In terahertz communication systems, amplifiers and other components can induce non-linear distortion in terms of amplitude and phase, resulting in system performance degradation. This paper presents a terahertz metamaterial waveguide to mitigate amplitude and phase distortions in some terahertz systems. A simple method based on free-space analysis is proposed for designing metamaterial waveguides in an enclosed space. The quasi-periodic metamaterial structures, which feature I-shaped resonant patterns, are integrated onto the inner walls of rectangular waveguides. The phase and amplitude of electromagnetic waves within the waveguide can be modulated by varying the dimensions and number of these resonators. Utilizing the effective medium theory and the equivalent circuits, the metamaterial waveguide’s phase and absorption modulation mechanisms are analyzed. Based on the proposed structure, a metamaterial waveguide with I-shaped resonators is designed and fabricated, and its abilities to modulate the phase and absorption of terahertz waves around 0.2 THz are demonstrated.

In complex marine environments, extracting target features from acoustic signal is very difficult, making the targets hard to be recognized. Therefore, it is necessary to perform denoising method on the acoustic signal to highlight the target features. However, training deep learning denoising models requires a large mount of acoustic data with labels and obtaining labels with real measured data is also extremely difficult. In this paper, an enhanced simulation algorithm, which considers integrating features of target line spectrum and ocean environmental noise, is proposed to construct a large-scale training sample set. Additionally, a deep convolutional denoising model is presented, which is first train on simulated data and directly applied to real measured data for denoising, enabling line spectrum to be significantly displayed in the time-frequency spectrogram. The results on simulation experiments and sea trials demonstrate that the proposed method can significantly reduce ocean noise while preserving the characteristics of target line spectrum. Furthermore, the experiments demonstrate that the proposed convolutional denoising model has transferability and generalization, making it suitable for denoising underwater acoustic signal in different marine areas.

In Underwater Acoustic Target Recognition (UATR), accurately extracting spectral lines from time–frequency spectra in complex ocean environments faces three critical challenges: low-frequency spectral confusion, line spectrum and noise mixture, and a computational efficiency vs. performance trade-off. To address these, we propose a deep learning-integrated framework based on application-oriented integration and adaptation of established techniques tailored to the underwater acoustic domain. The framework consists of the following: (1) the Line Spectrum Separation Network (LSS-Net), which integrates a Time–Frequency Joint LSTM and a Temporal Gated Cross-Attention (TGCA) module within an encoder–decoder architecture adapted for high-resolution underwater acoustic time–frequency spectra; (2) a physics-informed signal simulation approach that realistically models Doppler frequency drift and intensity fluctuations; and (3) a Peak-Tracking Line Extractor (PTLE) algorithm that leverages underwater acoustic-specific temporal constraints. The proposed framework achieves an MOTA of 0.89 on simulated data and 0.52 on real sea trial data, outperforming existing methods by 0.06-2.14 in MOTA and significantly suppressing high-resolution background noise.