DFE/THP duality for FBMC with highly frequency selective channels. - Hela Jedda; Leonardo Gomes Baltar; Oliver De Candido; Amine Mezghani; Josef A. Nossek (EUSIPCO, 2015)

SIMO/MISO MSE-Duality for Multi-User FBMC with Highly Frequency Selective Channels. - Oliver De Candido; Leonardo Gomes Baltar; Amine Mezghani; Josef A. Nossek (WSA, 2015)

Downlink Precoder and Equalizer Designs for Multi-User MIMO FBMC/OQAM. - Oliver De Candido; Sher Ali Cheema; Leonardo Gomes Baltar; Martin Haardt; Josef A. Nossek (WSA, 2016)

Are Traditional Signal Processing Techniques Rate Maximizing in Quantized SU-MISO Systems? - Oliver De Candido; Hela Jedda; Amine Mezghani; A. Lee Swindlehurst; Josef A. Nossek (GLOBECOM, 2017)

Interpretable Feature Generation using Deep Neural Networks and its Application to Lane Change Detection. - Oliver Gallitz; Oliver De Candido; Michael Botsch; Wolfgang Utschick (ITSC, 2019)

Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems. - Oliver De Candido; Hela Jedda; Amine Mezghani; A. Lee Swindlehurst; Josef A. Nossek (IEEE Trans. Wirel. Commun., 2019)

Interpretable Machine Learning Structure for an Early Prediction of Lane Changes. - Oliver Gallitz; Oliver De Candido; Michael Botsch; Ron Melz; Wolfgang Utschick (ICANN (1), 2020)

Towards Feature Validation in Time to Lane Change Classification using Deep Neural Networks. - Oliver De Candido; Michael Koller 001; Oliver Gallitz; Ron Melz; Michael Botsch; Wolfgang Utschick (ITSC, 2020)

Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture. - Oliver Gallitz; Oliver De Candido; Michael Botsch; Wolfgang Utschick (ITSC, 2021)

Parameter Sharing Reinforcement Learning for Modeling Multi-Agent Driving Behavior in Roundabout Scenarios. - Fabian Konstantinidis; Ulrich Hofmann 002; Moritz Sackmann; Jörn Thielecke; Oliver De Candido; Wolfgang Utschick (ITSC, 2021)

An Interpretable Lane Change Detector Algorithm based on Deep Autoencoder Anomaly Detection. - Oliver De Candido; Maximilian Binder; Wolfgang Utschick (IV, 2021)

Learning to Detect Adversarial Examples Based on Class Scores. - Tobias Uelwer; Felix Michels; Oliver De Candido (KI, 2021)

Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders. - Philipp Joppich; Sebastian Dorn; Oliver De Candido; Wolfgang Utschick; Jakob Knollmüller (CoRR, 2021)

An Analysis of Distributional Shifts in Automated Driving Functions in Highway Scenarios. - Oliver De Candido; Xinyang Li; Wolfgang Utschick (VTC Spring, 2022)

Detecting an Offset-Adjusted Similarity Score based on Duchenne Smiles. - Maximilian Henneberg; Chloe Eghtebas; Oliver De Candido; Kai Kunze; Jamie A. Ward (CHI Extended Abstracts, 2023)

On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows. - Jia Yu Tee; Oliver De Candido; Wolfgang Utschick; Philipp Geiger (ITSC, 2023)

Encouraging Validatable Features in Machine Learning-Based Highly Automated Driving Functions. - Oliver De Candido; Michael Koller 001; Wolfgang Utschick (IEEE Trans. Intell. Veh., 2023)

Interpretable Classifiers Based on Time-Series Motifs for Lane Change Prediction. - Kathrin Klein; Oliver De Candido; Wolfgang Utschick (IEEE Trans. Intell. Veh., 2023)

Evaluating AI-Based Components in Autonomous Railway Systems - A Methodology. - Jan Roßbach; Oliver De Candido; Ahmed Hammam; Michael Leuschel (KI, 2024)

Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations. - Ahmed Hammam; Bharathwaj Krishnaswami Sreedhar; Nura Kawa; Tim Patzelt; Oliver De Candido (CoRR, 2024)