A communication model of broadcast in wormhole-routed networks on-chip

This paper presents a novel analytical model to compute communication latency of broadcast as the most fundamental collective communication operation. The novelty of the model lies in its ability to predict the broadcast communication latency in wormhole-routed architectures employing asynchronous multi-port routers scheme. The model is applied to the Quarc NoC and its validity is verified by comparing the model predictions against the results obtained from a discrete-event simulator developed using OMNET++

Capacities of classical compound quantum wiretap and classical quantum compound wiretap channels

We determine the capacity of the classical compound quantum wiretapper channel with channel state information at the transmitter. Moreover we derive a lower bound on the capacity of this channel without channel state information and determine the capacity of the classical quantum compound wiretap channel with channel state information at the transmitter

Impacts of buffering voice calls in integrated voice and data services

In this study, we aim to analyse the relationship between various characteristics of a communication system with data and voice call requests. Queuing theory and Markov chain analysis are effectively used for this purpose. Such a study is useful for understanding how the proposed mathematical models behave which represents a system with integrated voice and data calls in homogenous wireless networks. We also propose to optimise the system characteristics in an attempt to provide better Quality of Service (QoS) for systems with integrated voice and data calls. The proposed models have two dimensions; one for voice calls and one for data calls. A channel is assigned for two input traffic call, namely, voice and data calls. The incoming voice and data calls are queued when the channel is busy. Since voice calls are delay-sensitive, priority is given to voice calls. Also, since there is only one channel, data calls are only serviced if there are no voice calls in the system. For such systems, it is important to analyse the impact of buffering the voice calls as well as data calls for various mean rates of call requests, and mean service times. The analytical models presented are generic which is applicable for various systems with similar characteristics. Numerical results are also provided. The results show that the proposed models can be used for optimisation of the performance of a given network

Ubiquitous e-learning System for dynamic mini-courseware assembling and delivering to mobile terminals

From 2000, mobile Learning (m-Learning) and ubiquitous Learning (u-Learning) has been the hottest research topic in e-learning, and now, integrating ubiquitous learning into mainstream of education and train has been the direction in the area, which demand new generational e-learning system. The paper introduces our research efforts in this direction. Based on the key concepts, such as ubiquitous learning object, mini-courseware, a new generational ubiquitous e-learning system is designed, which can be used for new requirements in m-Learning and u-Learning environments. In the system, learning resource related to a course is encapsulated into different ubiquitous learning objects, and mini-courseware can be assembled dynamically with learning resource extracted from these ubiquitous learning objects, accordingly, a mini-courseware player is designed for the situation. Based on these work, a resource based ubiquitous e-Learning system is designed considering pedagogical requirements under m-Learning and u-Learning environment

Applications of AI in Business, Industry, Government, Healthcare and Environment

The University of Maine Artificial Intelligence Initiative (UMaine AI) is a unique Maine-based venture that brings together university, industry, government, and community collaborators from Maine and beyond to advance the field of artificial intelligence, and through development of innovative technologies and applications find transformative solutions to enhance human life and societal well-being in Maine and beyond

Advances in Artificial Intelligence

The University of Maine Artificial Intelligence Initiative (UMaine AI) is a unique Maine-based venture that brings together university, industry, government, and community collaborators from Maine and beyond to advance the field of artificial intelligence, and through development of innovative technologies and applications find transformative solutions to enhance human life and societal well-being in Maine and beyond

Education and Workforce Development

The University of Maine Artificial Intelligence Initiative (UMaine AI) is a unique Maine-based venture that brings together university, industry, government, and community collaborators from Maine and beyond to advance the field of artificial intelligence, and through development of innovative technologies and applications find transformative solutions to enhance human life and societal well-being in Maine and beyond

Social, Ethical, Policy, and Legal Considerations

The University of Maine Artificial Intelligence Initiative (UMaine AI) is a unique Maine-based venture that brings together university, industry, government, and community collaborators from Maine and beyond to advance the field of artificial intelligence, and through development of innovative technologies and applications find transformative solutions to enhance human life and societal well-being in Maine and beyond

Integration of sensorimotor mappings by making use of redundancies

Hemion N, Joublin F, Rohlfing K. Integration of sensorimotor mappings by making use of redundancies. In: IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers, eds. The 2012 International Joint Conference on Neural Networks (IJCNN). Brisbane, Australia: IEEE; 2012