Antônio Augusto Fröhlich

Predictor-based compensators for networked control systems with stochastic delays and sampling intervals

Matheus Wagner, Marcelo Menezes Morato, Antônio Augusto Fröhlich, Julio Elias Normey-Rico

The stochastic nature of time delays in networked control systems poses significant challenges for controller synthesis and the corresponding analyses, leading to conservative designs and degraded performance. Existing approaches approximate stochastic delays by fixed, worst-case values, which limits their ability to describe …

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Energetic SmartData: A data-driven power management approach for cyber–physical systems

Antônio Augusto Fröhlich, Leonardo Passig Horstmann, Jozimar Custódio Xavier

Power management is a cornerstone for many Cyber-Physical Systems (CPSs), which relies on low-power circuits, dynamic power management algorithms and energy-aware software to match their requirements in terms of energy. As CPSs evolve towards data-centric designs to more promptly accommodate AI models …

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Linking Physical Fidelity to Downstream Performance in Physics-Informed Fault Diagnosis

Enzo Nicolás Spotorno, J. L. Ribeiro Filho, Antônio Augusto Fröhlich

A critical question in Scientific Machine Learning is whether improving a Physics-Informed Neural Network’s (PINN) physical fidelity translates into downstream task performance. We investigate this link using a three-stage diagnostic framework: PINN-based residual generation, Extreme Value Theory anomaly detection, and Siamese Neural …

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A Distributed and Adaptive Failure Detector for Vehicular Networks based on Cooperative Awareness Messages

Edson Tavares de Camargo, Antônio Augusto Fröhlich

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Adaptive QoS to Handle Transient Overloads in Time-Sensitive Vehicular Networks Caused by ADAS

Leonardo Passig Horstmann, Antônio Augusto Fröhlich, J. L. Ribeiro Filho, José Luís Conradi Hoffmann

Time-Sensitive Networking (TSN) has emerged as a key enabler for Advanced Driver Assistance Systems (ADASs), providing deterministic, low-latency communication essential for safety-critical applications. ADAS functionality relies on Computer Vision and Machine Learning techniques to process data from sensors such as cameras, LiDAR, …

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A Systematic Literature Review on Vehicular Collaborative Perception—A Computer Vision Perspective

Lei Wan, Jianxin Zhao, Andreas Wiedholz, Manuel Bied et autres

The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle …

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Enabling On-the-Fly Automotive Suspension Health Monitoring with Vision-based Vibration Model

Antônio Augusto Fröhlich, José Luís Conradi Hoffmann, Luiz Fernando Martins Pastuch

Predictive Maintenance plays a vital role in improving cost-efficiency of commercial fleets while improving reliability and safety metrics. This work presents a lightweight, vision-based framework for vibration-based suspension health monitoring that leverages video-derived vertical acceleration signals to detect anomalies on-the-fly. By classifying …

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Embedded Operation Effort Estimation: A Machine Learning Performance-Efficiency Trade-off Analysis

Enzo Nicolás Spotorno, Leonardo Passig Horstmann, José Luís Conradi Hoffmann, Antônio Augusto Fröhlich

Real-time monitoring of a vehicle’s operational effort is critical for enhancing dependability and enabling predictive maintenance in modern automotive systems. Implementing this on resource-constrained on-board computers, however, requires navigating a trade-off between prediction accuracy, data-label efficiency, and computational cost. This paper investigates …

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Seismic Data Compression with OBNZip: Implications for 4D Seismic Analysis

J. L. Ribeiro Filho, Pablo Machado Barros, Mateus Lucena, André Bulcão et autres

Efficient data storage and transmission in geophysical workflows rely on seismic data compression due to the massive, multi-dimensional datasets involved. This work presents OBNZip, a scalable modular compression framework that exploits sparsity, non-stationarity, and spatial redundancy in seismic data. We focus on …

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Physics-Informed Residual-Based Anomaly Detection and Open-Set Recognition System: A Case Study on Ring Bearings

Enzo Nicolás Spotorno, J. L. Ribeiro Filho, Antônio Augusto Fröhlich

Industrial anomaly detection often faces a trade-off between the interpretability of physics-based models and the flexibility of data-driven methods. This paper proposes a novel residual-based framework for anomaly detection and recognition in dynamic systems. Our approach employs a Physics-Informed Neural Network trained …

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A V2X Secrecy Forwarding Protocol for Intelligent Transport Systems

Thiago A. Bewiahn, Antônio Augusto Fröhlich

As vehicles become increasingly interconnected, ensuring secure, low-latency communication in Intelligent Transport Systems (ITS) is crucial. Traditional ITS security models, typically based on Public Key Infrastructure (PKI), introduce significant latency. In this work, we adapt and evaluate a V2X secrecy forwarding protocol …

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TruckRSS: Adding Variable Mass to the Responsibility-Sensitive Safety Model

José Luís Conradi Hoffmann, Antônio Augusto Fröhlich

The rising number of truck-related fatalities on Brazilian highways, alongside global increases in heavy-duty vehicle (HDV) incidents, highlights the need for advanced adaptive safety systems. Responsibility-Sensitive Safety (RSS) provides a verifiable framework for autonomous driving but struggles with HDV challenges such as …

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