252,924 research outputs found
Audio Classification in Speech and Music: A Comparison between a Statistical and a Neural Approach
We focus the attention on the problem of audio classification in speech and music for multimedia applications. In particular, we present a comparison between two different techniques for speech/music discrimination. The first method is based on Zero crossing rate and Bayesian classification. It is very simple from a computational point of view, and gives good results in case of pure music or speech. The simulation results show that some performance degradation arises when the music segment contains also some speech superimposed on music, or strong rhythmic components. To overcome these problems, we propose a second method, that uses more features, and is based on neural networks (specifically a multi-layer Perceptron). In this case we obtain better performance, at the expense of a limited growth in the computational complexity. In practice, the proposed neural network is simple to be implemented if a suitable polynomial is used as the activation function, and a real-time implementation is possible even if low-cost embedded systems are used
Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling
Research concerning organization and coordination within multi-agent systems
continues to draw from a variety of architectures and methodologies. The work
presented in this paper combines techniques from game theory and multi-agent
systems to produce self-organizing, polymorphic, lightweight, embedded agents
for systems scheduling within a large-scale real-time systems environment.
Results show how this approach is used to experimentally produce optimum
real-time scheduling through the emergent behavior of thousands of agents.
These results are obtained using a SWARM simulation of systems scheduling
within a High Energy Physics experiment consisting of 2500 digital signal
processors.Comment: Fourth International Conference on Hybrid Intelligent Systems (HIS),
Kitakyushu, Japan, December, 200
Hybrid-Vehfog: A Robust Approach for Reliable Dissemination of Critical Messages in Connected Vehicles
Vehicular Ad-hoc Networks (VANET) enable efficient communication between
vehicles with the aim of improving road safety. However, the growing number of
vehicles in dense regions and obstacle shadowing regions like Manhattan and
other downtown areas leads to frequent disconnection problems resulting in
disrupted radio wave propagation between vehicles. To address this issue and to
transmit critical messages between vehicles and drones deployed from service
vehicles to overcome road incidents and obstacles, we proposed a hybrid
technique based on fog computing called Hybrid-Vehfog to disseminate messages
in obstacle shadowing regions, and multi-hop technique to disseminate messages
in non-obstacle shadowing regions. Our proposed algorithm dynamically adapts to
changes in an environment and benefits in efficiency with robust drone
deployment capability as needed. Performance of Hybrid-Vehfog is carried out in
Network Simulator (NS-2) and Simulation of Urban Mobility (SUMO) simulators.
The results showed that Hybrid-Vehfog outperformed Cloud-assisted Message
Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP),
PEer-to-Peer protocol for Allocated REsource (PrEPARE), Fog-Named Data
Networking (NDN) with mobility, and flooding schemes at all vehicle densities
and simulation times
AER and dynamic systems co-simulation over Simulink with Xilinx System Generator
Address-Event Representation (AER) is a
neuromorphic communication protocol for transferring
information of spiking neurons implemented into VLSI chips.
These neuro-inspired implementations have been used to design
sensor chips (retina, cochleas), processing chips (convolutions,
filters) and learning chips, what makes possible the
development of complex, multilayer, multichip neuromorphic
systems. In biology one of the last steps of the processing is to
move a muscle, to apply the results of these complex
neuromorphic processing to the real world. One interesting
question is to be able to transform, or translate, the AER
information into robot movements, like for example, moving a
DC motor. This paper presents several ways to translate AER
spikes into DC motor power, and to control a DC motor speed,
based on Pulse Frequency Modulation. These methods have
been simulated into Simulink with Xilinx System Generator,
and tested into the AER-Robot platform.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0
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