Over the last decade there has been a proliferation of low-cost sensor networks that enable highly distributed sensor deployments in environmental applications. The technology is easily accessible and rapidly advancing due to the use of open-source microcontrollers. While this trend is extremely exciting, and the technology provides unprecedented spatial coverage, these sensors and associated microcontroller systems have not been well evaluated in the literature. Given the large number of new deployments and proposed research efforts using these technologies, it is necessary to quantify the overall instrument and microcontroller performance for specific applications. In this paper, an Arduino-based weather station system is presented in detail. These low-cost energy-budget measurement stations, or LEMS, have now been deployed for continuous measurements as part of several different field campaigns, which are described herein. The LEMS are low-cost, flexible, and simple to maintain. In addition to presenting the technical details of the LEMS, its errors are quantified in laboratory and field settings. A simple artificial neural network-based radiation-error correction scheme is also presented. Finally, challenges and possible improvements to microcontroller-based atmospheric sensing systems are discussed.

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Methods for 3D reconstruction such as Photometric stereo recover the shape and reflectance properties using multiple images of an object taken with variable lighting conditions from a fixed viewpoint. Photometric stereo assumes that a scene is illuminated only directly by the illumination source. As result, indirect illumination effects due to inter-reflections introduce strong biases in the recovered shape. Our suggested approach is to recover scene properties in the presence of indirect illumination. To this end, we proposed an iterative PS method combined with a reverted Monte-Carlo ray tracing algorithm to overcome the inter-reflection effects aiming to separate the direct and indirect lighting. This approach iteratively reconstructs a surface considering both the environment around the object and its concavities. We demonstrate and evaluate our approach using three datasets and the overall results illustrate improvement over the classic PS approaches.Index Terms-Photometric Stereo, 3D Reconstruction, Ray Tracing • a reverted Monte Carlo ray tracing algorithm to estimate the indirect lighting both from the environment and the object's concavities.

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