Ricardo Salvino Casado;Emerson Carlos Pedrino","doi":"10.1109/TLA.2024.10431422","DOIUrl":"https://doi.org/10.1109/TLA.2024.10431422","url":null,"abstract":"This article presents a Deep Learning-based approach for comparing automatic depth map estimation in indoor environments, with the aim of using them in navigation aid systems for visually impaired individuals. Depth map estimation is a laborious process, as most high-precision systems consist of complex stereo vision systems. The methodology utilizes Generative Adversarial Networks (GANs) techniques for generating depth maps from single RGB images. The study introduces methods for generating depth maps using pix2pix and CycleGAN. The major challenges still lie in the need to use large datasets, which are coupled with long training times. Additionally, a comparison of L1 Loss with a variation of the MonoDepth2 and DenseDepth systems was performed, using ResNet50 and ResNet18 as encoders, which are mentioned in this work, for comparison and validation of the presented method. The results demonstrate that CycleGAN is capable of generating more reliable maps compared to pix2pix and DepthNetResNet50, with an L1 Loss approximately 2,5 times smaller than pix2pix, approximately 2,4 times smaller than DepthNetResNet50, and approximately 14 times smaller than DepthNetResNet18.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10431422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Control and Grasping for a Robotic Hand with a Non-linked Double Tendon Arrangement 采用非链接双肌腱排列的机器人手的最佳控制和抓取功能

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431547

Erick J. Sánchez-Garnica;Liliam Rodríguez-Guerrero;Rocío Ortega-Palacios;Omar Jacobo Santos-Sánchez

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Assessing Human Settlement Sprawl in Mexico via Remote Sensing and Deep Learning 通过遥感和深度学习评估墨西哥的人类居住区扩张情况

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI: 10.1109/TLA.2024.10431421

Antonio Briseño Montes;Joaquin Salas;Elio Atenogenes Villaseñor Garcia;Ranyart Rodrigo Suarez;Danielle Wood

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Navigation of mobile robots using neural networks and genetic algorithms 利用神经网络和遗传算法实现移动机器人导航

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-01-23 DOI: 10.1109/TLA.2024.10412033

David Abad Perez;Basil Mohammed Al-Hadithi;Victor Cadix Martin

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Design and Comparative Analysis of THz Antenna through Machine Learning for 6G Connectivity 通过机器学习设计和比较分析用于 6G 连接的太赫兹天线

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-01-23 DOI: 10.1109/TLA.2024.10412032

Rachit Jain;Vandana Vikas Thakare;Pramod Kumar Singhal

{"title":"Design and Comparative Analysis of THz Antenna through Machine Learning for 6G Connectivity","authors":"Rachit Jain;Vandana Vikas Thakare;Pramod Kumar Singhal","doi":"10.1109/TLA.2024.10412032","DOIUrl":"https://doi.org/10.1109/TLA.2024.10412032","url":null,"abstract":"The rise of sixth-generation (6G) technology has become increasingly necessary to meet the growing demand for high-speed internet and the continuous advancements in technology. The development of an optimal antenna design is crucial to attain the required performance and capabilities. Traditional electromagnetic modeling approaches for antenna design are, however, time-consuming and computationally intensive requiring long simulation time and high-end computing systems. Therefore, Machine Learning (ML) technology can be utilized to deal with these limitations in the context of Terahertz (THz) antenna design, which has not been done before. The main objective of this work is to develop an antenna that operates in the THz Band, which is the essential 6G band for the future infrastructure revolution, and to predict and optimize the antenna's return loss using ML models like K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XG-Boost), Decision Tree, and Random Forest and Mean Squared Error (MSE) of 3.816. The findings show that all of these models perform accurately, particularly Random Forest having the highest accuracy of 82% in predicting the return loss. ML offers novel possibilities for the development of optimized and efficient 6G antennas for high-speed communication.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10412032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0