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Athanasios Voulodimos

Researcher at University of the West

Publications -  147
Citations -  4137

Athanasios Voulodimos is an academic researcher from University of the West. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 22, co-authored 135 publications receiving 2346 citations. Previous affiliations of Athanasios Voulodimos include Technological Educational Institute of Athens & National Technical University of Athens.

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Journal ArticleDOI

Deep Learning for Computer Vision: A Brief Review.

TL;DR: A brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders are provided.
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A complete farm management system based on animal identification using RFID technology

TL;DR: The platform apart from using a data repository through which the RFID tag numbers are associated with animal data records, introduces the use of rewritable tags for the storage of information that can be used to identify the animal in case it gets lost, or even recognize some basic information about it without the need of contacting the related database.
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Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing

TL;DR: The presented mechanism was designed and developed as a core component of an autonomous robotic inspector deployed and validated in the tunnels of Egnatia Motorway in Metsovo, Greece, and suggest a promising potential as a driver of autonomous concrete-lining tunnel-inspection robots.
Proceedings ArticleDOI

Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring

TL;DR: A Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, which is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increase.
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Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models

TL;DR: A non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation that harnesses the representational power of deep recurrent Long Short-Term Memory neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for.