Reviewerly/exHarmony huggingface dataset:
An Information Fusion Algorithm for INS/DVL/USBL Integrated Navigation System Based on Delay State Unscented Kalman Filter

In order to reduce the impact of the lagging measurement information introduced by the high delay of underwater acoustic communication, which makes the local data out of order during fusion, a state-expanded DS-UKF is proposed under the filtering framework of UKF. Algorithm provides a new way to deal with lag measurement problem more effectively. This method first builds a filtering model for the SINS/DVL/USBL integrated navigation system, takes the centralized Kalman filter as the framework, and takes the SINS/DVL/USBL system accuracy under the ideal no-delay state as the benchmark, and uses the unscented Kalman filter as the benchmark. Based on the filter, the idea of state expansion is introduced to compensate for the measurement delay. The simulation test shows that this method can guarantee the accuracy of the navigation system after introducing random delay in the fusion system, and improve the reliability and anti-jamming performance of the integrated navigation system.
The fusion-based Path Planning for Near Seabed Autonomous Underwater Vehicles

Autonomous underwater vehicles (AUVs) have played a huge role in deep-sea operations and exploration. However, with the limited energy, AUV faces obstacles in complex underwater environments such as seamounts and trenches during near bottom operations. Therefore, planning the reasonable and efficient path is one of the important conditions for AUV to achieve underwater operations <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . A new path planning method that combines improved RRT strategy and dynamic window analysis (DWA) is proposed to address the impact of underwater hydrodynamic forces and complex underwater environments on AUV during near bottom navigation. This proposed path planner has the following two advantages: one is to improve the global planning performance compared with the traditional RRT algorithm, and the other is to guarantee the real-time local planning capability via the DWA method. The superior performance of the developed planning algorithm is verified in the numerical examples.
Research on video-based moving object tracking

Video based motion object tracking is an important research topic in the field of computer vision. This paper studies the development of video tracking technology, compares and analyzes object tracking algorithms based on contrast analysis, motion detection, and matching, and summarizes the characteristics of each algorithm. In addition, through comprehensive research and analysis, this paper has designed and implemented vehicle video tracking based on SIFT (Scale-invariant Feature Transform) in dynamic backgrounds. This practical application environment is relatively complex, with dual motion of background and target, and there are also many noise points, lighting changes, and occlusions in the environment, effectively solving the problem of moving target tracking in dynamic and complex backgrounds.
Pedestrian Multi-Object Tracking with Bottleneck Transformer and Enhanced Feature Fusion

Due to balanced tracking accuracy and speed, Joint-detection-and-embedding (JDE) tracking paradigm has drawn great attention, which employs a single work to predict detection and appearance features simultaneously. Building on a strong baseline CSTrack, we replace the spatial convolutions in the final block of backbone with a Bottleneck Transformer, which models global relationships across objects and reduces the parameters. Besides, we introduce an enhanced feature fusion block with structural re-parameterization technique to augment multi-feature fusion for alleviating the contradiction between detection and identification embedding subtasks and maintaining the inference-time. The results on MOT16 and MOT17 datasets indicate that our method achieves competitive tracking results.
An Improved Transformer Few Shot Fault Diagnosis Method for Integrated Navigation Based on Generative Adversarial Network

In practical application, it is difficult to effectively collect fault information of integrated navigation system. Aiming at the problem of few fault samples in INS/GNSS navigation system, an unsupervised integrated navigation system fault diagnosis method (shot for GA-TRANFD) is proposed in this paper. The method uses unsupervised training style, which only needs the normal integrated navigation data to train the fault diagnosis model, so that effectively solves the few-shot data problem. GA-TRANFD enhances the robustness of feature extraction by using a pyramid structure feature extractor, and realizes sequence reconstruction and fault extraction by applying Transformer Network with the idea of generating antagonism, and realizes the function of fault diagnosis. Finally, a published UCR time series data set and integrated navigation hardware-in-the-loop simulation data are used to verify the model. Taking the F1 and ROC scores as the evaluation metrics, which are commonly used in the classification field to represent the accuracy of classification, the experiment shows that the improved model improves the F1 and ROC scores of the references by 20.7%.
Single Image Deraining with Generative Adversarial Network

Single image de-raining is an important computer vision task, which has attracted much attention from researchers in recently years. However, most current methods are still weak in image detail recovery. In this paper, we propose a rain removal algorithm based on the generative adversarial network (GAN) to better recover detailed information effectively. The generator utilizes U-net architecture along with a feature conversion module (FCM) to preserve image details. Additionally, we utilize high-level semantic loss, which uses the pre-trained VGG19 network to extract features between de-rained image and real image. The discriminator uses symmetric padding on each side of the feature maps to enlarge the receptive fields and even-sized kernels with little computational cost. We validate the effectiveness of our deraining algorithm through quantitative and qualitative analyses on Rain800, Rain200H and Rain200L datasets.
Parameter Tuning of PID Heading Controller for Self Navigation Model Based on Improved Particle Swarm Optimization

A nonlinear dynamic weight improved particle swarm optimization algorithm is proposed to address the difficulty of manually tuning the parameters of proportional integral differential (PID) in the self navigation model heading control system and the poor performance of the parameters obtained through tuning optimization. This algorithm is applied to the tuning process of PID parameters in the self navigation model heading control system. Taking the heading control of self navigation model as the research object, the mathematical model of the heading control system of self navigation model is firstly established, and an improved particle swarm optimization method is adopted to process the inertia weight with nonlinear dynamic decline, improve the search performance and optimization efficiency, and make up for the shortcomings of the traditional particle swarm optimization method. Finally, simulation comparison and analysis are carried out through Matlab/Simulink. The experimental results show that after using the improved particle swarm optimization method for parameter tuning, the self navigation model has better dynamic performance, shorter adjustment time, and also eliminates the difficulty of manual tuning, reducing the time for parameter tuning. According to the application of the ship’s manipulation index in the mathematical model in the second order, use the Matlab and Simulink software to implement the response model to simulate the ship’s rotation trajectory and the heading angle, and compare with the ship model rotation test data.
Attention-Based Grayscale Image Colorization

Image colorization has always been a hot topic in computer vision. Since the emergence of deep learning and its excellent performance in many image-processing tasks, image colorization methods based on convolutional neural networks (CNN) emerge in an endless stream, including automatic colorization methods and A coloring method based on a reference image. In the coloring method based on the reference image, an existing problem is that an algorithm for calculating the similarity between the grayscale image and the reference image needs to be designed to map the color blocks in the reference image to the grayscale image. And we use the attention mechanism to spread color implicitly, and propose an attention-guided image colorization network, which extracts features and corresponding color information from the reference image through a reference encoding sub-network, and simultaneously passes a target encoding sub-network The network extracts grayscale features from grayscale images, uses attention to calculate the correlation between the two types of features, and then restores the features and reconstructs colors through the decoding network. Our method can quickly capture the color of the reference feature, accurately transfer it to the target feature, and effectively solve the problem of small target loss during downsampling. By validating on multiple datasets, the results show that our method is effective, outperforming multiple state-of-the-art image colorization algorithms.
Multilevel Decomposition Time Aggregation Graph Convolution Networks for Skeleton-Based Action Recognition

Skeleton-based human action recognition has become a popular topic among researchers. This is because using skeletal data provides a robust solution to problems encountered in complex environments, such as changes in perspective and background interference. The robustness of skeletal data enables recognition methods to focus on more specific and relevant features. We propose a model called multilevel decomposition time aggregation graph convolution network (MDT-GCN), which utilizes a multilevel graph convolution kernel to capture higher-order spatial dependence relationships between joints. This is achieved by decomposing a human topology graph into smaller graphs, each of which has its own graph convolution kernel. To further enhance the performance of our model, we employ a two-flow framework and channel topology refinement strategy. Our experiments on the NTU-RGB+D60 and NTU-RGB+D120 datasets demonstrate that our MDT-GCN network outperforms the previous algorithm and significantly improves the accuracy of action recognition.
A Small Target Detection Algorithm For Aerial Images

This paper proposes an improved algorithm based on YOLOv5, named RepDrone-YOLOv5, to address the problem that the traditional detection algorithm has the problem of false detection and missed detection when detecting small targets due to the small target size, dense distribution and complex background of the UAV aerial imagery. First of all, in order to obtain and transmit a richer and more distinguished small target characteristics, adjust the sampling multiple and add a shallow features of the rich information in the model training process. At the same time, a RepVGG-based RepC3 feature extraction module is designed, by jointing local and global information to break the limitations of ordinary convolution extraction to obtain a greater experience. Further, an improved SPP module instead of the SPPF module to alleviate the effects of the loss of the pool layer on the loss of target information. Finally, an EIoU loss function optimizes the regression process of the prediction box and the detection box, and enhances the positioning ability of small targets. On the VisDrone2019 datasets, RepDroneYOLOv5 improves about 9 percentage points in mAP compared to the original YOLOv5 model, and the number of model parameters is reduced by about 80%.
Research on Dynamic Planning Method for Safe Path of UUV in Complex Fishing Net Deployed Waters

In response to the problem of long path planning time and poor maneuverability in avoiding close-range fishing nets in the complex fishing net deployment area of UUV in the near sea, this paper proposes a fusion algorithm combining improved RRT algorithm and improved DWA algorithm. In the sampling stage, the sampling point is adaptively moved a certain distance toward the task point to improve the directionality of the tree growth. When the expansion encounters obstacles, the algorithm will use steering and reselection strategies to solve the problems of blindness and the long path planning time of the RRT algorithm. After obtaining the global path, the sub-goal point for local dynamic path planning is adaptively selected according to the smoothness of the path to reduce the turning radius. In terms of dynamic obstacle avoidance, a fishing net obstacle avoidance strategy is added to the DWA algorithm to improve UUV ability to avoid close-range fishing nets. Simulation results show that the algorithm can plan a global path in a short time in complex sea areas and has a fast response speed to close-range fishing nets, making effective obstacle avoidance decisions and proving the effectiveness and practicability of the algorithm.
A 2D-Laser-based Pedestrian Detector for Mobile Robots via Self-Supervised Transfer Learning

The applications of mobile robots have gradually expanded to complex environments in human daily life. These environments often involve pedestrian activities that possess diverse purposes and statuses. Consequently, it becomes necessary to introduce pedestrian detection models that can predict the exact locations of pedestrians. However, the efficacy of 2D-Laser-based pedestrian detection models trained on open-source datasets is hindered by the difficulty of deploying them on mobile robots. The laser point cloud data from the mobile robot differs in device and resolution. Therefore, it’s non-trivial to deploy a pedestrian detection model trained on open-source datasets to a physical robot. To tackle this problem, we propose a pedestrian detection framework based on self-supervision. The framework is comprised of a pre-trained laser-based detector and a self-supervised data annotator. Our proposed method allows for rapid transfer learning by aligning the laser and camera. The data annotator employs instance segmentation on camera images to generate pseudo-labels for laser point clouds. These pseudo-labels are then used to bridge the gap between open-source data and laser point cloud data collected from mobile robots through transfer learning. Extensive experiments and visualizations reveal the efficacy of the proposed framework.
Calibration and 3D Measurement Methods for Stereo Structured Light System

To address the problem that the calibration of the stereo structured light system is difficult to find the corner points of the projection plane and the problem that there are invalid projection areas in the captured pictures, this paper proposes the method of nonlinear fitting and the statistical threshold to solve the above two problems. The absolute phase of the image can be obtained by overlaying the relative phase obtained from the four-step phase-shifting method and the corrected gray code decoding values, and then obtaining the relationship between the projector image coordinates and the camera image coordinates based on the absolute phase, and this method can ensure the accuracy and continuity of image decoding. According to the relationship between the corresponding coordinates in the two planes, a second-order polynomial is used to fit the nonlinear relationship between the camera pixel coordinates and the projector pixel coordinates, and then the coordinates of the checkerboard corner points in the camera image can be mapped into the projection plane to complete the projector calibration. In the 3D measurement process, we use the statistical threshold to determine the invalid projection areas to better complete the 3D reconstruction. Experimental results verify that the average accuracy of the system calibration was at 0.07 mm, which meets the expected requirements essentially. The invalid projection area was judged more accurately, and the algorithm was more operable.
Self-supervised Monocular Depth Estimation with Multi-Scale Feature Fusion

Self-supervised monocular depth estimation shows great potential without using ground truth depth as supervision. Depth information is the key information for scene understanding, however, real scenes are often complex, and the scales of different targets vary greatly. To alleviate the problems caused by scale changes and small targets, we propose a depth estimation method based on multi-scale feature fusion, which integrate the encoding features and decoding features at the same level more adequately. Specifically, we design a multi-scale feature fusion (MSFF) module, which contains two branches, performing global context aggregation and local context aggregation on features respectively. By further fusing the information of these two branches, the network can simultaneously pay attention to large targets with more global distribution and small targets with more local distribution. We conducted a series of experiments on the KITTI dataset, demonstrating that our method can achieve competitive results. The visualization results show that our method can obtain high-quality depth maps although the scales of targets in the scene vary greatly.
Real-Time Human Motion Capture Based on BiRNN and Inertial Sensor

This paper proposes a deep learning method using six sparse IMU inertial measurement units to realize body motion capture. It identifies the pattern of body posture task into multiple sub-tasks, estimates the whole body joint position from the blade joint position, and finally returns the rotation information of the whole joint to realize human posture recognition. For global translation tasks, RNN network fusion method are adopted to achieve better global translation effect. The method adopted in this paper compares the traditional visual method, and is not limited by space and funding. It can be captured through sparse IMU elements, and can achieve better results.
A Survey of Object Goal Navigation: Datasets, Metrics and Methods

Object Goal Navigation (ObjectNav) aims at directing an agent to a specified target object within an unseen scene. This task integrates advanced techniques, including visual perception, semantic prior learning, and visual navigation, which makes it a challenging and emerging task within the Embodied AI field. In this survey paper, we offer a comprehensive review of widely-used datasets and corresponding evaluation metrics for ObjectNav. Additionally, we provide an in-depth review and analysis of recently developed methods. Finally, we present discussions on potential future research directions.
Faster Implementation of the Dynamic Window Approach via Polar Coordinate Transformation

The dynamic window approach (DWA) is a collision avoidance strategy for mobile robots, optimized to guide a robot to its target while maintaining a safe distance from any visible obstacle in the vicinity. Although DWA has undergone several improvements and applications, its primary computational process has remained mostly unchanged, thereby resulting in a high level of complexity. Drawing inspiration from the velocity invariance assumption of DWA and polar coordinate transformations in the model, we propose a high-speed version of DWA.
Unmanned Surface Vehicle Path Planning Based on Improved Informed-RRT* algorithm

Aiming at the problems encountered by the InformedRapidly-exploring Random Trees Star (IRRT*) algorithm in the research of unmanned surface vehicle path planning in electronic chart environment, an improved guided IRRT* algorithm is proposed. Electronic chart data are processed and raster into raster maps. The target-oriented sampling area is adopted to strengthen the global random sampling point grow in the target-oriented area, and the aimlessness global exploration problem in initial stage of path planning is solved. Introduce adaptive step size and growth angle constraints, calculate obstacles appearance frequency and the number of samples fall into local loop, automatically adjust the step size to accelerate exploration speed in sparse marine environments. Introduce ancestor pruning strategy to obtain an optimal path and improve the overall effectiveness of algorithm. Compared with original algorithm, the algorithm proposed in this paper has higher efficiency and shorter path length, especially in electronic chart environment. Simulation results show that the guided Informed-RRT* algorithm proposed in this article converts targets into target-areas, accelerate searching speed and improving the overall ability of the algorithm.
Study on a Two-layer Path Planning Method of Spherical Multi-robot

A path planning method based on a two-layer coordination system is proposed for the compatibility requirement of path planning global optimum and local coordination in a spherical multi-robot system. In the first layer of the algorithm, the global path quality is improved by proposing a nonlinear sine factor and a sparrow search algorithm with improved levy flight strategy to plan the global collision-free optimal path for each robot. In the second layer, a rolling window method is used to determine the possible collision positions of each robot in the system, and a priority-based conflict resolution strategy is proposed to complete the local coordination of the path near the corresponding position. The experimental results show that the path planning method enables the robots to drive along the global optimal path, but still can carry out flexible and orderly local path coordination, which effectively improves the path planning performance of the system.
FPGA-based tunable Keccak core

Nowadays’ great emphasis on data security has led to a great need for hardware accelerated cryptograhic algorithms to cope with the high communication bandwidth demand. These hardware accelerators need to be highly tunable in order to support multiple operation modes for different security applications. In this paper, a Field Programmable Gate Array (FPGA)-based design and implementation of a tunable Keccak core is presented. The Keccak core’s performance and security parameters are configurable in the sense that bitrate, capacity, and the number of rounds can be user-specified with an extendable output length. Keccak’s sponge construction is exploited to enable different modes of operation. This level of flexibility makes the core a suitable fit for a large range of security requirements and applications. The implemented core can be operated as a sponge-based Pseudo Random Number Generation (PRNG). The design was implemented in VHDL (VHSCI Hardware Description Language) targeting a low-cost IntelFPGA Cyclone-V. The core achieved the following maximum throughput figures of: 11, 8.4, and 5.81Gbps for the three Secure Hash Algorithm-3 (SHA-3) variants 256, 384, and 512-bit respectively, while occupying only 8% of the FPGA’s area. The random sequences generated by the sponge-based PRNG successfully passed the National Institute of Standards and Technology (NIST) test suite. This paper demonstrated respectable area/performance results compared to other studies in literature.