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6. Quantum Technologies for future Quantum Optical Networks

Author

Paolo Martelli, Dileepsai Bodanapu, Alberto Gatto, Rafael J. Vicente, Ruben B. Mendez, Marco Brunero, Juan P. Brito, Vicente Martin, and Paolo Comi

Subjects
SDN, Quantum technology, Optical propagation, Quantum optical networks, Computer science, QKD, Wavelength-division multiplexing, Convergence (routing), Electronic engineering, Relevance (information retrieval), Quantum key distribution, Communications system, Quantum
Abstract

The recent evolutions in the convergence of QKD and classical communication systems are described, together with the relevance of SDN as a suitable approach for a real integration between QKD and WDM networks. Preliminary experimental results on a discrete-variable QKD prototype working in the O-band co-propagating with 40 50-GHz C-band information channels are reported, showing the capabilities of QKD in short/medium reach optical networks.

Published
2021
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7. Increasing network reliability by securing SDN communication with QKD

Author

Alberto Gatto, Paolo Comi, Fabrizio Bianchi, Rafa J. Vicente, Paolo Martelli, Vicente Martin, Marco Brunero, Juan P. Brito, and Ruben B. Mendez

Subjects
Computer science, business.industry, QKD, Reliability (computer networking), Physical layer, Quantum key distribution, Encryption, secure communications, SDN, Symmetric-key algorithm, Secure communication, symmetric encryption, Single point of failure, Software-defined networking, business, Computer network
Abstract

Modern communication networks can be flexibly shaped and controlled using Software Defined Networking (SDN) paradigm. However, besides unprecedented functional benefits, this solution exploits an architectural single point of failure. Securing the communication among SDN functional element is therefore a critical requirement for improving the network reliability. A novel approach to secure communications is provided by Quantum Key Distribution (QKD) that is a physical layer technology capable to securely deliver symmetric encryption keys leveraging on quantum physic properties. Our research demonstrates how to provide unconditionally secure communication at SDN level dramatically improving the reliability of the communication network itself.

Published
2021
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11. Quantum Technologies for future Quantum Optical Networks

Author

Gatto, Alberto, primary, Brito, Juan Pedro, additional, Brunero, Marco, additional, Bodanapu, Dileepsai, additional, Mendez, Ruben B., additional, Vicente, Rafael J., additional, Comi, Paolo, additional, Martin, Vicente, additional, and Martelli, Paolo, additional

Published
2021
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15. Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

Author

Kamal Nasrollahi, Golamreza Anbarjafari, Erika G. Spaich, Thomas B. Moeslund, Fatemeh Noroozi, Ramin Irani, Sergio Escalera, Ruben B. Bautista, Marco Bellantonio, Kaustubh Kulkarni, Christian B. Laursen, Ole Kæseler Andersen, and Mohammad A. Haque

Subjects
Vision, Computer science, RGBDT, Feature extraction, Pain, 02 engineering and technology, computer.software_genre, Facial recognition system, Database, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Thermal, Pain assessment, 0202 electrical engineering, electronic engineering, information engineering, Spatial analysis, Pose, RGB, Modalities, business.industry, Deep learning, Depth, Video, Visualization, Multimodal, 020201 artificial intelligence & image processing, Artificial intelligence, LSTM, Visual, business, computer, 030217 neurology & neurosurgery
Abstract

Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate.

Published
2018
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16. Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

Author

Haque, Mohammad A., primary, Bautista, Ruben B., additional, Noroozi, Fatemeh, additional, Kulkarni, Kaustubh, additional, Laursen, Christian B., additional, Irani, Ramin, additional, Bellantonio, Marco, additional, Escalera, Sergio, additional, Anbarjafari, Golamreza, additional, Nasrollahi, Kamal, additional, Andersen, Ole K., additional, Spaich, Erika G., additional, and Moeslund, Thomas B., additional

Published
2018
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