In recent years, multi-frame-based deformable convolution methods have gained widespread adoption in video compression artifact reduction. Although these methods have demonstrated state-of-the-art performance, they commonly rely on the simple U-Net architecture employed in Spatio-Temporal Deformable Fusion (STDF) for deformable convolution offset prediction. The inherent limitations of this U-Net architecture in spatio-temporal feature extraction lead to deviations in predicted offsets. To address these limitations and enhance artifact removal, we propose two key innovations. Firstly, we propose the Multi-scale Attention Prediction Network (MAPN) for offset prediction. This network enhances global perception and comprehensively captures critical information across the diverse dimensions of the feature map, enabling the extraction of richer spatio-temporal features and, consequently, more accurate offset predictions. Secondly, we design an efficient Multi-path Fusion Attention (MFA) module, which effectively restores details and mitigates artifacts at moving object boundaries without adversely affecting artifact removal in other regions. Extensive experiments on the MFQE 2.0 dataset show that our method consistently outperforms existing approaches in both fidelity and perceptual quality.

Although Bluetooth Low Energy (BLE) fingerprinting localization has become a hot research topic with encouraging results, it is difficult to predict the location depending on a short duration received signal strength indication (RSSI) sequence in realistic scenarios due to the severe fluctuation of RSSI. We introduce a new perspective to view the indoor positioning problem by radio map fingerprint. We argue that even though beacons may be independently deployed, the RSSI series bear certain spatial relation because of their copresence in the same physical space. The latent relation implicitly conveyed by the coexistence of their signals at various indoor locations. Unlike existing approaches that try to find a direct mapping between sensed signals and the corresponding location, we explore the spatial relation of beacons from the input data to estimate location. We propose a deep learning localization system, termed DRVAT, which is based on the distributed representation vector (DRV) and self-attention (AT) among the pairs of MAC-RSSI. First, we obtain DRVs which represent dense features in low dimensionality through pre-training on all MAC-RSSIs. Then we exploit self-attention mechanism to learn the latent spatial relation of beacons. Finally, MAC-RSSIs labeled with locations are used to fine-tune the model for estimating location. Localization accuracy results demonstrated the superior performance as compared with other positioning methods, and the visualization of DRV and attention mechanism are consistent with the spatial deployment of BLE.

Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single-source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system. To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed error model provides a solution for optimizing the deployment of positioning sources.

Multisource fusion localization is a mainstream scheme for acquiring accurate locations in complex indoor scenes. To overcome the interference of indoor structures on radio and illumination variation on visual features, the semantic maps provide an effective way for multisource fusion localization. However, due to the lack of visual depth information, solutions of indoor semantic maps suffer from large semantic segmentation errors for similar objects, which leads to the unstable performance of localization systems. To overcome the issue in semantic and fusion localization, we develop a localization system to demonstrate the use of restudy semantic map and self-adapting fusion localization would achieve centimeter-level positioning accuracy, termed VISEL. VISEL uses the proposed spatial attention-aware semantic model to enhance the discrimination of semantic features for capturing accurate semantic maps. On the basis of high-precision semantic maps, VISEL completes an enhanced particle filter fusion localization module with adaptive reassign weight to different localization modules, which successfully improves accuracy through complementary advantages between different signals while overcoming the drawbacks of each signal and interference of complex environment. The extensive experimental results show that VISEL outperforms current state-of-the-art positioning systems and achieves an average positioning accuracy of 0.4 m. VISEL utilizes semantic maps with depth features and enhanced particle filter to reduce the fusion localization error by 38%, which suggests the high-precision semantic maps with depth features could provide a robust solution for the fusion localization system for indoor complex scenes.

In the co-design process of hardware/software (HW/SW) system, especially for large and complicated embedded systems, HW/SW partitioning is a challenging step. Among different heuristic approaches, particle swarm optimization (PSO) has the advantages of simple implementation and computational efficiency, which is suitable for solving large-scale problems. This paper presents a conformity particle swarm optimization with fireworks explosion operation (CPSO-FEO) to solve large-scale HW/SW partitioning. First, the proposed CPSO algorithm simulates the conformist mentality from biology research. The CPSO particles with psychological conformist always try to move toward a secure point and avoid being attacked by natural enemy. In this way, there is a greater possibility to increase population diversity and avoid local optimum in CPSO. Next, to enhance the search accuracy and solution quality, an improved FEO with new initialization strategy is presented and is combined with CPSO algorithm to search a better position for the global best position. This combination can keep both the diversified and intensified searching. At last, the experiments on benchmarks and large-scale HW/SW partitioning demonstrate the efficiency of the proposed algorithm.