In the rapidly evolving field of wireless communication, Multiple Input Multiple Output (MIMO) networks have emerged as a pivotal technology, offering enhanced data rates and spectral efficiency by leveraging multiple antennas at both the transmitter and receiver. The introduction of Open Radio Access Network (O-RAN) architecture has further revolutionized this domain, enabling greater flexibility, scalability, and interoperability through its open interfaces and software-defined approach. This paper presents DRAGON, a novel Deep Reinforcement Learning (DRL)-based framework for joint Layer and Modulation and Coding Scheme (MCS) selection, tailored for downlink single-user MIMO networks under the O-RAN framework. Our approach is designed to be highly scalable, capable of efficiently managing a large number of configuration options in one-shot prediction, including up to 25 MCS and 4 layer choices. The proposed solution has been rigorously evaluated using an O-RAN-based simulation environment, demonstrating up to an 18% performance improvement over the state-of-the-art (SOTA) methods and achieving a best throughput of 87.4% when compared to the collected ground-truth dataset. Furthermore, our method supports real-time prediction, making it viable for practical deployment. In addition to these advancements, we explore the potential integration of our DRL-based solution with real-world platforms and discuss the extension of our approach to handle multi-user (MU) MIMO scenarios, paving the way for broader applications in next-generation wireless networks.

Fire is one of the mutable hazards that damage properties and destroy forests. However, fire-like objects easily influence many existing image-based fire detection methods. In order to improve the performance of fire identification, this paper proposes a new fire identification algorithm by merging fire segmentation and multi feature fusion of fire. First, the improved YCbCr models in the reflection and non-reflection environment are constructed according to the color model. Simultaneously, the reflection and non-reflection conditions can be judged according to the segmented area. Second, the seed points are determined according to the weighted average of centroid of each connected region. Simultaneously, the fine segmentation of fire image is implemented according to the entropy of average contrast and uniformity within the connected region. Experiments show that the segmentation is not affected by image noises. Finally, the quantitative indicators of fire identification are given according to the coefficient of variation of area, the dispersion of centroid and the circularity. Cases show that the proposed identification method of fire not only accurately identifies fire, but also has a lower computation complexity than the deep learning method.

Low/high or uneven luminance results in low contrast of remotely sensed images (RSIs), which makes it challenging to analyze their contents. In order to improve the contrast and preserving fine weak details of RSIs, this letter proposes a novel enhancement framework to correct luminance guided by weighted least squares (WLS), including the following key parts. First, an image is separated into a base layer and a detail layer by employing the WLS. Then, a learning network is proposed to correct luminance for the base layer enhancement. Next, an enhancement operator for improving the detail layer is computed by using the original image and the enhanced base layer. Finally, the output image is obtained with a fusion of the enhanced base and detail components. Both quantitatively and qualitatively experimental results verify that the proposed method performs better than the state of the arts in contrast improvement and detail preservation.

In the above article[1], the model in(1)should be revised as\begin{equation*}\min _{\mathcal{I}^{\mathcal{B}}}\left\{\left(\mathcal{I}-\mathcal{I}^{\mathcal{B}}\right)^{2}+\lambda\left(a_{x}(\mathcal{I})\left(\frac{\partial \mathcal{I}^{\mathcal{B}}}{\partial x}\right)^{2}+a_{y}(\mathcal{I})\left(\frac{\partial \mathcal{I}^{\mathcal{B}}}{\partial y}\right)^{2}\right)\right\} \end{equation*}to be minimized for${\mathcal {I}}^{\mathcal {B}}$.

Low-quality remotely sensed images (RSIs) are not beneficial for the analysis of many activities including agricultural growth, resident migration, forest fire, and etc. Many previous enhancement schemes improve their quality via changing their illumination. However, these approaches often fail in detail and brightness preservation as well as contrast improvement due to that the information from a single image is limited. To address this issue, an enhancement framework, named as global-local enhancement network (GLE-Net), is proposed to correct the intensity via learning extra information from collected training data, including the following three key steps: first, RSIs are decomposed by the discrete wavelet transformation (DWT) method into the low-frequency component and the detail components. Then, the low-frequency component is improved by the global enhancement network while the detail components are enhanced by the local enhancement network in parallel. Finally, the enhanced components are employed to produce high-quality images with the inverse DWT (IDWT) method. The quantitatively and qualitatively comparable experiments on both synthetic and real-world RSIs validate that the proposed GLE-Net method performs well on preserving brightness and fine details, and even outperforms the state-of-the-arts.

We report XLINK, a multi-path QUIC video transport solution with experiments in Taobao short videos. XLINK is designed to meet two operational challenges at the same time: (1) Optimized user-perceived quality of experience (QoE) in terms of robustness, smoothness, responsiveness, and mobility and (2) Minimized cost overhead for service providers (typically CDNs). The core of XLINK is to take the opportunity of QUIC as a user-space protocol and directly capture user-perceived video QoE intent to control multi-path scheduling and management. We overcome major hurdles such as multi-path head-of-line blocking, network heterogeneity, and rapid link variations and balance cost and performance.