An ambulatory pre-screening Point-of-Care (POC) device compatible with commercially available diapers has been developed to rapidly screen urine samples for incontinent or functionally impaired elderly. This POC device consists of a set of colorimetric reaction pads with accompanying reference colors. A smartphone with camera is a convenient tool for analysis of colorimetric assays; and a software application has been developed for smartphones to photograph the colorimetric assay and classify colorimetric reactions according to the reference colors. To facilitate detection of multiple biomarkers, e.g., 12 biomarkers with 2-7 references per biomarker, automatic localization of test/reference pads has been implemented through recognition of corner alignment marks and projective coordinate transformation for perspective removal. Each test run trains a set of classifiers from extracted reference data, which is used to classify the extracted test data. The smartphone application gives semi-quantitative results and functions independently of illumination intensity, illumination color, device type (smartphone brand/model), device settings (ISO, shutter speed, aperture) and automatic camera preprocessing. The smartphone application has been tested successfully on Samsung Galaxy S3, S6 Edge, S7 Edge, ZTE Nubia V7 mini and Iphone 6 in various illumination conditions.
Owing to a global effort towards reducing carbon emissions, electric vehicles (EVs) have emerged to replace the petroleum-fueled vehicles. However, the battery is a bottleneck restricting EVs from being utilized in the same way as petroleum-fueled vehicles. Lithium-ion batteries (LiBs) are commonly used in EVs, but have an optimal temperature range, and operation outside this range causes accelerated aging in the form of capacity fading and power fading, especially in cold climates. We propose that both state parameter estimation and thermal management are interconnected problems and should be addressed together: Battery health and performance depends on temperature, while temperature depends on operational conditions, battery health, structural design and thermal management. Temperature dependent decay accounting for heat generation in cells, temperature variation between cells and heat transfer with surroundings, can allow more accurate state parameter estimation and guide thermal management strategies. This review investigates how the dynamics of temperature dependence and heat generation are addressed in the literature related to estimation of battery state parameters. Approaches involving temperature were divided into three categories: 1) maintain constant ambient temperature and omit battery temperature, 2) verify at different ambient temperatures, and 3) use available data for cell and/or ambient temperature. A valid solution to the problem in real applications, must satisfy three criteria: a) suitable for online applications, b) scalable to battery packs, and c) applicable to dynamic battery cycling occurring during normal use. The most promising methods include coupled thermal and electric models with adaptive filtering, and recurrent neural network methods.
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