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Breath analysis has become increasingly important as a noninvasive process for the clinical diagnosis of patients suffering from various diseases. Many commercial gas preconcentration instruments are already being used to overcome the detection limits of commercial gas sensors for gas concentrations which are as low as ~100 ppb in exhaled breath. However, commercial instruments are large and expensive, and they require high power consumption and intensive maintenance. In the proposed study, a micro gas preconcentrator (μ-PC) filled with a carbon nanotube (CNT) foam as an adsorbing material was designed and fabricated for the detection of low-concentration ethane, which is known to be one of the most important biomarkers related to chronic obstructive pulmonary disease (COPD) and asthma. A comparison of the performance of two gas-adsorbing materials, i.e., the proposed CNT foam and commercial adsorbing material, was performed using the developed μ-PC. The experimental results showed that the synthesized CNT foam performs better than a commercial adsorbing material owing to its lower pressure drop and greater preconcentration efficiency in the μ-PC. The present results show that the application of CNT foam-embedded μ-PC in portable breath analysis systems holds great promise.

It is challenging to apply depth maps generated from sparse laser scan data to computer vision tasks, such as robot vision and autonomous driving, because of the sparsity and noise in the data. To overcome this problem, depth completion tasks have been proposed to produce a dense depth map from sparse LiDAR data and a single RGB image. In this study, we developed a deep convolutional architecture with cross guidance for multi-modal feature fusion to compensate for the lack of representation power of their modality. Two encoders, which are part of the proposed architecture, receive different modalities as inputs. They interact with each other by exchanging information in each stage through the attention mechanism during encoding. We also propose a residual atrous spatial pyramid block, comprising multiple dilated convolutions with different dilation rates, which are used to derive highly significant features. The experimental results of the KITTI depth completion benchmark dataset demonstrate that the proposed architecture shows higher performance than that of the other models trained in a two-dimensional space without pre-training or finetuning other datasets. INDEX TERMS Depth estimation, depth completion, LiDAR data, cross guidance, multi-scale dilated convolutional block. Recently, artificial neural network models with deep learning have been used in state-of-the-art technologies of pattern recognition and machine learning. In particular, convolutional neural networks (CNNs) exhibit excellent performance in many computer vision tasks. While conventional CNNs [3]-[5] comprise blocks of stacking convolution

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We generate networks and carbonization between individualized single-walled carbon nanotubes (SWCNTs) by an optimized plasmonic heating process using a halogen lamp to improve electrical properties for flow-induced energy harvesting. These properties were characterized by Raman spectra, a field-emission-scanning probe, transmission electron microscopy, atomic force microscopy and thermographic camera. The electrical sheet resistance of carbonized SWCNTs was decreased to 2.71 kΩ/□, 2.5 times smaller than normal-SWCNTs. We demonstrated flow-induced voltage generation on SWCNTs at various ion concentrations of NaCl. The generated voltage and current for the carbonized-SWCNTs were 9.5 and 23.5 times larger than for the normal-SWCNTs, respectively, based on the electron dragging mechanism.