Air monitoring: Research needs.
Sampling and analyses difficulties encountered in the ambient, source and exposure monitoring areas of environmental data sampling of inorganic particulate matter, inorganic gases, organic particulate matter, and organic gases are discussed. Attention is given to instrumentation such as tunable lasers, ion chromatography, optical emission spectroscopy, fiber glass filters, dichotomous samplers, Teflon and other polymeric membrane filters, and electrostatic samplers. A lack of species-tailored devices is noted, as is a need to develop numerical and statistical models that will decrease the required sampling volume for reliable estimates. Finally, better characterizations of the effects that sampling has on the medium sampled are recommended. 8 references.
Sampling and Analysis
Publisher Summary This chapter discusses sampling and analysis. In atmospheric analyses, the mass or concentration of the desired analyte is not measured directly. Instead, a numerical value of a physical or chemical property for the analyte, which can then be correlated with either mass or concentration, is obtained. The relationship between the measured physical or chemical property ( X i ) and the quantity ( q i ) for analyte species ( i ) is given by an analytical calibration function X i = f i ( q i ). If X i is determined for a series of standards of different known masses or concentrations, these results establish the analytical calibration curve, that is, the relationship between the analytical signal and the analyte mass or concentration. The choice of an appropriate calibration technique is affected by the analytical method, potential interferences in the sample matrix, the number of samples to be analyzed, the degree of accuracy necessary for the data's intended use, and the relative magnitude of the error contributed by the measurement process compared to combined errors from sampling, sample preparation, and data reduction. Knowledgeable interpretation of trace atmospheric measurement data is an important factor in a good-quality assurance program, and greater recognition of this principle would prevent considerable confusion.
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.