The quality of visual media is critically impacted by low illumination and the presence of airborne particulates, leading to challenges in brightness balance, color saturation, and texture clarity which are detrimental to various applications in image processing and computer vision. Addressing these challenges, this study introduces a novel image enhancement algorithm that significantly improves the quality of degraded images. Our proposed method, the multi-channel phase activation and multi-constraint dark channel prior (MMDCP), leverages an innovative approach by integrating the phase-adjusted Gaussian kernel function for brightness channel optimization in the Fourier transform frequency domain. This optimization is enhanced through the application of a saturated dark channel prior, achieving simultaneous brightness enhancement and color fidelity. Furthermore, we refine the dark channel prior deblurring algorithm by incorporating intensity, brightness, and color constraints to correct overexposure issues and color offsets in the reconstructed images. The efficacy of the MMDCP algorithm is demonstrated through extensive experimentation, comparing it against six contemporary image enhancement algorithms using two types of objective indicators and subjective assessments across four public datasets. The MMDCP algorithm consistently outperforms the existing methods, with a notable average improvement of 20% in PSNR and 19.6% in SSIM metrics, substantiating its superiority in enhancing brightness, detail, and color accuracy. This study’s results underline the MMDCP algorithm’s robustness and versatility in improving image quality across various conditions, including daytime, nighttime, indoor, and outdoor settings.

The abundant available spectrum resources have made millimeter wave (mmWave) communications the key feature of the fifth generation (5G) mobile communications, allowing ultra-high transmission capacity. Additionally, mmWave bands, already widely used in radar systems, show a great advantage in environment sensing. Based on these observations, to satisfy the ever-growing mobile service requirements, and meanwhile to improve the maintenance efficiency for high-speed railways (HSRs), in this paper, we present the joint sensing and communication HSR mmWave wireless network, where two mmWave beams are intelligently controlled to provide broadband communications and environment sensing, respectively. Moreover, to mitigate the inter-beam interference between communication beams and sensing beams, we propose a deep reinforcement learning (DRL) based beam management scheme, where the beamwidth and inter-beam spacing are adaptively adjusted according to dynamic wireless environments. Simulation results demonstrate that our proposed scheme can better balance the communication capacity and the sensing performance compared to conventional schemes with fixed beamwidth and inter-beam spacing.

The increasing popularity of social media facilitates the propagation of fake news, posing a major threat to the government and journalism, and thereby making how to detect fake news from social media an urgent requirement. In general, multimodal-based methods can achieve better performance because of the complementation among different modalities. However, the majority of them simply concatenate features from different modalities, failing to well preserve the mutual information in common features. To address this issue, a novel framework named semantic-enhanced multimodal fusion network is proposed for fake news detection, which can better capture mutual features among events and thus benefit the detection of fake news. This model consists of three subnetworks, namely multimodal fusion and event domain adaptation networks as well as the fake news detector. Specifically, the multimodal fusion network aims to extract deep features from texts and images and fuse them into a common semantic feature known as a snapshot. Then, the fake news detector can learn the representation of posts. Finally, the event domain adaptation network can single out and remove the peculiar features of each event, and keep shared features among events. The experimental results show that the proposed model outperforms some state-of-the-art approaches on two real-world multimedia data sets.

As one of the next generation of network architectures, Software-Defined Networking (SDN) decouples the forwarding and control function of the traditional network device. However, it also faces new threats from network attacks, such as the Low-rate Distributed Denial of Service (L-DDoS) attack. To resist L-DDoS attack in SDN, this work proposes METER, an enseMble discrEte wavelet Transform-based method for idEntifying low-Rate DDoS attack in SDN. The rationale for METER is to identify L-DDoS attacks based on a new metric referred to as the Ensemble Wavelet Energy Entropy set (EWEEs), which is calculated by a novel ensemble DWT method. Firstly, the ensemble wavelet coefficients matrix is attained by METER using the ensemble DWT method. After that, METER combines the related entropy values with wavelet energy of the ensemble wavelet coefficients matrix to obtain EWEEs. Furthermore, to increase the precision of the L-DDoS detection method, machine learning methods are used to mine the correlation between EWEEs and L-DDoS attacks for identifying L-DDoS. The experiments were implemented on the RYU controller and Mininet, which demonstrates that METER outperforms the baseline method, in terms of precision, accuracy, F -score, recall, ROC curve, and P-R curve.

As Internet of Things (IoT) technology is growing fast, it is clearly forseen that the number of IoT devices and the scale of connections will be further expanded. IoT is capable of taking advantage of the existing network infrastructure effectively, so as to achieve data sharing between devices. However, the large scale and complexity of the network structure will bring potential security risks to IoT system. The traditional access control model is more complex and centralized. This paper aims to build a trust-driven distributed self-collaborative security architecture of IoT based on blockchain and smart contracts, which could overcome the single-point failure problem of centralized entities. Implementation of the security architecture shows that it still possesses characteristics including scalability, lightweight, and fine granularity, while the secure access control preserves the privacy of IoT devices.