Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm

Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment. Nonetheless, two major obstacles still obstruct its deployment to real-world applications. The first issue is that they rarely take the entire pipeline's speed into account. The second is that they are incapable of dealing with some low-quality images (i.e., meter breakage, blur, and uneven scale). In this paper, we propose a human-like alignment and recognition algorithm to overcome these problems. More specifically, a Spatial Transformed Module(STM) is proposed to obtain the front view of images in a self-autonomous way based on an improved Spatial Transformer Networks(STN). Meanwhile, a Value Acquisition Module(VAM) is proposed to infer accurate meter values by an end-to-end trained framework. In contrast to previous research, our model aligns and recognizes meters totally implemented by learnable processing, which mimics human's behaviours and thus achieves higher performances. Extensive results verify the good robustness of the proposed model in terms of the accuracy and efficiency

Learning spatial and spectral features via 2D-1D generative adversarial network for hyperspectral image super-resolution

Three-dimensional (3D) convolutional networks have been proven to be able to explore spatial context and spectral information simultaneously for super-resolution (SR). However, such kind of network can’t be practically designed very ‘deep’ due to the long training time and GPU memory limitations involved in 3D convolution. Instead, in this paper, spatial context and spectral information in hyperspectral images (HSIs) are explored using Two-dimensional (2D) and Onedimenional (1D) convolution, separately. Therefore, a novel 2D-1D generative adversarial network architecture (2D-1DHSRGAN) is proposed for SR of HSIs. Specifically, the generator network consists of a spatial network and a spectral network, in which spatial network is trained with the least absolute deviations loss function to explore spatial context by 2D convolution and spectral network is trained with the spectral angle mapper (SAM) loss function to extract spectral information by 1D convolution. Experimental results over two real HSIs demonstrate that the proposed 2D-1D-HSRGAN clearly outperforms several state-of-the-art algorithms

SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation

Recently, self-supervised monocular depth estimation has gained popularity with numerous applications in autonomous driving and robotics. However, existing solutions primarily seek to estimate depth from immediate visual features, and struggle to recover fine-grained scene details with limited generalization. In this paper, we introduce SQLdepth, a novel approach that can effectively learn fine-grained scene structures from motion. In SQLdepth, we propose a novel Self Query Layer (SQL) to build a self-cost volume and infer depth from it, rather than inferring depth from feature maps. The self-cost volume implicitly captures the intrinsic geometry of the scene within a single frame. Each individual slice of the volume signifies the relative distances between points and objects within a latent space. Ultimately, this volume is compressed to the depth map via a novel decoding approach. Experimental results on KITTI and Cityscapes show that our method attains remarkable state-of-the-art performance (AbsRel = $0.082$ on KITTI, $0.052$ on KITTI with improved ground-truth and $0.106$ on Cityscapes), achieves $9.9\%$, $5.5\%$ and $4.5\%$ error reduction from the previous best. In addition, our approach showcases reduced training complexity, computational efficiency, improved generalization, and the ability to recover fine-grained scene details. Moreover, the self-supervised pre-trained and metric fine-tuned SQLdepth can surpass existing supervised methods by significant margins (AbsRel = $0.043$, $14\%$ error reduction). self-matching-oriented relative distance querying in SQL improves the robustness and zero-shot generalization capability of SQLdepth. Code and the pre-trained weights will be publicly available. Code is available at \href{https://github.com/hisfog/SQLdepth-Impl}{https://github.com/hisfog/SQLdepth-Impl}.Comment: 14 pages, 9 figure

Glycosylated MoS2 Sheets for Capturing and Deactivating E. coli Bacteria: Combined Effects of Multivalent Binding and Sheet Size

Molybdenum disulfide (MoS2) holds great promise for antibacterial applications owing to its strong photothermal performance and biocompatibility. Most of its antibacterial explorations have sought enhanced antibacterial potency through designing new hybrid inorganic materials, the relationship between its physiochemical properties and antibacterial activities has yet to be explored. This work is the first to investigate the combination effects of different sized and functionalized MoS2 sheets on their antibacterial activities. The bacterial capture abilities of 3 µm mannosylated, galactosylated, and glucosylated sheets, as well as 300 nm mannosylated sheets, all with similar sugar densities, are compared. Only mannosylated MoS2 sheets are found to agglutinate normal Escherichia coli (E. coli) and large mannosylated MoS2 sheets show the strongest E. coli agglutination. Despite slightly weaker photothermal performance under near-infrared (NIR) laser irradiation, large mannosylated MoS2 sheets exhibit higher antibacterial activity than the smaller sheets. By much stronger specific multivalent binding, large sheets capture E. coli more efficiently and compensate for their reduced photothermal activity. Besides providing a facile approach to eliminate E. coli bacteria, these findings offer valuable guidance for future development of 2D nanomaterial-based antibacterial agents and filter holder materials, where large-functionalized sheets can capture and eliminate bacteria powerfully

The Micro-Changes of Fly Ash in the Utilization of “Dip in One Acid Twice/Unite Two Kinds of Alkalis”

Determined the new technology of element leaching in fly ash’s utilization---- “dip in one acid twice/unite two kinds of alkalis” through comparison tests, the technique consist of four phases: acid leaching、alkali dissolution, calcination and second acid leaching, the maximum fine utilization rates of silicon, aluminum, iron are respectively 97.07%, 86.67%, 96.54%, the total utilization rate is 100%. Analyzed the micro-changes of fly ash in the utilization process by X-ray diffraction and scanning electron microscope, the results show that: (1)there are mineral changes exist in acid leaching process, and some amorphous active substance is dissolved, it destroy the surface structure of fly ash, conducive to the conduct of following response; (2)after alkali leaching, most of the amorphous SiO2 is dissolved, crystalline SiO2 (quartz) has not changed; (3)after calcination with sodium carbonate, all the mine phases are transformed into nepheline and a small amount of pyroxene which are layer (film) structure , except a small amount of residual quartz crystal;(4)after the second acid leaching, the fly ash is transformed into silica II which mainly constitute by the amorphous SiO2.特集 : 「資源、新エネルギー、環境、防災研究国際セミナー

Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg¨ ha´1 ) was significantly lower than that of the SF (114 Mg¨ ha´1 ). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0–10 cm layer accounted for more than one third of the total soil C stock over 0–50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg¨ ha´1 , respectively, 64% of which was soil C for both stands (205 Mg¨ ha´1 for the SF and 127 Mg¨ ha´1 for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C

Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg¨ ha´1 ) was significantly lower than that of the SF (114 Mg¨ ha´1 ). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0–10 cm layer accounted for more than one third of the total soil C stock over 0–50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg¨ ha´1 , respectively, 64% of which was soil C for both stands (205 Mg¨ ha´1 for the SF and 127 Mg¨ ha´1 for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C