Bibliografie tematiche / Non-uniform wavelet sampling

Letteratura scientifica selezionata sul tema "Non-uniform wavelet sampling"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Non-uniform wavelet sampling":

Pelissier, Michael, e Christoph Studer. "Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion". IEEE Transactions on Circuits and Systems I: Regular Papers 65, n. 2 (febbraio 2018): 471–84. http://dx.doi.org/10.1109/tcsi.2017.2729779.

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Aldroubi, Akram. "Non-uniform weighted average sampling and reconstruction in shift-invariant and wavelet spaces". Applied and Computational Harmonic Analysis 13, n. 2 (settembre 2002): 151–61. http://dx.doi.org/10.1016/s1063-5203(02)00503-1.

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Sun, Zongzheng, Yinghui Quan e Zhixing Liu. "A Non-Uniform Interrupted-Sampling Repeater Jamming Method for Intra-Pulse Frequency Agile Radar". Remote Sensing 15, n. 7 (30 marzo 2023): 1851. http://dx.doi.org/10.3390/rs15071851.

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Abstract (sommario):

The existing research proposes an intra-pulse frequency agile radar waveform with “active” anti-jamming characteristics. It uses the discontinuity and periodicity of the interrupted-sampling repeater jamming and combines the anti-jamming algorithm to effectively suppress interrupted-sampling repeater jamming. In order to improve the jamming effectiveness of the interferer for the intra-pulse frequency agile waveform, this paper proposes to jam the intra-pulse frequency agile radar by using a non-uniform interrupted-sampling and forwarding method under parameter constraints. The proposed method first obtains the sub-pulse width of the intra-pulse frequency agile radar waveform by parameter estimation of the intercepted intra-pulse frequency agile radar signal through time–frequency ridge extraction and wavelet transform. Then, we construct non-uniform interrupted-sampling repeater jamming based on sub-pulse width constraint interference parameters. Theoretical analysis and results show that the non-uniform interrupted-sampling forwarding under parameter constraints makes it challenging to suppress interference in multiple domains, such as the time–frequency and pulse compression domain for intra-pulse frequency agile radar, which significantly improves the jamming capability of the jammer for intra-pulse frequency agile radar.

De Vito, Luca, Grazia Iadarola, Francesco Lamonaca, Francesco Picariello, Sergio Rapuano e Ioan Tudosa. "Non-Uniform Wavelet Bandpass Sampling Analog-to-Information Converter: A hardware implementation and its experimental assessment". Measurement 134 (febbraio 2019): 739–49. http://dx.doi.org/10.1016/j.measurement.2018.11.015.

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Chui, Charles K., Yu-Ting Lin e Hau-Tieng Wu. "Real-time dynamics acquisition from irregular samples — With application to anesthesia evaluation". Analysis and Applications 14, n. 04 (27 aprile 2016): 537–90. http://dx.doi.org/10.1142/s0219530515500165.

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Abstract (sommario):

Although most digital representations of information sources are obtained by uniform sampling of some continuous function representations, there are many important events for which only irregular data samples are available, including trading data of the financial market and various clinical data, such as the respiration signals hidden in ECG measurements. For such digital information sources, the only available effective smooth function interpolation scheme for digital-to-analog (D/A) conversion algorithms are mainly for offline applications. Hence, in order to adapt the powerful continuous-function mathematical approaches for real-time applications, it is necessary to introduce an effective D/A conversion scheme as well as to modify the desired continuous-function mathematical method for online implementation. The powerful signal processing tool to be discussed in this paper is the synchrosqueezed continuous wavelet transform (SST), which requires computation of the continuous wavelet transform (CWT), as well as its derivative, of the analog signal of interest. An important application of this transform is to extract information, such as the underlying dynamics, hidden in the signal representation. The first objective of this paper is to introduce a unified approach to remove the two main obstacles for adapting the SST approach to irregular data samples in order to allow online computation. Firstly, for D/A conversion, a real-time algorithm, based on spline functions of arbitrarily desired order, is proposed to interpolate the irregular data samples, while preserving all polynomials of the same spline order, with assured maximum order of approximation. Secondly, for real-time dynamic information extraction from an oscillatory signal via SST, a family of vanishing-moment and minimum-supported spline-wavelets (to be called VM wavelets) are introduced for online computation of the CWT and its derivative. The second objective of this paper is to apply the proposed real-time algorithm and VM wavelets to clinical applications, particularly to the study of the “anesthetic depth” of a patient during surgery, with emphasis on analyzing two dynamic quantities: the “instantaneous frequencies” and the “non-rhythmic to rhythmic ratios” of the patient’s respiration, based on a one-lead electrocardiogram (ECG) signal. Indeed, the “R-peaks” of the ECG signal, which constitute a waveform landmark for clinical evaluation, are non-uniform samples of the respiratory signal. It is envisioned that the proposed algorithm and VM wavelets should enable real-time monitoring of “anesthetic depth”, during surgery, from the respiration signal via ECG measurement.

He, Yi, Peng Cheng, Shanmin Yang e Jianwei Zhang. "Three-Dimensional Face Recognition Using Solid Harmonic Wavelet Scattering and Homotopy Dictionary Learning". Entropy 24, n. 11 (13 novembre 2022): 1646. http://dx.doi.org/10.3390/e24111646.

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Data representation has been one of the core topics in 3D graphics and pattern recognition in high-dimensional data. Although the high-resolution geometrical information of a physical object can be well preserved in the form of metrical data, e.g., point clouds/triangular meshes, from a regular data (e.g., image/audio) processing perspective, they also bring excessive noise in the course of feature abstraction and regression. For 3D face recognition, preceding attempts focus on treating the scan samples as signals laying on an underlying discrete surface (mesh) or morphable (statistic) models and by embedding auxiliary information, e.g., texture onto the regularized local planar structure to obtain a superior expressive performance to registration-based methods, but environmental variations such as posture/illumination will dissatisfy the integrity or uniform sampling condition, which holistic models generally rely on. In this paper, a geometric deep learning framework for face recognition is proposed, which merely requires the consumption of raw spatial coordinates. The non-uniformity and non-grid geometric transformations in the course of point cloud face scanning are mitigated by modeling each identity as a stochastic process. Individual face scans are considered realizations, yielding underlying inherent distributions under the appropriate assumption of ergodicity. To accomplish 3D facial recognition, we propose a windowed solid harmonic scattering transform on point cloud face scans to extract the invariant coefficients so that unrelated variations can be encoded into certain components of the scattering domain. With these constructions, a sparse learning network as the semi-supervised classification backbone network can work on reducing intraclass variability. Our framework obtained superior performance to current competing methods; without excluding any fragmentary or severely deformed samples, the rank-1 recognition rate (RR1) achieved was 99.84% on the Face Recognition Grand Challenge (FRGC) v2.0 dataset and 99.90% on the Bosphorus dataset.

Martín del Campo, Gustavo, Yuriy Shkvarko, Andreas Reigber e Matteo Nannini. "TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach". Remote Sensing 10, n. 11 (17 novembre 2018): 1822. http://dx.doi.org/10.3390/rs10111822.

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Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods.

Manokhin, Mikhail, Paul Chollet e Patricia Desgreys. "Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications". Sensors 24, n. 3 (3 febbraio 2024): 999. http://dx.doi.org/10.3390/s24030999.

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Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications.

Sun, Chengfa, Hui Cui, Weidong Zhou, Weiwei Nie, Xiuying Wang e Qi Yuan. "Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning". International Journal of Neural Systems 29, n. 10 (dicembre 2019): 1950021. http://dx.doi.org/10.1142/s0129065719500217.

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Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a [Formula: see text]-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level [Formula: see text]-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.

Davis, Anthony B., Alexander Marshak, Robert F. Cahalan e Warren J. Wiscombe. "Interactions: Solar and Laser Beams in Stratus Clouds, Fractals & Multifractals in Climate & Remote-Sensing Studies". Fractals 05, supp02 (ottobre 1997): 129–66. http://dx.doi.org/10.1142/s0218348x97000875.

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Recent research on cloud structure and cloud-radiation interaction at NASA's Goddard Space Flight Center is presented as a show case of interdisciplinary work where fractals and multifractals play a central role. Focus has been primarily on stratocumulus because of their first-order effect on the Earth's energy balance (hence the global climate) due to their unusual horizontal extension and persistence. These cloud layers have quasi-flat upper/lower boundaries and appear to be quite uniform but are highly variable inside. The general strategy has been: utilization of spatial statistics of in situ and remotely sensed data pertaining to cloud structure to constrain stochastic cloud models used in turn for radiative transfer simulations where artificial radiation fields are generated; these fields are compared to actual measurements. and so on, until a degree of closure is achieved. The major trends have been: i) computation and understanding of cloudradiative properties from the large scales of interest to Global Climate Models (over 102 km) down to the smallest observable scales (less than 10 m); ii) from predicting the outcome of "ideal" measurements to those of "real" ones with limited accuracy, sampling and averaging; iii) from passive to active remote-sensing methods; and iv) shifting from standard to wavelet-based analysis/modeling techniques. In terms of potential for impact on geophysical research at large, the most important contributions are: a) criteria for and measures of nonstationarity and intermittency in scale-invariant data; b) so-called "bounded" multifractal cascade models having a continuously variable degree of nonstationarity: c) a parameterization of the bulk effect of fractal variability on large-scale planetary albedo; and d) the basic scaling theory of radiative "smoothing" that explains non-trivially related multiple scattering phenomena in both solar- and lidar-based remote sensing. The last item also suggests new methods of observing clouds and new ways of processing cloud radiance data to retrieve physical cloud properties.

Tesi sul tema "Non-uniform wavelet sampling":

Back, Antoine. "Conception et intégration d'un convertisseur analogique-paramètres flexible pour les capteurs intelligents". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT020.

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Avec le fort développement de l'Internet des Objets (IoT), il devient nécessaire de converger vers de nouveaux capteurs dit intelligents. Ces capteurs doivent permettre d'analyser l'environnement extérieur, comprendre le contexte dans lequel ils sont utilisés et être conscient des besoins utilisateurs. Ils doivent cependant rester petits, fiables, bon marché et avoir une autonomie de plusieurs années. La conversion analogique-paramètre (Analog-to-Feature, A2F) est une nouvelle méthode d'acquisition pensée pour les appareils IoT, et semble être une solution adaptée pour de tels capteurs. Cette conversion consiste à extraire des paramètres directement sur le signal analogique. Une sélection pertinente des paramètres permet d'extraire uniquement l'information nécessaire à une tache particulière. Le convertisseur proposé est basé sur la technique de l'échantillonnage non-uniforme en ondelettes (NUWS). L'architecture mélange le signal analogique avec des ondelettes paramétrables avant d'intégrer et convertir le signal en données numériques. L'objectif de la thèse est de proposer une méthode pour concevoir un convertisseur A2F générique basé sur le NUWS. Il est ainsi nécessaire de définir les caractéristiques des ondelettes afin d'acquérir une large gamme de signaux basse fréquence (ECG, EMG, EEG, parole…). Cette étape nécessite l'utilisation d'algorithmes de sélection de paramètres et d'algorithmes d'apprentissage automatique pour sélectionner le meilleur ensemble d'ondelettes pour une application donnée et qui doit permettre de définir les spécifications du convertisseur. L'étape de sélection des paramètres doit tenir compte des contraintes de mise en œuvre pour optimiser au mieux la consommation d'énergie. Un algorithme de sélection de paramètres est proposé pour choisir des ondelettes pour une application donnée, afin de maximiser la précision de classification tout en diminuant la consommation d'énergie, grâce à un modèle de consommation réalisé dans une technologie CMOS 0.18μm
The Internet of Things (IoT) is currently experiencing huge developments. IoT includes lots of different devices such as Wireless Sensors Networks (WSN) or wearable electronics that rely on wireless communications. These networks need to understand the context in which they are used. This mean that the system must know what is happening around it, i.e. sense the environment, and understands the needs of the user. This requires always-on sensing on many sensors while being small, cheap, reliable and having a lifetime of several years. Analog-to-Feature (A2F) conversion is a new acquisition method that was thought for IoT devices. The converter aims at extracting useful features directly on the analog signal. By carefully choosing a set of features, it is possible to acquire only the relevant information for a given task. The proposed converter is based on the Non-Uniform Wavelet Sampling (NUWS) architecture. The architecture mixes the analog signal with tunable wavelets prior to integration and digital conversion. The aim of the thesis is to propose a method to design a generic A2F converter based on the NUWS. It includes the definition of the wavelet parameters in order to acquire a broad range of low frequency signals (ECG, EMG, EEG, speech …). This step requires the use of feature selection algorithms and machine learning algorithms for selecting the best set of wavelets for a given application and should be used to define the specifications for the converter. The feature selection step must be aware of physical implementation constraints to optimize energy consumption as much as possible. A feature selection algorithm is proposed to choose wavelets for a given application, in order to maximize classification accuracy while decreasing power consumption, through a power model designed in CMOS 0.18μm

Capitoli di libri sul tema "Non-uniform wavelet sampling":

"Non-uniform sampling in multiply generated shift-invariant subspaces of 𝐿^{𝑝}(ℝ^{𝕕})". In Wavelet Analysis and Applications, 1–8. Providence, Rhode Island: American Mathematical Society, 2002. http://dx.doi.org/10.1090/amsip/025/01.

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Atti di convegni sul tema "Non-uniform wavelet sampling":

Walter, G. G. "Non-uniform sampling in wavelet subspaces". In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758335.

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Malhotra, Hari Krishan, e Lalit Kumar Vashisht. "Construction of Non-Uniform Parseval Wavelet Frames for L2 (R) via UEP". In 2019 13th International conference on Sampling Theory and Applications (SampTA). IEEE, 2019. http://dx.doi.org/10.1109/sampta45681.2019.9030867.

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Bo, Liu, Zhang Qi, Liu Guofu e Xie Xiufen. "Non-uniform Sampling Signal Spectral Estimation of Tire Pressure Monitoring System Using Wavelet Transform". In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4351052.

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Pelissier, Michael, Gilles Masson, Laurent Ouvry, Luis Felipe Fonseca Dias e Marguerite Marnat. "Hardware platform of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF signal activity detection". In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2018. http://dx.doi.org/10.1109/iscas.2018.8351834.

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