In order to improve the control performance of the Cyber-Physical Systems (CPSs), an integrated modelling-control-design trio framework is established in this paper. In the modelling part, CPS has two components, cyber component and physical component, so the system may be subject to (possibly fast) time-varying cyber interference and (possibly fast) time-varying physical uncertainty. A dynamic model encompassing these two phases of the CPS is established. In the control part, a novel control design is proposed based on the dynamic model. The problem of constraint-following for CPS operating under cyber interference and physical uncertainty is considered. In the design part, the choice of control parameters is investigated. This procedure consists of two stages. The first stage is to design a control scheme based on feasible control design parameters, so that it can guarantee the performance in the case of both cyber interference and physical uncertainty. The second stage is to seek the optimal design among the feasible control design parameters, which is resolved by an intelligent multi-agent game-theoretic approach. We invoke both Nash game and Stackelberg strategy to choose the optimal parameters. Interestingly, the optimal parameters obtained from different game settings are the same. This shows the conception of optimality we established spans in a broader context. The robustness and superiority of the system performance are demonstrated in the intelligent transportation system.

Patch-based denoising algorithms like BM3D have achieved outstanding performance. An important idea for the success of these methods is to exploit the recurrence of similar patches in an input image to estimate the underlying image structures. However, in these algorithms, the similar patches used for denoising are obtained via Nearest Neighbour Search (NNS) and are sometimes not optimal. First, due to the existence of noise, NNS can select similar patches with similar noise patterns to the reference patch. Second, the unreliable noisy pixels in digital images can bring a bias to the patch searching process and result in a loss of color fidelity in the final denoising result. We observe that given a set of good similar patches, their distribution is not necessarily centered at the noisy reference patch and can be approximated by a Gaussian component. Based on this observation, we present a patch searching method that clusters similar patch candidates into patch groups using Gaussian Mixture Model-based clustering, and selects the patch group that contains the reference patch as the final patches for denoising. We also use an unreliable pixel estimation algorithm to pre-process the input noisy images to further improve the patch searching. Our experiments show that our approach can better capture the underlying patch structures and can consistently enable the state-of-the-art patch-based denoising algorithms, such as BM3D, LPCA and PLOW, to better denoise images by providing them with patches found by our approach while without modifying these algorithms.

This paper presents a method for capturing high-speed video using an asynchronous camera array. Our method sequentially fires each sensor in a camera array with a small time offset and assembles captured frames into a high-speed video according to the time stamps. The resulting video, however, suffers from parallax jittering caused by the viewpoint difference among sensors in the camera array. To address this problem, we develop a dedicated novel view synthesis algorithm that transforms the video frames as if they were captured by a single reference sensor. Specifically, for any frame from a non-reference sensor, we find the two temporally neighboring frames captured by the reference sensor. Using these three frames, we render a new frame with the same time stamp as the non-reference frame but from the viewpoint of the reference sensor. Specifically, we segment these frames into super-pixels and then apply local content-preserving warping to warp them to form the new frame. We employ a multi-label Markov Random Field method to blend these warped frames. Our experiments show that our method can produce high-quality and high-speed video of a wide variety of scenes with large parallax, scene dynamics, and camera motion and outperforms several baseline and state-of-the-art approaches.

Depth captured by consumer RGB-D cameras is often noisy and misses values at some pixels, especially around object boundaries. Most existing methods complete the missing depth values guided by the corresponding color image. When the color image is noisy or the correlation between color and depth is weak, the depth map cannot be properly enhanced. In this paper, we present a depth map enhancement algorithm that performs depth map completion and de-noising simultaneously. Our method is based on the observation that similar RGB-D patches lie in a very low-dimensional subspace. We can then assemble the similar patches into a matrix and enforce this low-rank subspace constraint. This low-rank subspace constraint essentially captures the underlying structure in the RGB-D patches and enables robust depth enhancement against the noise or weak correlation between color and depth. Based on this subspace constraint, our method formulates depth map enhancement as a low-rank matrix completion problem. Since the rank of a matrix changes over matrices, we develop a data-driven method to automatically determine the rank number for each matrix. The experiments on both public benchmarks and our own captured RGB-D images show that our method can effectively enhance depth maps.

In Robocup, the field lines provide robots with the most useful information to make self-locating and strategies. For real-time competitions, the detection must be efficient. In this paper, we present a novel method of line detection based on fast edge extraction and modified Hough Transform. In this study, the condition of different objects' distances is considered according to the camera angle. In the first step of edge extraction, an image is roughly scanned. A local-precise scanning is then implemented in regions where there may be edge points. After all feature points are picked out, a modified Hough Transform is used to extract lines. The result demonstrates that combining with real-time conditions, such as the angle of the camera and the distances of the objects on court, plays an important role in object detection.