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Xinwei Luo

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3 papers

2 author rows

EAAI Journal 2025 Journal Article

A deep learning based iterative denoising algorithm for multiple frequency lines recovery

Qifan Shen
Xinwei Luo
Long Chen

Passive detection technology constitutes a crucial research direction in underwater acoustic target detection. It has been the subject of ongoing investigations to address the pressing need for stealth capabilities. The most formidable hurdle that all types of detectors must overcome is the extraction of line spectral components relevant to the target, given the convoluted underwater environment teeming with significant noise pollution. In this paper, a pioneering deep learning-based algorithm, known as the Additive Diffusion Probabilistic Denoising Model (ADPDM), is proposed to rectify the performance inadequacies of neural network-based approaches when operating under low signal-to-noise ratios (SNRs). To begin with, the ADPDM was ingeniously crafted. It was designed to astutely modify the representation of underwater signals by transforming the generative inference process of the diffusion model into a deterministic recovery strategy. Subsequently, the ADPDM was expanded into the complex-valued time–frequency joint domain, in order to take full advantage of the multi-dimensional information representation brought about by the lofargram. Moreover, an accelerating inference algorithm was adopted and calibrated to be fully compatible with the ADPDM framework. In contrast to the prevailing frequency line trackers that predominantly concentrate on discerning the frequency positions of the line spectrum, the ADPDM is dedicated to unearthing and reconstructing the latent line spectrum components concealed within the observed signal. This, in turn, paves the way for more effective subsequent detection or estimation operations. Empirical results demonstrated that the frequency lines within the signal enhanced by the ADPDM can be detected with remarkable efficacy, even when a relatively less sophisticated tracker is employed. On the basis of these findings, the detection performance metrics of the ADPDM have been shown to outstrip those of the current state-of-the-art (SOTA) methods, both those founded on deep learning and the hidden Markov model (HMM), across the entire spectrum of experimental SNRs.

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EAAI Journal 2024 Journal Article

A ship-radiated noise classification method based on domain knowledge embedding and attention mechanism

Lu Chen
Xinwei Luo
Hanlu Zhou

Ship classification based on machine learning (ML) has proven to be a significant underwater acoustic research direction. One of the critical challenges rests with how to embed domain signal knowledge into ML models to obtain suitable features that highly correlate with the classification and create better predictors. In this paper, a novel ML-based ship classification model, Hierarchical Underwater Acoustic Transformer (HUAT), is proposed to improve the classification performance. Firstly, the Detection of Envelope Modulation on Noise (DEMON) spectra of ship-radiated noise signals are estimated by cyclostationary analysis. The motivation for using a DEMON-based preprocessing scheme is that valuable propeller information can be revealed by exploiting the second-order cyclostationarity of ship-radiated noise signals. Secondly, the useful features of DEMON spectra are enhanced using a multi-head self-attention module, and the potential features of the Mel spectrograms are extracted employing a Convolutional Neural Network (CNN) module. The two kinds of features are fused to provide ship classification patterns. The challenge of feature learning in the deep classification model is reduced by leveraging domain-related classification knowledge. Finally, the Swin Transformer, based on shifted window self-attention mechanism, is used to learn high-level feature representations and conduct ship classification. Experimental results show that the HUAT model achieves excellent classification performance on ship-radiated noise datasets, ShipsEar and DeepShip. And its classification efficiency is better than the model based on traditional Transformer architecture. In addition, the proposed method provides technical support for the underwater intelligent system capable of automatically sensing sailing vessels and recognizing vessel types.

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ICML Conference 2023 Conference Paper

SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference

Ran Ran
Xinwei Luo
Wei Wang
Tao Liu 0023
Gang Quan
Xiaolin Xu 0001
Caiwen Ding
Wujie Wen

Homomorphic Encryption (HE) is a promising technology to protect clients’ data privacy for Machine Learning as a Service (MLaaS) on public clouds. However, HE operations can be orders of magnitude slower than their counterparts for plaintexts and thus result in prohibitively high inference latency, seriously hindering the practicality of HE. In this paper, we propose a HE-based fast neural network (NN) inference framework–SpENCNN built upon the co-design of HE operation-aware model sparsity and the single-instruction-multiple-data (SIMD)-friendly data packing, to improve NN inference latency. In particular, we first develop an encryption-aware HE-group convolution technique that can partition channels among different groups based on the data size and ciphertext size, and then encode them into the same ciphertext by novel group-interleaved encoding, so as to dramatically reduce the number of bottlenecked operations in HE convolution. We further tailor a HE-friendly sub-block weight pruning to reduce the costly HE-based convolution operation. Our experiments show that SpENCNN can achieve overall speedups of 8. 37$\times$, 12. 11$\times$, 19. 26$\times$, and 1. 87$\times$ for LeNet, VGG-5, HEFNet, and ResNet-20 respectively, with negligible accuracy loss. Our code is publicly available at https: //github. com/ranran0523/SPECNN.

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