The electrocardiogram (ECG) is a diagnostic tool that provides insights into the hearts electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, LiouvilleWeyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWTs strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.

Combination time-frequency and empirical wavelet transform methods for removal of composite noise in EMG signals

Electrocardiogram (ECG) is a very important tool for cardiac health status measurement and detection of various diseases at their early stage. The electrocardiogram (ECG) is one of the major physiological signals generated from heart's rhythmic polarization and depolarization. This work presents the application of the parametric and nonparametric time-frequency techniques of different ECG signals. The biomedical ECG signals used in this paper are a normal and an abnormal ECG. The results show a good localization of QRS complexes, T waves and determine frequency interval of QRS complex on the time-frequency plan. The results demonstrate the effectiveness the parametric time-frequency compared with the non parametric time-frequency techniques, and also show high accuracy of frequency change of the electrocardiogram ECG signals. The available ECG records in the MIT-BIH arrhythmia database are used to illustrate the effectiveness of the proposed methods.

Smart greenhouses (SGHs) are controlled-environment agricultural systems that leverage digital technologies to optimize crop production and resource management. In particular, recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have enabled the development of intelligent monitoring, predictive modeling, and automated decision-support systems within these environments. Against this backdrop, this comprehensive review synthesizes over 130 studies published between 2020 and 2025, with a focus on AI-driven monitoring, predictive modeling, and decision-support frameworks in SGH environments. More specifically, key application domains include microclimate regulation, crop growth assessment, disease and pest detection, yield estimation, and robotic harvesting. Moreover, particular attention is given to the interplay between AI methodologies and their data sources, encompassing IoT sensor networks, RGB, multispectral, and hyperspectral imaging, as well as multimodal data-fusion approaches. In addition, publicly available datasets, model architectures, and performance metrics are consolidated to support reproducibility and cross-study comparison. Nevertheless, persistent challenges are critically discussed, including data heterogeneity, limited model generalization across sites, interpretability constraints, and practical barriers to deployment. Finally, emerging research directions are identified, notably multimodal learning, edge-AI integration, standardized benchmarks, and scalable system architectures, with the overarching objective of guiding the development of robust, sustainable, and operationally feasible AI-enabled SGH systems.

Electrocardiogram signals (ECG) are among the most important sources of diagnostic information in healthcare. During ECG measurement, there may be various noises such as muscle contraction (electromyography), baselines wander, and power-line interferences, which interfered with the ECG information identification that causing a misinterpretation of the ECG signal. In this paper, the denoising techniques of the Empirical Mode Decomposition (EMD), the Ensemble Empirical Mode Decomposition (EEMD) and the Discrete Wavelet Transform (DWT) were used. These techniques are applied to a noisy electrocardiogram abnormal signal obtained by adding white noise and natural noise. A comparative performance study of these three techniques was used. The abnormal signal used is obtained from the patient with a malignant ventricular arrhythmia. From the obtained results, the EEMD technique is a powerful tool for elimination and restoration the original signal than the other techniques used in this paper.