Paper-based immunoassays are becoming powerful and low-cost diagnostic tools, especially in resource-limited settings. Inexpensive methods for quantifying these assays have been shown using desktop scanners, which lack portability, and cameras, which suffer from the ever changing ambient light conditions. In this work, we introduce a novel approach of quantifying colors of colorimetric diagnostic assays with a smartphone that allows high accuracy measurements in a wide range of ambient conditions, making it a truly portable system. Instead of directly using the red, green, and blue (RGB) intensities of the color images taken by a smartphone camera, we use chromaticity values to construct calibration curves of analyte concentrations. We demonstrate the high accuracy of this approach in pH measurements with linear response ranges of 1-12. These results are comparable to those reported using a desktop scanner or silicon photodetectors. To make the approach adoptable under different lighting conditions, we developed a calibration technique to compensate for measurement errors due to variability in ambient light. This technique is applicable to a number of common light sources, such as sun light, fluorescent light, or smartphone LED light. Ultimately, the entire approach can be integrated in an "app" to enable one-click reading, making our smartphone based approach operable without any professional training or complex instrumentation.

Automatic registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR) is a challenging task due to the significant non-linear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the Histogram of Orientated Phase Congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation, and use the extended model to build HOPCncc. Then a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images, and has been tested with a variety of optical, LiDAR, SAR and map data. The results show that HOPCncc is robust against complex non-linear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration.

Monitoring aspects of human performance during various activities has recently become a highly investigated research area. Many new commercial products are available now to monitor human physical activity or responses while performing activities ranging from playing sports, to driving, and even sleeping. However, monitoring cognitive performance biomarkers, such as neuropeptides, is still an emerging field due to the complicated sample collection and processing, as well as the need for a clinical lab to perform analysis. Enzyme-linked immunosorbent assays (ELISAs) provide specific detection of biomolecules with high sensitivity (picomolar concentrations). Even with the advantage of high sensitivity, most ELISAs need to be performed in a laboratory setting and require around 6 h to complete. Transitioning this assay to a platform where it reduces cost, shortens assay time, and is able to be performed outside a lab is invaluable. Recently developed paper diagnostics provide an inexpensive platform on which to perform ELISAs; however, the major limiting factor for moving out of the laboratory environment is the measurement and analysis instrumentation. Using something as simple as a digital camera or camera-enabled Windows- or Android-based tablets, we are able to image paper-based ELISAs (P-ELISAs), perform image analysis, and produce response curves with high correlation to target biomolecule concentration in the 10 pM range. Neuropeptide Y detection was performed. Additionally, silver enhancement of Au NPs conjugated with IgG antibodies showed a concentration-dependent response to IgG, thus eliminating the need for an enzyme-substrate system. Automated image analysis and quantification of antigen concentrations are able to be performed on Windows- and Android-based mobile platforms.

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