Title

Multimodal Species Identification In Wireless Sensor Networks

Keywords

Evolved Classifiers; NEAT; PSO; Species Classification

Abstract

This paper deals with a multimodal approach to identifying species in a Versatile Service-Oriented Wireless Mesh Sensor Network. This type of network is distinguished by the presence of heterogeneous networks, which may posses low storage capabilities. Hence, an optimal multimodal classifier is introduced, which employs audio and image features to enhance its performance in noisy environments. The classifier is a neural network which is evolved with an evolutionary algorithm. Results demonstrate that the classifier can achieve high performance, which is not degraded as it scales to classifying more classes. © 2011 IEEE.

Publication Date

12-1-2011

Publication Title

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

Number of Pages

385-388

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CAMSAP.2011.6136033

Socpus ID

84857176647 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84857176647

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