149,646 research outputs found
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
The propagation of sound in a shallow water environment is characterized by
boundary reflections from the sea surface and sea floor. These reflections
result in multiple (indirect) sound propagation paths, which can degrade the
performance of passive sound source localization methods. This paper proposes
the use of convolutional neural networks (CNNs) for the localization of sources
of broadband acoustic radiated noise (such as motor vessels) in shallow water
multipath environments. It is shown that CNNs operating on cepstrogram and
generalized cross-correlogram inputs are able to more reliably estimate the
instantaneous range and bearing of transiting motor vessels when the source
localization performance of conventional passive ranging methods is degraded.
The ensuing improvement in source localization performance is demonstrated
using real data collected during an at-sea experiment.Comment: 5 pages, 5 figures, Final draft of paper submitted to 2018 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
15-20 April 2018 in Calgary, Alberta, Canada. arXiv admin note: text overlap
with arXiv:1612.0350
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
Multi-mode resource constrained multi-project scheduling and resource portfolio problem
This paper introduces a multi-project problem environment which involves
multiple projects with assigned due dates; with activities that have alternative
resource usage modes; a resource dedication policy that does not allow
sharing of resources among projects throughout the planning horizon; and a
total budget. There are three issues to face when investigating this multiproject environment. First, the total budget should be distributed among
different resource types to determine the general resource capacities which
correspond to the total amount for each renewable resource to be dedicated
to the projects. With the general resource capacities at hand, the next issue
is to determine the amounts of resources to be dedicated to the individual
projects. With the dedication of resources accomplished, the scheduling
of the projects' activities reduces to the multi-mode resource constrained
project scheduling problem (MRCPSP) for each individual project. Finally
the last issue is the effcient solution of the resulting MRCPSPs. In this paper,
this multi-project environment is modeled in an integrated fashion and designated as the Resource Portfolio Problem. A two-phase and a monolithic
genetic algorithm are proposed as two solution approaches each of which
employs a new improvement move designated as the combinatorial auction
for resource portfolio and the combinatorial auction for resource dedication.
Computational study using test problems demonstrated the effectiveness of
the solution approach proposed
Giving Neurons to Sensors: An Approach to QoS Management Through Artificial Intelligence in Wireless Networks
For the latest ten years, many authors have focused their investigations
in wireless sensor networks. Different researching issues have
been extensively developed: power consumption, MAC protocols, selforganizing
network algorithms, data-aggregation schemes, routing protocols,
QoS management, etc. Due to the constraints on data processing
and power consumption, the use of artificial intelligence has been historically
discarded. However, in some special scenarios the features of
neural networks are appropriate to develop complex tasks such as path
discovery. In this paper, we explore the performance of two very well
known routing paradigms, directed diffusion and Energy-Aware Routing,
and our routing algorithm, named SIR, which has the novelty of being
based on the introduction of neural networks in every sensor node. Extensive
simulations over our wireless sensor network simulator, OLIMPO,
have been carried out to study the efficiency of the introduction of neural
networks. A comparison of the results obtained with every routing protocol
is analyzed. This paper attempts to encourage the use of artificial
intelligence techniques in wireless sensor nodes
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