RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing

Stairs are common building structures in urban environments, and stair detection is an important part of environment perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a neural network architecture with RGB and depth map inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB map and the depth map and effectively combine the information from the RGB map and the depth map in different scenes. In addition, we design a line clustering algorithm for the postprocessing of detection results, which can make full use of the detection results to obtain the geometric stair parameters. Experiments on our dataset show that our method can achieve better accuracy and recall compared with existing state-of-the-art deep learning methods, which are 5.64% and 7.97%, respectively, and our method also has extremely fast detection speed. A lightweight version can achieve 300 + frames per second with the same resolution, which can meet the needs of most real-time detection scenes

Shifting the paradigm in RNA virus detection: integrating nucleic acid testing and immunoassays through single-molecule digital ELISA

In this review article, we explore the characteristics of RNA viruses and their potential threats to humanity. We also provide a brief overview of the primary contemporary techniques used for the early detection of such viruses. After thoroughly analyzing the strengths and limitations of these methods, we highlight the importance of integrating nucleic acid testing with immunological assays in RNA virus detection. Although notable methodological differences between nucleic acid testing and immune assays pose challenges, the emerging single-molecule immunoassay-digital ELISA may be applied to technically integrate these techniques. We emphasize that the greatest value of digital ELISA is its extensive compatibility, which creates numerous opportunities for real-time, large-scale testing of RNA viruses. Furthermore, we describe the possible developmental trends of digital ELISA in various aspects, such as reaction carriers, identification elements, signal amplification, and data reading, thus revealing the remarkable potential of single-molecule digital ELISA in future RNA virus detection

StairNetV3: Depth-aware Stair Modeling using Deep Learning

Vision-based stair perception can help autonomous mobile robots deal with the challenge of climbing stairs, especially in unfamiliar environments. To address the problem that current monocular vision methods are difficult to model stairs accurately without depth information, this paper proposes a depth-aware stair modeling method for monocular vision. Specifically, we take the extraction of stair geometric features and the prediction of depth images as joint tasks in a convolutional neural network (CNN), with the designed information propagation architecture, we can achieve effective supervision for stair geometric feature learning by depth information. In addition, to complete the stair modeling, we take the convex lines, concave lines, tread surfaces and riser surfaces as stair geometric features and apply Gaussian kernels to enable the network to predict contextual information within the stair lines. Combined with the depth information obtained by depth sensors, we propose a stair point cloud reconstruction method that can quickly get point clouds belonging to the stair step surfaces. Experiments on our dataset show that our method has a significant improvement over the previous best monocular vision method, with an intersection over union (IOU) increase of 3.4 %, and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications. Our dataset is available at https://data.mendeley.com/datasets/6kffmjt7g2/1

When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation

With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously. By introducing deep learning theory into multi-task learning, we first propose a novel multi-task deep learning framework with two main structures: encoder and decoder. The encoder is constructed to extract sufficient image features in different scales for the decoder, while the decoder is a tasks-specific structure which employs these extracted features adaptively and optimally to meet the different feature demands of the recognition and segmentation. Therefore, the proposed framework has the ability to achieve superior recognition and segmentation performance. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, experimental results show the superiority of the proposed framework in terms of recognition and segmentation

In Situ X-ray Absorption Spectroscopy Studies of Kinetic Interaction between Platinum(II) Ions and UiO-66 Series Metal–Organic Frameworks

The interaction of guest Pt(II) ions with UiO-66–X (X = NH2, H, NO2, OMe, F) series metal–organic frameworks (MOFs) in aqueous solution was investigated using in situ X-ray absorption spectroscopy. All of these MOFs were found to be able to coordinate with Pt(II) ions. The Pt(II) ions in UiO-66–X MOFs generally coordinate with 1.6–2.4 Cl and 1.4–2.4 N or O atoms. We also studied the time evolution of the coordination structure and found that Pt(II) maintained a coordination number of 4 throughout the whole process. Furthermore, the kinetic parameters of the interaction of Pt(II) ions with UiO-66–X series MOFs (X = NH2, H, NO2, OMe, F) were determined by combinational linear fitting of extended X-ray absorption fine structure (EXAFS) spectra of the samples. The Pt(II) adsorption rate constants were found to be 0.063 h–1 for UiO-66–NH2 and 0.011–0.017 h–1 for other UiO-66–X (X = H, NO2, OMe, F) MOFs, which means that Pt(II) adsorption in UiO-66–NH2 is 4–6 times faster than that in other UiO-66 series MOFs. FTIR studies suggested that the carboxyl groups could be the major host ligands binding with Pt(II) ions in UiO-66 series MOFs, except for UiO-66–NH2, in which amino groups coordinate with Pt(II) ions

Experimental analysis on residual ultimate bearing capacity of thin plate with internal explosion dent damage under biaxial compression

ObjectivesIn modern naval warfare, advancements in weaponry have significantly increased the vulnerability of ships to explosion impacts. Precision-guided weapons, in particular, pose a significant threat, as internal explosions within ship cabins can cause extensive damage to thin-walled structures. This damage not only compromises the ship's structural integrity but also affects its overall functionality and safety. To accurately assess a ship's ability to withstand such damage and make informed battlefield decisions, it is crucial to evaluate the residual load-bearing capacity of damaged structures under complex sea conditions. This analysis is essential for evaluating the ship's damage tolerance and determining its ability to safely return to port. MethodsThis study focuses on the behavior of hull plates damaged by in-cabin explosions. A series of meticulously designed model tests were conducted, aiming to analyze the residual load-bearing capacity of thin plates exhibiting dent damage under biaxial compression. The use of biaxial compression is highly relevant, as it replicates the complex stress states experienced by ship hulls in actual sea conditions. To measure the detailed mechanical behavior of the damaged plates, the digital image correlation (DIC) method was employed. This advanced technique enabled the creation of a three-dimensional full-field strain measurement system, which recorded the out-of-plane deformation of the plates with high precision. By analyzing this data, the study explored the failure modes of dent-damaged thin plates under biaxial compression, illuminating the mechanisms through which such damage progresses and ultimately leads to structural failure. ResultsThe experimental results provided significant insights into the behavior of damaged thin plates under biaxial compression. A key finding was that, regardless of the applied loading ratio, the presence of dent damage led to a substantial reduction in the residual load-bearing capacity of the thin plates. In some cases, this reduction reached up to 19.96%, demonstrating the severe impact of even minor damage on the structural performance of the plates. Furthermore, all tested plates ultimately failed due to significant plastic deformation at the intersection of the loading edges, which underscores the localized nature of the damage and its catastrophic consequences for structural integrity. Another key finding was that an increase in the load at one end of the biaxial compression resulted in a notable decline in the ultimate bearing capacity at the other end. ConclusionsThis study provides valuable insights into assessing the damage survivability of ships under complex stress conditions. The findings help naval personnel better understand the structural state of damaged ships, enabling them to make informed decisions regarding mission continuation or safe return to port. Additionally, the research provides a basis for future research focused on optimizing ship structural design and enhancing damage-tolerance capabilities. Overall, this study plays a vital role in ensuring the safety and operational effectiveness of ships in combat and their safe return to port