TY - GEN
T1 - Agile Edge Classification of Ocean Sounds
AU - Neophytou, Stelios
AU - Tsiantis, Pavlos
AU - Alexopoulos, Ilias
AU - Kyriakides, Ioannis
AU - Veyrac, Camille De
AU - Abdi, Ehson
AU - Hayes, Daniel R.
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/28
Y1 - 2020/10/28
N2 - The maritime environment is characterized by a scarcity of resources of power, sensing, processing, and communications. The resource constraints impose limitations in information acquisition which involves data collection and data processing to yield meaningful statistics. The contribution of this work is on custom software and hardware methods for low power, low data-rate processing for the application of classification of ocean sounds. The combination of light processing software and custom hardware allow the development of efficient cyber-physical maritime IoT systems. A simulation-based study is provided to evaluate the ability of the software method for agile learning of features for ocean sounds classification. In addition, a practical implementation on a custom hardware emulator is provided to demonstrate the potential of the method to classify ocean sounds on low power, inexpensive seaborne IoT nodes.
AB - The maritime environment is characterized by a scarcity of resources of power, sensing, processing, and communications. The resource constraints impose limitations in information acquisition which involves data collection and data processing to yield meaningful statistics. The contribution of this work is on custom software and hardware methods for low power, low data-rate processing for the application of classification of ocean sounds. The combination of light processing software and custom hardware allow the development of efficient cyber-physical maritime IoT systems. A simulation-based study is provided to evaluate the ability of the software method for agile learning of features for ocean sounds classification. In addition, a practical implementation on a custom hardware emulator is provided to demonstrate the potential of the method to classify ocean sounds on low power, inexpensive seaborne IoT nodes.
UR - http://www.scopus.com/inward/record.url?scp=85099758773&partnerID=8YFLogxK
U2 - 10.1109/UEMCON51285.2020.9298142
DO - 10.1109/UEMCON51285.2020.9298142
M3 - Conference contribution
AN - SCOPUS:85099758773
T3 - 2020 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
SP - 343
EP - 348
BT - 2020 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
Y2 - 28 October 2020 through 31 October 2020
ER -