Article
Engineering, Multidisciplinary
Xuezhong Wang
Summary: This paper proposes a radar electronic signal recognition algorithm based on wavelet transform and deep learning, which improves the recognition effect and robustness of electronic radar through research on signal preprocessing and feature extraction methods, as well as the design of an optimized convolution neural network.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Chemistry, Analytical
Young In Jang, Jae Young Sim, Jong-Ryul Yang, Nam Kyu Kwon
Summary: This study aims to find the optimal mother wavelet function and wavelet decomposition level for denoising Doppler cardiogram signals. The results show that Db9 and Sym9 are the most efficient mother wavelet functions, and the best decomposition level is seven.
Article
Mathematics, Applied
U. K. Mandal, Sandeep Kumar Verma, Akhilesh Prasad
Summary: The paper aims to study the composition of continuous Kontorovich-Lebedev wavelet transform and wave packet transform based on the Kontorovich-Lebedev transform. Estimates for these transforms are obtained, along with Plancherel's relation for their composition. Additionally, a reconstruction formula for WPT associated with KL-transform is derived, and Calderon's formula related to KL-transform using its convolution property is obtained.
Article
Computer Science, Artificial Intelligence
Chunwei Tian, Menghua Zheng, Wangmeng Zuo, Bob Zhang, Yanning Zhang, David Zhang
Summary: This paper proposes a multi-stage image denoising CNN with wavelet transform, using dynamic convolution, wavelet transform and enhancement, and residual block to improve denoising performance. Experimental results show that the proposed method outperforms popular denoising methods.
PATTERN RECOGNITION
(2023)
Article
Engineering, Multidisciplinary
Claudia Barile, Caterina Casavola, Giovanni Pappalettera, Vimalathithan Paramsamy Kannan
Summary: Signal-based acoustic emission data were analyzed in this research to identify damage modes in CFRP composites. Novel methodologies were introduced, and the 'dmey' wavelet was chosen for damage process identification through WPT, which showed consistent results with shifting in spectral density for characterizing damage modes.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Chemistry, Analytical
Zengyuan Liu, Xiujuan Feng, Chengliang Dong, Mingzhi Jiao
Summary: This paper proposes an analytical method of PID signal with the adaptive weight of small wave packet decomposition node to suppress noise caused by the photoionization detector monitoring signal of VOCs. The PID signal is transmitted to the upper machine software through a single-chip microcontroller. By comparing with traditional wavelet packet denoising method, the superiority of the proposed method in denoising signals of PID is verified. This method lays a foundation for accurate VOCs monitoring in a high humidity environment by eliminating noise generated by local non-uniformity on the photocathode surface of PID ionization chamber.
Article
Engineering, Biomedical
Mahesh Chandra, Pankaj Goel, Ankita Anand, Asutosh Kar
Summary: The improved high-speed adaptive filter-based denoising architectures proposed in this paper outperform existing adaptive filter architectures and wavelet-based architectures, offering design flexibility and efficiency in denoising ECG signals in noisy environments for low-cost high-performance applications in the medical field. These architectures also require significantly less hardware compared to state-of-the-art wavelet-based architectures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Electrical & Electronic
C. Gouveia, D. Albuquerque, P. Pinho, J. Vieira
Summary: In this paper, the effectiveness of using Discrete Wavelet Transform in cardiac signal extraction is demonstrated through comparisons with other commonly used methods. The evaluation criteria include heart rate calculation accuracy, peak detection consistency, and the ability to assess vital signs of subjects with different physical stature. Real application scenarios with non-controlled monitoring environment conditions are also considered to test the efficiency of these methods.
IEEE SENSORS JOURNAL
(2022)
Article
Automation & Control Systems
Guoliang Lu, Xin Wen, Guangshuo He, Xiaojian Yi, Peng Yan
Summary: This article introduces a new dynamic modeling approach GMWPCs, which integrates WPD and graph theory to extract correlation information for early warning detection and fault identification. Experimental results validate the effectiveness and suitability of the proposed framework.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Automation & Control Systems
Guoliang Lu, Xin Wen, Guangshuo He, Xiaojian Yi, Peng Yan
Summary: In this article, a new dynamic modeling approach called GMWPCs is proposed for health monitoring of rolling element bearings (REBs) by integrating wavelet packet decomposition (WPD) and graph theory. The GMWPCs can enhance the analysis of WPD and enable early detection and fault identification in REBs, demonstrating effectiveness for real engineering applications.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Mechanical
Jian Cheng, Yu Yang, Xin Li, Junsheng Cheng
Summary: This article introduces the problems of several commonly used signal decomposition methods in gear fault diagnosis and proposes a novel multi-layer decomposition method called symplectic geometry packet decomposition (SGPD). The SGPD method combines symplectic geometry theory and the multi-layer decomposition idea of wavelet packet to minimize noise and retain fault information during the decomposition of non-steady signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Geosciences, Multidisciplinary
Liangsheng He, Hao Wu, Xiaotao Wen
Summary: Convolutional Neural Network (CNN) is widely used in seismic data denoising. However, traditional methods based on CNN ignore the multi-scale features in the wavelet domain, leading to decreased denoising accuracy. To address this, a method called WINNet_ACSP is proposed, which combines the lifting wavelet transform principle with atrous convolutions spatial pyramid (ACSP) to extract multi-scale information from noisy seismic data. The proposed method effectively removes random noise and preserves detailed information. Transfer learning is also employed to overcome the limited training sample size of seismic data. Experimental results demonstrate the effectiveness of the method.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Tianwei Lan, Zhaofa Zeng, Liguo Han, Jingwen Zeng
Summary: The neural network denoising technique has achieved impressive results in automatically learning the effective signal from data without any assumptions. However, it has been found that the performance of this method declines with increasing pollution levels when processing contaminated seismic data. Combining wavelet transform and a residual neural network has shown promising results in suppressing random noise data.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Electrical & Electronic
Apostolos Evangelidis, Dimitris Kugiumtzis
Summary: A mode decomposition algorithm called Maximal Spectral Overlap Wavelet Transform (MSO-WT) is proposed to decompose nonlinear and non-stationary signals into intrinsic modes. The algorithm overcomes the limitations of traditional algorithms in terms of noise and sampling sensitivity by using adaptive decomposition and synthesis methods. The effectiveness of the algorithm is demonstrated through synthetic signal experiments. Additionally, the algorithm is used to develop a new phase synchronization index, MCMPC, and compared with the MPC index for detecting phase synchronization between Rossler and Mackey-Glass systems. Furthermore, the MCMPC index is applied to construct a brain connectivity network and detect epileptic seizures using multichannel electroencephalogram data.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Acoustics
Shaobo Ji, Ying Jiang, Guoqiang Wang, Guohong Tian, Zeting Yu, Xin Lan, Wenwen Wang, Yong Cheng
Summary: The correlation between combustion status and block vibration was studied under different engine speed, torque, and oil temperature conditions. Results showed that frequency components below 8 kHz had a similar trend with the peak combustion pressure at different speed conditions. Frequency components from 8 kHz to 25 kHz increased with engine speed and were mainly affected by inertia force. The spectrum energy of frequency components increased with engine torque. The maximum spectrum energy of frequency components below 20 kHz did not show a specific trend due to the opposite effects of combustion pressure and oil damping at different oil temperatures. Frequency components higher than 20 kHz increased with increasing oil temperature, indicating their greater sensitivity to oil damping.
Article
Engineering, Electrical & Electronic
Zou Deyue, Gao Siyu, Li Xinyue, Zhao Wanlong
Summary: In this paper, an integrated navigation/communication signal is proposed by combining Cyclic Code Shift Keying (CCSK) as a communication signal with traditional Global Navigation Satellite System (GNSS) signal. A fuzzy judging algorithm and segmenting correlation domain are used to improve the transmission robustness of the integrated signal. Additionally, a strategy is proposed to solve the incompatibility problem between the integrated signal and binary communication system using interleaving coding and dual-thresholds optimization. The proposed algorithm significantly improves the fault-tolerant rate and reduces the Bit Error Rate (BER), enhancing the reliability of the communication system.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Guoxing Huang, Yunfei Xiang, Shibiao Deng, Yu Zhang, Jingwen Wang
Summary: This paper proposes a dual-channel cooperative sub-Nyquist sampling system for LFM-BPSK hybrid modulated signal. The system can solve the frequency ambiguity problem caused by low sampling rate and estimate the phases of binary symbols and the positions of discontinuities through self-mixing technology.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
A. Yousefian Darani, Y. Khedmati Yengejeh, G. Navarro, H. Pakmanesh, J. Sharafi
Summary: The development and use of network technology have brought society into the internet world, where communication through digital data takes place. However, the protection of digital resources has become a significant challenge, which steganography is seen as a possible solution for. This paper presents an image steganography algorithm that hides a grayscale secret image within an RGB color cover image, with a genetic algorithm used to optimize the selection of suitable hiding places. The proposed method is empirically demonstrated to be effective in terms of transparency, security, and resistance.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Xiao Tan, Zhiwei Yang, Xianghai Li, Yongfei Mao, Di Jiang
Summary: A novel gridless SR-STAP algorithm applicable to a non-ULA with AAPEs is proposed in this study. By establishing the ANM-STAP model and iteratively updating the clutter subspace and AAPEs, the performance of STAP is improved.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Irfan Manisali, Okyanus Oral, Figen S. Oktem
Summary: In this paper, a novel non-iterative deep learning-based reconstruction method for real-time near-field MIMO imaging is proposed. The method achieves high image quality with low computational cost at compressive settings through two stages of processing. A large-scale dataset is also developed for training the neural networks. The effectiveness of the method is demonstrated through experimental data and extensive simulations.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Haibin Li, Dengchao Wu, Wenming Zhang, Cunjun Xiao
Summary: Workplace safety accidents are a pervasive issue worldwide, with 67.95% of construction accidents attributed to the lack of helmet-wearing. Existing helmet detection algorithms suffer from underperformance in real-world scenarios due to various challenges. This study introduces a lightweight helmet detection algorithm, YOLO-PL, which achieves state-of-the-art performance by optimizing network structure and incorporating lightweight modules.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Fen Liu, Jianfeng Chen, Kemeng Li, Jisheng Bai, Weijie Tan, Chang Cai, Muhammad Saad Ayub
Summary: In this paper, a semi-tensor product-based multi-modal factorized multilinear (STP-MFM) pooling method is proposed for information fusion in sentiment analysis. Experimental results demonstrate that the proposed method outperforms baselines in terms of accuracy, training speed, and model complexity.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Xiaoyu Zhao, Xiaoqian Chen, Zhiqiang Gong, Wen Yao, Yunyang Zhang, Xiaohu Zheng
Summary: In this paper, a contrastive enhancement approach is proposed to mine latent prototypes from background regions and leverage latent classes to improve the utilization of similarity information between prototype and query features. The proposed modules, including a latent prototype sampling module and a contrastive enhancement module, significantly improve the performance of state-of-the-art methods for few-shot segmentation.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Xiaoyong Lyu, Baojin Liu, Wenbing Fan, Zhi Quan
Summary: This paper provides an in-depth analysis of the channel estimate model (CEM) in passive radar using orthogonal frequency division multiplexing (OFDM) waveforms. The authors propose a new CEM that takes into consideration the influence of inter-carriers interference (ICI). The theoretical analysis and simulation results support the effectiveness of the new CEM and the proposed method for canceling ICI.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Ruisheng Rana, Jinping Wang, Bin Fang, Weiming Yang
Summary: This paper introduces an improved neighborhood preserving embedding method (NPEAE), which utilizes a linear autoencoder to achieve more accurate and effective data projection from high-dimensional space to low-dimensional space. NPEAE performs better in recognition accuracy compared to other comparative methods.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Yuehao Guo, Xianpeng Wang, Jinmei Shi, Lu Sun, Xiang Lan
Summary: This article presents a fast real-valued tensor propagator method for FDA-MIMO radar. The method improves estimation accuracy by utilizing the original structural information of multidimensional data and eliminates the high computational complexity of high-order singular value decomposition. The proposed algorithm achieves parameter estimation at low snapshots and has lower computational complexity than other algorithms at high snapshots.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Bang Huang, Wen-Qin Wang, Weijian Liu, Shunsheng Zhang, Yizhen Jia
Summary: This paper studies the robust moving target detection problem in FDA-MIMO radar with an unknown covariance matrix in Gaussian clutter. The proposed approach adopts the subspace method and proposes three robust adaptive detectors, which are validated to be effective.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Asutosh Kar
Summary: In this paper, a class of variable step size adaptive algorithms is developed for hybrid narrow-band active noise control (HNANC) systems and compared with the existing state-of-the-art methods. Two algorithms are proposed for HNANC systems operating in multiple noise environments, and their performance is analyzed. The results demonstrate significant improvement in noise reduction compared to the counterparts.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Shouqi Wang, Zhigang Feng
Summary: With the rapid development of artificial intelligence and sensor technology, intelligent fault diagnosis methods based on deep learning are widely used in industrial production. In practical applications, complex noise environments and huge model parameters affect the performance and cost-effectiveness of diagnostic models. In this paper, a lightweight intelligent fault diagnosis model using multi-sensor data fusion is proposed to address these issues. By processing vibration signals from different sensors of rolling bearings using variational mode decomposition (VMD) and designing unique grayscale feature maps based on each intrinsic modal function (IMF) component, the proposed model achieves high accuracy diagnosis in noisy environments while meeting the requirements of small, light, and fast production. The ultra-lightweight GoogLeNet model (ULGoogLeNet) is constructed by adjusting the traditional GoogLeNet structure, and the ultra-lightweight subspace attention module (ULSAM) is introduced to reduce model parameters and enhance feature extraction capability. Experimental results on two datasets demonstrate the effectiveness and superiority of the proposed method.
DIGITAL SIGNAL PROCESSING
(2024)
Article
Engineering, Electrical & Electronic
Jichen Yang, Fangfan Chen, Yu Cheng, Pei Lin
Summary: Recently, there has been significant attention on multi-speaker multimedia speaker recognition (MMSR). This study explores innovative techniques for integrating audio and visual cues from the front-end representations of both speaker's voice and face. Experimental results show that these techniques achieve considerable improvements in the performance of MMSR.
DIGITAL SIGNAL PROCESSING
(2024)
