35 citations · 40 across the 16 of their papers we have counts for
16 papers
CDKF-Track: Cluster-aware Data-Driven Kalman Filtering for Cooperative 3D Multi-Object Tracking
Maria Damanaki, Nikos Piperigkos, Alexandros Gkillas +1
Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decision-making. Singleagent MOT…
Adversarially Guided Diffusion for LiDAR Range Image Synthesis
Stavros Bouras, Antonios Makris, Alexandros Gkillas +2
LiDAR semantic segmentation is a key perception task in autonomous driving, where false predictions can affect downstream planning and safety-critical decision-making. Although adv…
Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications
Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos +1
This work tackles the challenge of enhancing low-resolution LiDAR sensors for SLAM applications through a novel Deep Unrolling-based Super-Resolution (SR) model. We integrate an ou…
Guided Model-based LiDAR Super-Resolution for Resource-Efficient Automotive scene Segmentation
Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
High-resolution LiDAR data plays a critical role in 3D semantic segmentation for autonomous driving, but the high cost of advanced sensors limits large-scale deployment. In contras…
Integrated Simulation Framework for Adversarial Attacks on Autonomous Vehicles
Christos Anagnostopoulos, Ioulia Kapsali, Alexandros Gkillas +2
Autonomous vehicles (AVs) rely on complex perception and communication systems, making them vulnerable to adversarial attacks that can compromise safety. While simulation offers a…
Efficient Model-Based Purification Against Adversarial Attacks for LiDAR Segmentation
Alexandros Gkillas, Ioulia Kapsali, Nikos Piperigkos +1
LiDAR-based segmentation is essential for reliable perception in autonomous vehicles, yet modern segmentation networks are highly susceptible to adversarial attacks that can compro…
A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm
Alexandros Gkillas, Christos Anagnostopoulos, Nikos Piperigkos +16
This paper introduces a holistic perception system for internal and external monitoring of autonomous vehicles, with the aim of demonstrating a novel AI-leveraged self-adaptive fra…
Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking
Maria Damanaki, Ioulia Kapsali, Nikos Piperigkos +2
The critical perception capabilities of EdgeAI systems, such as autonomous vehicles, are required to be resilient against adversarial threats, by enabling accurate identification a…
Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing
Maria Damanaki, Nikos Piperigkos, Alexandros Gkillas +1
Multi-Object Tracking (MOT) plays a crucial role in autonomous driving systems, as it lays the foundations for advanced perception and precise path planning modules. Nonetheless, s…
Personalized Federated Learning for Cross-view Geo-localization
Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos +1
In this paper we propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. We address the challenges of data privacy and het…
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis
Alexandros Gkillas, Aris Lalos
Anomaly and missing data constitute a thorny problem in industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, howev…
Federated Data-Driven Kalman Filtering for State Estimation
Nikos Piperigkos, Alexandros Gkillas, Christos Anagnostopoulos +1
This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More spe…
A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution
Alexandros Gkillas, Dimitris Ampeliotis, Kostas Berberidis
In recent literature there are plenty of works that combine handcrafted and learnable regularizers to solve inverse imaging problems. While this hybrid approach has demonstrated pr…
Deep Equilibrium Models Meet Federated Learning
Alexandros Gkillas, Dimitris Ampeliotis, Kostas Berberidis
In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networ…
Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising
Alexandros Gkillas, Dimitris Ampeliotis, Kostas Berberidis
In this study, the problem of computing a sparse representation of multi-dimensional visual data is considered. In general, such data e.g., hyperspectral images, color images or vi…
A cross-domain recommender system using deep coupled autoencoders
Alexandros Gkillas, Dimitrios Kosmopoulos
Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework ha…