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TOEformer: Temporal order enhanced Transformer for time series forecasting

Engineering Applications of Artificial Intelligence 2025年 155卷

作者： Li, Qiang Qin, Jiwei Wang, Dacheng Qin, Xizhong Cui, Daishun Xie, Jiachen Sun, Dezhi School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

Transformers have shown excellent performance in time series forecasting, but a recent study points out that existing Transformer-based time series forecasting models cannot preserve temporal order well. To address this problem, we preserve temporal order information by integrating linear and Transformer structures and predicting trend and seasonal data, respectively, which compensates for the inadequacy of the Transformer in this regard by leveraging linear models to preserve the temporal order information of the original sequence. Besides, directly combining the seasonal and trend part prediction results increases the overall prediction error. For this problem, this paper proposes a new loss function, the relative value exponential loss, which reduces the impact of the problem on predictions by constraining the network to generate more seasonal-trend pairs that can offset each other's errors. Experimental results on six benchmark public datasets show that our temporal order enhanced Transformer (TOEformer) can preserve temporal order information and produce better forecasting performance in more than 85% of multivariate prediction setups compared to state-of-the-art time series forecasting models. The proposed relative value exponential loss effectively mitigates the overall error growth problem. © 2025

关键词： Forecasting

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A Singing Melody Extraction Network Via Self-Distillation and Multi-Level Supervision

A Singing Melody Extraction Network Via Self-Distillation an...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ying Hu Jiabo Jing Fan Li Lijun He Li Lin Wenzhong Yang Key Laboratory of Signal Detection and Processing Urumqi China School of Computer Science and Technology Xinjiang University Urumqi China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China Xinjiang Institute of Electronics Research Shares Co. Ltd Urumgi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Extracting singing melody from polyphonic music is an important topic in the field of music information retrieval. In this paper, we propose a singing melody extraction network consisting of five stacked multi-scale feature time-frequency aggregation (MF-TFA) modules. In the same network, deeper layers generally contain more contextual information than shallower layers. To help the shallower layers enhance the ability of task-relevant feature extraction, we propose a self-distillation and multi-level supervision (SD-MS) method, which leverages the feature distillation from the deepest layer to the shallower one and multi-level supervision to guide network training. Visualization analysis shows that by introducing SD-MS, the same-level layer in the network can obtain a clearer representation of fundamental frequency components, while the shallower layers can even learn more task-relevant semantic information. Ablation study results indicate that SD-MS applies to existing melody extraction models and can consistently improve performance. Experimental results show that our proposed method, MF-TFA with SD-MS, outperforms six compared state-of-the-art methods, achieving overall accuracy (OA) scores of 87.1%, 89.9%, and 76.6% on the ADC 2004, MIREX 05, and MEDLEY DB datasets, respectively. The main code will be available at https://***/SmoothJing/MF-TFA_SD-MS.

关键词： Training Visualization Time-frequency analysis Semantics signal processing Feature extraction Multiple signal classification Data mining Speech processing Music information retrieval

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Fine-Grained Spatial-Temporal Framework for Engagement Prediction 2nd

Fine-Grained Spatial-Temporal Framework for Engagement Pred...

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2nd CSIG Conference on Emotional Intelligence, CEI 2024

作者： Wang, Shanshan Zheng, Yong Wang, Keyang Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Beijing China Institutes of Physical Science and Information Technology Anhui University Anhui China Zhejiang Dahua Technology Co. Ltd. Zhejiang China School of Computer Science and Technology Anhui University Anhui China

ISBN: (纸本)9789819650835

The engagement prediction task aims to identify the current level of involvement of individuals based on the information presented in video clips. In recent years, engagement prediction has attracted considerable attention due to its significance in real-life scenarios. We observed that an individual’s engagement status is often primarily manifested in the facial region. This observation prompted us to solve the engagement prediction problem by exploiting facial information to learn features related to engagement. In this paper, we propose a novel model for learning fine-grained features, called Fine-Grained Spatial-Temporal Framework (FGST). Specifically, we consider both spatial and temporal aspects. In the spatial domain, we propose an entropy-weighted method based on Fourier transform. By applying Fourier transform to video frames and introducing the concept of information entropy, we can segregate engagement-related information from engagement-unrelated information and mining difficult samples. This aids in enabling the model to learn features conducive to engagement prediction. In the temporal domain, we calculate the differences between adjacent frames and sum these differences to obtain regions of pixel variation between video frames, thereby generates saliency weight and improving model performance. Experiments on several public datasets demonstrate the proposed model can outperform the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fourier transforms

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ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Conformer-Based Diffusion Model

ZCS-CDiff: A Zero-Shot Code-Switching TTS System with Confor...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ke Chen Zhihua Huang Liang He Yonghong Yan School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-Switching (CS) Text-To-Speech (TTS) models have gained attention due to the increasing prevalence of multilingual communication. However, existing models struggle to meet the growing demand for personalized CS TTS, particularly in Zero-Shot (ZS) scenarios where the model should generate speech for an unseen speaker from reference speech. In this paper, we propose ZCS-CDiff, a zero-shot code-switching TTS system with the Conformer-based diffusion model. We disentangle speech features and use the diffusion model to precisely model these disentangled attributes, resulting in high-quality ZS CS speech. Additionally, we introduce a Conformer-based WaveNet as the denoising network within the diffusion model to further improve the accuracy of modeling different attributes. We also designed a speaker-assist module to help the model better handle speaker information extracted from the reference speech, resulting in CS speech with higher speaker similarity. Experimental results and ablation studies demonstrate that the ZS CS speech generated by ZCS-CDiff has good speech naturalness, intelligibility, and speaker similarity while confirming the effectiveness of our design choices. Audio samples are available 1 .

关键词： Training Adaptation models Accuracy Speech coding Noise reduction Speech enhancement signal processing Diffusion models Probabilistic logic Text to speech

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SFUnet-DCNN: An effective approach for LPI radar waveform recognition under low SNR conditions

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IEEE Sensors Journal 2025年

作者： Chen, Yi Quan, Daying Yu, Kaiyin Wang, Xiaofeng Jin, Ning Xing, Mengdao Wu, Jiongfeng China Jiliang University School of Information Engineering Hangzhou310000 China Xidian University National Key Laboratory of Radar Signal Processing Xi’an710071 China Xidian University Collaborative Innovation Center of Information Sensing and Understanding Xi’an710071 China Xidian University Academy of Advanced Interdisciplinary Research Xi’an710071 China Zhejiang Chitic-Safeway New Energy Technology Co. Ltd Hangzhou310000 China

Low Probability of Intercept (LPI) radar waveform recognition is crucial in modern electronic defense, providing decision-makers with essential insights. However, LPI radar signals are highly susceptible to noise interference, especially in low signal-to-Noise Ratio (SNR) environments, significantly reducing recognition accuracy. To address this challenge, we propose a novel LPI radar waveform recognition approach called SFUnet-DCNN, which can achieve robust recognition performance under low SNR scenarios. First, the LPI radar signals are transformed into Time-Frequency Images (TFIs) using the Choi-Williams Distribution (CWD). Subsequently, the TFIs at various SNR levels are fed into a Deep Convolutional Neural Network (DCNN) with Spatial Frequency Blocks (SFBs), which extracts effective features for the SNR estimation and the residual attention classification network. The SFUnet denoising network is activated specifically to reconstruct low SNR TFIs, which are then reprocessed by the DCNN. Finally, the residual attention classification network generates modulation-type predictions. Extensive simulation experiments demonstrate the impressive recognition performance of our approach compared with State-Of-The-Art (SOTA) baselines. Remarkably, even at an SNR of -10 dB, it achieves an overall recognition accuracy of up to 95%. Furthermore, we have verified the effectiveness of the proposed model using actual measurement data, emphasizing its practical applicability, which confirms that the proposed approach is a powerful tool for enhancing radar signal recognition in challenging environments. © 2001-2012 IEEE.

关键词： Convolutional neural networks

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Optically transparent transmissive orbital angular momentum (OAM) metasurface with high transmittance and broadband characteristics

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Optics Express 2025年第12期33卷 24385-24395页

作者： Yong Cai Yuying Dai Yue Ma Gang Wang Guanghui Xu Xingang Ren Hui Wang Xiaohu Zhao Xianliang Wu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei 230039 China Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Anhui University Hefei 230039 China wang_toward@*** ghxu86@*** Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei 230039 China xgren@*** Hefei Normal University Hefei 230009 China

This work proposes a high-performance broadband orbital angular momentum (OAM) beam-generating metasurface based on optically transparent media. A broadband polarization-converting transmissive metasurface, which operates in the 18 - 24 (28.57% fractional bandwidth) GHz range, has been realized by collaboratively designing dual C-split ring resonator structures and bidirectional lattice-patterned surface networks. The array structure demonstrates typical helical phase distribution features in the K-band, and experimental results verify simulation design. Innovatively employing a composite structure of quartz glass substrate and patterned conductive thin films, the design achieves compatible optimization of 77% visible-light transmittance and microwave broadband performance. This approach breaks through the mutually exclusive limitations of traditional metasurfaces’ optical/electromagnetic properties, providing a highly integrated solution for transparent antenna radomes in satellite communication systems and multi-mode beam multiplexing applications.

关键词： Orbital angular momentum multiplexing Phase plates Ring resonators Spatial light modulators Spiral phase Thin films

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DOA estimation of wideband signals based on iterative spectral reconstruction

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Journal of Systems Engineering and Electronics 2017年第6期28卷 1039-1045页

作者： Shun He Zhiwei Yang Guisheng Liao National Laboratory of Radar Signal Processing Xidian University Xi’an 710071 China Communication and Information Engineering Collage Xi’an University of Science and Technology Xi’an 710054 China

In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation(DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral(Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio(SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation.

关键词： direction of arrival(DOA) wideband source coherent source minimum variance spectral

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Ionosphere correction algorithm for spaceborne SAR imaging

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Journal of Systems Engineering and Electronics 2016年第5期27卷 993-1000页

作者： Lin Yang Mengdao Xing Guangcai Sun School of Electronic Science and Engineering National University of Defense TechnologyChangsha 410073China National Laboratory of Radar Signal Processing Xidian UniversityXi'an 710071China

For spaceborne synthetic aperture radar （SAR） imaging,the dispersive ionosphere has significant effects on the propagationof the low frequency （especially P-band） radar signal. Theionospheric effects can be a significant source of the phase error inthe radar signal, which causes a degeneration of the image qualityin spaceborne SAR imaging system. The background ionosphericeffects on spaceborne SAR through modeling and simulation areanalyzed, and the qualitative and quantitative analysis based onthe spatio-temporal variability of the ionosphere is given. A novelionosphere correction algorithm （ICA） is proposed to deal with theionospheric effects on the low frequency spaceborne SAR radarsignal. With the proposed algorithm, the degradation of the imagequality caused by the ionosphere is corrected. The simulation resultsshow the effectiveness of the proposed algorithm.

关键词： synthetic aperture radar （SAR） radar imaging highresolution ionospheric effects total electron content （TEC）.

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Research on cancer rapid diagnosis technology based on adaptive channel attention mechanism and two-dimensional spectral fusion

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Microchemical Journal 2025年 213卷

作者： Liu, Wei Chang, Chenjie Li, Jiahe Lv, Xiaoyi Liu, Yang Chen, Chen Chen, Cheng College of Software Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi 830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang Cloud Computing Engineering Technology Research Center Karamay 834000 China

With the widespread use of Artificial Intelligence (AI) in biomedicine, integrating infrared and Raman spectra with biological information offers new prospects for enhanced disease diagnosis. For these two types of data, more information can be extracted using multimodal data fusion compared to single modality, but existing studies tend to only fuse one-dimensional spectral data, ignoring the richer information of the spectra when they are converted to two-dimensional images. In this study, we propose a rapid diagnostic method for cancer, which first converts one-dimensional (1D) Raman and Infrared spectra into two-dimensional (2D) spectral images using Short-Time Fourier Transform (STFT), respectively. Then the features are fused by our designed Adaptive Graph Channel Attention Spacial Cross-Attention Fusion Module (ASCFM). This fusion module innovatively uses the adaptive graph channel attention mechanism to strengthen the model's ability to capture the key information of different spectral features, and utilizes the spatial attention fusion module and the cross-attention module for spatial information extraction and fusion. The output fused features effectively avoid feature information loss through this multimodal feature extraction and fusion, which greatly improves the performance of the model. In this study, Gradient-weighted Class Activation Mapping is also used to make the decision-making process of the neural network more interpretable. The results show that the accuracy of 2D Raman spectral images fused with 2D infrared spectral images by ASCFM followed by ResidualNetwork (ResNet) for diagnosis of lung cancer and glioma datasets is 98.88 % and 96.38 %, which suggests that it is a highly promising adjunctive technique for cancer diagnosis. © 2025

关键词： Auxiliary diagnostic technology Deep learning Infrared spectroscopy Lung cancer Multimodal fusion Raman spectroscopy

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A multi-target tracking algorithm based on Gaussian mixture model

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Journal of Systems Engineering and Electronics 2020年第3期31卷 482-487页

作者： SUN Lili CAO Yunhe WU Wenhua LIU Yutao National Laboratory of Radar Signal Processing Xidian UniversityXi’an 710071China Science and Technology on Communication Networks Laboratory Shijiazhuang 050081China

Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is *** algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior *** with the traditional data association algorithm,this algorithm has better tracking performance and less computational *** results demonstrate the effectiveness of the proposed algorithm.

关键词： multiple-target tracking Gaussian mixture model(GMM) data association expectation maximization(EM)algorithm

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