Research Activities

 

5G Radio technologies

Over the last years, the proliferation of smart devices has brought pervasive communications everywhere and anytime. Consequently, wireless network architectures and techniques have to handle the increase in traffic and in demand for radio resources. At the TLC&RS lab we focus on:
    • network architecture development based on information sharing and cognitive radio network to enable smart architecture solutions as well as device to device communication.
    • strategies for spectrum sharing and information fusion/processing optimization from maps.
    • signal processing techniques for mmWave technology.

A. Vizziello, R. Amadeo: "Energy Efficient Information Sharing in Social Cognitive Radio Networks", 11th EAI International Conference on Cognitive Radio Oriented Wireless Networks, Crowncom 2016, Grenoble, France, May 30-June 1, 2016.

A. Vizziello, and J. Perez-Romero: "System Architecture in Cognitive Radio Networks using a Radio Environment Map", in Proc. of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management, CogART 2011, (invited paper), Barcelona, Spain, 26-29 Oct. 2011.

Networking and Multimedia

As telecommunications services become pervasive, methods to adapt to heterogeneous environments are much needed. In order to be efficient, adaptation must be automatic and involve both network and service aspects. With this in mind, the research activities of the Networking and Multimedia (NaM) group of the TLC and RS lab cover aspect of video coding using scalability and multiple descriptions techniques on one side and cognitive systems on the other. User cooperation is also envisaged exploiting the concept of “sociality” among devices and among users.

L. Favalli, M. Folli, A. Lombardo, D. Reforgiato, G. Schembra, “Design of a service platform for delay-sensitive video streaming applications based on multicast p2p and scalable MDC encoding”, Computer Communications (Elsevier),Volume 35, Issue 18, 1 November 2012, Pages 2254–2263. Issn: 0140-3664, DOI:10.1016/j.comcom.2012.08.006

A. Vizziello, L. Favalli,” Smart social architecture for green distributed networks,” Journal of High Speed Networks, IOS Press, ISSN 0926-6801 (Print), 1875-8940 (Online), Vol. 19, Number 3 / 2013, pp 237-250, DOI: 10.3233/JHS-130475

A. Morales Figueroa , L.Favalli, “Buffer management for scalable video streaming,” EAI Endorsed Transactions on Mobile Communications and Applications vol 3, 17(8): e2, sept. 2017, DOI: 10.4108/eai.13-9-2017.153337

Health Applications

5G technologies will be applied to several vertical sectors, such as factories, eHealth, automotive, energy, media and management. We focus on eHealth applications, which requires specific requirements in terms of latency, reliability, positioning accuracy.

In particular, implanted sensors will enable the next generation of healthcare by in-situ testing of abnormal physiological conditions, personalized medicine and proactive drug delivery. These implants need to transmit measurements to an external processing center for real time monitoring, to receive updates on drug delivery volumes, and to directly start actions by embedded actuators. We propose to use galvanic coupling (GC) technology to connect implants, which uses the natural electrical conduction properties of human tissues, and enables direct communication among the sensors implanted in the body.

M. Swaminathan, A. Vizziello, D. Duong, P. Savazzi, K. Chowdhury: "Beamforming in the Body: Energy-efficient and Collision-free Communication for Implants", IEEE INFOCOM 2017, Atlanta, GA, USA, 1-4 May 2017.

Digital Signal Processing and IP Core Design

The research activities regard advance signal processing techniques for system design and simulation of innovative transceivers to be used in modern wired and wireless standard communications. Moreover, fast hardware prototyping techniques are used for experimental performance evaluation of the studied techniques. In more details, the main topic of interests are:
    • adaptive modulation and coding
    • advanced channel estimation and synchronization techniques
    • MIMO and massive-MIMO architectures

P. Savazzi, A. Vizziello, “A novel physical layer scheme based on superposition codes,” EURASIP Journal on Wireless Communications and Networking, December 2017, 2017:146, DOI: 10.1186/s13638-017-0927-y, Springer International Publishing.

Anna Vizziello, Pietro Savazzi, Roberta Borra,” Joint Phase Recovery for XPIC System Exploiting Adaptive Kalman Filtering,” IEEE Communications Letters, Volume: 20, Issue: 5, May 2016, DOI: 10.1109/LCOMM.2016.2542798.

Andrea Marinoni, Pietro Savazzi, Paolo Gamba, "Efficient detection and decoding of q-ary LDPC coded signals over partial response channels," Eurasip Journal on Wireless Communications and Networking, vol. 2013:18, ISSN: 1687-1499, January 2013, doi: 10.1186/1687-1499-2013-18.

Wireless Sensing for Space, Terrestrial and Biomedical Applications

This research line is devoted to both explore novel wireless communication protocols and develop novel DSP techniques for processing the data collected by different type of sensor technologies:
    • wireless temperature monitoring on aerospace platforms
    • fast and energy efficiente identification of UWB sensor tags
    • beamforming techniques for energy efficient intrabody communications

Meenupriya Swaminathan, Anna Vizziello, Davy Duong, Pietro Savazzi and Kaushik R. Chowdhury,” Beamforming in the Body: Energy-efficient and Collision-free Communication for Implants,” IEEE International Conference on Computer Communications, INFOCOM 2017, 1-4 May 2017, Atlanta, GA, USA.

Emanuele Goldoni, Pietro Savazzi, Anna Vizziello, “A Novel Channel Coding Scheme for RFID Generation-2 Systems,” Electronics 2017, 6(1), 4; doi:10.3390/
Anna Vizziello, Pietro Savazzi, “Efficient RFID Tag Identification Exploiting Hybrid UHFUWB Tags and Compressive Sensing,” IEEE Sensors Journal, Volume: 16, Issue: 12, June15, 2016, DOI: 10.1109/JSEN.2016.2551375.

P. Gamba, E. Goldoni, P. Savazzi, P.G. Arpesi, C. Sopranzi, J-F. Dufour, and M. Lavagna, “Wireless Passive Sensors for Remote Sensing of Temperature on Aerospace Platforms,” IEEE Sensors Journal, vol. 14, no. 11, November 2014, doi: 10.1109/JSEN.2014.2353623.

Earth observation and risk

Implementation of EO-based methods to extract indicators intended for dynamic vulnerability and recovery/reconstruction monitoring. This is done in the framework of assessing risk of natural disasters and considers several different methods and data sources, focussing on satellite data but including crowdsourced and in-situ data.

Dell’Acqua, Fabio, and Daniele De Vecchi. "Potentials of Active and Passive Geospatial Crowdsourcing in Complementing Sentinel Data and Supporting Copernicus Service Portfolio." Proceedings of the IEEE 105.10 (2017): 1913-1925.

Iannelli, Gianni Cristian, and Fabio Dell’Acqua. "Extensive Exposure Mapping in Urban Areas through Deep Analysis of Street-Level Pictures for Floor Count Determination." Urban Science 1.2 (2017): 16.

Uprety, Pralhad, Fumio Yamazaki, and Fabio Dell'Acqua. "Damage detection using high-resolution SAR imagery in the 2009 L'Aquila, Italy, Earthquake." Earthquake Spectra 29.4 (2013): 1521-1535.

Earth Observation & Big data

At TLC&RS Lab we work to achieve efficient management, accurate mining and fast processing of large scale heterogeneous data to detect data patterns and correlations that may not be immediately visible at a first glimpse. Thus, we aim to develop smart methods to assess the knowledge contained in these data, in order to combine high accuracy with fast processing and to promote research on multiple scales. To this aim, nonlinear pattern recognition algorithms relying on information theory, non-Euclidean geometry, data driven discovery and nonlinear optimization are designed and implemented. Indeed, we point to establish a framework of smart learning for a scalable, flexible, efficient and accurate information assessment from big datasets collected by highly diverse information sources, so that it would be possible to precisely characterize the interplay between anthropogenic extents and environment and climate,  and assess the impact of physical and environmental phenomena on human life style.

A. Marinoni, G.C. Iannelli, P. Gamba, “An information theory-based scheme for efficient classification of remote sensing data”, IEEE Trans. On Geoscience and Remote Sensing, 55(10): 5864-5876, Oct. 2017.

A. Marinoni, P. Gamba, “An Efficient Approach for Local Affinity Pattern Detection in Remotely Sensed Big Data,” IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing 8(10):1-12, Oct. 2015.

A. Marinoni, P. Gamba: “Big Data for human-environment interaction assessment: challenges and opportunities”, ESA Big Data from Space Conference (BiDS), Nov. 12-14, 2014, Frascati, Italy.

Global Urban Remote Sensing

Mapping urban areas and their features using remote sensing is a challenge including the need to exploit multiple sensors, work on huge amount of data, and design techniques to extract information at multiple scales, from the whole urban area to its blocks and land uses, down to the single building and road. The TLC & RS lab has a long experience in urban extent extraction using radar and multispectral sensors, multi-scale and multitemporal data fusion at the feature and decision level, leading to seminal research papers in the this topic area.

Global Mapping of Human Settlement - Experiences, Datasets, and Prospects, P. Gamba, M. Herold  (Eds), CRC Press, June 2009.

P. Gamba, “Human settlements: a global challenge for EO data processing and interpretation”, Proceedings of the IEEE, vol. 101, no. 3, pp. 570-581, March. 2013.

A. Salentinig, P. Gamba, “A general framework for urban area extraction exploiting multi-resolution SAR data fusion”, IEEE J. of Selected Topics in Applied Earth Observation and Remote Sensing, vol. 9, no. 5, pp. 2009-2018, 2016.

Hyperspectral Remote Sensing

The possibility to characterize materials and surface using a large number of wavelengths by means of hyperspectral sensors calls for specialized techniques to select and extract relevant information. Moreover, the relatively coarse spatial resolution of hyperspectral sensors, especially from spaceborne platforms, leads to the need of unmoving techniques to extra material abundances. From the surface of the Earth to those of the Moon and other planer, the TLC & RS lab has been designing and optimizing non-linear unmixing techniques that provided superior results in many applications. That is the privilege of working in the actual place of the “Pavia University“ hyperspectral benchmark data set!

A. Marinoni, and P. Gamba, “A Novel Approach for Efficient p-linear Hyperspectral Unmixing”, IEEE J. of Selected Topics in Signal Processing, vol. 9, no. 6, pp. 1156-1168, 2015.

A. Marinoni, A. Plaza, P. Gamba, “Harmonic Mixture Modeling for Efficient Nonlinear Hyperspectral Unmixing”, IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 9, pp. 4247-4256, 2016.

A. Marinoni, A. Plaza, P. Gamba, “A novel pre-unmixing framework for efficient detection of linear mixtures in hyperspectral images”, IEEE Trans. on Geoscience and Remote Sensing, vol. 55, no. 8, pp. 4325-4333, Aug. 2017.