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Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation.

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X − Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

Due to the poor visibility of the deep-sea environment, acoustic signals are often collected and analyzed to explore the behavior of marine species. With the progress of underwater signal-acquisition technology, the amount of acoustic data obtained from the ocean has exceeded the limit that human can process manually, so designing efficient marine-mammal classification algorithms has become a research hotspot. In this paper, we design a classification model based on a multi-channel parallel structure, which can process multi-dimensional acoustic features extracted from audio samples, and fuse the prediction results of different channels through a trainable full connection layer. It uses transfer learning to obtain faster convergence speed, and introduces data augmentation to improve the classification accuracy. The k-fold cross-validation method was used to segment the data set to comprehensively evaluate the prediction accuracy and robustness of the model. The evaluation results showed that the model can achieve a mean accuracy of 95.21% while maintaining a standard deviation of 0.65%. There was excellent consistency in performance over multiple tests.

Sensory data collection is one of the most important concerns in underwater sensor networks (USNs). Because full connectivity cannot be guaranteed, mobile data gathering with autonomous underwater vehicles (AUVs) is widely used in sparse three-dimensional (3D) USNs to solve energy-imbalance problems between different sensor nodes. AUVs with relatively abundant energy and storage can collect sensory data from one sensor node to transmit to another node, so as to avoid energy-intensive multi-hop transmission. As a result, movement control strategy and data collecting path planning for AUVs are very crucial for the performance of data acquisition. This paper proposes a smooth 3D Dubins curves based mobile data gathering mechanism to overcome the kinematic nonholonomic constraints of AUVs. The objective of our proposed method is to collect sensory data along smooth 3D Dubins paths, which are interpolated by continuous Bezier curves in the Z-axis from 2D Dubins curves. Extensive simulation results verify that the proposed method has a more efficient performance in terms of path smoothness and energy consumption; thus it is very suitable for mobile data collection in 3D underwater sensor networks.

Atopic dermatitis (AD) is a common recurrent chronic inflammatory skin disease, and there is increasing evidence of a possible association between AD and autoimmune diseases. This study aimed to summarize existing epidemiological studies on the association between AD and autoimmune diseases and to perform a meta-analysis of combinable results. We conducted a thorough search for cohort studies, case-control studies and cross-sectional studies across the PubMed, Cochrane Library, and Embase databases, from their inception to May 24, 2024, using medical subject headings and relevant keywords. All data were meticulously analyzed using Stata statistical software version 17.0. The protocol was registered on PROSPERO (CRD42024547282). A total of 26 cohort studies, comprising 1,629,723 patients with atopic dermatitis and 15,106,889 control subjects, were included in this meta-analysis. These studies were published between 2014 and 2024 and included 19 cohort studies, 2 case-control studies, and 5 cross-sectional studies. The current study demonstrated a significant association of atopic dermatitis with autoimmune diseases[HR 1.49, 95% CI (1.31-1.70); 0.001], including celiac disease, systemic lupus erythematosus, Sjogren's syndrome, ankylosing spondylitis, alopecia areata, rheumatoid arthritis, vitiligo, thyroid dysfunction, ulcerative colitis. The results of our study indicate a clear association between atopic dermatitis and autoimmune diseases, both in adults and children. Additionally, women were more likely to have autoimmune disease complications than men. However, due to the limited number of participants in our study, further research is needed to thoroughly investigate the relationship. https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024547282.

To address the problem of three-dimensional (3D) localization in underwater environments, this paper proposes a 3D positioning method based on magnetic induction communication (MI in short). Dual transmitters equipped with 3D coils are used to transmit magnetic field signals, while a single receiver with 3D coils is adopted to receive signals. Three-dimensional position calculation is realized by collecting induced voltage from the receiving 3D coils, which enables any device at a known position in space to provide positioning services for other devices. To eliminate abnormal data and suppress environmental noise interference in underwater received signals and further improve positioning performance, an improved density clustering algorithm named the Density-Based Spatial Clustering Method in Magnetic Positioning is proposed to remove erroneous positioning data. In addition, Kalman filtering is introduced to jointly suppress environmental noise interference. Experimental results demonstrate that the average positioning error of the proposed localization method is 0.67 m and maximum positioning error is 0.83 m; therefore, this paper provides a novel technical solution for underwater positioning in non-line-of-sight environments.

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

To investigate the influence of different water quality conditions on the underwater transmission performance of laser communication signals, this paper systematically analyzes the absorption and scattering characteristics of the underwater laser communication channel, and constructs a transmission model of laser propagation in water, so as to explore the transmission influence mechanism under typical water quality environments. On this basis, a system of in situ measurements for underwater laser channel attenuation is designed and constructed, and several sets of experiments are carried out to verify the rationality and applicability of the model. The collected experimental data are denoised by the fusion of wavelet analysis and adaptive Kalman filtering (DWT-AKF in short) algorithm, and compared with the data measured by an underwater hyperspectral Absorption Coefficient Spectrophotometer (ACS in short), which shows that the channel attenuation coefficients of the model inversion and the measured values are in high agreement. The research results provide a reliable theoretical basis and experimental support for the performance optimization and engineering design of the underwater laser communication system.

Trajectory tracking control refers to the movement of an unmanned underwater vehicle (UUV) along a desired trajectory, which is a critical technology for the underwater tasks of UUVs. However, in actual scenarios, the reaction torque of propellers induces roll motion in UUVs, and the communication resource and computational resource of UUVs are limited, which affects the trajectory tracking performance of UUVs severely. Hence, this paper introduces an event triggering mechanism to design the double-loop integrated sliding mode control (EDLISMC), which is used for the trajectory tracking control of UUVs. This method designs the kinematic model and dynamic model of 6 degree of freedom (DoF) UUVs under the influence of reaction torque. Then, this method derives the dual loop integral sliding mode controller and designs the event triggering mechanism based on the relative threshold to reduce unnecessary control signals and improve the control efficiency of UUVs. In addition, this method uses a positive lower bound method to verify that the proposed event triggering mechanism does not have Zeno behavior and adopts the Lyapunov theorem to analyze the stability of EDLISMC. Finally, this paper conducts simulations on the simulink component of MATLAB. The relevant simulation proves that the proposed method can complete the trajectory tracking control of UUVs under the influence of reaction torque and it is superior to other methods in terms of resource consumption.

Vehicle detection based on unmanned aerial vehicle (UAV) aerial images plays a significant role in areas such as traffic monitoring and management, disaster relief, and more, garnering extensive attention from researchers in recent years. However, datasets acquired from UAV platforms inevitably suffer from issues such as imbalanced class distribution, severe background interference, numerous small objects, and significant target scale variance, presenting substantial challenges to practical vehicle detection applications based on this platform. Addressing these challenges, this paper proposes an object detection model grounded in a background suppression pyramid network and multi-scale task adaptive decoupled head. Firstly, the model implements a long-tail feature resampling algorithm (LFRA) to solve the problem of imbalanced class distribution in the dataset. Next, a background suppression pyramid network (BSPN) is integrated into the Neck segment of the model. This network not only reduces the interference of redundant background information but also skillfully extracts features of small target vehicles, enhancing the ability of the model to detect small objects. Lastly, a multi-scale task adaptive decoupled head (MTAD) with varied receptive fields is introduced, enhancing detection accuracy by leveraging multi-scale features and adaptively generating relevant features for classification and detection. Experimental results indicate that the proposed model achieves state-of-the-art performance on lightweight object detection networks. Compared to the baseline model PP-YOLOE-s, our model improves the AP50:95 on the VisDrone-Vehicle dataset by 1.9%.