Introduction: In this paper, a novel method in the estimation and prediction of PM(10) is introduced using wavelet transform-based artificial neural networks (WT-ANN).
Discussion: First, the application of wavelet transform, selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in input training set data, is presented. Afterward, the circular statistical indices which are used in this method are formally introduced in order to investigate the relation between PM(10) levels and circular meteorological variables. Then, the results of the simulation of PM(10) based on WT-ANN by use of MATLAB software are discussed. The results of the above-mentioned simulation show an enhanced accuracy and speed in PM(10) estimation/prediction and a high degree of robustness compared with traditional ANN models.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s11356-011-0554-9 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network.
Med Image Anal
January 2025
Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 440-746, South Korea. Electronic address:
This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (US) images using artificial neural networks. The HCC-Net integrates the discrete wavelet transform (DWT) to decompose US images into four sub-band images, a lesion detector for hierarchical lesion localization, and a pattern-augmented classifier for generating pattern-enhanced lesion images and subsequent classification. The lesion detection uses a hierarchical coarse-to-fine approach to minimize missed lesions.
View Article and Find Full Text PDFEpilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model.
Diagnostics (Basel)
January 2025
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction.
View Article and Find Full Text PDFTunable Q-factor Wavelet Transform-Based Features in the Classification of Phonation Types in the Singing and Speaking Voice.
J Voice
November 2024
Department of Information and Communications Engineering, Aalto University, Espoo 02150, Finland.
Phonation is the use of the laryngeal system, with the help of an air-stream provided by the respiratory system, to generate audible sounds. Humans are capable of generating voices of various phonation types (eg, breathy, neutral, and pressed), and these types are used both in singing and speaking. In this study, we propose to use features derived using the tunable Q-factor wavelet transform (TQWT) for classification of phonation types in the singing and speaking voice.
View Article and Find Full Text PDFWavelet transform-based mode decomposition for EEG signals under general anesthesia.
PeerJ
November 2024
University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Background: Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method.
Methods: To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals the serial interface of a bispectral index (BIS) monitor.
Veni, Vidi, Vici-When Is Home Video Seizure Monitoring Helpful?
Epilepsy Curr
May 2024
Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Seizure detection is vital for managing epilepsy as seizures can lead to injury and even death, in addition to impacting quality of life. Prompt detection of seizures and intervention can help prevent injury and improve outcomes for individuals with epilepsy. Wearable sensors show promising results for automated detection of certain seizures, but they have limitations such as patient tolerance, impracticality for newborns, and the need for recharging.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!