This article investigates the control problem of bearing-based formation tracking for underactuated autonomous underwater vehicles (AUVs) considering actuator constraints and unknown disturbances. A leader-follower structure is adopted, where the leaders move with an unknown reference velocity. For the followers, an integrated strategy is proposed, which includes i) a bearing-based control method composed of a reference velocity estimator, a virtual velocity for achieving the desired formation, and an adaptive robust formation controller to track the virtual velocity under disturbances; and ii) a parameter tuning method based on control parameterization approaches and heuristic algorithms. By employing the cascade system theory, asymptotic convergence of errors in the overall system is proved in the presence of unknown disturbances. The tuning method optimizes controller gains to ensure, all while preserving the convergence properties of the closed-loop error system constraint feasibility and performance optimality. As a result, convergence, robustness, feasibility, and optimality are all achieved. Extension to the case where AUVs have sideslip motions in 3-D space is also discussed. Simulation results are presented to demonstrate the effectiveness of the proposed strategy.

For video bit-depth enhancement (VBDE) tasks, inter-frame information is critical for removing false contours and recovering the details in low bit-depth (LBD) videos. However, due to different structural distortions and complex motions in the neighboring frames, it is difficult to effectively utilized inter-frame information. Most algorithms rely on alignment operations to provide information of neighboring frames, suffering from slow inference speed due to the complex alignment module design. Meanwhile, most existing methods sequentially perform the intra-frame feature extractions and inter-frame information fusions, but fail to efficiently fuse spatio-temporal information. Therefore, in this paper, we propose a two-stage progressive group (TSPG) network to find complementary information related to the target frame without adopting an alignment operation. To simultaneously achieve intra-frame feature extractions and inter-frame feature fusions, we propose a parallel spatio-temporal fusion (PSTF) module with a dual-branch spatial-temporal residual (DSTR) block to focus on more useful temporal information while ensuring a faster inference speeds. Extensive experiments on public datasets demonstrate that our proposed multi-stage spatio-temporal fusion network (named MSTFN) can quickly and effectively eliminate false contours and recover high quality target frames. Furthermore, our method outperforms the state-of-the-art methods in terms of both PSNR and SSIM, and can reach faster inference speeds.

This article studies the bearing-based formation tracking control problem of multiple double-integrator agents. A leader-following structure, where the leader moves with the reference dynamics, is adopted. Different from the existing methods, which require complete information of the time-varying reference velocity, in this article, only the time-varying reference orientation information is known by part of the followers and the amplitude of the reference velocity is unknown. To solve the problem, this article proposes a velocity-estimation-based control scheme, which consists of an estimator for estimating the varying rate of the reference orientation, an adaptation law for estimating the amplitude of the reference velocity, and bearing-based control inputs for tracking the leader and achieving the bearing-based formation based on the estimations. Moreover, the scaling formation maneuver can be achieved by using an auxiliary distance measurement. It shows that both the estimation errors and control errors converge to zero under the connectivity of the topology and properties of bearing rigidity. The closed-loop system is analyzed to be semiglobally uniformly asymptotically stable based on the cascaded system theory. Numerical simulations are presented to demonstrate the effectiveness of our method.

360$^\circ$video becomes increasingly attractive in smart factories due to its immersive experience for industrial surveillance. However, transmitting this kind of video requires a fairly large demand on the bandwidth due to its high-resolution and panoramic view. Moreover, low-latency responses of the video to users' head movements are required. This leads to the tradeoff between high video quality and low-latency response under limited bandwidth in factories. This article proposes a perception-sensitivity (PS)-based 360$^\circ$tiled video streaming method called PSTile for industrial surveillance. Specifically, a PS tiling strategy is constructed for valid tile grouping based on the designed PS index. Then, a tile bitrate adaptation problem is formulated to allocate bitrates to valid tiles. It jointly optimizes surveillance accuracy, end-to-end latency, and quality-of-experience of users under accuracy, latency, and bandwidth constraints. Simulations demonstrate that PSTile achieves at least 32.6% lower end-to-end latency and 19.3% higher average video bitrate than grid-tiling and clus-tiling methods.

Video bit-depth enhancement (VBDE) reconstructs high-bit-depth (HBD) frames from a low-bit-depth (LBD) video sequence. As neighboring frames contain a considerable amount of complementary information related to the center frame, it is vital for VBDE to exploit neighboring frames as much as possible. Conventional VBDE algorithms with explicit alignment across frames attempt to warp each neighboring frame to the center frame with estimated optical flow, taking into account only pairwise correlation. Most spatiotemporal fusion approaches involve direct concatenation or 3D convolution and treat all features equally, failing to focus on information related to the center frame. Therefore, in this paper, we introduce an improved nonlocal block as a global attentive alignment (GAA) module, which takes the whole input video sequence into consideration to capture features that are globally correlated, to perform implicit alignment. Furthermore, given the bulk of features extracted from the center and neighboring frames, we propose target-guided attention (TGA). TGA can exploit more center-frame-related details and facilitate feature fusion. The proposed network (dubbed TANet) is capable of effectively eliminating false contours and recovering the center frame in high quality, as demonstrated by the experimental results. TANet outperforms state-of-the-art models in terms of both PSNR and SSIM with low time consumption.