kumoh national institute of technology
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1. Embedded neural network for real-time animal behavior classification 2. Choosing an integrated RF module for a wildlife monitoring WSN 3. Deployment of a ser of camera trap networks for wildlife inventory in western amazon rainforest
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Date : 2018-02-09
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This work [1] presents an animal behavior recognition, classification and monitoring system based on a wireless sensor network and a smart collar device, provided with inertial sensors and an embedded multi-layer perceptron-based feed-forward neural network, to classify the different gaits or behaviors based on the collected information.

The paper [2] considers the choice of a radio transceiver module for use in a wireless sensor network aimed at wildlife monitoring.
In this application, power consumption and communication range in difficult terrain are key challenges.

Authors of [3] deploy An MWSN for animal monitoring in the Amazon rainforest of Peru. Tapirduino, the developed trap camera sensor node, is composed by an Arduino-like PCB, a CMOS camera, an IR flash, a PIR sensor, a SD card, and 900-MHz radio. The challenges tackled in this project were to overcome the high attenuation of radio frequency in the middle of the jungle and to reduce energy consumption until achieving a very low level.
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