With the successful launch of BIRD satellite in October 2001, new possibilities of the observation of hot events like forest fires, volcanic eruptions a.o. from space are opened. The BIRD (Bi-spectral infrared Detecti...
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(纸本)081944586X
With the successful launch of BIRD satellite in October 2001, new possibilities of the observation of hot events like forest fires, volcanic eruptions a.o. from space are opened. The BIRD (Bi-spectral infrared Detection) is the first satellite which is equipped with space instrumentation dedicated to recognize high temperature events. Current remote sensing systems have the disadvantage that they were not designed for the observation of hot events. Starting with the FIRES Phase A Study, the principle requirements and ideas for a fire recognition system were defined. With the German BIRD demonstrator mission, a feasible approach of these ideas has been realized and work now in space. This mission shall answer technological and scientific questions related to the operation of a compact bi-spectral infrared push-broom sensor and related to the detection and investigation of fires from space. The payload of BIRD is a multi-sensor system designed to fulfil the scientific requirements under the constraints of a micro satellite. The paper describes the basic ideas for fire detection and the estimation of fire temperature, fire size, and energy release in the sub-pixel domain and describes the technical solution for the infraredsensor system on board of BIRD.
BackgroundPressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. ...
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BackgroundPressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the *** designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body *** experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to *** proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.
Thermal imaging sensors have been increasingly integrated in a wide range of smart building and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable for applications that require ...
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Thermal imaging sensors have been increasingly integrated in a wide range of smart building and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable for applications that require non-intrusive monitoring with proper privacy protection. In this paper, we present an in-depth investigation of a low-resolution thermal imaging sensor (i.e., Melexis MLX90640) focusing on algorithm design issues and solutions when detecting moving objects. This type of sensors are designed to operate with a two-subpage chessboard reading pattern, which gives rise to blob displacements across two subpages when target objects are in motion. We have conducted systematic characterization of the sensor and demonstrated issues through experimental measurements and analysis. We have also proposed a subpage bilinear interpolation method and an enhanced sensor data preprocessing method for occupancy estimation with moving objects. The performance of the proposed method is analyzed by training and testing classification algorithms using two datasets collected with objects of different moving speeds. Our performance results indicate that the proposed method could be used for occupancy estimation in various smart building and Internet of Things applications.
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