Existing multi-color space guided techniques for underwater image enhancement (UIE) fail to take the advantages of the XYZ color space for preserving underwater image details, meanwhile, existing UIE datasets, typically containing low-quality reference images of distorted colors and blurred structures, lead to inaccurate enhancement mapping between low-quality and high-quality images. To overcome these above limitations, we propose a Multi-Color Space Fusion Network (MCSF-Net) for UIE. The MCSF-Net incorporates a Multi-dimensional Feature Fusion Block (MFFB) and weighted feature fusion scheme to effectively integrate complementary features from both XYZ and RGB color spaces. Moreover, we establish a Large-Scale Mixed UIE dataset (LSMU) by using nine no-reference metrics to filter out low-quality reference images from eight public UIE datasets, enabling more effective network learning. Extensive experiments on mainstream datasets demonstrate that the proposed method outperforms several leading approaches in both color restoration and detail enhancement of various underwater images. The code and dataset for MCSF-Net will be available athttps://github.com/WYJGR/MCSF-Net.

Visual dense SLAM can facilitate pose estimation and map reconstruction for sensor carriers in unknown environments. However, in uncontrolled environments such as offices, shopping malls, and train stations, frequent occurrences of people walking back and forth or temporary movement of objects within the scene are common. Most existing visual dense SLAM systems do not account for these dynamic factors, leading to localization drift and map distortion. In this paper, we propose DGS-SLAM, a system capable of achieving robust localization and high-fidelity static map reconstruction in dynamic environments. We utilize semantic 3D Gaussians for scene representation, effectively eliminating interference from dynamic objects and refining the reconstruction of static background. We enhance the tracking accuracy and mapping quality of dense SLAM by using a distance distribution-based Gaussian pruning algorithm and implementing a coarse-to-fine tracking strategy with bundle adjustment and differentiable rendering. We perform qualitative and quantitative evaluations on two publicly available dynamic environment datasets. The results indicate that our method effectively reduces the interference caused by dynamic objects, enabling visual dense SLAM to maintain competitive tracking accuracy and mapping performance in dynamic environments.

The success of 3D Gaussian splatting in 3D reconstruction has recently led to efforts to integrate it with SLAM systems. However, most existing research has focused on indoor tracking and mapping, while outdoor Gaussian SLAM methods still heavily rely expensive LiDAR sensor. To address these challenges, we propose LOG-SLAM, a novel method for large-scale outdoor tracking and mapping using Gaussian Splatting. Our approach supports tracking through monocular or visual-inertial input, progressively constructing the 3D Gaussian map from depth and pose estimates obtained during the tracking process. Additionally, we introduce a submap-based strategy for managing large-scale maps, enabling the reconstruction of kilometer-scale environments. A loop closure detection module is also incorporated to reduce accumulated errors. Furthermore, we present a novel dynamic object removal method based on rendering loss that mitigates the interference of dynamic objects on the reconstruction. Our experiments on KITTI and KITTI-360 demonstrate that our method achieves localization performance comparable to traditional SLAM systems, while outperforming recent GS/NeRF-based SLAM approaches in terms of mapping and rendering quality.

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://github.com/cathnat/Epileptic_Classification.

Neural implicit representation has recently achieved significant advancements, especially in the field of SLAM(Simultaneous Localization and Mapping). Previous NeRF-based SLAM methods have difficulties with object-level localization and reconstruction and struggle in dynamic and illumination-varied environments. We propose ONeK-SLAM, a robust object-level SLAM system that effectively combines feature points and neural radiance fields. ONeK-SLAM uses the joint information at the object level to improve localization accuracy and enhance reconstruction details. Moreover, our approach detects and eliminates dynamic objects based on the joint errors, while also harnessing the illumination invariance offered by feature points. Consequently, ONeK-SLAM achieves high-precision localization and detailed object-level mapping, even in dynamic and illumination-varying environments. Our evaluations, conducted on three public datasets that include both dynamic and variable lighting sequences, demonstrate that our method outperforms recent NeRF-based SLAM method in both localization and reconstruction.

In large-scale outdoor environments, vehicles often encounter situations like retracing their path or turning around, leading to many reverse loop closures where the vehicles traverse previously covered paths from opposite viewpoints. Existing monocular SLAM methods, due to insufficient utilization of semantic information and neglect of leveraging reverse loop closures, result in significant scale drift and pose drift when confronted with such scenarios. In this paper, we introduce SMORE-SLAM, a semantic monocular SLAM with scale correction and reverse loop closure module. We constrain scale drift by harnessing semantic information across a wide spatial extent. Furthermore, we detect and correct reverse loop closures using semantic point cloud to reduce pose drift. Experimental results on the KITTI odometry dataset and the Oxford RobotCar dataset demonstrate the capability of our research in scale correction and reverse loop closure detection, enabling a reduction in trajectory errors of monocular SLAM.

Abstract Parameter servers (PSs) placement is one of the most important factors for global model training on distributed deep learning. This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of storage capacity and the number of local PSs. Then, we provide the proof for the NP-hardness of the proposed problem. The whole training epochs are divided into two parts, i.e. the first epoch and the other epochs. For the first epoch, an approximation algorithm and a rounding algorithm are proposed in this paper, to solve the proposed problem. For the other epochs, an adjustment algorithm is proposed, by continuously adjusting the decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that the proposed approximation algorithm and rounding algorithm perform better than existing works for all cases, in terms of the training time of global model. Meanwhile, the training time of global model for the proposed approximation algorithm is very close to that for optimal solution generated by the brute-force approach for all cases. Besides, the integrated algorithm outperforms the existing works when the available storage capacity varies during the training.

Load balance in vehicular ad hoc networks (VANETs) is a challenge in vehicle-to-vehicle computation offloading, due to stochastic requests of users, heterogeneous service capabilities and high mobility of vehicles, etc. This paper aims to fill this gap by formulating a problem for load balance in a VANET, with the objective of minimizing the maximum load under transmit power, storage capacity, per task completion time and energy consumption constraints. The formulated problem is proved to be NP-hard, then it is investigated by decomposing it into two subproblems, i.e., how to offload tasks for the case of fixed transmit power and how to adjust transmit power for the given offloading decision. For the first subproblem, an approximation algorithm is proposed by offloading the tasks in the vehicle with the maximum load to the vehicle with minimum load. Meanwhile, a deep reinforcement learning algorithm is proposed, in order to focus on the network dynamics. A coalition based algorithm, a distributed coalition based algorithm, as well as an incentive algorithm based on deep reinforcement learning, are proposed to maximize the total payoff for the selfishness of vehicles. For the second subproblem, an adjustment strategy for transmit power is customized to further reduce the computing load. The algorithms are evaluated on an integrated simulation platform with open street map, SUMO, NS-3 and dataset of Google cluster-usage traces. Simulation results show that, the proposed algorithms outperform three state-of-the-art works for most cases, in terms of the maximum load. The proposed distributed algorithm can significantly accelerate the proposed centralized algorithm with acceptable increase in maximum load. Besides, the load can be further reduced by the proposed adjustment strategy.

Fog computing has become an emerging distributed computing paradigm to provide services with low latency and high throughput. However, load unbalance is serious due to the difference in geography, which results in performance deterioration and low utilization of resources in the fog network. In this paper, the load is the tradeoff between the delay and energy consumption for fog nodes. Meanwhile, the problem of minimizing the maximum load in the homogeneous fog network is formulated and its NP-hardness is proved. Then, a greedy algorithm is proposed for solving the problem by giving the preference to offloading the task in the fog node with the maximum load to the fog node with the minimum load in the network. Moreover, for solving the problem with consideration of selfishness of fog nodes, a coalition based algorithm is proposed to encourage the fog nodes with a light load to share their resources to reduce the maximum load. We evaluate the performance of the proposed algorithms on NS-3 and simulation results show that the proposed algorithms outperform the existing algorithm about 40% in terms of the maximum load.

State estimation with sensors is essential for mobile robots. Due to different performance of sensors in different environments, how to fuse measurements of various sensors is a problem. In this paper, we propose a tightly coupled multi-sensor fusion framework, Lvio-Fusion, which fuses stereo camera, Lidar, IMU, and GPS based on the graph optimization. Especially for urban traffic scenes, we introduce a segmented global pose graph optimization with GPS and loop-closure, which can eliminate accumulated drifts. Additionally, we creatively use a actor-critic method in reinforcement learning to adaptively adjust sensors’ weight. After training, actor-critic agent can provide the system better and dynamic sensors’ weight. We evaluate the performance of our system on public datasets and compare it with other state-of-the-art methods, which shows that the proposed method achieves high estimation accuracy and robustness to various environments. And our implementations are open source and highly scalable.

Vehicular fog computing (VFC) has become an appealing paradigm to provide services for vehicles and traffic systems. However, high mobility is one of the great challenges to the communication and computation service qualities in VFC. A network model for directional vehicle mobility is proposed in this paper to guarantee the service qualities of vehicles in VFC. In the model, vehicles are configured into three vehicular subnetworks according to their turning directions at the next crossing. For each subnetwork, vehicles communicate with each other via vehicle-to-vehicle communication, and with roadside units via vehicle-to-infrastructure communication. The aim is to minimize the average response time of the tasks originated from vehicles. By carefully choosing neighboring vehicles as task processing helpers, a greedy algorithm is proposed to solve the mentioned optimization problem. Besides, two bipartite matching based algorithms, named BMA1and BMA2, are proposed by exploiting Kuhn-Munkras approach and minimum-cost maximum-flow approach, respectively. Performance of the proposed model and the offloading algorithms are evaluated on the combined simulation platform by open street map, SUMO and NS-3. Simulation results show that, the proposed model outperforms four existing models in terms of average response time, when the five models have similar number of unsuccessful tasks. Moreover, the proposed BMA1and BMA2are superior to the existing greedy algorithm in terms of the average response time of tasks, and the proposed greedy algorithm significantly accelerates the generation of offloading decisions in comparison to BMA1, BMA2and the existing greedy algorithm.