Keynote 1: Wireless Sensor Network for rural area monitoring and precision agriculture


Farmers and agriculturalists require the new technology to improve their crops and obtain highest performance from their framing.

Constant monitoring is needed to know the estate of the crops during their growing process and act as soon as any type of disease or plague affects to the plantation.

Wireless Sensor Network appears as the key technology to address these problems. It brings real time monitoring in order to provide precision agriculture.

This speech will show the sensors and sensor nodes developed by our team for this purpose. Later we will review some Wireless technologies currently used for wireless sensor networks.

Finally, we will show several wireless sensor networks deployed for agriculture precision and our last works on Smart Systems and Artificial Intelligence in Wireless Sensor Networks


Prof. Jaime LLORET, Polytechnic University of Valencia - Spain


Jaime LLORET received his B.Sc.+M.Sc. in Physics in 1997, his B.Sc.+M.Sc. in electronic Engineering in 2003 and his Ph.D. in telecommunication engineering (Dr.Ing.) in 2006. He is a Cisco Certified Network Professional Instructor. He worked as a network designer and administrator in several enterprises. He is currently Associate Professor in the Polytechnic University of Valencia. He is the Chair of the Integrated Management Coastal Research Institute (IGIC) and he is the head of the "Active and collaborative techniques and use of technologic resources in the education (EITACURTE)" Innovation Group. He is the director of the University Diploma “Redes y Comunicaciones de Ordenadores” and he has been the director of the University Master "Digital Post Production" for the term 2012-2016.

He was Vice-chair for the Europe/Africa Region of Cognitive Networks Technical Committee (IEEE Communications Society) for the term 2010-2012 and Vice-chair of the Internet Technical Committee (IEEE Communications Society and Internet society) for the term 2011-2013. He has been Internet Technical Committee chair (IEEE Communications Society and Internet society) for the term 2013-2015. He has authored 22 book chapters and has more than 480 research papers published in national and international conferences, international journals (more than 220 with ISI Thomson JCR). He has been the co-editor of 40 conference proceedings and guest editor of several international books and journals. He is editor-in-chief of the "Ad Hoc and Sensor Wireless Networks" (with ISI Thomson Impact Factor), the international journal "Networks Protocols and Algorithms", and the International Journal of Multimedia Communications. Moreover, he is Associate Editor-in-Chief of “Sensors” in the Section sensor Networks, he is advisory board member of the "International Journal of Distributed Sensor Networks" (both with ISI Thomson Impact factor), and he is IARIA Journals Board Chair (8 Journals). Furthermore, he is (or has been) associate editor of 46 international journals (16 of them with ISI Thomson Impact Factor). He has been involved in more than 450 Program committees of international conferences, and more than 150 organization and steering committees. He has led many local, regional, national and European projects. He is currently the chair of the Working Group of the Standard IEEE 1907.1. Since 2016 he is the Spanish researcher with highest h-index in the TELECOMMUNICATIONS journal list according to Clarivate Analytics Ranking. He has been general chair (or co-chair) of 52 International workshops and conferences. He is IEEE Senior, ACM Senior and IARIA Fellow.

Keynote 2: Texture and colour descriptors for visual recognition: historical overview and applications to computer vision and robotics


Texture and colour, along with shape, gloss and transparency are the visual features that mostly determine the appearance of objects, materials and scenes. As a consequence, the automatic characterisation of colour and texture via suitable descriptors plays a crucial role in a wide range of applications and very diverse areas – for instance defect detection, surface grading and sorting (process supervision); scene recognition (autonomous vehicles); materials categorisation (flexible manufacturing systems); computer-assisted diagnosis and prognostication (health management). No surprise, then, that representing colour and texture in a compact and effective way has been attracting a lot of research interest for at least four decades.

In the last few years texture and colour analysis has undergone major changes. Traditionally, the approach to the problem was essentially model-driven, and consisted of designing suitable descriptors by hand (hence the term 'hand-crafted' or 'engineered' methods). This paradigm has recently been shifting towards data-driven approaches in which the descriptors are no longer designed a priori, but 'learned' from the data (deep learning, convolutional networks).

The aim of this talk is to introduce some basic concepts in colour and texture analysis with specific focus on the differences between traditional (hand-crafted) approaches and those based on deep learning. The speech will also discuss some applications of colour and texture analysis closely related to computer vision and robotics, such as surface inspection and grading, defect detection and development of autonomous systems.


Prof. Francesco BIANCONI, University of Perugia - Italy


Francesco BIANCONI received the M.Eng. In Mechanical Engineering from the University of Perugia, Italy, and the Ph.D. in Computer-aided Design from a consortium of Italian universities. He has been a Visiting Researcher with the University of Vigo, Spain; the University of East Anglia, U.K.; Queen Mary University of London, U.K. and City, University of London, U.K.

He is currently a Professor with the Department of Engineering, University of Perugia, where he conducts research on computer vision, image processing, and pattern recognition with special focus on texture and colour analysis.

He is a IEEE Senior Member, Chartered Engineer and Court-Appointed Expert.