Thematische Bibliographien / Wavelet Scattering Transform

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Auswahl der wissenschaftlichen Literatur zum Thema „Wavelet Scattering Transform“

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Veröffentlicht am 25. Januar 2025

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Zeitschriftenartikel zum Thema "Wavelet Scattering Transform"

Yaou, M. H., und W. T. Chang. „Wavelet transform in scattering data interpolation“. Electronics Letters 29, Nr. 21 (1993): 1835. http://dx.doi.org/10.1049/el:19931221.

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Wang, Juan, Jiangshe Zhang und Jie Zhao. „Texture Classification Using Scattering Statistical and Cooccurrence Features“. Mathematical Problems in Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/3946312.

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Texture classification is an important research topic in image processing. In 2012, scattering transform computed by iterating over successive wavelet transforms and modulus operators was introduced. This paper presents new approaches for texture features extraction using scattering transform. Scattering statistical features and scattering cooccurrence features are derived from subbands of the scattering decomposition and original images. And these features are used for classification for the four datasets containing 20, 30, 112, and 129 texture images, respectively. Experimental results show that our approaches have the promising results in classification.

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Liu, Zhishuai, Guihua Yao, Qing Zhang, Junpu Zhang und Xueying Zeng. „Wavelet Scattering Transform for ECG Beat Classification“. Computational and Mathematical Methods in Medicine 2020 (09.10.2020): 1–11. http://dx.doi.org/10.1155/2020/3215681.

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An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.

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Marzog, Heyam A., und Haider J. Abd. „Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction“. Applied Computational Intelligence and Soft Computing 2022 (19.09.2022): 1–8. http://dx.doi.org/10.1155/2022/9884076.

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The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.

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Shi, Jun, Yanan Zhao, Wei Xiang, Vishal Monga, Xiaoping Liu und Ran Tao. „Deep Scattering Network With Fractional Wavelet Transform“. IEEE Transactions on Signal Processing 69 (2021): 4740–57. http://dx.doi.org/10.1109/tsp.2021.3098936.

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Lone, Ab Waheed, und Nizamettin Aydin. „Wavelet Scattering Transform based Doppler signal classification“. Computers in Biology and Medicine 167 (Dezember 2023): 107611. http://dx.doi.org/10.1016/j.compbiomed.2023.107611.

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D. S. Aabdalla, Islam, und D. Vasumathi. „Wavelet Scattering Transform for ECG Cardiovascular Disease Classification“. International Journal of Artificial Intelligence & Applications 15, Nr. 1 (29.01.2024): 101–13. http://dx.doi.org/10.5121/ijaia.2024.15107.

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Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this field is increasingly on prediction, with a growing dependence on machine learning techniques. This study aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet database by employing machine learning (ML). The study proposed several multi-class classification models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral features, and wavelet scattering, to extract features and capture unique characteristics from the ECG dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease. Furthermore, it could prove to be a valuable resource for future medical research projects aimed at improving the diagnosis and treatment of cardiovascular diseases.

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Khemani, Varun, Michael H. Azarian und Michael G. Pecht. „Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery“. Electronics 11, Nr. 3 (02.02.2022): 451. http://dx.doi.org/10.3390/electronics11030451.

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Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.

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Velicheti, Phani Datta, John F. Wu und Andreea Petric. „Quantifying Roman WFI Dark Images with the Wavelet Scattering Transform“. Publications of the Astronomical Society of the Pacific 135, Nr. 1050 (01.08.2023): 084502. http://dx.doi.org/10.1088/1538-3873/acf073.

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Abstract The Nancy Grace Roman Space Telescope will survey a large area of the sky at near-infrared wavelengths with its Wide Field Instrument (WFI). The performance of the 18 WFI H4RG-10 detectors will need to be well-characterized and regularly monitored in order for Roman to meet its science objectives. Weak lensing science goals are particularly sensitive to instrumental distortions and patterns that might masquerade as astronomical signals. We apply the wavelet scattering transform in order to analyze localized signals in Roman WFI images that have been taken as part of a dark image test suite. The scattering transform quantifies shapes and clustering information by reducing images into nonlinear combinations of wavelet modes on multiple size scales. We show that these interpretable scattering statistics can separate rare correlated patterns from typical noise signals, and we discuss the results in context of power spectrum analyses and other computer vision methods.

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Omer, Osama A., Mostafa Salah, Ammar M. Hassan, Mohamed Abdel-Nasser, Norihiro Sugita und Yoshifumi Saijo. „Blood Pressure Estimation from Photoplythmography Using Hybrid Scattering–LSTM Networks“. BioMedInformatics 4, Nr. 1 (09.01.2024): 139–57. http://dx.doi.org/10.3390/biomedinformatics4010010.

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One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, machine/deep learning has been leveraged for learning a BP from photoplethysmography (PPG) signals. Hence, continuous BP monitoring can be introduced, based on simple wearable contact sensors or even remotely sensed from a proper camera away from the clinical setup. However, the available training dataset imposes many limitations besides the other difficulties related to the PPG time series as high-dimensional data. This work presents beat-by-beat continuous PPG-based BP monitoring while accounting for the aforementioned limitations. For a better exploration of beats’ features, we propose to use wavelet scattering transform as a better descriptive domain to cope with the limitation of the training dataset and to help the deep learning network accurately learn the relationship between the morphological shapes of PPG beats and the BP. A long short-term memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over other domains. The learning scenarios are carried out on a beat basis where the input corresponding PPG beat is used for predicting BP in two scenarios; (1) Beat-by-beat arterial blood pressure (ABP) estimation, and (2) Beat-by-beat estimation of the systolic and diastolic blood pressure values. Different transformations are used to extract the features of the PPG beats in different domains including time, discrete cosine transform (DCT), discrete wavelet transform (DWT), and wavelet scattering transform (WST) domains. The simulation results show that using the WST domain outperforms the other domains in the sense of root mean square error (RMSE) and mean absolute error (MAE) for both of the suggested two scenarios.

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Dissertationen zum Thema "Wavelet Scattering Transform"

Waldspurger, Irène. „Wavelet transform modulus : phase retrieval and scattering“. Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0036/document.

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Les tâches qui consistent à comprendre automatiquement le contenu d’un signal naturel, comme une image ou un son, sont en général difficiles. En effet, dans leur représentation naïve, les signaux sont des objets compliqués, appartenant à des espaces de grande dimension. Représentés différemment, ils peuvent en revanche être plus faciles à interpréter. Cette thèse s’intéresse à une représentation fréquemment utilisée dans ce genre de situations, notamment pour analyser des signaux audio : le module de la transformée en ondelettes. Pour mieux comprendre son comportement, nous considérons, d’un point de vue théorique et algorithmique, le problème inverse correspondant : la reconstruction d’un signal à partir du module de sa transformée en ondelettes. Ce problème appartient à une classe plus générale de problèmes inverses : les problèmes de reconstruction de phase. Dans un premier chapitre, nous décrivons un nouvel algorithme, PhaseCut, qui résout numériquement un problème de reconstruction de phase générique. Comme l’algorithme similaire PhaseLift, PhaseCut utilise une relaxation convexe, qui se trouve en l’occurence être de la même forme que les relaxations du problème abondamment étudié MaxCut. Nous comparons les performances de PhaseCut et PhaseLift, en termes de précision et de rapidité. Dans les deux chapitres suivants, nous étudions le cas particulier de la reconstruction de phase pour la transformée en ondelettes. Nous montrons que toute fonction sans fréquence négative est uniquement déterminée (à une phase globale près) par le module de sa transformée en ondelettes, mais que la reconstruction à partir du module n’est pas stable au bruit, pour une définition forte de la stabilité. On démontre en revanche une propriété de stabilité locale. Nous présentons également un nouvel algorithme de reconstruction de phase, non-convexe, qui est spécifique à la transformée en ondelettes, et étudions numériquement ses performances. Enfin, dans les deux derniers chapitres, nous étudions une représentation plus sophistiquée, construite à partir du module de transformée en ondelettes : la transformée de scattering. Notre but est de comprendre quelles propriétés d’un signal sont caractérisées par sa transformée de scattering. On commence par démontrer un théorème majorant l’énergie des coefficients de scattering d’un signal, à un ordre donné, en fonction de l’énergie du signal initial, convolé par un filtre passe-haut qui dépend de l’ordre. On étudie ensuite une généralisation de la transformée de scattering, qui s’applique à des processus stationnaires. On montre qu’en dimension finie, cette transformée généralisée préserve la norme. En dimension un, on montre également que les coefficients de scattering généralisés d’un processus caractérisent la queue de distribution du processus
Automatically understanding the content of a natural signal, like a sound or an image, is in general a difficult task. In their naive representation, signals are indeed complicated objects, belonging to high-dimensional spaces. With a different representation, they can however be easier to interpret. This thesis considers a representation commonly used in these cases, in particular for theanalysis of audio signals: the modulus of the wavelet transform. To better understand the behaviour of this operator, we study, from a theoretical as well as algorithmic point of view, the corresponding inverse problem: the reconstruction of a signal from the modulus of its wavelet transform. This problem belongs to a wider class of inverse problems: phase retrieval problems. In a first chapter, we describe a new algorithm, PhaseCut, which numerically solves a generic phase retrieval problem. Like the similar algorithm PhaseLift, PhaseCut relies on a convex relaxation of the phase retrieval problem, which happens to be of the same form as relaxations of the widely studied problem MaxCut. We compare the performances of PhaseCut and PhaseLift, in terms of precision and complexity. In the next two chapters, we study the specific case of phase retrieval for the wavelet transform. We show that any function with no negative frequencies is uniquely determined (up to a global phase) by the modulus of its wavelet transform, but that the reconstruction from the modulus is not stable to noise, for a strong notion of stability. However, we prove a local stability property. We also present a new non-convex phase retrieval algorithm, which is specific to the case of the wavelet transform, and we numerically study its performances. Finally, in the last two chapters, we study a more sophisticated representation, built from the modulus of the wavelet transform: the scattering transform. Our goal is to understand which properties of a signal are characterized by its scattering transform. We first prove that the energy of scattering coefficients of a signal, at a given order, is upper bounded by the energy of the signal itself, convolved with a high-pass filter that depends on the order. We then study a generalization of the scattering transform, for stationary processes. We show that, in finite dimension, this generalized transform preserves the norm. In dimension one, we also show that the generalized scattering coefficients of a process characterize the tail of its distribution

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Rahmani, Maryam. „On the calculation of time-domain impulse-response of systems from band-limited scattering-parameters using wavelet transform“. Thesis, Mississippi State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10270053.

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In the aspect of electric-ship grounding, the time-domain behavior of the ship hull is needed. The grounding scheme impacts the nature of voltage transients during switching events and faults, identifiability and locatability of ground faults, fault current levels, and power quality. Due to the large size of ships compared with the wavelengths of the desired signals, time-domain measurement or simulation is a time-consuming process. Therefore, it is preferred that the behavior be studied in the frequency-domain. In the frequency-domain one can break down the whole ship hull into small blocks and find the frequency behavior of each block (scattering parameters) in a short time and then con- nect these blocks and find the whole ship hull scattering parameters. Then these scattering parameters should be transferred to the time-domain. The problem with this process is that the measured frequency-domain data (or the simulated data) is band-limited so, while calculating time-domain solutions, due to missing DC and low frequency content the time-domain response encounters causality, passivity and time-delay problems. Despite availability of several software and simulation packets that convert frequency-domain information to time-domain, all are known to suffer from the above mentioned problems. This dissertation provides a solution for computing the Time-Domain Impulse-Response for a system by using its measured or simulated scattering parameters. In this regard, a novel wavelet computational approach is introduced.

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Moufidi, Abderrazzaq. „Machine Learning-Based Multimodal integration for Short Utterance-Based Biometrics Identification and Engagement Detection“. Electronic Thesis or Diss., Angers, 2024. http://www.theses.fr/2024ANGE0026.

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Le progrès rapide et la démocratisation de la technologie ont conduit à l’abondance des capteurs. Par conséquent, l’intégration de ces diverses modalités pourrait présenter un avantage considérable pour de nombreuses applications dans la vie réelle, telles que la reconnaissance biométrique ou la détection d’engagement des élèves. Dans le domaine de la multimodalité, les chercheurs ont établi des architectures variées de fusion, allant des approches de fusion précoce, hybride et tardive. Cependant, ces architectures peuvent avoir des limites en ce qui concerne des signaux temporels d’une durée courte, ce qui nécessite un changement de paradigme vers le développement de techniques d’apprentissage automatique multimodales qui promettent une précision et une efficacité pour l’analyse de ces données courtes. Dans cette thèse, nous nous appuyons sur l’intégration de la multimodalité pour relever les défis précédents, allant de l’identification biométrique supervisée à la détection non supervisée de l’engagement des étudiants. La première contribution de ce doctorat porte sur l’intégration de la Wavelet Scattering Transform à plusieurs couches avec une architecture profonde appelée x-vectors, grâce à laquelle nous avons amélioré la performance de l’identification du locuteur dans des scénarios impliquant des énoncés courts tout en réduisant le nombre de paramètres nécessaires à l’entraînement. En s’appuyant sur les avantages de la multimodalité, on a proposé une architecture de fusion tardive combinant des vidéos de la profondeur des lèvres et des signaux audios a permis d’améliorer la précision de l’identification dans le cas d’énoncés courts, en utilisant des méthodes efficaces et moins coûteuses pour extraire des caractéristiques spatio-temporelles. Dans le domaine des défis biométriques, il y a la menace de l’émergence des "deepfakes". Ainsi, nous nous sommes concentrés sur l’élaboration d’une méthode de détection des "deepfakes" basée sur des méthodes mathématiques compréhensibles et sur une version finement ajustée de notre précédente fusion tardive appliquée aux vidéos RVB des lèvres et aux audios. En utilisant des méthodes de détection d’anomalies conçues spécifiquement pour les modalités audio et visuelles, l’étude a démontré des capacités de détection robustes dans divers ensembles de données et conditions, soulignant l’importance des approches multimodales pour contrer l’évolution des techniques de deepfake. S’étendant aux contextes éducatifs, la thèse explore la détection multimodale de l’engagement des étudiants dans une classe. En utilisant des capteurs abordables pour acquérir les signaux du rythme cardiaque et les expressions faciales, l’étude a développé un ensemble de données reproductibles et un plan pour identifier des moments significatifs, tout en tenant compte des nuances culturelles. L’analyse des expressions faciales à l’aide de Vision Transformer (ViT) fusionnée avec le traitement des signaux de fréquence cardiaque, validée par des observations d’experts, a mis en évidence le potentiel du suivi des élèves afin d’améliorer la qualité d’enseignement
The rapid advancement and democratization of technology have led to an abundance of sensors. Consequently, the integration of these diverse modalities presents an advantage for numerous real-life applications, such as biometrics recognition and engage ment detection. In the field of multimodality, researchers have developed various fusion ar chitectures, ranging from early, hybrid, to late fusion approaches. However, these architec tures may have limitations involving short utterances and brief video segments, necessi tating a paradigm shift towards the development of multimodal machine learning techniques that promise precision and efficiency for short-duration data analysis. In this thesis, we lean on integration of multimodality to tackle these previous challenges ranging from supervised biometrics identification to unsupervised student engagement detection. This PhD began with the first contribution on the integration of multiscale Wavelet Scattering Transform with x-vectors architecture, through which we enhanced the accuracy of speaker identification in scenarios involving short utterances. Going through multimodality benefits, a late fusion architecture combining lips depth videos and audio signals further improved identification accuracy under short utterances, utilizing an effective and less computational methods to extract spatiotemporal features. In the realm of biometrics challenges, there is the threat emergence of deepfakes. There-fore, we focalized on elaborating a deepfake detection methods based on, shallow learning and a fine-tuned architecture of our previous late fusion architecture applied on RGB lips videos and audios. By employing hand-crafted anomaly detection methods for both audio and visual modalities, the study demonstrated robust detection capabilities across various datasets and conditions, emphasizing the importance of multimodal approaches in countering evolving deepfake techniques. Expanding to educational contexts, the dissertation explores multimodal student engagement detection in classrooms. Using low-cost sensors to capture Heart Rate signals and facial expressions, the study developed a reproducible dataset and pipeline for identifying significant moments, accounting for cultural nuances. The analysis of facial expressions using Vision Transformer (ViT) fused with heart rate signal processing, validated through expert observations, showcased the potential for real-time monitoring to enhance educational outcomes through timely interventions

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Westermark, Pontus. „Wavelets, Scattering transforms and Convolutional neural networks : Tools for image processing“. Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-337570.

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Pike, Christopher James. „High resolution acoustic investigations of sub-seabed soils : relationship of wavelet transformed acoustic image to soil properties and some geotechnical parameters“. Thesis, Bangor University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265234.

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Strömbom, Johannes. „Natural Fingerprinting of Steel“. Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85531.

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A cornerstone in the industry's ongoing digital revolution, which is sometimes referred to as Industry 4.0, is the ability to trace products not only within the own production line but also throughout the remaining lifetime of the products. Traditionally, this is done by labeling products with, for instance, bar codes or radio-frequency identification (RFID) tags. In recent years, using the structure of the product itself as a unique identifier, a "fingerprint", has become a popular area of research. The purpose of this work was to develop software for an identification system using laser speckles as a unique identifier of steel components. Laser speckles, or simply speckles, are generated by illuminating a rough surface with coherent light, typically laser light. As the light is reflected, the granular pattern known as speckles can be seen by an observer. The complex nature of a speckle pattern together with its sensitivity to changes in the setup makes it robust against false-positive identifications and almost impossible to counterfeit. Because of this, speckles are suitable to be used as unique identifiers. In this work, three different identification algorithms have been tested in both simulations and experiments. The tested algorithms included one correlation-based, one method based on local feature extraction, and one method based on global feature extraction. The results showed that the correlation-based identification is most robust against speckle decorrelation, i.e changes in the speckle pattern, while being quite computationally expensive. The local feature-based method was shown to be unfit for this current application due to its sensitivity to speckle decorrelation and erroneous results. The global feature extraction method achieved high accuracy and fast computational speed when combined with a clustering method based on overlapping speckle patterns and a k-nearest neighbours (k-NN) search. In all the investigated methods, parallel calculations can be utilized to increase the computational speed.

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Huang, Jiunn-Ming, und 黃俊銘. „On the Application of Wavelet Transform to Wave Scattering Problems“. Thesis, 2000. http://ndltd.ncl.edu.tw/handle/65878253962566121085.

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博士
國立交通大學
電信工程系
88
The fundamental principles and characteristics of wavelet transform are discussed and applied to solve electromagnetic integral equations (IE). The major drawback of conventional method of moments (MoM) is the full matrix generation and huge computation time. Since different wavelets will results in diverse sparsity and computation time depends on the number of non-zero elements. The purposes of this dissertation are focus on the design of different wavelets to sparsify MoM impedance matrix without sacrificing much accuracy. We also present the concept of visible energy (VE) as a criterion to select a more suitable wavelet. The VE index is useful in designing new wavelets for electromagnetic IE. The lower VE index is, the more sparsity is the impedance matrix. In this dissertation, we first introduce the mathematical preliminaries of wavelets and conventional MoM for the sake of completeness. The VE concept is introduced thereafter and applied to Daubechies wavelet design. Daubechies wavelet is a minimum-phase one. However, we found that mix-phase wavelets are more useful in numerical solution of IE for their lower VE index. To acquire more sparsity and verify the validity of VE, we design another type of wavelet by optimization technique. That is the lattice-structure quadrature mirror filter (QMF) which is widely used in digital signal processing community. Much more sparsity is arrived by the QMF and best basis selection algorithm of wavelet packet. For example, the impedance matrix of a cylindrical scatterer is almost diagonalized. This result suggested that more wavelts from different fields could contribute to solve scattering IE efficiently by VE selection. This approach also extends the degree of freedom. Last but not least, we pinpointed several possible extensions and applications for further studied in electromagnetic field.

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„Fast Numerical Algorithms for 3-D Scattering from PEC and Dielectric Random Rough Surfaces in Microwave Remote Sensing“. Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.38433.

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abstract: We present fast and robust numerical algorithms for 3-D scattering from perfectly electrical conducting (PEC) and dielectric random rough surfaces in microwave remote sensing. The Coifman wavelets or Coiflets are employed to implement Galerkin’s procedure in the method of moments (MoM). Due to the high-precision one-point quadrature, the Coiflets yield fast evaluations of the most off-diagonal entries, reducing the matrix fill effort from O(N^2) to O(N). The orthogonality and Riesz basis of the Coiflets generate well conditioned impedance matrix, with rapid convergence for the conjugate gradient solver. The resulting impedance matrix is further sparsified by the matrix-formed standard fast wavelet transform (SFWT). By properly selecting multiresolution levels of the total transformation matrix, the solution precision can be enhanced while matrix sparsity and memory consumption have not been noticeably sacrificed. The unified fast scattering algorithm for dielectric random rough surfaces can asymptotically reduce to the PEC case when the loss tangent grows extremely large. Numerical results demonstrate that the reduced PEC model does not suffer from ill-posed problems. Compared with previous publications and laboratory measurements, good agreement is observed.
Dissertation/Thesis
Doctoral Dissertation Electrical Engineering 2016

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Buchteile zum Thema "Wavelet Scattering Transform"

Szczęsna, Agnieszka, Dariusz Augustyn, Henryk Josiński, Adam Świtoński, Paweł Kasprowski und Katarzyna Harężlak. „Novel Photoplethysmographic Signal Analysis via Wavelet Scattering Transform“. In Computational Science – ICCS 2022, 641–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08757-8_53.

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Moufidi, Abderrazzaq, David Rousseau und Pejman Rasti. „Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification“. In Artificial Neural Networks in Pattern Recognition, 97–106. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20650-4_8.

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Destouet, Gabriel, Cécile Dumas, Anne Frassati und Valérie Perrier. „Wavelet Scattering Transform and Ensemble Methods for Side-Channel Analysis“. In Constructive Side-Channel Analysis and Secure Design, 71–89. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68773-1_4.

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Al-Taee, Ahmad A., Rami N. Khushaba, Tanveer Zia und Adel Al-Jumaily. „Feature Extraction Using Wavelet Scattering Transform Coefficients for EMG Pattern Classification“. In Lecture Notes in Computer Science, 181–89. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_15.

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Ismael, Mustafa R., Haider J. Abd und Mohammed Taih Gatte. „Recognition of APSK Digital Modulation Signal Based on Wavelet Scattering Transform“. In Lecture Notes in Networks and Systems, 469–78. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0825-5_51.

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Oommen, Deepthi, und J. Arunnehru. „Early Diagnosis of Alzheimer’s Disease from MRI Images Using Scattering Wavelet Transforms (SWT)“. In Soft Computing and its Engineering Applications, 249–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05767-0_20.

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Wang, Ziyu, Feifei Liu, Shengxiang Xia, Shuhua Shi, Lin Wang, Zheng Xu, Sen Ai und Zhengyong Huang. „A New Method for Human Activity Recognition of Photoplethysmography Signals Using Wavelet Scattering Transform“. In Machine Learning and Artificial Intelligence. IOS Press, 2023. http://dx.doi.org/10.3233/faia230780.

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Exercise is an indispensable part of people’s lives and is closely related to their health. Human Activity Recognition (HAR), which involves detects and analyzes human body activity, has become the focus of current research. Photoplethysmography (PPG) has advantages such as convenience for detection and low cost, and is widely used in wearable devices becoming an ideal choice for HAR. In this study, we used wavelet scattering transform (WST) to extract features from PPG and then performed activity recognition on it. We achieved excellent classification accuracy of 92.54% and 97.76% respectively in the experiments of three-class and four-class exercise detection. The results showed this method based on wavelet scattering transform and PPG can accurately detect exercise types and provide effective support for HAR.

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„Deep Learning-Based Texture Classification by Scattering Transform with Wavelet“. In Series in Machine Perception and Artificial Intelligence, 465–73. 3. Aufl. WORLD SCIENTIFIC, 2024. http://dx.doi.org/10.1142/9789811284052_0014.

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Yang, Ziang, Biyu Zhou, Xuehai Tang, Ruixuan Li und Songlin Hu. „Breaking the Weak Semantics Bottleneck of Transformers in Time Series Forecasting“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240645.

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Transformer with self-attention was initially crafted to model language sequences, where discrete tokens (i.e., words) showcase high semantic density. However, when applied to time series token inputs (i.e., datapoints) with weak-density semantics and temporal redundancy, it faces challenges as these time-domain tokens impede its ability to capture the intricate latent properties of time series dynamics. While time-frequency transformation presents a viable solution by bringing forth a new space with heightened expressive power, existing approaches fall short of fully exploiting its potential. In response to these limitations, we propose a general-purpose transformer-based model, named Scattering Transformer, for multivariate time series forecasting and self-supervised representation learning. It is based on two innovative components: i) scattering self-attention mechanism incorporating wavelet key/value and standard query to unify the learning of cross-domain relationships between the time and wavelet domains; and ii) stochastic scaling positional encoding scheme that relies solely on order information, emulating longer sequence positions to generalize up to ultra-long horizon case. Extensive experiments on eight real-world benchmarks show the potential of our Scattering Transformer as a robust and versatile solution, showcasing its quadruple efficacy of non-stationary forecasting, ultra-long horizons forecasting, representation learning, and reduction in time and space complexity.

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Konferenzberichte zum Thema "Wavelet Scattering Transform"

Kong, Fantong, Yongxiang Liu, Hanchang Xu und Biao Wang. „Underwater Acoustic Classification Using Wavelet Scattering Transform and Convolutional Neural Network“. In 2024 OES China Ocean Acoustics (COA), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/coa58979.2024.10723696.

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Zhao, Heng, Yanyan Zhang, Qiujuan Lyu, Jiamin Fang, Jie Zhang, Sipu Zhang und Shiyu Ge. „Feature extraction and attribute recognition of particle light scattering signals based on wavelet scattering transform and long and short term memory network“. In Eleventh International Symposium on Precision Mechanical Measurements, herausgegeben von Liandong Yu, Lianqing Zhu, Zai Luo und Haojie Xia, 120. SPIE, 2024. http://dx.doi.org/10.1117/12.3033121.

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Guven, Oguzhan, Bryan Y. Wang und Yildiz Bayazitoglu. „Solving Radiative Transfer Equation in Scattering Plane-Parallel Medium Using Wavelets Approximation“. In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-33887.

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Wavelet analysis is presented for solving the radiative transfer equation for a scattering medium in a one-dimensional plane-parallel geometry. Some properties of the wavelet transform for numerical approximation of radiative heat transfer are demonstrated. The governing equations are reduced to a system of first-order ordinary differential equations. Linear anisotropic scattering is assumed in order to compare the results with the previous researchers. The method of analysis is quite general since it only requires that the scattering phase function is square integrable. The numerical solutions indicate that wavelet approximation is promising.

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AlBader, Mesaad, und Hamid A. Toliyat. „Wavelet Scattering Transform Based Induction Motor Current Signature Analysisa“. In 2020 International Conference on Electrical Machines (ICEM). IEEE, 2020. http://dx.doi.org/10.1109/icem49940.2020.9270810.

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Saranraj S, Padmapriya V, Sudharsan S, Piruthiha D und Venkateswaran N. „Palm print biometric recognition based on Scattering Wavelet Transform“. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2016. http://dx.doi.org/10.1109/wispnet.2016.7566183.

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Shen, Shi’an, Xiaokai Wang, Yanhui Zhou, Zhensheng Shi, Wenchao Chen und Cheng Wang. „3D scattering wavelet transform CNN for seismic fault detection“. In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3594177.1.

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Romanenko, S. N., L. M. Karpukov und R. D. Pulov. „Application of wavelet transform to the solution of scattering problems“. In 2007 6th International Conference on Antenna Theory and Techniques. IEEE, 2007. http://dx.doi.org/10.1109/icatt.2007.4425153.

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GHEZAIEL, Wajdi, Luc BRUN und Olivier LEZORAY. „Wavelet Scattering Transform and CNN for Closed Set Speaker Identification“. In 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2020. http://dx.doi.org/10.1109/mmsp48831.2020.9287061.

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Wang, Yajing, und Hui Yang. „Simulation of dynamic light scattering signal based on wavelet transform“. In 2010 International Conference on Educational and Information Technology (ICEIT). IEEE, 2010. http://dx.doi.org/10.1109/iceit.2010.5607795.

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Barbolla, Dora Francesca, Lara De Giorgi und Giovanni Leucci. „Discrete Wavelet Transform to reduce surface scattering in GPR sections“. In 2023 IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage. Budapest: IMEKO, 2023. http://dx.doi.org/10.21014/10.21014/tc4-arc-2023.031.

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