Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference

MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices

[EN] Multimedia cloud computing has appeared as a very attractive environment for the business world in terms of providing cost-effective services with a minimum of entry costs and infrastructure requirements. There are some architecture proposals in the related literature, but there is no multimedia cloud computing architecture with hybrid features specifically designed for mobile devices. In this article, we propose a new multimedia hybrid cloud computing architecture and protocol. It merges existing private and public clouds and combines IaaS, SaaS and SECaaS cloud computing models in order to find a common platform to deliver real time traffic from heterogeneous multimedia and social networks for mobile users. The developed protocol provides suitable levels of QoS, while providing a secure and trusted cloud environment.Jimenez, JM.; Díaz Santos, JR.; Lloret, J.; Romero Martínez, JO. (2019). MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices. IEEE Network. 33(1):106-112. https://doi.org/10.1109/MNET.2018.1300246S10611233

A software-defined architecture for next-generation cellular networks

In the recent years, mobile cellular networks are undergoing fundamental changes and many established concepts are being revisited. New emerging paradigms, such as Software-Defined Networking (SDN), Mobile Cloud Computing (MCC), Network Function Virtualization (NFV), Internet of Things (IoT),and Mobile Social Networking (MSN), bring challenges in the design of cellular networks architectures. Current Long-Term Evolution (LTE) networks are not able to accommodate these new trends in a scalable and efficient way. In this paper, first we discuss the limitations of the current LTE architecture. Second, driven by the new communication needs and by the advances in aforementioned areas, we propose a new architecture for next generation cellular networks. Some of its characteristics include support for distributed content routing, Heterogeneous Networks(HetNets) and multiple Radio Access Technologies (RATs). Finally, we present simulation results which show that significant backhaul traffic savings can be achieved by implementing caching and routing functions at the network edge