TEMPERATURE COEFFICIENTS OF TOPCON RADIATION THERMOMETERS
− The ambient or instrument temperature affects the outputs of a standard radiation thermometer through various parts such as a detector, a feedback resistance and an interference filter. Usually the output was corrected by one coefficient assuming the linear dependence. If the filter wavelength changes, the dependence is different at each target temperature. We measured the temperature coefficients of Topcon radiation thermometers at different target temperatures and found the target temperature dependence as well as the feed back resistance dependence. Therefore a high stability radiation thermometer requires controlling the detector, filter and amplifier temperatures.
Calibration and characterization of Chino 900 nm silicon narrow-band radiation thermometer
This paper describes the calibration and characterization of the Chino 900 nm standard radiation thermometer, IR-RST-90 W. The gain ratio, fixed-point calibrations, spectral responsivity, nonlinearity, size-of-source effect, distance effect, zero-offset stability and temperature drift were measured. Some problems were found for this thermometer regarding the out-of-band suppression, the size-of-source effect and the temperature drifts. A new type radiation thermometer was manufactured for improving the quality to use as a standard radiation thermometer.
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.