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SBrT2026 · 2026

XLIV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais

303 artigos · página 21 de 31
DOI 10.14209/sbrt.2026.1571299795

Estimação de Componentes Harmônicas em Sistemas Elétricos Utilizando Combinação Convexa de Filtros Adaptativos

Hemerson Diego Azevedo Barros, Caio Vinícius Santos Dezidério, Thiago Silva Fernandes, Francisco das Chagas de Souza
This paper proposes the CONV-TNLMS architecture for harmonic component estimation in electrical power systems, based on the convex combination of scalar NLMS and tensor NLMS adaptive filters operating in parallel. Tensor modeling via Kronecker product decomposition reduces the number of adapted parameters, while the convex combination mechanism with...
Adaptive filteringConvex combinationTensor NLMSHarmonic estimation
DOI 10.14209/sbrt.2026.1571299796

Statistical-CSI-Based RIS User Clustering: From Interference-Free TDMA to Simultaneous Multiuser Transmission

Pedro Teixeira Sega, José Carlos Marinello
Reconfigurable intelligent surfaces (RISs) can improve wireless coverage and spectral efficiency, but practical deployments are limited by channel estimation overhead and by the number of RIS reconfigurations within a transmission frame. Statistical channel state information (S-CSI) is a low-overhead alternative for RIS phase-shift design and user...
SINRStatistical CSIUser clusteringTDMA
DOI 10.14209/sbrt.2026.1571299803

Performance Analysis of LoRa CSS Communication in Dense Foliage Environments

Paulo Oliveira Mendes, Davi Sampaio Timbó, Samuel do Nascimento Arruda, Isabela Avelino Freire, Alberto Lima, Francisco Helder Candido Filho
Communication in forest and dense foliage environments is challenging because vegetation obstructs the Fresnel zone, increases multipath propagation, and attenuates radiofrequency signals through leaves, trunks, and moisture. This paper evaluates the performance of LoRa Chirp Spread Spectrum (CSS) communication in a dense foliage scenario using Heltec V3...
LoRaCSSMeshtasticdense foliage
DOI 10.14209/sbrt.2026.1571299813

Utilização De Estrutura PBG Quase-Periódica Baseada Em Azulejo De Penrose No Projeto De Antenas De Microfita

Sebastião Ferreira Baracho Neto, José Patrocínio da Silva, Idalmir de Souza Queiroz Junior
Este artigo apresenta o projeto e a análise de uma antena de microfita com estrutura PBG (Photonic Band Gap) baseada no padrão quasiperiódico de Penrose. A antena foi projetada em substrato FR4 (εᵣ = 4,4) utiliza furos de ar no substrato para obter melhores resultados nos parâmetros da antena. O uso de um PBG quasiperiódico inspirado em Penrose trouxe...
Azulejo de PenrosePBGAntena de MicrofitaPadrão Quase-periódico
DOI 10.14209/sbrt.2026.1571299814

Subject-Independent Estimation of Vertical Ground Reaction Force from a Wearable Accelerometer: A Reproducible Benchmark of Accuracy, Robustness, and Inference Latency

Sérgio Rodrigues Lima Júnior, Ronaldo F Zampolo, Antonio Pereira Jr
Accurate estimation of vertical ground reaction force (vGRF) outside laboratory environments remains a key challenge in wearable biomechanics and rehabilitation monitor- ing. This study investigates subject-independent vGRF estimation from a single triaxial tibial accelerometer using a fully repro- ducible experimental pipeline. A leave-one-subject-out...
ground reaction force estimationwearable accelerometerssubject-independent validationmachine learning
DOI 10.14209/sbrt.2026.1571299820

Study on coexisting 6G D-MIMO and massive MIMO networks operating in TDD

Mariana Marques Ferreira, Maria Clara R. Lobão, Yuri C. B. Silva, Igor M. Guerreiro, Roberto Pinto Antonioli, Behrooz Makki
In this study, the coexistence between a multiple-input and multiple-output (MIMO) primary network (PN) and a distributed MIMO (D-MIMO) secondary network (SN) is analyzed when both networks operate in time division duplex (TDD) and their uplink (UL) and downlink (DL) phases are not synchronized. The impact of the interference is observed when the...
6GCoexistenceCross-link InterferenceDistributed MIMO
DOI 10.14209/sbrt.2026.1571299825

Blink Identification Using Video Magnification for Temporal Analysis of Eyelid Motion

Maria Luiza Cavalcante Cernadas, Victor H. R. Cardoso, Joao Weyl Costa, Moisés Felipe Silva
This work proposes a blink detection method based on video magnification and evaluates its performance against the Eye Aspect Ratio (EAR), a widely used geometric landmark based approach. The methods were assessed using 221 manually annotated blink events collected from two subjects performing fixation and reading tasks at three acquisition distances....
Blink DetectionVideo MagnificationNon-contact MonitoringPartial Blink
DOI 10.14209/sbrt.2026.1571299831

Load Balancing Strategies for Terrestrial and Non-Terrestrial Network Coexistence

Francisco Cassimiro de Sousa Albuquerque, F. Rodrigo P Cavalcanti, Victor Farias Monteiro, Lucas dos Santos Fonseca
Non-Terrestrial Networks (NTNs) are commonly envisioned as a means of extending coverage beyond the reach of terrestrial infrastructure. However, they can also be used to complement Terrestrial Networks (TNs) in supporting traffic demand. This paper evaluates load balancing strategies for TN-NTN coexistence through bidirectional user offloading and...
Non-Terrestrial NetworksLEO SatellitesOffloadingLoad Balancing
DOI 10.14209/sbrt.2026.1571299837

Evaluation of Power Control Strategies for Dense Wireless Networks

Caio Rodrigues dos Santos, F. Rodrigo P Cavalcanti
This paper presents a comparative analysis of power control algorithms for dense wireless uplink networks. Three approaches are evaluated under noise-limited and interference-limited scenarios: Distributed Power Control (DPC), MaxSum, and MaxProd. Results show that DPC provides higher energy efficiency, while MaxProd achieves a balanced trade-off between...
6GPower ControlDense Wireless NetworkEnergy Efficiency
DOI 10.14209/sbrt.2026.1571299841

Multivariate Time Series Classification in Resource-Constrained Embedded Systems

João Vítor Correia Pessoa, Rafael da Silva Chaves, Marcello Luiz Rodrigues de Campos
Monitoring systems in oil wells generate large volumes of multivariate time-series data that often need to be used for local decision-making, motivating the use of embedded machine learning solutions for real-time anomaly detection at the edge. Implementing machine learning models on resource-constrained single-board computers requires efficient...
neural network quantizationedge inferenceoil well event detectionpost-training quantization