Outputs (53)

RSS Indoor Localization Based on a Single Access Point (2019)
Journal Article
Kokkinis, A., Kanaris, L., Liotta, A., & Stavrou, S. (2019). RSS Indoor Localization Based on a Single Access Point. Sensors, 19(17), Article 3711. https://doi.org/10.3390/s19173711

This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The proposed approach demonstrates that different RSS value... Read More about RSS Indoor Localization Based on a Single Access Point.

An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging (2019)
Journal Article
Wang, Y., Song, W., Fortino, G., Qi, L., Zhang, W., & Liotta, A. (2019). An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging. IEEE Access, 7, 140233-140251. https://doi.org/10.1109/ACCESS.2019.2932130

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhance... Read More about An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging.

Intelligence at the Edge of Complex Networks: The Case of Cognitive Transmission Power Control (2019)
Journal Article
Pace, P., Fortino, G., Zhang, Y., & Liotta, A. (2019). Intelligence at the Edge of Complex Networks: The Case of Cognitive Transmission Power Control. IEEE Wireless Communications, 26(3), 97-103. https://doi.org/10.1109/mwc.2019.1800354

The rapid proliferation of new devices has led to the Internet of Things (IoT), a network where virtually any object equipped with a radio interface can be connected. Accordingly, networks are exploding in terms of the number of devices but also in c... Read More about Intelligence at the Edge of Complex Networks: The Case of Cognitive Transmission Power Control.

Runtime evaluation of cognitive systems for non-deterministic multiple output classification problems (2019)
Journal Article
Gopalakrishna, A. K., Ozcelebi, T., Lukkien, J. J., & Liotta, A. (2019). Runtime evaluation of cognitive systems for non-deterministic multiple output classification problems. Future Generation Computer Systems, 100, 1005-1016. https://doi.org/10.1016/j.f

Cognitive applications that involve complex decision making such as smart lighting have non-deterministic input-output relationships, i.e., more than one output may be acceptable for a given input. We refer them as non-deterministic multiple output c... Read More about Runtime evaluation of cognitive systems for non-deterministic multiple output classification problems.

Recent Advances in the Processing and Rendering Algorithms for Computer-Generated Holography (2019)
Journal Article
Corda, R., Giusto, D., Liotta, A., Song, W., & Perra, C. (2019). Recent Advances in the Processing and Rendering Algorithms for Computer-Generated Holography. Electronics, 8(5), https://doi.org/10.3390/electronics8050556

Digital holography represents a novel media which promises to revolutionize the way the users interacts with content. This paper presents an in-depth review of the state-of-the-art algorithms for advanced processing and rendering of computer-generate... Read More about Recent Advances in the Processing and Rendering Algorithms for Computer-Generated Holography.

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks (2019)
Journal Article
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371

High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserv... Read More about Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks.

Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities (2019)
Journal Article
Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., Brindley, P., & Liotta, A. (2019). Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.or

The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing dat... Read More about Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities.

Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance (2018)
Journal Article
Cauteruccio, F., Fortino, G., Guerrieri, A., Liotta, A., Mocanu, D. C., Perra, C., Terracina, G., & Torres Vega, M. (2019). Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. Info

Heterogeneous wireless sensor networks are a source of large amount of different information representing environmental aspects such as light, temperature, and humidity. A very important research problem related to the analysis of the sensor data is... Read More about Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance.

Interference graphs to monitor and control schedules in low-power WPAN (2018)
Journal Article
van der Lee, T., Liotta, A., & Exarchakos, G. (2019). Interference graphs to monitor and control schedules in low-power WPAN. Future Generation Computer Systems, 93, 111-120. https://doi.org/10.1016/j.future.2018.10.014

Highlights • This study presents the complete and slotted interference graph model. • The service uses the complete interference graph to evaluate the network. • Slotted interference graphs are used to reschedule problematic connections. • Rea... Read More about Interference graphs to monitor and control schedules in low-power WPAN.

Deep learning with multi-scale feature fusion in remote sensing for automatic oceanic eddy detection (2018)
Journal Article
Du, Y., Song, W., He, Q., Huang, D., Liotta, A., & Su, C. (2019). Deep learning with multi-scale feature fusion in remote sensing for automatic oceanic eddy detection. Information Fusion, 49, 89-99. https://doi.org/10.1016/j.inffus.2018.09.006

Oceanic eddies are ubiquitous in global oceans and play a major role in ocean energy transfer and nutrients distribution, thus being significant for understanding ocean current circulation and marine climate change. They are characterized by a combin... Read More about Deep learning with multi-scale feature fusion in remote sensing for automatic oceanic eddy detection.