A Time-Efficient Self-Test Method for Evaluating Thermal Parameters of Uncooled Infrared Detectors

Hou, Ying; Fu, Jianyu; Liu, Chao; Lu, Yihong; Li, Zhenfeng; Chen, Dapeng

doi:10.1109/tim.2022.3178487

Cited by 4 publications

(1 citation statement)

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“…So, assessing them is crucial for the performance improvement of the detectors. In [34], a self-test approach for evaluating the thermal characteristics of uncooled IR focal plane arrays is developed. This method was inspired by the conventional optical frequency domain concept and Joule's heating effect of the pixels.…”

Section: B Heat Detectorsmentioning

confidence: 99%

Review Study on Recent Developments in Fire Sensing Methods

Sulthana,

Wise,

Ravikumar

et al. 2023

IEEE Access

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Scientific society has envisioned a considerable advancement in various fire detection methods due to the development in the field of machine learning, information technology, sensors, and signal processing technology. These intelligent processing technologies help in reducing the detection time and false alerts from the sensors. Over the past few decades, there is substantial improvement in the computing power of computers and a decrease in the cost of image sensors, enabling video-based fire detection technology for real-time applications. The ability to differentiate between fire and non-fire threats is improved with the development of the Internet of Things (IoT) or Wireless Sensor Networks (WSN). Unmanned Aerial Vehicles (UAVs) are becoming a more realistic solution for monitoring and detecting fire due to their remote sensing capabilities. This paper summarizes various fire detection methods and the technologies behind them. The issues related to the present fire detection methods and future research initiatives are discussed. The primary aspects of the fire signatures like flame, smoke, ambient temperature, and surrounding gaseous levels concerning different sensors are analyzed with their benefits and drawbacks based on evaluating a range of parameters.

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Section: B Heat Detectorsmentioning

confidence: 99%