163,578 research outputs found
Building Programmable Wireless Networks: An Architectural Survey
In recent times, there have been a lot of efforts for improving the ossified
Internet architecture in a bid to sustain unstinted growth and innovation. A
major reason for the perceived architectural ossification is the lack of
ability to program the network as a system. This situation has resulted partly
from historical decisions in the original Internet design which emphasized
decentralized network operations through co-located data and control planes on
each network device. The situation for wireless networks is no different
resulting in a lot of complexity and a plethora of largely incompatible
wireless technologies. The emergence of "programmable wireless networks", that
allow greater flexibility, ease of management and configurability, is a step in
the right direction to overcome the aforementioned shortcomings of the wireless
networks. In this paper, we provide a broad overview of the architectures
proposed in literature for building programmable wireless networks focusing
primarily on three popular techniques, i.e., software defined networks,
cognitive radio networks, and virtualized networks. This survey is a
self-contained tutorial on these techniques and its applications. We also
discuss the opportunities and challenges in building next-generation
programmable wireless networks and identify open research issues and future
research directions.Comment: 19 page
Cognitive and neuromuscular robotic rehabilitation framework
© Springer International Publishing AG 2017.This paper presents a cognitive and neuromuscular robotic rehabilitation framework to support enhanced control of arm movement for humans with muscular control impairment, typically with some level of memory deficiency due to, for example, suffering from a stroke. It describes the design, development and integration of the framework architecture as well as a Baxter robot based demonstration platform. Three key elements of the proposed framework (rehabilitation module, workspace and rehabilitation scenarios) have been described in detail. In the rehabilitation sessions, the users and the robot are asked to work together to place cubes so as to form a predefined shape. The robot and the user hold the same object in order to move it to a particular destination according to a rehabilitation scenario. If the robot detects a force from the user directed in the wrong direction during the navigation then it resists and corrects the movement in order to assist the user towards the right direction. The assistive support scenarios were designed to evaluate the achieved enhancement of precision, efficiency and dexterity of arm movements. The proposed rehabilitation framework provides a modular, automated and open-source platform for researchers and practitioners in neuromuscular rehabilitation applications
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments
In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge.
We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and
future challenges
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
Simulation of complex environments:the Fuzzy Cognitive Agent
The world is becoming increasingly competitive by the action of liberalised national and global markets. In parallel these markets have become increasingly complex making it difficult for participants to optimise their trading actions. In response, many differing computer simulation techniques have been investigated to develop either a deeper understanding of these evolving markets or to create effective system support tools. In this paper we report our efforts to develop a novel simulation platform using fuzzy cognitive agents (FCA). Our approach encapsulates fuzzy cognitive maps (FCM) generated on the Matlab Simulink platform within commercially available agent software. We firstly present our implementation of Matlab Simulink FCMs and then show how such FCMs can be integrated within a conceptual FCA architecture. Finally we report on our efforts to realise an FCA by the integration of a Matlab Simulink based FCM with the Jack Intelligent Agent Toolkit
Spectrum sharing security and attacks in CRNs: a review
Cognitive Radio plays a major part in communication technology by resolving the shortage of the spectrum through usage of dynamic spectrum access and artificial intelligence characteristics. The element of spectrum sharing in cognitive radio is a fundament al approach in utilising free channels. Cooperatively communicating cognitive radio devices use the common control channel of the cognitive radio medium access control to achieve spectrum sharing. Thus, the common control channel and consequently spectrum sharing security are vital to ensuring security in the subsequent data communication among cognitive radio nodes. In addition to well known security problems in wireless networks, cognitive radio networks introduce new classes of security threats and challenges, such as licensed user emulation attacks in spectrum sensing and misbehaviours in the common control channel transactions, which degrade the overall network operation and performance. This review paper briefly presents the known threats and attacks in wireless networks before it looks into the concept of cognitive radio and its main functionality. The paper then mainly focuses on spectrum sharing security and its related challenges. Since spectrum sharing is enabled through usage of
the common control channel, more attention is paid to the
security of the common control channel by looking into its
security threats as well as protection and detection mechanisms. Finally, the pros and cons as well as the comparisons of different CR - specific security mechanisms are presented with some open research issues and challenges
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