Cross-technology communication(CTC) enables seamless interactions between
diverse wireless technologies. Most existing work is based on reversing the
transmission path to identify the appropriate payload to generate the waveform
that the target devices can recognize. However, this method suffers from many
limitations, including dependency on specific technologies and the necessity
for intricate algorithms to mitigate distortion. In this work, we present
NNCTC, a Neural-Network-based Cross-Technology Communication framework inspired
by the adaptability of trainable neural models in wireless communications. By
converting signal processing components within the CTC pipeline into neural
models, the NNCTC is designed for end-to-end training without requiring labeled
data. This enables the NNCTC system to autonomously derive the optimal CTC
payload, which significantly eases the development complexity and showcases the
scalability potential for various CTC links. Particularly, we construct a CTC
system from Wi-Fi to ZigBee. The NNCTC system outperforms the well-recognized
WEBee and WIDE design in error performance, achieving an average packet
reception rate(PRR) of 92.3% and an average symbol error rate(SER) as low as
1.3%.

基于神经网络的跨技术通信系统（NNCTC）通过将 CTC 管道中的信号处理组件转化为神经模型，实现了无需标记数据的端到端训练，从而使开发复杂性大大降低，并展示了各种 CTC 链接的可扩展性潜力。NNCTC 系统在错误性能上优于 WEBee 和 WIDE 设计，其平均分组接收率 (PRR) 达到 92.3％，平均符号错误率 (SER) 仅为 1.3％。

NNCTC：基于神经网络的物理层跨技术通信

NNCTC: Physical Layer Cross-Technology Communication via Neural Networks

Modern neural-network-based no-reference image- and video-quality metrics
exhibit performance as high as full-reference metrics. These metrics are widely
used to improve visual quality in computer vision methods and compare video
processing methods. However, these metrics are not stable to traditional
adversarial attacks, which can cause incorrect results. Our goal is to
investigate the boundaries of no-reference metrics applicability, and in this
paper, we propose a fast adversarial perturbation attack on no-reference
quality metrics. The proposed attack (FACPA) can be exploited as a
preprocessing step in real-time video processing and compression algorithms.
This research can yield insights to further aid in designing of stable
neural-network-based no-reference quality metrics.

本文研究了基于神经网络的无参考质量度量方法的适用范围，并提出了一种快速的对抗扰动攻击方法（FACPA），可以用作实时视频处理和压缩算法的预处理步骤，提高稳定性。

无参考图像和视频质量评价的快速对抗基于 CNN 的扰动攻击

Fast Adversarial CNN-based Perturbation Attack on No-Reference Image-  and Video-Quality Metrics

A neural-network-based approach is presented to efficiently implement digital
backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting
"learned" DBP significantly reduces the complexity compared to conventional DBP
implementations.

该研究提出了一种基于神经网络的方法来实现数字反向传播，针对 32x100 公里的光纤链路，所得到的 “学习” 数字反向传播相对于传统实现方法大大降低了复杂性。