Geometrical models for simulating the properties of underwater bodies using digital image processing

Al-Shimiry, Intisar F. H.; Al-Zuky, Ali A.; Al-Obaidi, Fatin E. M.

doi:10.1063/5.0068897

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Cellular & capillaries communications elements are utilized in this experiment. For long-distance connectivity, Global Systems for Mobile Communication (GSM), Third Generation (3G), and Long Term Evolution (LTE) were utilized [14,15]. Sensors were put up inside the observing region and communicated via sensing devices in habitats environmental monitoring.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Approach to Diagnose the Animal Health Continuous Monitoring Using IoT Based Sensory Data

Kandepan¹,

Venkatesan²

2022

I2M

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Monitoring the health of humans in everyday life is quite challenging. Smart wearables have been increasingly important in recent technologies for monitoring and indicating our current health status, which is utilised to appropriately diagnose our health. Monitoring animals' health raises several concerns. We must wait for veterinary specialists to assess and diagnose whether the animals are suffering only in known regions. The result is delayed treatment and degradation of animal health. Therefore, primary health diagnosis is needed. However, mounting equipment was required to identify these features. This research leads to the recognition of the behaviour of animals through this technique, which facilitates the assessment of the health of animals. The use of Wireless Sensor Networks (WSN) and IoT features an Animals Smart Healthcare Monitoring (ASHM) system for this research to track animal behavior with high precision using sensors and to detect animals' health with great precision. We proposed designing a system that would track the movement and health of an animal. This can be achieved by developing a system with animal smart health monitoring system. The sensor can be mounted on the animal body to get the desired physiological parameters like temperature, heart rate, respiratory rate, ECG, and Blood pressure. This health surveillance system method has reduced the mean latency by 75% and provides results with an output accuracy of 98%.

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Section: Literature Surveymentioning

confidence: 99%