XLIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais

Neste trabalho, propomos um esquema de marca d'água em imagens baseado em aprendizado profundo, no qual as camadas de inserção e extração são treinadas de forma iterativa para garantir alta robustez contra ataques. A camada de inserção adota uma estrutura paralela com redes neurais convolucionais inspiradas na rede Inception, enquanto a camada de...

Artificial Intelligence in Electronic Warfare has gained prominence, particularly for Automatic Modulation Clas- sification tasks. Deep learning methods have demonstrated ro- bustness and high accuracy in addressing this challenge. This study proposed and tested Long Short-Term Memory and Convo- lutional Neural Network architectures for Automatic...

This paper investigates the use of the Cubature Kalman Filter (CKF) for estimating the trajectory of a projectile with known ballistic parameters using simulated radar measurements from a Weapon Locating Radar. The CKF is a nonlinear filtering technique with enhanced accuracy and numerical stability compared to the Extended Kalman Filter and the...

The widespread adoption of 5G capable smartphones has made high-speed connectivity accessible to most users, with Enhanced Mobile Broadband (eMBB) technology playing a pivotal role in delivering faster download and upload rates. The diverse range of services enabled by 5G networks demands innovative solutions for optimizing resource allocation and...

In this work we introduce new PRNGs based on linear recurrences over the Galois field GF(2^e). We analyze the period properties of the linear recurrence and identify the conditions under which the proposed PRNGs achieve the maximum period of 2^e − 1. A logical circuit diagram using balanced binary tree structures is proposed and its complexity is...

This paper aims to develop a system that detects potential fires in residential kitchens. The system collects real-time data on changes in temperature, CO2, and cooking gas leaks using sensors and transmits this information to residents through an Android application. The machine learning model embedded in the system achieved an accuracy of 99.5\% during...

The expansion of the Internet of Things (IoT) presents significant security challenges due to the limited resources of many IoT devices and their insufficient protection mechanisms. Lightweight protoc

Neste trabalho, investigamos o desempenho da técnica Long-Range Frequency Hopping Spread Spectrum (LR-FHSS), com foco na relação de trade-off entre goodput e Pontualidade da Informação (em inglês Age of Information, AoI). Especificamente, avaliamos o desempenho de diferentes taxas de dados (em inglês Data Rates, DR) padronizadas do LR-FHSS, analisando...

This paper presents the development of an intelligent baby monitoring system for deaf parents, utilizing IoT (Internet of Things) and machine learning. The system captures the baby's cries through a microphone, processes the data in a microcontroller using MFCC (Mel-Frequency Cepstral Coefficients) and LSTM (Long Short-Term Memory) networks, and sends...

The growing demand for energy efficiency in mobile networks has driven the adoption of virtualized Base Stations running on general-purpose processors. This paper compares machine learning models for estimating power consumption using data from four processor architectures. Extreme Gradient Boosted Trees Regressions delivered the most accurate and robust...