The precise localization of Unmanned Aerial Vehicles (UAVs) is a critical prerequisite for mission success in smart urban environments driven by the Internet of Things (IoT). Traditional Global Navigation Satellite System (GNSS)-based positioning schemes are vulnerable to signal interference and spoofing attacks, highlighting the importance of visual scene matching as a robust solution for anti-jamming localization. However, this technology faces challenges such as sensitivity to perspective variations and trajectory discontinuities. To address these limitations, this paper presents a novel visual scene matching-based UAV localization framework. First, a spatiotemporally constrained candidate map retrieval strategy is proposed, which enables efficient and accurate candidate retrieval by coupling single-frame probability estimation with a Hidden Markov Model. Second, a pose recovery algorithm based on cross-frame elevation difference constraints is introduced, by leveraging feature matches between adjacent UAV frames and between UAV frames and satellite images, the elevation information of satellite images is estimated and 2D-3D correspondences between UAV images and satellite images is established. This constructs a 3D constraint model that enables the UAV to obtain 3D coordinates independent of perspective variations, thereby maintaining localization accuracy under oblique views. Finally, a factor graph joint optimization framework is developed to fuse local relative poses with satellite-based absolute geographic poses, thereby ensuring smooth and stable localization performance. Extensive experiments on public and real-world datasets demonstrate that the proposed method significantly improves localization accuracy and validates its effectiveness in supporting collaborative IoT agent operations in GNSS-denied environments.

Accurate monitoring of the state of health (SOH) and remaining useful life (RUL) of lithium batteries is a key technology to realize their stable and economic operation. The current data-driven methods have problems such as local prediction errors caused by capacity self-recovery effect, model drift phenomenon, and inaccurate trend prediction caused by complex degradation process. In this article, we propose a joint SOH and RUL forecasting based on the fusion of kolmogorov-Arnold network (KAN), deep bidirectional long short-term memory network (DBLSTM) and adaptive mechanism method (KAN-HDBLSTM-AM). The method innovatively introduces an adaptive mechanism that considers the capacity degradation trend for different prediction steps, suppresses the accumulation of errors in the model prediction process, and further improves the prediction accuracy by combining the nonlinear approximation capability of KAN and the mastery of data temporal characteristics of DBLSTM. Experimental validation shows that the proposed method can effectively overcome the prediction errors caused by capacity self-recovery and model drift, and can retain sufficient accuracy under long prediction steps, with the prediction trend being more in line with the actual change trend and the prediction error being smaller compared with other methods.

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

This paper presents PathCE, a path-centric cardinality estimation framework for subgraph matching. PathCE improves estimation accuracy by utilizing statistics from short graph queries. At its core is a novel data structure called the path-centric summary graph (PSG), which captures short path query statistics from a data graph G and represents them in a new graph G . Given a graph query Q and a PSG graph G for G, PathCE decomposes Q into a simpler query G , where each edge in G corresponds to a sub-path query in Q with statistics included in G . PathCE estimates the cardinality using G and G , requiring significantly fewer estimation iterations while ensuring that the estimate remains an upper bound on the true cardinality of Q ( G ). It also includes PSGBuilder, a parallelly scalable algorithm that constructs PSG's for any given graph in linear time, efficiently scaling with the number of processors. Empirical results on real-world and synthetic datasets show that PathCE outperforms state-of-the-art baselines in accuracy, estimation latency, and summary construction efficiency.

Stationary functional brain networks (sFBNs) and dynamic functional brain networks (dFBNs) derived from resting-state functional MRI characterize the complex interactions of the human brain from different aspects and could offer complementary information for brain disease analysis. Most current studies focus on sFBN or dFBN analysis, thus limiting the performance of brain network analysis. A few works have explored integrating sFBN and dFBN to identify brain diseases, and achieved better performance than conventional methods. However, these studies still ignore some valuable discriminative information, such as the distribution information of subjects between and within categories. This paper presents a Double Collaborative Learning Network (DCLNet), which takes advantage of both collaborative encoder and collaborative contrastive learning, to learn complementary information of sFBN and dFBN and distribution information of subjects between inter- and intra-categories for brain disease classification. Specifically, we first construct sFBN and dFBN using traditional correlation-based methods with rs-fMRI data, respectively. Then, we build a collaborative encoder to extract brain network features at different levels (i.e., connectivity-based, brain-region-based, and brain-network-based features), and design a prune-graft transformer module to embed the complementary information of the features at each level between two kinds of FBNs. We also develop a collaborative contrastive learning module to capture the distribution information of subjects between and within different categories, thereby learning the more discriminative features of brain networks. We evaluate the DCLNet on two real brain disease datasets with rs-fMRI data, with experimental results demonstrating the superiority of the proposed method.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs. Recently, the transformer technique has demonstrated remarkable performance in various tasks, which is attributed to its effective self-attention mechanism in capturing the global temporal feature representations. However, it cannot effectively model the local network characteristics of FCNs. To this end, in this paper, we propose a novel network structure for Local sequential feature Coupling Global representation learning (LCGNet) to take advantage of convolutional operations and self-attention mechanisms for enhanced FCN representation learning. Specifically, we first build a dynamic FCN for each subject using an overlapped sliding window approach. We then construct three sequential components (i.e., edge-to-vertex layer, vertex-to-network layer, and network-to-temporality layer) with a dual backbone branch of CNN and transformer to extract and couple from local to global topological information of brain networks. Experimental results on two real datasets (i.e., ADNI and ADHD-200) with rs-fMRI data show the superiority of our LCGNet.

Audio-visual signals can be used jointly for robotic perception as they complement each other. Such multi-modal sensory fusion has a clear advantage, especially under noisy acoustic conditions. Speaker localization, as an essential robotic function, was traditionally solved as a signal processing problem that now increasingly finds deep learning solutions. The question is how to fuse audio-visual signals in an effective way. Speaker tracking is not only more desirable, but also potentially more accurate than speaker localization because it explores the speaker's temporal motion dynamics for smoothed trajectory estimation. However, due to the lack of large annotated dataset, speaker tracking is not well studied as speaker localization. In this paper, we study robotic speaker Direction of Arrival (DoA) estimation with a focus on audio-visual fusion and tracking methodology. We propose a Cross-Modal Attentive Fusion (CMAF) mechanism, which explores self-attention to learn intra-modal temporal dependencies, and cross-attention mechanism for inter-modal alignment. We also collect a realistic dataset on a robotic platform to support the study. The experimental results demonstrate that our proposed network outperforms the state-of-the-art audio-visual localization and tracking methods under noisy conditions, with an improved accuracy of 5.82% and 3.62% at SNR = −20 dB, respectively.

Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of brain disorders, such as Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding methods have been designed for FC network analysis. However, these studies usually use a pre-defined threshold or connection percentage to threshold whole FC networks, thus ignoring the diversity of temporal correlation (e.g., strong associations) between brain regions in subject groups. In this work, we propose a distribution-guided network thresholding learning (DNTL) method for FC network analysis in brain disorder identification with rs-fMRI. Specifically, for each connection of a pair of brain regions, we propose to determine its specific threshold based on the distribution of connection strength (i.e., temporal correlation) between subject groups (e.g., patients and normal controls). The proposed DNTL can adaptively yield an FC-specific threshold for each connection in an FC network, thus preserving diversity of temporal correlation among different brain regions. Experiment results on 365 subjects from two datasets (i.e., ADNI and ADHD-200) suggest that the DNT method outperforms state-of-the-art methods in brain disorder identification with rs-fMRI data.

Drought is one of the most common natural disasters which may harm ecosystem and economy, and thus, it is important to accurately grasp the change of drought. In this study, an innovative multisource remote sensing drought index improved Temperature-Vegetation-Soil Moisture Dryness Index (iTVMDI) based on passive microwave remote sensing data of FengYun (FY)3B and optical/infrared data is proposed to monitor the drought in Shandong Province, China in 2016. The monitoring results were verified by meteorological data. Results showed that iTVMDI has a negative correlation (R=-0.73) with precipitation and the average correlation coefficient with temperature is 0.76. Overall, iTVMDI can be applied to monitor the temporal and spatial variation of drought conditions.

Drought is one of the major natural disasters which not only causes great damage to ecosystems and the environment, but also seriously affects social and economic activities and residents' lives. In this study, the MODIS- Albedo, LST and NDVI data was used to downscale the FY-3B soil moisture product at 25km to 1km by multivariate statistical regression method. Then the drought of Shandong Province was monitored using the downscaling results in 2016. The results indicate that drought monitoring using multisource remote sensing data is very useful for drought monitoring.