The WAC is a 7-color push-frame camera (100 and 400 m/pixel visible and UV, respectively), while the two NACs are monochrome narrow-angle linescan imagers (0.5 m/pixel). The primary mission of LRO is to obtain measurements of the Moon that will enable future lunar human exploration. The overarching goals of the LROC investigation include landing site identification and certification, mapping of permanently polar shadowed and sunlit regions, meter-scale mapping of polar regions, global multispectral imaging, a global morphology base map, characterization of regolith properties, and determination of current impact hazards.

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Abstract-Traditional approaches to transmit information reliably over an error-prone network employ either Forward Error Correction (FEC) or retransmission techniques. In this paper we consider an application of network coding to increase the bandwidth efficiency of reliable broadcast in a wireless network. In particular, we propose two schemes which employ network coding to reduce the number of retransmissions as a result of packet losses. Our proposed schemes combine different lost packets from different receivers in such a way that multiple receivers are able to recover their lost packets with one transmission by the source. The advantages of the proposed schemes over the traditional wireless broadcast are shown through simulations and theoretical analysis. Specifically, we provide a few results on the retransmission overhead of the proposed schemes under different channel conditions.

In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results.