The WISENET Centre and Mechatronics Centre at UiA have joined forces in a new multidisciplinary frontier research project that combines Artificial Intelligence and Robotics. They have received NOK 16 million (total budget 20 NOK million) from the Research Council of Norway to develop the next-generation artificial intelligence for industrial collaborative robots. The project is called Collective Efficient Deep Learning and Networked Control for Multiple Collaborative Robot Systems (DEEPCOBOT). The project period for DEEPCOBOT is from 2020 to 2025. The plan is for the project to have three PhD candidates and one postdoctoral fellow.
News published at UiA web site here
- Henning Idsøe, Linga Reddy Cenkeramaddi, Soumya J and Baltasar Beferull Lozano, "Phase-noise Impact on the Performance of mmWave-RADARs", IEEE International Conference on Advanced Networks and Telecommunication Systems, 2019.
- Anders Frøytlog, Magne Haglund, Linga Reddy Cenkeramaddi and Baltasar Beferull Lozano, "Design and implementation of a long-range low-power wake-up radio and customized DC-MAC protocol for LoRaWAN", IEEE International Conference on Advanced Networks and Telecommunication Systems, 2019
Leila Ben Saad is presenting her research work in a Top IEEE Global Conference on Signal and Information Processing
Leila Ben Saad, Elvin Isufi and Baltasar Beferull-Lozano, "Graph Filtering with Quantization over Random Time-varying graphs", IEEE Global Conference on Signal and Information Processing 2019.
This paper is a collaboration with Delft University and an extended version is upcoming.
These are the papers accepted at IEEE SPAWC 2019:
Mohamed Elnourani, Baltasar Beferull-Lozano, Daniel Romero, Siddharth Deshmukh, "Reliable Underlay Device-to-Device Communications on Multiple Channels", IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019.
Leila Ben Saad, Baltasar Beferull-Lozano, "Graph Filtering of Time-Varying Signals over Asymmetric Wireless Sensor Networks", IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019.
The authors: Leila Ben Saad, Cesar Asensio-Marco and Baltasar Beferull-Lozano
The large number of nodes forming current sensor networks has made essential to introduce distributed mechanisms in many traditional applications. In the emerging field of graph signal processing, the distributed mechanism of information potentials constitutes a distributed graph filtering process that can be used to solve many different problems. An important limitation of this algorithm is that it is inherently iterative, which implies that the nodes incur in a repeated communication cost along the exchange periods of the filtering process. Since the sensor nodes are battery powered and radio communications are energy demanding operations, in this work, we propose to redesign the network topology in order to reduce the total energy consumption of the filtering process. An accurate energy model is proposed and extensive numerical results are presented to show the efficiency of our methodology according to this energy model.
The authors: Leila Ben Saad, Thilina Weerasinghe and Baltasar Beferull-Lozano
Graph filters, which are considered as the workhorses of graph signal analysis in the emerging field of signal processing on graphs, are useful for many applications such as distributed estimation in wireless sensor networks. Many of these tasks are based on basic distributed operators such as consensus, which are carried out by sensor devices under limited energy supply. To cope with the energy constraints, this paper focuses on designing the network topology in order to maximize the network lifetime when applying graph filters. None of the existing works in the literature have studied such problem when graph filters are used. The problem is a complex combinatorial problem and in this work, we propose two efficient heuristic algorithms for solving it. We show by simulations that they provide good performance and increase significantly the network lifetime.