Article
Computer Science, Artificial Intelligence
Xiaonan Cui, Dinghan Hu, Peng Lin, Jiuwen Cao, Xiaoping Lai, Tianlei Wang, Tiejia Jiang, Feng Gao
Summary: Accurate classification of children's epilepsy syndromes is crucial for the diagnosis and treatment of epilepsy. This paper presents a study on the classification of two common epilepsy syndromes using a novel feature fusion model based on deep transfer learning and conventional time-frequency representation. Experimental results demonstrate high classification accuracy of the proposed algorithm.
Article
Chemistry, Multidisciplinary
Bo Zhang, Tao Xu, Wen Chen, Chongyang Zhang
Summary: This study extracts relevant features from seismic signals using mel spectrogram and mel frequency cepstral coefficient (MFCC) and applies a deep learning model with a hierarchical structure for earthquake prediction. The results show significant improvement in predictive performance, and the combination of 1D-CNN and 2D-CNN demonstrates unique advantages in handling time-series problems.
APPLIED SCIENCES-BASEL
(2023)
Article
Acoustics
Zilong Zhou, Jinkun Zhang, Ruishan Cheng, Yichao Rui, Xin Cai, Lu Chen
Summary: The study proposes a denoising method based on CEEMDAN and WPTD to reduce the noise of blasting vibration signals. The method decomposes the signals and applies wavelet packet threshold denoising to effectively reduce noise and preserve vibration components.
Article
Engineering, Multidisciplinary
Omair Rashed Abdulwareth Almanifi, Ahmad Fakhri Ab Nasir, Mohd Azraai Mohd Razman, Rabiu Muazu Musa, Anwar P. P. Abdul Majeed
Summary: This paper examines the performance of transfer learning in detecting heart murmurs. By evaluating three transfer learning models, the results indicate that using Spectrograms as the signal representation method yields the best results.
ALEXANDRIA ENGINEERING 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
Automation & Control Systems
Ashish Kumar, Harshit Tomar, Virender Kumar Mehla, Rama Komaragiri, Manjeet Kumar
Summary: This paper studies various denoising techniques for removing noise from ECG signals, and proposes a denoising technique based on stationary wavelet transform, which outperforms other methods by preserving more ECG signal components.
Article
Geochemistry & Geophysics
Kecheng Chen, Xiaorong Pu, Yazhou Ren, Hang Qiu, Fanqiang Lin, Saimin Zhang
Summary: This article proposes a novel denoising framework for TEM signals using deep convolutional neural networks, which transforms the denoising task into an image denoising task. The framework includes a new signal-to-image transformation method and a deep CNN-based denoiser, achieving better performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
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, Multidisciplinary
Tong Liu, YuCheng Jin, Shuo Wang, QinWen Zheng, Guoan Yang
Summary: This paper proposes a denoising method of acoustic emission (AE) signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the problem of weak AE signals being submerged in strong background noise in the actual operating conditions of the engine. The proposed method decomposes the engine background noise signals and noise-containing fault AE signals using wavelet packet, enhances the local analysis capability of the autoencoder, and analyzes the differences between the background noise signals and the noise-containing fault signals. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different signal-to-noise ratios (SNR).
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Musatafa Abbas Abbood Albadr, Sabrina Tiun, Masri Ayob, Manal Mohammed, Fahad Taha AL-Dhief
Summary: Spoken language identification involves determining and classifying natural language from given content and datasets. The proposed method in this study focuses on reducing MFCC feature dimensions to optimize identification efficiency.
COGNITIVE COMPUTATION
(2021)
Article
Chemistry, Analytical
Yunbo Shi, Juanjuan Zhang, Jingjing Jiao, Rui Zhao, Huiliang Cao
Summary: This paper explores wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis for high-G accelerometers. Through numerical simulation and testing, it provides better methods for dynamic calibration and parameter extraction of high-G accelerometers.
Article
Computer Science, Information Systems
Yunji Zhao, Nannan Zhang, Zhihao Zhang, Xiaozhuo Xu
Summary: This research proposes a bearing fault diagnosis method based on MFCC and DSFAN, which achieve accurate fault diagnosis by extracting fault features. By preprocessing the original signals and extracting features using MFCC, the DSFAN network model is constructed to extract global constraint features and distributed constraint features for bearing fault diagnosis. Experimental results demonstrate the excellent performance of the proposed MFCC-DSFAN method for fault diagnosis.
Article
Engineering, Multidisciplinary
Yunji Zhao, Baofu Qin, Yuhang Zhou, Xiaozhuo Xu
Summary: This paper proposes a bearing fault diagnosis method based on inverted Mel-scale frequency cepstrum coefficients and deformable convolution networks. By reconstructing the traditional Mel-scale frequency cepstrum coefficients filter bank, the frequency-domain characteristics of bearing vibration signals are obtained, the fault information contained in the high-frequency region is highlighted, and the influence of time series distribution inconsistency between training samples and testing samples on the diagnosis accuracy is reduced. The introduction of deformable convolution networks model further improves the spatial discrimination between different fault categories and improves the accuracy of fault diagnosis.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Qinghua Luo, Xiaozhen Yan, Chunyu Ju, Yunsai Chen, Zhenhua Luo
Summary: The paper introduces a USBL positioning system with Kalman filtering to improve accuracy by exploring a new element array and combining Kalman filters to accurately capture and filter acoustic signals, obtaining high-precision phase difference information and determining the coordinates of the underwater target. Comprehensive evaluation results demonstrate the effectiveness of the proposed USBL positioning method based on the Kalman filter algorithm.
Article
Engineering, Electrical & Electronic
Jakub Svatos, Jan Holub
Summary: Automatic acoustic measurement detection systems are increasingly necessary for pointing out dangerous events in both military and civil areas. This system detects, localizes, and classifies acoustic impulse events, such as gunshots, using Mel frequency transformation and a support vector machine (SVM) algorithm. It can accurately locate the event and estimate the caliber of the firearm used. Tested with various firearms and ammunition, this system can reliably detect, localize, and classify hazardous acoustic events by training the classifier with diverse impulse acoustic events.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Islam Mansour, Mohamed Aboualalaa, Ahmed Allam, Adel B. Abdel-Rahman, Mohammed Abo-Zahhad, Ramesh K. Pokharel
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2018)
Article
Computer Science, Information Systems
Tonny Ssettumba, Ahmed H. Abd El-Malek, Maha Elsabrouty, Mohammed Abo-Zahhad
IEEE INTERNET OF THINGS JOURNAL
(2019)
Article
Chemistry, Analytical
Ahmed Gomaa, Moataz M. Abdelwahab, Mohammed Abo-Zahhad, Tsubasa Minematsu, Rin-ichiro Taniguchi
Article
Engineering, Electrical & Electronic
Ahmad Abdalrazik, Adel B. Abdel-Rahman, Ahmed Allam, Mohammed Abo-Zahhad
INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES
(2020)
Article
Engineering, Electrical & Electronic
Islam Mansour, Mohamed Aboualalaa, Adel Barakat, Ahmed Allam, Adel B. Abdel-Rahman, Mohammed Abo-Zahhad, Ramesh K. Pokharel
IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS
(2020)
Proceedings Paper
Telecommunications
Kenneth Okello, Ahmed H. Abd El-Malek, Maha Elsabrouty, Mohammed Abo-Zahhad
2019 INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB)
(2019)
Proceedings Paper
Telecommunications
Kenneth Okello, Ahmed H. Abd El-Malek, Maha Elsabrouty, Mohammed Abo-Zahhad
2019 INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB)
(2019)
Proceedings Paper
Telecommunications
Mennatallah A. Rostom, Ahmed H. Abd El-Malek, Maha Elsabrouty, Mohammed Abo-Zahhad
2019 INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB)
(2019)
Article
Computer Science, Information Systems
Mohamed Aboualalaa, Islam Mansour, Hala Elsadek, Adel B. Abdel-Rahman, Ahmed Allam, Mohammed Abo-Zahhad, Kuniaki Yoshitomi, Ramesh K. Pokharel
Proceedings Paper
Acoustics
Sherif Seha, Georgios Papangelakis, Dimitrios Hatzinakos, Ali Shahidi Zandi, Felix J. E. Comeau
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Malissa Maria Mahmud, Chandra Reka Ramachandiran, Othman Ismail
2019 10TH INTERNATIONAL CONFERENCE ON E-EDUCATION, E-BUSINESS, E-MANAGEMENT AND E-LEARNING (IC4E 2019)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Mohamed Aboualalaa, Islam Mansour, Mohamed Mansour, Adel Bedair, Ahmed Allam, Mohammed Abo-Zahhad, Hala Elsadek, Kuniaki Yoshitomi, Ramesh K. Pokharel
2018 IEEE WIRELESS POWER TRANSFER CONFERENCE (WPTC)
(2018)
Proceedings Paper
Telecommunications
Tonny Ssettumba, Ahmed H. Abd El-Malek, Maha Elsabrouty, Mohamed Abo-Zahhad
2018 14TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB 2018)
(2018)
Proceedings Paper
Telecommunications
Abdelsalam Sayed-Ahmed, Maha Elsabrouty, Ahmed H. Abd El-Malek, Mohammed Abo-Zahhad
2018 14TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB 2018)
(2018)
Proceedings Paper
Computer Science, Information Systems
Ahmed Gomaa, Moataz M. Abdelwahab, Mohammed Abo-Zahhad
2018 IEEE 61ST INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS)
(2018)
