XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais

This paper focuses in the analysis of Multichannel Blind Deconvolution (MBD) techniques over noiseless and noisy data. We use both REgularization by Denoising (RED) and the multichannel blind criterion as a new approach to regularize the ill posed inverse problem of image deblurring. Tests with different optimization techniques, such as fixed step,...

We present two methods based on Barlow Twins, a self-supervised method, to improve training with smaller batches. The first method randomly drops features from the output before computing the loss to reduce the variance. The second method introduces a queue of outputs from previous batches to improve the loss estimate during training. The first method,...

In this work, the metric learning is adopted to improve the classification of non-stationary acoustic sources. The proposed strategy aims to overcome the statistical differences that arise from the non-stationary behavior by learning an optimal function that minimizes intra-class and maximizes inter-class distances. A convolutional neural network with...

We report on using support vector classifiers (SVCs) to mitigate the residual and fiber-induced nonlinear distortions in a digital coherent optical communication system employing dual-polarization 16-ary quadrature amplitude modulation with a data rate of 100 Gbps. Simulation results reveal that SVC can partially tackle the effect of the...

Space division multiplexing (SDM) is a promising solution to increase the capacity of current optical networks. However, the capacity increase provided by SDM is limited by mode-dependent gain (MDG) generated in amplifiers. To enable the study of MDG-impaired SDM transmission, analytical models can be employed to simulate SDM channels at different MDG...

This paper addresses the problem of automated diagnosis of rolling bearing faults in rotating machinery using vibration data, which is crucial for efficient and continuous operation in the industry. A recent study has revealed that the traditional train-test split widely used in research suffered from data leakage, resulting in overly optimistic outcomes...

Este artigo apresenta uma abordagem sistemática para a seleção de hiperparâmetros no treinamento de duas arquiteturas relevantes de aprendizado profundo da literatura, usadas na classificação de patologias cardíacas. Especificamente, utilizando a base de dados multirrótulo PTB-XL ECG e métricas apropriadas, foi desenvolvido um script em Python para...

This paper describes the use of a suite of software tools that can be used as part of an integrated learning environment, including text, interactivity and projects for the teaching of undergraduate linear algebra.

Creating accurate and reliable low-resource automatic speech recognition (ASR) models remains challenging due to limited curated data. This work presents the development of a bilingual ASR model for Brazilian Portuguese and South-American Spanish. The model utilizes the Wav2Vec2.0 architecture and is trained on multiple speech datasets. It combines...

A comunicação pelo corpo humano (HBC) é um método de comunicação sem fio para redes corporais que apresenta vantagens com respeito ao consumo, segurança e interferência sobre outros métodos baseados em radiação. Neste trabalho são descritos o projeto e as simulações pós-leiaute de um receptor para HBC com acoplamento capacitivo desenvolvido em tecnologia...