Metal-organic framework (MOF)-derived porous metal/C composites have drawn considerable attention from the microwave absorption field owing to their large pore volumes and surface areas. Exploring single-MOF-derived materials with high intensity and broadband absorption is largely needed but remains a challenge. Here, porous Co/ZnO/C (CZC) microrods were fabricated easily from cuboid-shaped heterobimetallic MOFs. CZC provides an efficient platform for integrating different semiconductors (ZnO), magnetic metal (Co), and carbon sources into one particle, which enhances the electromagnetic (EM) wave-absorbing ability. The carbonization temperature which is critical for EM parameters was studied in detail. CZC annealed at 700 °C outperformed those obtained at 600 or 800 °C in terms of microwave wave-absorbing properties. The reflection loss (RL) was optimized to -52.6 (or -20.6) dB at 12.1 (or 14.8) GHz with an effective bandwidth (RL ≤ -10 dB) of 4.9 (or 5.8) GHz at the coating thickness of 3.0 (or 2.5) mm. Such enhancement of EM wave-absorbing capabilities is ascribed to the well-built porous structure, dielectric loss, and magnetic loss. This work offers a new way to prepare porous magnetic metal/C composites with excellent microwave-absorbing properties starting from heterobimetallic MOFs.

Flower-like phosphorus-doped g-C3N4 with a high surface area was synthesized using cyanuric acid–melamine supramolecular precursors which were absorbed by phosphoric acid.

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As one of the core contents of intelligent monitoring, target tracking is the basis for video content analysis and processing. In visual tracking, due to occlusion, illumination changes, and pose and scale variation, handling such large appearance changes of the target object and the background over time remains the main challenge for robust target tracking. In this paper, we present a new robust algorithm (STC-KF) based on the spatio-temporal context and Kalman filtering. Our approach introduces a novel formulation to address the context information, which adopts the entire local information around the target, thereby preventing the remaining important context information related to the target from being lost by only using the rare key point information. The state of the object in the tracking process can be determined by the Euclidean distance of the image intensity in two consecutive frames. Then, the prediction value of the Kalman filter can be updated as the Kalman observation to the object position and marked on the next frame. The performance of the proposed STC-KF algorithm is evaluated and compared with the original STC algorithm. The experimental results using benchmark sequences imply that the proposed method outperforms the original STC algorithm under the conditions of heavy occlusion and large appearance changes.

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In this paper, we propose an improved generative adversarial network (GAN) for image compression artifacts reduction task (artifacts reduction by GANs, ARGAN). The lossy compression leads to quite complicated compression artifacts, especially blocking artifacts and ringing effects. To handle this problem, we choose generative adversarial networks as an effective solution to reduce diverse compression artifacts. The structure of "U-NET" style is adopted as the generative network in the GAN. A discriminator network is designed in a convolutional manner to differentiate the restored images from the ground truth distribution. This approach can help improve the performance because the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an end-to-end mapping from input degraded image to corresponding restored image, but also learns a loss function to train this mapping. Benefit from the improved GANs, we can achieve desired results without hand-engineering the loss functions. The experiments show that our method achieves better performance than the state-of-the-art methods.

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