Microfluidics-based biochips consist of microfluidic arrays on rigid substrates through which, movement of fluids is tightly controlled to facilitate biological reactions. Biochips are soon expected to revolutionize biosensing, clinical diagnostics, and drug discovery. Critical to the deployment of biochips in such diverse areas is the dependability of these systems. Thus, robust testing techniques are required to ensure an adequate level of system dependability. Due to the underlying mixed technology and energy domains, such biochips exhibit unique failure mechanisms and defects. In this article we present a highly effective fault diagnosis strategy that uses a single source and sink to detect and locate multiple faults in a microfluidic array, without flooding the array, a problem that has hampered realistic implementations of all existing strategies. The strategy renders itself well for a built-in self-test that could drastically reduce the operating cost of microfluidic biochips. It can be used during both the manufacturing phase of the biochip, as well as field operation. Furthermore, the algorithm can pinpoint the actual fault, as opposed to merely the faulty regions that are typically identified by strategies proposed in the literature. Also, analytical results suggest that it is an effective strategy that can be used to design highly dependable biochip systems.

A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct methods and tightly-coupled optimization techniques that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

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Microfluidics-based biochips consist of microfluidic arrays on rigid substrates through which movement of fluids is tightly controlled to facilitate biological reactions. Biochips are soon expected to revolutionize biosensing, clinical diagnostics, environmental monitoring, and drug discovery. Critical to the deployment of the biochips in such diverse areas is the dependability of these systems. Thus robust testing and diagnosis techniques are required to ensure adequate level of system dependability. Due to the underlying mixed technology and mixed energy domains, such biochips exhibit unique failure mechanisms and defects. In this article efficient parallel testing and diagnosis algorithms are presented that can detect and locate single as well as multiple faults in a microfluidic array without flooding the array, a problem that has hampered realistic implementation of several existing strategies. The fault diagnosis algorithms are well suited for built-in self-test that could drastically reduce the operating cost of microfluidic biochip. Also, the proposed alogirthms can be used both for testing and fault diagnosis during field operation as well as increasing yield during the manufacturing phase of the biochip. Furthermore, these algorithms can be applied to both online and offline testing and diagnosis. Analytical results suggest that these strategies that can be used to design highly dependable biochip systems.

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