We present a portable multi-contrast microscope capable of producing bright-field, dark-field, and differential phase contrast images of thin biological specimens on a smartphone platform. The microscopy method is based on an imaging scheme termed “color-coded light-emitting-diode (LED) microscopy (cLEDscope),” in which a specimen is illuminated with a color-coded LED array and light transmitted through the specimen is recorded by a color image sensor. Decomposition of the image into red, green, and blue colors and subsequent computation enable multi-contrast imaging in a single shot. In order to transform a smartphone into a multi-contrast imaging device, we developed an add-on module composed of a patterned color micro-LED array, specimen stage, and miniature objective. Simple installation of this module onto a smartphone enables multi-contrast imaging of transparent specimens. In addition, an Android-based app was implemented to acquire an image, perform the associated computation, and display the multi-contrast images in real time. Herein, the details of our smartphone module and experimental demonstrations with various biological specimens are presented.

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Conventional methods for analyzing heavy metal contamination in soil and water generally require laboratory equipped instruments, complex procedures, skilled personnel and a significant amount of time. With the advancement in computing and multitasking performances, smartphone-based sensors potentially allow the transition of the laboratory-based analytical processes to field applicable, simple methods. In the present work, we demonstrate the novel miniaturized setup for simultaneous sample preparation and smartphone-based optical sensing of arsenic As(III) in the contaminated soil. Colorimetric detection protocol utilizing aptamers, gold nanoparticles and NaCl have been optimized and tested on the PDMS-chip to obtain the high sensitivity with the limit of detection of 0.71 ppm (in the sample) and a correlation coefficient of 0.98. The performance of the device is further demonstrated through the comparative analysis of arsenic-spiked soil samples with standard laboratory method, and a good agreement with a correlation coefficient of 0.9917 and the average difference of 0.37 ppm, are experimentally achieved. With the android application on the device to run the experiment, the whole process from sample preparation to detection is completed within 3 hours without the necessity of skilled personnel. The approximate cost of setup is estimated around 1 USD, weight 55 g. Therefore, the presented method offers the simple, rapid, portable and cost-effective means for onsite sensing of arsenic in soil. Combined with the geometric information inside the smartphones, the system will allow the monitoring of the contamination status of soils in a nation-wide manner.

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In this paper, a fault detection scheme for DC-link voltage sensor and its fault tolerant control strategy for three-phase AC/DC/AC PWM converters are proposed, where the Luenberger observer is applied to estimate the DC-link voltage. The Luenberger observer is based on a converter model, which is derived from the voltage equations of a grid-side converter and the power balance on a DC link. A fault of the voltage sensor is detected by comparing the measured value of the DC-link voltage with the estimated one. When a sensor fault is detected, a fault tolerant control strategy is performed, where the estimated DC-link voltage is used for the feedback control. The estimation error from the observer is about 1.5 V, which is sufficiently accurate for feedback control. In addition, it is shown that the observer performance is robust to parameter variations of the converter. The validity of the proposed method has been verified by simulation and experimental results.