Continuously preventing weak password attacks is one of the most important initiatives to secure IoT systems. Password strength meters can guide users to create secure passwords, but in our investigation, we found that current password strength meters in mainstream IoT systems overestimate the strength of passwords with digit segments, leading users to choose passwords they thought were secure but are actually not. Therefore, we conduct a more in-depth and comprehensive study on the semantic characteristics of digit segments in passwords than ever before. We obtained unpublished high-frequency digit semantic patterns through semantic extraction methods and improved the PCFG attack by utilizing these newly discovered semantic pattern characteristics. The experimental results show that the semantic characteristics of digit segments have an important impact on the strength of user passwords. Finally, we propose a feasible scheme to improve the password strength meter for IoT systems based on the high-frequency semantic characteristics of digit segments.

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a rich initial training dataset for training the base model. For complex sequences that are difficult to analyze, especially under challenging snowy weather conditions, AIS uses scene reconstruction and prediction inconsistency to iteratively select training samples, refining the model to handle a wide range of real-world scenarios. Experiments across datasets and weather conditions validate our approach’s effectiveness. Notably, our method matches the performance of full-dataset training with just 52% of the sequence volume, demonstrating the training efficiency and robustness of our active training paradigm. By optimizing the training process, our approach sets the stage for more agile and reliable LiDAR odometry systems, capable of navigating diverse environmental conditions with greater precision.

Deep neural networks have been shown to be effective for unsupervised monocular visual odometry that can predict the camera’s ego-motion based on an input of monocular video sequence. However, most existing unsupervised monocular methods haven’t fully exploited the extracted information from both local geometric structure and visual appearance of the scenes, resulting in degraded performance. In this paper, a novel geometry-aware network is proposed to predict the camera’s ego-motion by learning representations in both 2D and 3D space. First, to extract geometry-aware features, we design an RGB-PointCloud feature fusion module to capture information from both geometric structure and the visual appearance of the scenes by fusing local geometric features from depth-map-derived point clouds and visual features from RGB images. Furthermore, the fusion module can adaptively allocate different weights to the two types of features to emphasize important regions. Then, we devise a relevant feature filtering module to build consistency between the two views and preserve informative features with high relevance. It can capture the correlation of frame pairs in the feature-embedding space by attention mechanisms. Finally, the obtained features are fed into the pose estimator to recover the 6-DoF poses of the camera. Extensive experiments show that our method achieves promising results among the unsupervised monocular deep learning methods on the KITTI odometry and TUM-RGBD datasets.

Large-area hyperspectral remote sensing monitoring of physiological parameters of reclaimed vegetation is a crucial means of environmental supervision in rare earth (RE) mining areas. The variation analysis of reclaimed vegetation characteristics under environmental stress in mining areas provides the necessary foundation for accurate dynamic monitoring of ecological restoration. In this article, the original spectral reflectance of six typical reclaimed vegetations in the RE mining area and their corresponding vegetations in the normal environment are measured. The spectral variations between reclaimed and normal vegetations are then compared and analyzed. To extract more hidden information in spectral features, the original spectrum is transformed using common derivative transform. In addition, wavelet transform (WT) and fractional differential (FD) transform are used to strengthen the detailed information of the original spectrum and explore the spectral features of reclaimed vegetation under environmental stress in RE mining areas. It can be deduced that in the first derivative spectrum, the reclaimed vegetations show a blueshift in the red edge position except forWetland pine. This indicates that reclaimed vegetation is affected by external factors, such as environmental stresses to different degrees in the mining area. WT can extract the original spectral features with a high degree of influence, highlighting the detailed information with obvious changes and sharp fluctuations in spectral information features, in which the reclaimed vegetations and normal vegetations had obvious differences at the d5 scale. These results provide technical support for process management and formulation of reclamation measures of reclaimed vegetation in RE mining areas, and therefore contribute to the ecological reconstruction of RE reclamation mining areas.

A dual-threshold method is proposed in this paper to reduce the spurious photon events in photon counting imaging with a thermoelectric cooling electron multiplying charge coupled device (EMCCD). Using the thresholds in both spatial and time domains, the read noise, dark signal and clock induced charges (CIC) can be filtered simultaneously. The theory and measurements of the dual-threshold technique are presented. The results show that this method works better than 5σ threshold method in restoring photon images with the EMCCD of the working temperature at -20 °C. The sensitivity of thermoelectric cooling EMCCDs can be effectively improved by the dual-threshold technique. Photon images could be still restored under the illumination level of 0.1e-/pixel/frame.