A novel adaptive terahertz system for reliable and efficient maritime communications under hostile sea conditions

Terahertz (THz) frequency band has been widely used in indoor, outdoor and space communications due to its advantage of large available bandwidth. However, limited research has been conducted to apply THz technique in maritime communications as hostile sea conditions cause significant path loss, thus leading to unacceptable signal error rate. In this paper, we propose a novel adaptive THz maritime communication system to tackle the above-mentioned challenge. Specifically, we design a joint source-channel coding scheme by using system random linear network coding (sRLNC) and Reed-Solomon (RS) to ensure transmission reliability. To further improve the transmission efficiency, we propose a novel triple-channel communication architecture facilitated by a very high frequency (VHF) feedback channel. With this design, the source data can be transmitted via the THz main channel while the coding redundancy is dispatched in the auxiliary channel. Meanwhile, the feedback channel allows sender to use an adaptive mechanism to achieve the transmission efficiency with higher transmission rate over long communication distance. In addition, we adopt the Doppler frequency offset in maritime environment to compensate both relative movement between communication parties and adversarial maritime factors, e.g., strong wind and extreme sea states. Simulation results demonstrate that our proposed THz system has remarkable capability not only to improve the communication efficiency up to 20Gbps compared to those conventional high frequency (HF), VHF and millimeter wave communication systems but also to transmit data over a longer distance with lower BERs.</p

A compositional transformer based autoencoder for image style transfer

Image style transfer has become a key technique in modern photo-editing applications. Although significant progress has been made to blend content from one image with style from another image, the synthesized image may have a hallucinatory effect when the texture from the style image is rich when processing high-resolution image style transfer tasks. In this paper, we propose a novel attention mechanism, named compositional attention, to design a compositional transformer-based autoencoder (CTA) to solve this above-mentioned issue. With the support from this module, our model is capable of generating high-quality images when transferring from texture-riched style images to content images with semantics. Additionally, we embed region-based consistency terms in our loss function for ensuring internal structure semantic preservation in our synthesized image. Moreover, information theory-based CTA is discussed and Kullback–Leibler divergence loss is introduced to preserve more brightness information for photo-realistic style transfer. Extensive experimental results based on three benchmark datasets, namely Churches, Flickr Landscapes, and Flickr Faces HQ, confirmed excellent performance when compared to several state-of-the-art methods. Based on a user study assessment, the majority number of users, ranging from 61% to 66%, gave high scores on the transfer effects of our method compared to 9% users who supported the second best method. Further, for the questions of realism and style transfer quality, we achieved the best score, i.e., an average of 4.5 out of 5 compared to other style transfer methods