Compressive Sensing for Efficiently Collecting Wildlife Sounds with Wireless Sensor Networks

Diaz, Javier J. M.; Colonna, Juan Gabriel; Soares, Rodrigo B.; Figueiredo, Carlos M. S.; Nakamura, Eduardo F.

doi:10.1109/icccn.2012.6289298

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…Aide et al [1] propose the use of a lossless compression codec FLAC, while Diaz et al [8] used a framework based on compressive sensing. In these cases, the amount of information transmitted is smaller and, as additional benefit, it is possible to recover the audio in the sink.…”

Section: Related Workmentioning

confidence: 99%

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A Distributed Approach for Classifying Anuran Species Based on Their Calls

Colonna

Cristo

Nakamura

2014

2014 22nd International Conference on Pattern Recognition

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In this work, we evaluate the performance of a distributed classification system in a Wireless Sensor Network for monitoring anurans. Our aim is to study how to take advantage of the collaborative nature of the sensor network to improve the recognition of anuran calls. To accomplish this, we evaluate four low-cost techniques (majority vote, weighted majority vote, arithmetic and geometric combinators) to combine three classifiers commonly used in sensor applications (Quadratic Discriminant Analysis, Naive Bayes, and Decision Trees) and trained to identify anuran calls. We investigate how the environment perceptions of the sensors can be used to discard confusing scenarios, i.e., scenarios in which there are multiple calls from different species at same time. Our best combination strategy achieved a gain of about 11% over a sensor taken in isolation. We also found that, by using the entropy of the species estimates, the sensor committee is able to effectively identify confusing scenarios, increasing gains over the isolated sensor to about 20%.

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Section: Related Workmentioning

confidence: 99%

“…A different strategy to reduce the transmission cost is to compress the audio before transmission [1,8]. Aide et al [1] propose the use of a lossless compression codec FLAC, while Diaz et al [8] used a framework based on compressive sensing.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Approach for Classifying Anuran Species Based on Their Calls

Colonna

Cristo

Nakamura

2014

2014 22nd International Conference on Pattern Recognition

Self Cite

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“…Sensor nodes are responsible in sensing any changes in the WSN environment, communicate between the other sensor nodes during packets transmission, and sometimes perform basic computation operation with the collected data. WSN has been applied in many real applications such as healthcare [2,3], military [4], environment monitoring [5,6] and industrial [7]. Sensor nodes that consist of source and destination nodes are geographically placed by using static, dynamic and mobile modes depending on its usage and functionalities [8].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Ant Colony System for Reducing Packet Loss in Wireless Sensor Network

Nasir

Ku-Mahamud

Kamioka³

2018

IJGDC

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“…In several applications, we may be interested in counting the number of occurrences of a specific event -our class of interest. For instance, we may be interested in counting the number of times a specific species of frog croack in the field (DIAZ et al, 2012;LICHMAN, 2013) while disregarding other species and other animals altogether. Although we may have enough data to fairly characterize the class of interest, that is, the expected event, it may be impractical to collect enough data to fully characterize everything that is not expected.…”

Section: Introductionmentioning

confidence: 99%

“…As the data size is restricted, we only considered the two biggest families of frogs as the classes of the data, ending up with 6,585 entries. The positive class is the Hylidae family, and the negative class is the Leptodactylidae family (DIAZ et al, 2012;LICHMAN, 2013);…”

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confidence: 99%

Non-stationary and unpredictable data distributions in classification and quantification

Reis¹

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This work is dedicated to all people who are unable to dedicate something. I also thank my advisor Prof. Batista, for whom, at times, I have been a heavier burden than he could have anticipated. He did not drop the weight, and for that he has my gratitude. Once Prof. Batista moved overseas, Prof. Parros welcomed me with open arms, and I cannot thank her enough for personally supporting my final months in this journey. Finally, I'm immensely thankful that I could collaborate with André Maletzke, who rocketed many of my ideas off the ground. Additional thanks go to FAPESP and CAPES, that financially supported this thesis (grants 2017/22896-7 and PROEX-6909543/D, respectively). "'All conditioned things are impermanent'-when one sees this with wisdom, one turns away from suffering." (The Buddha) Palavras-chave: dados não estacionários, aprendizado com dados positivos e não-rotulados, quantificação, classificação, mudança de conceito.

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Compressive Sensing for Efficiently Collecting Wildlife Sounds with Wireless Sensor Networks

Cited by 8 publications

References 17 publications

A Distributed Approach for Classifying Anuran Species Based on Their Calls

A Distributed Approach for Classifying Anuran Species Based on Their Calls

Enhanced Ant Colony System for Reducing Packet Loss in Wireless Sensor Network

Non-stationary and unpredictable data distributions in classification and quantification

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