keyword
MENU ▼
Read by QxMD icon Read
search

Discrete wavelet transform

keyword
https://www.readbyqxmd.com/read/28813000/an-ameliorated-prediction-of-drug-target-interactions-based-on-multi-scale-discrete-wavelet-transform-and-network-features
#1
Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo
The prediction of drug-target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug-target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns...
August 16, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28807374/design-and-application-of-discrete-wavelet-packet-transform-based-multiresolution-controller-for-liquid-level-system
#2
Rimi Paul, Anindita Sengupta
A new controller based on discrete wavelet packet transform (DWPT) for liquid level system (LLS) has been presented here. This controller generates control signal using node coefficients of the error signal which interprets many implicit phenomena such as process dynamics, measurement noise and effect of external disturbances. Through simulation results on LLS problem, this controller is shown to perform faster than both the discrete wavelet transform based controller and conventional proportional integral controller...
August 11, 2017: ISA Transactions
https://www.readbyqxmd.com/read/28764415/sparse-dictionary-for-synthetic-transmit-aperture-medical-ultrasound-imaging
#3
Ping Wang, Jin-Yang Jiang, Na Li, Han-Wu Luo, Fang Li, Shi-Gang Cui
It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform...
July 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28755816/analysis-of-the-tennis-racket-vibrations-during-forehand-drives-selection-of-the-mother-wavelet
#4
Y Blache, C Hautier, F Lefebvre, A Djordjevic, T Creveaux, I Rogowski
The time-frequency analysis of the tennis racket and hand vibrations is of great interest for discomfort and pathology prevention. This study aimed to (i) to assess the stationarity of the vibratory signal of the racket and hand and (ii) to identify the best mother wavelet to perform future time-frequency analysis, (iii) to determine if the stroke spin, racket characteristics and impact zone can influence the selection of the best mother wavelet. A total of 2364 topspin and flat forehand drives were performed by fourteen male competitive tennis players with six different rackets...
July 20, 2017: Journal of Biomechanics
https://www.readbyqxmd.com/read/28747669/compressive-sampling-based-on-frequency-saliency-for-remote-sensing-imaging
#5
Jin Li, Zilong Liu, Fengdeng Liu
In saliency-based compressive sampling (CS) for remote sensing image signals, the saliency information of images is used to allocate more sensing resources to salient regions than to non-salient regions. However, the pulsed cosine transform method can generate large errors in the calculation of saliency information because it uses only the signs of the coefficients of the discrete cosine transform for low-resolution images. In addition, the reconstructed images can exhibit blocking effects because blocks are used as the processing units in CS...
July 26, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28741115/the-dtl-erg-electrode-comes-in-different-shapes-and-sizes-are-they-all-good
#6
Jungeun Woo, Suna Jung, Mathieu Gauvin, Pierre Lachapelle
PURPOSE: Although the DTL fiber electrode has been in use in the ERG field for more than four decades, its composition was never clearly defined. We compared five different types of conductive (DTL type) yarn (differing in terms of mass, number of filaments, and crimping degree) in order to determine whether we could identify one that would be better suited for the recording of ERGs. METHODS: Photopic flash ERGs were recorded from five subjects using the following DTL electrodes: 27/7, 22/1, 11/1, 11/1*2, and 22/1*2...
July 24, 2017: Documenta Ophthalmologica. Advances in Ophthalmology
https://www.readbyqxmd.com/read/28711988/emotion-recognition-based-on-eeg-features-in-movie-clips-with-channel-selection
#7
Mehmet Siraç Özerdem, Hasan Polat
Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems...
July 15, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28706561/automatic-detection-of-epilepsy-and-seizure-using-multiclass-sparse-extreme-learning-machine-classification
#8
Yuanfa Wang, Zunchao Li, Lichen Feng, Chuang Zheng, Wenhao Zhang
An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28692997/automated-classification-and-removal-of-eeg-artifacts-with-svm-and-wavelet-ica
#9
Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Paul Cumming, Masahiro Iwahashi
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28691485/single-drop-raman-imaging-exposes-the-trace-contaminants-in-milk
#10
Zong Tan, Ting-Ting Lou, Zhi-Xuan Huang, Jing Zong, Ke-Xin Xu, Qi-Feng Li, Da Chen
Better milk safety control can offer important means to promote public health. However, few technologies can detect different types of contaminants in milk simultaneously. In this regard, the present work proposes a single-drop Raman imaging (SDRI) strategy for semiquantitation of multiple hazardous factors in milk solutions. By developing SDRI strategy that incorporates the coffee-ring effect (a natural phenomenon often presents in a condensed circle pattern after a drop evaporated) for sample pretreatment and discrete wavelet transform for spectra processing, the method serves well to expose typical hazardous molecular species in milk products, such as melamine, sodium thiocyanate and lincomycin hydrochloride, with little sample preparation...
August 2, 2017: Journal of Agricultural and Food Chemistry
https://www.readbyqxmd.com/read/28682261/towards-on-demand-deep-brain-stimulation-using-online-parkinson-s-disease-prediction-driven-by-dynamic-detection
#11
Ameer Mohammed, Majid Zamani, Richard Bayford, Andreas Demosthenous
In Parkinson's disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection...
July 3, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28653015/classification-of-breast-masses-in-ultrasound-images-using-self-adaptive-differential-evolution-extreme-learning-machine-and-rough-set-feature-selection
#12
Kadayanallur Mahadevan Prabusankarlal, Palanisamy Thirumoorthy, Radhakrishnan Manavalan
A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28593391/wavelet-decomposition-analysis-in-the-two-flash-multifocal-erg-in-early-glaucoma-a-comparison-to-ganglion-cell-analysis-and-visual-field
#13
Livia M Brandao, Matthias Monhart, Andreas Schötzau, Anna A Ledolter, Anja M Palmowski-Wolfe
PURPOSE: To further improve analysis of the two-flash multifocal electroretinogram (2F-mfERG) in glaucoma in regard to structure-function analysis, using discrete wavelet transform (DWT) analysis. METHODS: Sixty subjects [35 controls and 25 primary open-angle glaucoma (POAG)] underwent 2F-mfERG. Responses were analyzed with the DWT. The DWT level that could best separate POAG from controls was compared to the root-mean-square (RMS) calculations previously used in the analysis of the 2F-mfERG...
August 2017: Documenta Ophthalmologica. Advances in Ophthalmology
https://www.readbyqxmd.com/read/28574347/anisotropic-discrete-dual-tree-wavelet-transform-for-improved-classification-of-trabecular-bone
#14
Hind Oulhaj, Mohammed Rziza, Aouatif Amine, Hechmi Toumi, Eric Lespessailles, Mohammed El Hassouni, Rachid Jennane
This paper deals with a new Anisotropic Discrete Dual-Tree Wavelet Transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional Discrete Dual-Tree Wavelet Transform (DDTWT) by using the anisotropic basis functions associated with the Hyperbolic Wavelet Transform (HWT) instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform. The Generalized Gaussian Distribution (GGD) is used to model the distribution of the sub-band coefficients...
May 26, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28558318/a-new-near-lossless-eeg-compression-method-using-ann-based-reconstruction-technique
#15
Behzad Hejrati, Abdolhossein Fathi, Fardin Abdali-Mohammadi
Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compression methods utilize fixed transforms such as discrete cosine transform (DCT) and wavelet and usually cannot efficiently extract signal redundancy especially for non-stationary signals such as electroencephalogram (EEG). In this paper, we first propose learning-based adaptive transform using combination of DCT and artificial neural network (ANN) reconstruction technique...
August 1, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28546862/block-sparsity-based-joint-compressed-sensing-recovery-of-multi-channel-ecg-signals
#16
Anurag Singh, Samarendra Dandapat
In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance...
April 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529760/denoising-techniques-in-adaptive-multi-resolution-domains-with-applications-to-biomedical-images
#17
Salim Lahmiri
Variational mode decomposition (VMD) is a new adaptive multi-resolution technique suitable for signal denoising purpose. The main focus of this work has been to study the feasibility of several image denoising techniques in empirical mode decomposition (EMD) and VMD domains. A comparative study is made using 11 techniques widely used in the literature, including Wiener filter, first-order local statistics, fourth partial differential equation, nonlinear complex diffusion process, linear complex diffusion process (LCDP), probabilistic non-local means, non-local Euclidean medians, non-local means, non-local patch regression, discrete wavelet transform and wavelet packet transform...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529759/high-frequency-based-features-for-low-and-high-retina-haemorrhage-classification
#18
Salim Lahmiri
Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD)...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28499122/emd-dwt-based-transform-domain-feature-reduction-approach-for-quantitative-multi-class-classification-of-breast-lesions
#19
Sharmin R Ara, Syed Khairul Bashar, Farzana Alam, Md Kamrul Hasan
Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i...
September 2017: Ultrasonics
https://www.readbyqxmd.com/read/28489019/heart-sound-classification-from-unsegmented-phonocardiograms
#20
Philip Langley, Alan Murray
Objective Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. Approach PCG segments of 5 second duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 second segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation...
May 10, 2017: Physiological Measurement
keyword
keyword
25002
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"