Kaihao Shi^1,2, Lin Yuan^1,2,*, Qimeng Yu³, Zhongting Shen², Yingtan Yu², Chenwei Nie¹, Xingjian Zhou³, Jingcheng Zhang³

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2033-2054, 2025, DOI:10.32604/phyton.2025.066286 - 31 July 2025

Abstract Plant diseases are a major threat that can severely impact the production of agriculture and forestry. This can lead to the disruption of ecosystem functions and health. With its ability to capture continuous narrow-band spectra, hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing. However, existing continuous wavelet analysis (CWA) methods suffer from feature redundancy issues, while the continuous wavelet projection algorithm (CWPA), an optimization approach for feature selection, has not been fully validated to monitor plant diseases. This study utilized rice bacterial leaf blight (BLB) as an example by… More >

Selime Tuba Pesen, Mehmet Ali Altuncu^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5597-5611, 2025, DOI:10.32604/cmc.2025.066702 - 30 July 2025

Abstract EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses. However, the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging. Therefore, advanced deep learning methods are needed to extract meaningful features and improve classification performance. This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network (TCN) mechanisms for EEG-based emotion recognition. EEG signals are first converted into scalogram images using Continuous Wavelet Transform (CWT), and classification is performed on these images. Swin Transformer is… More >

Zhicheng Liu¹, Long Zhao^1,*, Guanru Wen¹, Peng Yuan², Qiu Jin¹

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 541-555, 2023, DOI:10.32604/sdhm.2023.029760 - 17 November 2023

Abstract The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element… More > Graphic Abstract

Sidra Naseem¹, Kashif Javed¹, Muhammad Jawad Khan¹, Saddaf Rubab², Muhammad Attique Khan³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 471-486, 2021, DOI:10.32604/cmc.2021.018239 - 04 June 2021

Abstract Electroencephalography is a common clinical procedure to record brain signals generated by human activity. EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications, but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience. Various EEG analysis and classification techniques have been proposed to address this problem however, the conventional classification methods require identification and learning of specific EEG characteristics beforehand. Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification. One of the great implementations of deep… More >

Ahmed Silik^1,2, Mohammad Noori^3,*, Wael A. Altabey^1,4, Ramin Ghiasi¹, Zhishen Wu¹

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 1-22, 2021, DOI:10.32604/sdhm.2021.012751 - 22 March 2021

Abstract A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable… More >

Zheng Li^1,2, Wei Zhang¹, Kezhuang Gong¹

CMC-Computers, Materials & Continua, Vol.9, No.3, pp. 209-228, 2009, DOI:10.3970/cmc.2009.009.209

Abstract In this paper, continuous wavelet transform has been applied to inclusion detection in cantilever beams. By means of FEM, a cantilever beam with an inclusion is subjected to an impact on its free end, and its stress wave propagation process is calculated. Here, two kinds of inclusions which are distinct in material behavior have been discussed. And we change the inclusion's sizes in the beam and set it in three different positions to simulate some complicated situations. For soft inclusion, the results show that the arrival times of incident and reflective wave are distinguishable by… More >