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ABRAMOVICH, FELIX, ANESTIS ANTONIADIS, THEOFANIS SAPATINAS, and BRANI VIDAKOVIC. "OPTIMAL TESTING IN A FIXED-EFFECTS FUNCTIONAL ANALYSIS OF VARIANCE MODEL." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 04 (December 2004): 323–49. http://dx.doi.org/10.1142/s0219691304000639.
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We consider the testing problem in a fixed-effects functional analysis of variance model. We test the null hypotheses that the functional main effects and the functional interactions are zeros against the composite nonparametric alternative hypotheses that they are separated away from zero in L2-norm and also possess some smoothness properties. We adapt the optimal (minimax) hypothesis testing procedures for testing a zero signal in a Gaussian "signal plus noise" model to derive optimal (minimax) non-adaptive and adaptive hypothesis testing procedures for the functional main effects and the functional interactions. The corresponding tests are based on the empirical wavelet coefficients of the data. Wavelet decompositions allow one to characterize different types of smoothness conditions assumed on the response function by means of its wavelet coefficients for a wide range of function classes. In order to shed some light on the theoretical results obtained, we carry out a simulation study to examine the finite sample performance of the proposed functional hypothesis testing procedures. As an illustration, we also apply these tests to a real-life data example arising from physiology. Concluding remarks and hints for possible extensions of the proposed methodology are also given.
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Lee, Gihyoun, Seung Hyun Lee, Sang Hyeon Jin, and Jinung An. "Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model." Journal of Near Infrared Spectroscopy 26, no. 2 (February 13, 2018): 79–86. http://dx.doi.org/10.1177/0967033518757231.
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Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subject's movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms.
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KABEER, V., and N. K. NARAYANAN. "WAVELET-BASED ARTIFICIAL LIGHT RECEPTOR MODEL FOR HUMAN FACE RECOGNITION." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 05 (September 2009): 617–27. http://dx.doi.org/10.1142/s0219691309003124.
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This paper presents a novel biologically-inspired and wavelet-based model for extracting features of faces from face images. The biological knowledge about the distribution of light receptors, cones and rods, over the surface of the retina, and the way they are associated with the nerve ends for pattern vision forms the basis for the design of this model. A combination of classical wavelet decomposition and wavelet packet decomposition is used for simulating the functional model of cones and rods in pattern vision. The paper also describes the experiments performed for face recognition using the features extracted on the AT & T face database (formerly, ORL face database) containing 400 face images of 40 different individuals. In the recognition stage, we used the Artificial Neural Network Classifier. A feature vector of size 40 is formed for face images of each person and recognition accuracy is computed using the ANN classifier. Overall recognition accuracy obtained for the AT & T face database is 95.5%.
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Xie, Shengkun, and Sridhar Krishnan. "Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis." Medical & Biological Engineering & Computing 51, no. 1-2 (October 9, 2012): 49–60. http://dx.doi.org/10.1007/s11517-012-0967-8.
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CUI, YIBO, CHI ZHANG, LINYUAN WANG, BIN YAN, and LI TONG. "DENSE-GWP: AN IMPROVED PRIMARY VISUAL ENCODING MODEL BASED ON DENSE GABOR FEATURES." Journal of Mechanics in Medicine and Biology 21, no. 05 (April 7, 2021): 2140017. http://dx.doi.org/10.1142/s0219519421400170.
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Brain visual encoding models based on functional magnetic resonance imaging are growing increasingly popular. The Gabor wavelet pyramid model (GWP) is a classic example, exhibiting a good prediction performance for the primary visual cortex (V1, V2, and V3). However, the local variations in the visual stimulation are quite convoluted in terms of spatial frequency, orientation, and position, posing a challenge for visual encoding models. Whether the GWP model can thoroughly extract informative and effective features from visual stimulus remains unclear. To this end, this paper proposes a dense GWP visual encoding model by ameliorating the composition of the Gabor wavelet basis from three aspects: spatial frequency, orientation, and position. The improved model named Dense-GWP model could extract denser features from the image stimulus. A regularization optimization algorithm was used to select informative and effective features, which were crucial for predicting voxel activity in the region of interest. Extensive experimental results showed that the Dense-GWP model exhibits an improved prediction performance and can therefore help further understand the human visual perception mechanism.
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Jian, Zini, Xianpei Wang, Xueting Liu, Meng Tian, Quande Wang, and Jiangxi Xiao. "Research on BOLD-fMRI Data Denoising Based on Bayesian Estimation and Adaptive Wavelet Threshold." Oxidative Medicine and Cellular Longevity 2021 (February 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/8819384.
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The acquisition of functional magnetic resonance imaging (fMRI) images of blood oxygen level-dependent (BOLD) effect and the signals to be analyzed is based on weak changes in the magnetic field caused by small changes in blood oxygen physiological levels, which are weak signals and complex in noise. In order to model and analyze the pathological and hemodynamic parameters of BOLD-fMRI images effectively, it is urgent to use effective signal analysis techniques to reduce the interference of noise and artifacts. In this paper, the noise characteristics of functional magnetic resonance imaging and the traditional signal denoising methods are analyzed. The Bayesian decision criterion takes into account the probability of the total occurrence of all kinds of references and the loss caused by misjudgment and has strong discriminability. So, an improved adaptive wavelet threshold denoising method based on Bayesian estimation is proposed. By using the correlation characteristics of multiscale wavelet coefficients, the corresponding wavelet components of useful signals and noises are processed differently; while retaining useful frequency information, the noise is weakened to the greatest extent. The new adaptive threshold wavelet denoising method based on Bayesian estimation is applied to the actual experiment, and the results of OEF (oxygen extraction fraction) are optimized. A series of simulation experiments are carried out to verify the effectiveness of the proposed method.
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Inoussa, Garba, Hui Peng, and Jun Wu. "Nonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model." Neurocomputing 86 (June 2012): 59–74. http://dx.doi.org/10.1016/j.neucom.2012.01.010.
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Al-Hiyali, Mohammed Isam, Norashikin Yahya, Ibrahima Faye, and Ahmed Faeq Hussein. "Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network." Sensors 21, no. 16 (August 4, 2021): 5256. http://dx.doi.org/10.3390/s21165256.
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The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
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BARDET, JEAN-MARC, and PIERRE BERTRAND. "DEFINITION, PROPERTIES AND WAVELET ANALYSIS OF MULTISCALE FRACTIONAL BROWNIAN MOTION." Fractals 15, no. 01 (March 2007): 73–87. http://dx.doi.org/10.1142/s0218348x07003356.
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In some applications, for instance, finance, biomechanics, turbulence or internet traffic, it is relevant to model data with a generalization of a fractional Brownian motion for which the Hurst parameter H is dependent on the frequency. In this contribution, we describe the multiscale fractional Brownian motions which present a parameter H as a piecewise constant function of the frequency. We provide the main properties of these processes: long-memory and smoothness of the paths. Then we propose a statistical method based on wavelet analysis to estimate the different parameters and prove a functional Central Limit Theorem satisfied by the empirical variance of the wavelet coefficients.
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Fernandez Rojas, Raul, Mingyu Liao, Julio Romero, Xu Huang, and Keng-Liang Ou. "Cortical Network Response to Acupuncture and the Effect of the Hegu Point: An fNIRS Study." Sensors 19, no. 2 (January 18, 2019): 394. http://dx.doi.org/10.3390/s19020394.
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Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS.
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Kumar, P., and K. N. Rai. "Numerical solution of generalized DPL model using wavelet method during thermal therapy applications." International Journal of Biomathematics 12, no. 03 (April 2019): 1950032. http://dx.doi.org/10.1142/s1793524519500323.
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In this paper, generalized dual-phase-lag (DPL) model has been studied for the numerical analysis of spatial variation of temperature within living biological tissues during thermal therapy applications. A new hybrid numerical scheme based on finite difference scheme and Chebyshev wavelet Galerkin method are used to solve the generalized DPL model with constant heat flux boundary condition. Multi-resolution and multi-scale computational property of Chebyshev wavelet in the present case localizes small scale variations of solution and fast switching of functional bases. Our study demonstrates that due to presence of coupling factor (convection–perfusion), generalized DPL model predicts lower temperature than classical DPL and Pennes model at the tumor position. Higher values of phase lag times results in lower temperature at the tumor position. But, in case of variation of phase lag time due to temperature gradient, the nature of temperature profile also depends on the spatial coordinate. The effect of the blood temperature, porosity and interfacial convective heat transfer on temperature distribution has been investigated. It is found that larger values of porosity and interfacial convective heat transfer results in lower temperature at the tumor position. Also, both porosity and interfacial convective heat transfer are pronounced more at higher values. The whole analysis is presented in dimensionless form.
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Jiang, Dong Huan, and Guang Bao Xu. "Image Zooming Based on Cartoon and Texture Decomposition." Advanced Materials Research 457-458 (January 2012): 1002–7. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1002.
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A new algorithm for image zooming based on cartoon and texture decomposition is presented in this paper. The basic idea is to first decompose the image into cartoon and texture, and then zoom each part separately with different image zooming algorithms. Finally, the zoomed images will be synthesized into one image. The zoomed parts of the image are found by minimizing the different variational functional in the wavelet domain which use the Besov norm to measure the regularity of the parts. Unlike the traditional image zooming by interpolation, the variation model and image cartoon-texture decomposition is incorporated in the zooming algorithm. Experimental results have verified the validity of the new algorithm.
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Djilas, Milan, Christine Azevedo-Coste, David Guiraud, and Ken Yoshida. "Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded with Thin-Film Intrafascicular Electrodes." Computational Intelligence and Neuroscience 2010 (2010): 1–13. http://dx.doi.org/10.1155/2010/836346.
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Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback.
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Ge, Qi, Zhuo-Chen Lin, Yong-Xiang Gao, and Jin-Xin Zhang. "A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer’s Disease." Healthcare 8, no. 4 (November 11, 2020): 476. http://dx.doi.org/10.3390/healthcare8040476.
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(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain’s electrical activity, can be a promising diagnostic tool for Alzheimer’s disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding’s D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.
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Zhao, Bin, Song Zhang, and Jianfeng Li. "Influence of surface functional parameters on friction behavior and elastic–plastic deformation of grinding surface in mixed lubrication state." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 233, no. 6 (October 26, 2018): 870–83. http://dx.doi.org/10.1177/1350650118806375.
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Mixed lubrication is a common lubrication regime in sliding contact and has received much attention in recent research. The influences of surface topography on friction performance in this lubrication state are significant owing to the coexistence of fluid–solid contact and solid–solid contact conditions. First, an accuracy surface model is built based on wavelet transform results. Then, the average Reynolds equation is revised for a grinding surface to be used in simulation. Third, four surface roughness parameters ( Sa, Sbi, S ci, and Svi) are selected to characterize surface topography. Additionally, the impacts on the solid–solid contact area, friction coefficient, and surface flattening are investigated. Finally, optimizations of surface roughness parameters directed toward energy saving and sliding stability are conducted and verified. Simulation and experiment methods are jointly applied to guarantee the accuracy of this research. The result of this study can provide theoretical support for machining contact surfaces.
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Martinelli, Daniele, Gloria Castellazzi, Roberto De Icco, Ana Bacila, Marta Allena, Arianna Faggioli, Grazia Sances, et al. "Thalamocortical Connectivity in Experimentally-Induced Migraine Attacks: A Pilot Study." Brain Sciences 11, no. 2 (January 27, 2021): 165. http://dx.doi.org/10.3390/brainsci11020165.
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In this study we used nitroglycerin (NTG)-induced migraine attacks as a translational human disease model. Static and dynamic functional connectivity (FC) analyses were applied to study the associated functional brain changes. A spontaneous migraine-like attack was induced in five episodic migraine (EM) patients using a NTG challenge. Four task-free functional magnetic resonance imaging (fMRI) scans were acquired over the study: baseline, prodromal, full-blown, and recovery. Seed-based correlation analysis (SCA) was applied to fMRI data to assess static FC changes between the thalamus and the rest of the brain. Wavelet coherence analysis (WCA) was applied to test time-varying phase-coherence changes between the thalamus and salience networks (SNs). SCA results showed significantly FC changes between the right thalamus and areas involved in the pain circuits (insula, pons, cerebellum) during the prodromal phase, reaching its maximal alteration during the full-blown phase. WCA showed instead a loss of synchronisation between thalami and SN, mainly occurring during the prodrome and full-blown phases. These findings further support the idea that a temporal change in thalamic function occurs over the experimentally induced phases of NTG-induced headache in migraine patients. Correlation of FC changes with true clinical phases in spontaneous migraine would validate the utility of this model.
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Sharma, Manish, Virendra Patel, Jainendra Tiwari, and U. Rajendra Acharya. "Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals." Diagnostics 11, no. 8 (July 30, 2021): 1380. http://dx.doi.org/10.3390/diagnostics11081380.
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Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.
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Pradhan, Ashwini, Debahuti Mishra, Kaberi Das, Ganapati Panda, Sachin Kumar, and Mikhail Zymbler. "On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model." Mathematics 9, no. 17 (August 30, 2021): 2095. http://dx.doi.org/10.3390/math9172095.
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Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used.
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Morris, Jeffrey S., and Raymond J. Carroll. "Wavelet-based functional mixed models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68, no. 2 (April 2006): 179–99. http://dx.doi.org/10.1111/j.1467-9868.2006.00539.x.
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Bennett, S. H. "Modeling methodology for vascular input impedance determination and interpretation." Journal of Applied Physiology 76, no. 1 (January 1, 1994): 455–84. http://dx.doi.org/10.1152/jappl.1994.76.1.455.
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The significance of pulse wave reflections in the pulmonary vascular system is elaborated using a new method to determine the broadband frequency response of input impedance up to frequencies of 100 Hz. A simple data model, based on the signal construct of a wavelet, is used to generalize and reconcile the common approaches to vascular frequency response estimation so that an accurate response can be calculated from physiological waveforms. Input impedance interpretation is accomplished using a structural and functional modeling methodology. To identify internal structural system properties, the methodology of inverse scattering is used to relate observed pulse wave echoes in the frequency response to a longitudinal distribution of reflection sites of anatomic significance. To identify functional interactions with pulmonary vascular wave mechanics, a time series analysis methodology is proposed to describe vascular interactions using a generalized principle of superposition. The methods of determination and interpretation are applied to a sample pressure-flow data set from the pulmonary circulation of a lamb experiencing vascular-ventilatory interaction. The example suggests that the frequency response is consistent with a discrete longitudinal distribution of reflection sites that may be affected by the ventilator.
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CHUA, GEK-HUEY, ARUN KRISHNAN, KUO-BIN LI, and MASARU TOMITA. "MULTIRESOLUTION ANALYSIS UNCOVERS HIDDEN CONSERVATION OF PROPERTIES IN STRUCTURALLY AND FUNCTIONALLY SIMILAR PROTEINS." Journal of Bioinformatics and Computational Biology 04, no. 06 (December 2006): 1245–67. http://dx.doi.org/10.1142/s0219720006002442.
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Physicochemcial properties of amino acids are important factors in determining protein structure and function. Most approaches make use of averaged properties over entire domains or even proteins to analyze their structure or function. This level of coarseness tends to hide the richness of the variability in the different properties across functional domains. This paper studies the conservation of physicochemical properties in a functionally similar family of proteins using a novel wavelet-based technique known as multiresolution analysis. Such an analysis can help uncover characteristics that can otherwise remain hidden. We have studied the protein kinase family of sequences and our findings are as follows: (a) a number of different properties are conserved over the functional catalytic domain irrespective of the sequence identities; (b) conservation of properties can be observed at different frequency levels and they agree well with the known structural/functional properties of the subdomains for the protein kinase family; (c) structural differences between the different kinase family members are reflected in the waveforms; and (d) functionally important mutations show distortions in the waveforms of conserved properties. The potential usefulness of the above findings in identifying functionally similar sequences in the twilight and midnight zones is demonstrated through a simple prediction model for the protein kinase family which achieved a recall of 93.7% and a precision of 96.75% in cross-validation tests.
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Borkowski, Andrzej, and Wiesław Kosek. "Theoretical geodesy." Geodesy and Cartography 64, no. 2 (December 1, 2015): 261–79. http://dx.doi.org/10.1515/geocart-2015-0015.
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Abstract The paper presents a summary of research activities concerning theoretical geodesy performed in Poland in the period of 2011-2014. It contains the results of research on new methods of the parameter estimation, a study on robustness properties of the M-estimation, control network and deformation analysis, and geodetic time series analysis. The main achievements in the geodetic parameter estimation involve a new model of the M-estimation with probabilistic models of geodetic observations, a new Shift-Msplit estimation, which allows to estimate a vector of parameter differences and the Shift-Msplit(+) that is a generalisation of Shift-Msplit estimation if the design matrix A of a functional model has not a full column rank. The new algorithms of the coordinates conversion between the Cartesian and geodetic coordinates, both on the rotational and triaxial ellipsoid can be mentioned as a highlights of the research of the last four years. New parameter estimation models developed have been adopted and successfully applied to the control network and deformation analysis. New algorithms based on the wavelet, Fourier and Hilbert transforms were applied to find time-frequency characteristics of geodetic and geophysical time series as well as time-frequency relations between them. Statistical properties of these time series are also presented using different statistical tests as well as 2nd, 3rd and 4th moments about the mean. The new forecasts methods are presented which enable prediction of the considered time series in different frequency bands.
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Hu, Xiao-Juan, Lei Zhang, Jia-Tuo Xu, Bao-Cheng Liu, Jian-Ying Wang, Yan-Long Hong, Li-Ping Tu, and Ji Cui. "Pulse Wave Cycle Features Analysis of Different Blood Pressure Grades in the Elderly." Evidence-Based Complementary and Alternative Medicine 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/1976041.
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Background and Objective. The same range of blood pressure values may reflect different vascular functions, especially in the elderly. Therefore, a single blood pressure value may not comprehensively reveal cardiovascular function. This study focused on identifying pulse wave features in the elderly that can be used to show functional differences when blood pressure values are in the same range. Methods. First, pulse data were preprocessed and pulse cycles were segmented. Second, time domain, higher-order statistics, and energy features of wavelet packet decomposition coefficients were extracted. Finally, useful pulse wave features were evaluated using a feature selection and classifier design. Results. A total of 6,075 pulse wave cycles were grouped into 3 types according to different blood pressure levels and each group was divided into 2 categories according to a history of hypertension. The classification accuracy of feature selection in the 3 groups was 97.91%, 95.24%, and 92.28%, respectively. Conclusion. Selected features could be appropriately used to analyze cardiovascular function in the elderly and can serve as the basis for research on a cardiovascular risk assessment model based on Traditional Chinese Medicine pulse diagnosis.
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Wu, Hongchang. "Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2021 (June 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/1823930.
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The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super-resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.
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Бурау, Надія Іванівна, and Ольга Ярославівна Паздрій. "ІНТЕРПРЕТАЦІЯ ВІБРАЦІЙНИХ СИГНАЛІВ СКЛАДНОЇ РОТОРНОЇ СИСТЕМИ НА ОСНОВІ ФРАКТАЛЬНОГО АНАЛІЗУ." Aerospace technic and technology, no. 7 (August 31, 2019): 114–21. http://dx.doi.org/10.32620/aktt.2019.7.16.
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The work analyzes vibration signals obtained by simulating a turbine of a complex rotor system, for example, an aviation gas turbine engine, under conditions of stationary and non-stationary excitations. Four modes of vibration excitation are considered: stationary poly-harmonic excitation with the frequency of rotor rotation and super-harmonic components; stationary poly-harmonic excitation with the frequency of rotor rotation and sub-harmonic components; non-stationary vibration excitation with a linear increase in the rotor speed with super-harmonic and sub-harmonic components of the instantaneous rotor speed. In the course of the turbine model, vibration signals are generated, which are further analyzed without taking into account and taking into account additive noise. For signal processing, fractal and time-scale (wavelet) analysis were used. The determination of the fractal structure of the simulated vibration signals is made based on R / S analysis, or the method of normalized scope, as a result of which the Hurst exponent is determined. The Hurst exponent is a number that is interpreted as the ratio of the “strength” of a trend to the signal noise level and is used in the study to interpret the received vibration signals. The results showed that the vibration signals obtained in all considered modes of vibration excitation without taking into account the additive noise, in terms of the Hurst exponent, are classified as anti-persistent trend-non-stable signals. Taking into account additive noise, the Hurst exponent increases, the vibration properties in stationary excitation modes approach persistence and the appearance of a trend, and in non-stationary vibration excitation signals approach to processes such as white noise. For the vibration signal obtained at stationary poly-harmonic excitation with super-harmonic components, a preliminary wavelet - decomposition was carried out into a set of approximations and details, followed by determination of the Hurst exponent for each element of decomposition. The results obtained showed an ambiguous change in the Hurst exponent for various decomposition elements. The obtained results can be used to improve the methodological and algorithmic support systems for functional diagnostics of complex rotor systems with the appearance and propagation of damage to their rotating elements.
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Giacofci, M., S. Lambert-Lacroix, G. Marot, and F. Picard. "Wavelet-Based Clustering for Mixed-Effects Functional Models in High Dimension." Biometrics 69, no. 1 (February 4, 2013): 31–40. http://dx.doi.org/10.1111/j.1541-0420.2012.01828.x.
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Clement, Lieven, Kristof De Beuf, Olivier Thas, Marnik Vuylsteke, Rafael A. Irizarry, and Ciprian M. Crainiceanu. "Fast Wavelet Based Functional Models for Transcriptome Analysis with Tiling Arrays." Statistical Applications in Genetics and Molecular Biology 11, no. 1 (January 6, 2012): 1–36. http://dx.doi.org/10.2202/1544-6115.1726.
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Lee, Wonyul, and Jeffrey S. Morris. "Identification of differentially methylated loci using wavelet-based functional mixed models." Bioinformatics 32, no. 5 (November 11, 2015): 664–72. http://dx.doi.org/10.1093/bioinformatics/btv659.
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Malloy, E. J., J. S. Morris, S. D. Adar, H. Suh, D. R. Gold, and B. A. Coull. "Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models." Biostatistics 11, no. 3 (February 15, 2010): 432–52. http://dx.doi.org/10.1093/biostatistics/kxq003.
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Lancia, Leonardo, Philip Rausch, and Jeffrey S. Morris. "Automatic quantitative analysis of ultrasound tongue contours via wavelet-based functional mixed models." Journal of the Acoustical Society of America 137, no. 2 (February 2015): EL178—EL183. http://dx.doi.org/10.1121/1.4905881.
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Morris, Jeffrey S., Cassandra Arroyo, Brent A. Coull, Louise M. Ryan, Richard Herrick, and Steven L. Gortmaker. "Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles." Journal of the American Statistical Association 101, no. 476 (December 1, 2006): 1352–64. http://dx.doi.org/10.1198/016214506000000465.
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Morris, Jeffrey S., Philip J. Brown, Richard C. Herrick, Keith A. Baggerly, and Kevin R. Coombes. "Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models." Biometrics 64, no. 2 (June 2008): 479–89. http://dx.doi.org/10.1111/j.1541-0420.2007.00895.x.
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SRIKANTH, R., and A. G. RAMAKRISHNAN. "WAVELET-BASED ESTIMATION OF HEMODYNAMIC RESPONSE FUNCTION FROM fMRI DATA." International Journal of Neural Systems 16, no. 02 (April 2006): 125–38. http://dx.doi.org/10.1142/s012906570600055x.
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We present a new algorithm to estimate hemodynamic response function (HRF) and drift components of fMRI data in wavelet domain. The HRF is modeled by both parametric and nonparametric models. The functional Magnetic resonance Image (fMRI) noise is modeled as a fractional brownian motion (fBm). The HRF parameters are estimated in wavelet domain by exploiting the property that wavelet transforms with a sufficient number of vanishing moments decorrelates a fBm process. Using this property, the noise covariance matrix in wavelet domain can be assumed to be diagonal whose entries are estimated using the sample variance estimator at each scale. We study the influence of the sampling rate of fMRI time series and shape assumption of HRF on the estimation performance. Results are presented by adding synthetic HRFs on simulated and null fMRI data. We also compare these methods with an existing method,1 where correlated fMRI noise is modeled by a second order polynomial functions.
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HAMRITA, MOHAMED ESSAIED, NIDHAL BEN ABDALLAH, and ANOUAR BEN MABROUK. "A WAVELET METHOD COUPLED WITH QUASI-SELF-SIMILAR STOCHASTIC PROCESSES FOR TIME SERIES APPROXIMATION." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 05 (September 2011): 685–711. http://dx.doi.org/10.1142/s0219691311004353.
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Scaling laws and generally self-similar structures are now well known facts in financial time series. Furthermore, these signals are characterized by the presence of stochastic behavior allowing their analysis with pure functional methods being incomplete. In the present paper, some existing models are reviewed and modified, based on wavelet theory and self-similarity, to recover multi-scaling cases for approximating financial signals. The resulting models are then tested on some empirical examples and analyzed for error estimates.
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Liu, Huan, Shu Zhang, Xi Jiang, Tuo Zhang, Heng Huang, Fangfei Ge, Lin Zhao, et al. "The Cerebral Cortex is Bisectionally Segregated into Two Fundamentally Different Functional Units of Gyri and Sulci." Cerebral Cortex 29, no. 10 (December 12, 2018): 4238–52. http://dx.doi.org/10.1093/cercor/bhy305.
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Abstract The human cerebral cortex is highly folded into diverse gyri and sulci. Accumulating evidences suggest that gyri and sulci exhibit anatomical, morphological, and connectional differences. Inspired by these evidences, we performed a series of experiments to explore the frequency-specific differences between gyral and sulcal neural activities from resting-state and task-based functional magnetic resonance imaging (fMRI) data. Specifically, we designed a convolutional neural network (CNN) based classifier, which can differentiate gyral and sulcal fMRI signals with reasonable accuracies. Further investigations of learned CNN models imply that sulcal fMRI signals are more diverse and more high frequency than gyral signals, suggesting that gyri and sulci truly play different functional roles. These differences are significantly associated with axonal fiber wiring and cortical thickness patterns, suggesting that these differences might be deeply rooted in their structural and cellular underpinnings. Further wavelet entropy analyses demonstrated the validity of CNN-based findings. In general, our collective observations support a new concept that the cerebral cortex is bisectionally segregated into 2 functionally different units of gyri and sulci.
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Tognarelli, Andrea, Eusebio Stucchi, Alessia Ravasio, and Alfredo Mazzotti. "High-resolution coherency functionals for velocity analysis: An application for subbasalt seismic exploration." GEOPHYSICS 78, no. 5 (September 1, 2013): U53—U63. http://dx.doi.org/10.1190/geo2012-0544.1.
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We tested the properties of three different coherency functionals for the velocity analysis of seismic data relative to subbasalt exploration. We evaluated the performance of the standard semblance algorithm and two high-resolution coherency functionals based on the use of analytic signals and of the covariance estimation along hyperbolic traveltime trajectories. Approximate knowledge of the wavelet was exploited to design appropriate filters that matched the primary reflections, thereby further improving the ability of the functionals to highlight the events of interest. The tests were carried out on two synthetic seismograms computed on models reproducing the geologic setting of basaltic intrusions and on common midpoint gathers from a 3D survey. Synthetic and field data had a very low signal-to-noise ratio, strong multiple contamination, and weak primary subbasalt signals. The results revealed that high-resolution coherency functionals were more suitable than semblance algorithms to detect primary signals and to distinguish them from multiples and other interfering events. This early discrimination between primaries and multiples could help to target specific signal enhancement and demultiple operations.
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FAUST, OLIVER, PENG CHUAN ALVIN ANG, SUBHA D. PUTHANKATTIL, and PAUL K. JOSEPH. "DEPRESSION DIAGNOSIS SUPPORT SYSTEM BASED ON EEG SIGNAL ENTROPIES." Journal of Mechanics in Medicine and Biology 14, no. 03 (March 13, 2014): 1450035. http://dx.doi.org/10.1142/s0219519414500353.
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Electroencephalography (EEG) is a measure which represents the functional activity of the brain. We show that a detailed analysis of EEG measurements provides highly discriminant features which indicate the mental state of patients with clinical depression. Our feature extraction method revolves around a novel processing structure that combines wavelet packet decomposition (WPD) and non-linear algorithms. WPD was used to select appropriate EEG frequency bands. The resulting signals were processed with the non-linear measures of approximate entropy (ApEn), sample entropy (SampEn), renyi entropy (REN) and bispectral phase entropy ( P h). The features were selected using t-test and only discriminative features were fed to various classifiers, namely probabilistic neural network (PNN), support vector machine (SVM), decision tree (DT), k-nearest neighbor algorithm (k-NN), naive bayes classification (NBC), Gaussian mixture model (GMM) and Fuzzy Sugeno Classifier (FSC). Our classification results show that, with a classification accuracy of 99.5%, the PNN classifier performed better than the rest of classifiers in discriminating between normal and depression EEG signals. Hence, the proposed decision support system can be used to diagnose, and monitor the treatment of patients suffering from depression.
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Kim, Paul Y., Khan M. Iftekharuddin, Pinakin G. Davey, Gabor Holló, Martha Tóth, Anita Garas, and Edward A. Essock. "Selective fusion of structural and functional data for improved glaucoma detection." Modeling and Artificial Intelligence in Ophthalmology 1, no. 3 (June 19, 2017): 82–99. http://dx.doi.org/10.35119/maio.v1i3.40.
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This work proposes novel selective feature fusion of structural and functional data for improved glaucoma detection. The structural data such as retinal nerve fiber layer (RNFL) thickness measurement acquired by scanning laser polarimetry (SLP) is fused with the functional visual field (VF) measurement recorded from the standard automated perimetry (SAP) test. The proposed selective feature fusion exploits correspondence between structural and functional data obtained over multiple sectors. The correlation coefficients for corresponding structural-function sector pairs are used as weights in subsequent feature selection. The sectors are ranked according to the correlation coefficients and the first four highly ranked sectors are retained. Following our prior work, fractal analysis (FA) features for both structural and functional data are obtained and fused for each selected sectors respectively. These fused FA features are then used for glaucoma detection. The novelty of this work stems from (i) locating structure-functional sectoral correspondence; (ii) selecting only a few interesting sector pairs using correlation coefficient between structure-function data; (iii) obtaining novel FA features from these pairs; and (iv) fusing these features for glaucoma detection. Such a method is distinctively different from other existing methods that exploit structure-function models in that structure-function sectoral correspondences have been weighted and, based on such weights, only portions of the sectors are retained for subsequent fusion and classification of structural and functional features. For statistical analysis of the glaucoma detection results, sensitivity, specificity and area under receiver operating characteristic curve (AUROC) are calculated. Performance comparison is obtained with those of existing feature-based techniques such as wavelet-Fourier analysis (WFA) and fast-Fourier analysis (FFA). Comparisons of AUROC values show that our novel selective feature fusion method for discrimination of glaucomatous and ocular normal patients slightly outperforms other existing techniques with AUROCs of 0.98, 0.98 and 0.99 for WFA, FFA and FA respectively.
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Kazashi, Yoshihito. "Quasi–Monte Carlo integration with product weights for elliptic PDEs with log-normal coefficients." IMA Journal of Numerical Analysis 39, no. 3 (May 23, 2018): 1563–93. http://dx.doi.org/10.1093/imanum/dry028.
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Abstract Quasi–Monte Carlo (QMC) integration of output functionals of solutions of the diffusion problem with a log-normal random coefficient is considered. The random coefficient is assumed to be given by an exponential of a Gaussian random field that is represented by a series expansion of some system of functions. Graham et al. (2015, Quasi-Monte Carlo finite element methods for elliptic PDEs with lognormal random coefficients. Numer. Math., 131, 329–368) developed a lattice-based QMC theory for this problem and established a quadrature error decay rate ≈ 1 with respect to the number of quadrature points. The key assumption there was a suitable summability condition on the aforementioned system of functions. As a consequence, product-order-dependent weights were used to construct the lattice rule. In this paper, a different assumption on the system is considered. This assumption, originally considered by Bachmayr et al. (2017c, Sparse polynomial approximation of parametric elliptic PDEs. Part I: affine coefficients. ESAIM Math. Model. Numer. Anal., 51, 321–339) to utilise the locality of support of basis functions in the context of polynomial approximations applied to the same type of the diffusion problem, is shown to work well in the same lattice-based QMC method considered by Graham et al.: the assumption leads us to product weights, which enables the construction of the QMC method with a smaller computational cost than Graham et al. A quadrature error decay rate ≈ 1 is established, and the theory developed here is applied to a wavelet stochastic model. By a characterisation of the Besov smoothness, it is shown that a wide class of path smoothness can be treated with this framework.
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Quintanilla, Jorge G., Shlomo Shpun, José Jalife, and David Filgueiras-Rama. "Novel approaches to mechanism-based atrial fibrillation ablation." Cardiovascular Research 117, no. 7 (March 21, 2021): 1662–81. http://dx.doi.org/10.1093/cvr/cvab108.
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Abstract Modern cardiac electrophysiology has reported significant advances in the understanding of mechanisms underlying complex wave propagation patterns during atrial fibrillation (AF), although disagreements remain. One school of thought adheres to the long-held postulate that AF is the result of randomly propagating wavelets that wonder throughout the atria. Another school supports the notion that AF is deterministic in that it depends on a small number of high-frequency rotors generating three-dimensional scroll waves that propagate throughout the atria. The spiralling waves are thought to interact with anatomic and functional obstacles, leading to fragmentation and new wavelet formation associated with the irregular activation patterns documented on AF tracings. The deterministic hypothesis is consistent with demonstrable hierarchical gradients of activation frequency and AF termination on ablation at specific (non-random) atrial regions. During the last decade, data from realistic animal models and pilot clinical series have triggered a new era of novel methodologies to identify and ablate AF drivers outside the pulmonary veins. New generation electroanatomical mapping systems and multielectrode mapping catheters, complimented by powerful mathematical analyses, have generated the necessary platforms and tools for moving these approaches into clinical procedures. Recent clinical data using such platforms have provided encouraging evidence supporting the feasibility of targeting and effectively ablating driver regions in addition to pulmonary vein isolation in persistent AF. Here, we review state-of-the-art technologies and provide a comprehensive historical perspective, characterization, classification, and expected outcomes of current mechanism-based methods for AF ablation. We discuss also the challenges and expected future directions that scientists and clinicians will face in their efforts to understand AF dynamics and successfully implement any novel method into regular clinical practice.
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Fang, Yin-Ying, Chi-Fang Chen, and Sheng-Ju Wu. "Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan." ANZIAM Journal 59 (July 25, 2019): C318—C357. http://dx.doi.org/10.21914/anziamj.v59i0.12655.
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Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets.
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Atkinson, Andrew D., Raymond R. Hill, Joseph J. Pignatiello, G. Geoffrey Vining, Edward D. White, and Eric Chicken. "Exposing System and Model Disparity and Agreement Using Wavelets." Journal of Verification, Validation and Uncertainty Quantification 3, no. 2 (June 1, 2018). http://dx.doi.org/10.1115/1.4041265.
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Model verification and validation (V&V) remain a critical step in the simulation model development process. A model requires verification to ensure that it has been correctly transitioned from a conceptual form to a computerized form. A model also requires validation to substantiate the accurate representation of the system it is meant to simulate. Validation assessments are complex when the system and model both generate high-dimensional functional output. To handle this complexity, this paper reviews several wavelet-based approaches for assessing models of this type and introduces a new concept for highlighting the areas of contrast and congruity between system and model data. This concept identifies individual wavelet coefficients that correspond to the areas of discrepancy between the system and model.
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Wang, Shunfang, and Xiaoheng Wang. "Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion." BMC Bioinformatics 20, S25 (December 2019). http://dx.doi.org/10.1186/s12859-019-3276-5.
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Abstract Background Protein structural class predicting is a heavily researched subject in bioinformatics that plays a vital role in protein functional analysis, protein folding recognition, rational drug design and other related fields. However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes. Results We constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at https://github.com/Xiaoheng-Wang12/Wang-xiaoheng/tree/master. Conclusions Experimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.
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Wang, Xianghai, Wenya Zhang, Rui Li, and Ruoxi Song. "The UDWT image denoising method based on the PDE model of a convexity-preserving diffusion function." EURASIP Journal on Image and Video Processing 2019, no. 1 (October 14, 2019). http://dx.doi.org/10.1186/s13640-019-0480-1.
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Abstract It is a great challenge to maintain details while suppressing and eliminating noise of the image. Considering the nonconvexity property of the diffusion function and the hypersensitivity of the Laplace operator to noise in the Y-K model, a fourth-order PDE image denoising model (Con_G&L model) is proposed in this paper. This model is constructed by a new convexity-preserving diffusion function which guarantees the corresponding energy functional has a globally unique minimum solution. At the same time, the Gaussian filter is combined with the Laplace operator in this model, and as a result, the noisy image is smoothed before the diffusion process, which improves the ability of capturing the details and edges of the noisy image greatly. Furthermore, by analyzing the statistical properties of the undecimated discrete wavelet transform (UDWT) coefficients of noisy image, we observe that the noise information is mainly distributed in the high-frequency sub-bands, and based on this, the proposed Con_G&L model is applied in the high-frequency sub-bands of the UDWT to get the denoising method. The proposed method removes the image noise effectively with the image texture and other details of the image being maintained. Meanwhile, the generation of false edges and the staircase effect can be suppressed. A large number of simulation experiments verify the effectiveness of the proposed method.
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Cong, Hu, Wang Peng, Zhou Tian, Martin Vallières, Xu Chuanpei, Zhu Aijun, and Zhang Benxin. "FDG-PET/CT Radiomics Models for The Early Prediction of Loco-regional Recurrence in Head and Neck Cancer." Current Medical Imaging Formerly Current Medical Imaging Reviews 16 (July 12, 2020). http://dx.doi.org/10.2174/1573405616666200712181135.
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: Purpose Both CT and PET radiomics is considered as a potential prognostic biomarker in head and neck cancer. This study investigate the value of fused pre-treatment functional imaging (18F-FDG PET/CT) radiomics for modeling of local recurrence of head and neck cancers. Material and Methods: Firstly, 298 patients is divided into a training set (n = 192) and verification set (n = 106). Secondly, PETs and CTs are fused based on wavelet transform. Thirdly, radiomics features are extracted from the 3D tumor area from PETCT fusion. The training set is used to select the features reduction and predict local recurrence, and the random forest prediction models combining radiomics and clinical variables are constructed. Finally, the ROC curve and K-M analysis are used to evaluate the prediction efficiency of the model on the validation set. Results: Two PET / CT fusion radiomics features and three clinic parameters are extracted to construct the radiomics model. AUC value in the verification set 0.70 is better than no fused sets 0.69. The accuracy of 0.66 is not the highest value (0.67). Either consistency index CI 0.70 (from 0.67 to 0.70) or the p-value 0.025 (from 0.03 to 0.025) get the best result in all four models. Conclusion: The radiomics model based on the fusion of PETCT is better than the model based on PET or CT alone in predicting local recurrence, the inclusion of clinical parameters may result in more accurate predictions, which has certain guiding significance for the development of personalized precise treatment scheme.
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Abdelkader, Eslam Mohammed, Osama Moselhi, Mohamed Marzouk, and Tarek Zayed. "A Grey Wolf Optimization-Based Method for Segmentation and Evaluation of Scaling in Reinforced Concrete Bridges." International Journal of Information Technology & Decision Making, June 5, 2021, 1–54. http://dx.doi.org/10.1142/s0219622021500425.
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Bridges are prone to severe deterioration agents which promote their degradation over the course of their lifetime. Furthermore, maintenance budgets are being trimmed. This state of circumstances entails the development of a computer vision-based method for the condition assessment of bridge elements in an attempt to circumvent the drawbacks of visual inspection-based models. Scaling is progressive local flaking or loss in the surface portion of concrete that affects the functional and structural integrity of reinforced concrete bridges. As such, this research study proposes a self-adaptive three-tier method for the automated detection and assessment of scaling severity levels in reinforced concrete bridges. The first tier relies on the integration of cross entropy function and grey wolf optimization (GWO) algorithm for the segmentation of scaling pixels. The second tier is designated for the autonomous interpretation of scaling area. In this model, a hybrid feature extraction algorithm is proposed based on the fusion of singular value decomposition and discrete wavelet transform for the efficient and robust extraction of the most dominant features in scaling images. Then an integration of Elman neural network and GWO algorithm is proposed for the sake of improving the prediction accuracies of scaling area though optimization of both structure and parameters of Elman neural network. The third tier aims at establishing a unified scaling severity index to assess the extent of severities of scaling according to its area and depth. The developed method is validated through multi-layered comparative analysis that involved performance evaluation comparisons, statistical comparisons and box plots. Results demonstrated that the developed scaling detection model significantly outperformed a set of widely-utilized classical segmentation models achieving mean squared error, mean absolute error, peak signal to noise ratio and cross entropy of 0.175, 0.407, 55.754 and 26011.019, respectively. With regards to the developed scaling evaluation model, it accomplished remarkable better and more robust performance that other meta-heuristic-based Elman neural network models and conventional prediction models. In this context, it obtained mean absolute percentage error, root-mean squared error and mean absolute error 1.513%, 29.836 and 12.066, respectively, as per split validation. It is anticipated that the developed integrated computer vision-based method could serve as the basis of automated, reliable and cost-effective inspection platform of reinforced concrete bridges which can assist departments of transportation in taking effective preventive maintenance and rehabilitation actions.
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Luongo, G., S. Schuler, M. W. Rivolta, O. Doessel, R. Sassi, and A. Loewe. "236Automatic classification of 20 different types of atrial tachycardia using 12-lead ECG signals." EP Europace 22, Supplement_1 (June 1, 2020). http://dx.doi.org/10.1093/europace/euaa162.048.
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Abstract Funding Acknowledgements Supported by the European Union"s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.766082 (MY-ATRIA) Background Atrial Flutter (AFl) as a common reentrant atrial tachycardia is driven by self-sustainable mechanisms that cause excitation to propagate along pathways different from sinus rhythm. Intracardiac electrophysiological mapping and catheter ablation is often performed without prior knowledge of the mechanism perpetuating AFl in a given patient, likely prolonging the procedure time of these invasive interventions. We investigated the feasibility of automatically identifying 20 different AFl types based on the non-invasive 12-lead electrocardiogram (ECG) using machine learning. Methods Electrophysiological fast marching computer simulations of 20 different atrial tachycardia scenarios (micro-/macro-reentry, scar-related/anatomical/functional, figure-of-eight, focal, different locations) were performed and propagated to the standard 12-lead ECG based on the Courtemanche atrial action potential model. The virtual study population comprised combinations of 8 different anatomical bi-atrial models with 2 orientational variants each and 8 different torso models yielding a total of 2512 ECGs. From each ECG, we extracted 114 features from different domains (e.g., time, frequency, entropy, wavelet, non-linear recurrence analysis). The dataset was randomly split into 1256 training samples, 628 validation samples and 628 test samples while maintaining a balanced AFl type distribution. A radial basis neural network (RBNN) was trained as a classifier after selection of the most informative features. Results The RBNN yielded a test set accuracy of 90% regarding the identification of the AFl mechanism using 10 features (from different domains). The most discriminative single feature was the cycle length that alone led to a test set accuracy of 74%, while the remaining feature set without cycle length (9 features) reduced the test set accuracy to 33%. The machine learning approach generalized well regarding unseen torso geometries (90% accuracy if training was performed on only 7 torso models) but rather poor regarding atrial anatomies (23% if the atrial anatomical model was not seen during training) indicating that more than the currently used 8 atrial models should be included during training to cover the relevant anatomical variability. Conclusions Our results show that a machine learning classifier can potentially identify a high number of different AFl types using the 12-lead ECG. This non-invasive method can aid in planning and tailoring AFl treatment for patients. Application to clinical data is necessary as a next step to pave the way for clinical translation. Abstract Figure.
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Denault, William R. P., Julia Romanowska, Øystein A. Haaland, Robert Lyle, Jack A. Taylor, Zongli Xu, Rolv T. Lie, Håkon K. Gjessing, and Astanand Jugessur. "Wavelet Screening identifies regions highly enriched for differentially methylated loci for orofacial clefts." NAR Genomics and Bioinformatics 3, no. 2 (April 9, 2021). http://dx.doi.org/10.1093/nargab/lqab035.
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Abstract DNA methylation is the most widely studied epigenetic mark in humans and plays an essential role in normal biological processes as well as in disease development. More focus has recently been placed on understanding functional aspects of methylation, prompting the development of methods to investigate the relationship between heterogeneity in methylation patterns and disease risk. However, most of these methods are limited in that they use simplified models that may rely on arbitrarily chosen parameters, they can only detect differentially methylated regions (DMRs) one at a time, or they are computationally intensive. To address these shortcomings, we present a wavelet-based method called ‘Wavelet Screening’ (WS) that can perform an epigenome-wide association study (EWAS) of thousands of individuals on a single CPU in only a matter of hours. By detecting multiple DMRs located near each other, WS identifies more complex patterns that can differentiate between different methylation profiles. We performed an extensive set of simulations to demonstrate the robustness and high power of WS, before applying it to a previously published EWAS dataset of orofacial clefts (OFCs). WS identified 82 associated regions containing several known genes and loci for OFCs, while other findings are novel and warrant replication in other OFCs cohorts.
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Nawabi, Jawed, Helge Kniep, Gerhard Schön, Jens Fiehler, and Uta Hanning. "Abstract WP423: Advanced Machine Learning in Action: Radiomic Based Outcome Prediction of Acute Intracranial Hemorrhage on Computed Tomography." Stroke 51, Suppl_1 (February 2020). http://dx.doi.org/10.1161/str.51.suppl_1.wp423.
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Background: Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes 1 . We hypothesized that machine learning algorithms could automatically analyze non-contrast computed tomography (NECT) of the head and predict clinical outcome of ICH patients 2 . Methods: 300 NECTs with acute spontaneous ICH between 2014-2019 were retrospectively included from the database at a tertiary university hospital. A binary outcome was defined as Modified Ranking Scale (mRS) 0-3 (good outcome) and mRS 4-6 (bad outcome) at discharge. Radiomic features including shape, histogram and texture markers were extracted from non- , wavelet- and log-sigma-filtered images using regions of interest of ICH. The quantitative predictors were evaluated utilizing random forest algorithms with 5-fold model-external cross-validation. Results: The model achieved an area under the ROC curve of 0.81 (95% CI [0.077; 0.86]; P<0.01), specificities and sensitivities reached 78% at Youden’s Index optimal cut-off point for the prediction of functional clinical outcome at discharge (mRS). Discussion: In conclusion, quantitative features of acute NECT images in a machine learning algorithm provided high discriminatory power in predicting functional outcome. In clinical routine, this proposed approach could allow early triage of high-risk patients for poor outcome. Indication of source:1 Qureshi, A. I. et al. Intracerebral haemorrhage. Lancet. 2009. 2 Mohammad R. Arbabshirani et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine. 2018.
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Li, Yang, Xianliang Shi, Hongdong Diao, Min Zhang, and Yadong Wu. "Optimization of warehouse management based on artificial intelligence technology." Journal of Intelligent & Fuzzy Systems, March 22, 2021, 1–8. http://dx.doi.org/10.3233/jifs-189843.
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This paper analyzes the artificial intelligence algorithms related to the storage path optimization problem and focuses on the ant colony algorithm and genetic algorithm with better applicability. The genetic algorithm is used to optimize the parameters of the ant colony algorithm, and the performance of the ant colony algorithm is improved. A typical route optimization problem model is taken as an example to prove the effectiveness of parameter optimization. This paper proposes a combined forecasting method through data preprocessing algorithm and artificial intelligence optimization. The combined prediction method first uses wavelet transform threshold processing to remove the noise data in the original data and then uses three separate methods to reduce noise. Forecast warehouse data and obtain intermediate forecast results. This article analyzes warehouse management and can solve the problems in the company’s warehouse management from the aspects of warehouse design and planning, warehouse design, and integrated warehouse management. After comparative analysis and selection, this paper uses the SLP method to rationally adjust and arrange the relative position and area of each functional area of the warehouse, and improve the evaluation index system. Experimental research shows that under the guidance of this article to optimize storage strategy, cargo location layout, and warehousing workflow, the employee reward mechanism mobilizes the enthusiasm of employees, improves work efficiency, and reduces storage costs. The above-mentioned various optimization and storage improvement measures finally reduced the total storage cost by 17%, effectively achieving the goal of cost control.