Relevant bibliographies by topics / Non-uniform wavelet sampling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Non-uniform wavelet sampling'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Non-uniform wavelet sampling"

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Pelissier, Michael, and Christoph Studer. "Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion." IEEE Transactions on Circuits and Systems I: Regular Papers 65, no. 2 (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, no. 2 (2002): 151–61. http://dx.doi.org/10.1016/s1063-5203(02)00503-1.

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Sun, Zongzheng, Yinghui Quan, and Zhixing Liu. "A Non-Uniform Interrupted-Sampling Repeater Jamming Method for Intra-Pulse Frequency Agile Radar." Remote Sensing 15, no. 7 (2023): 1851. http://dx.doi.org/10.3390/rs15071851.

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

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Chui, Charles K., Yu-Ting Lin, and Hau-Tieng Wu. "Real-time dynamics acquisition from irregular samples — With application to anesthesia evaluation." Analysis and Applications 14, no. 04 (2016): 537–90. http://dx.doi.org/10.1142/s0219530515500165.

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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-fu
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He, Yi, Peng Cheng, Shanmin Yang, and Jianwei Zhang. "Three-Dimensional Face Recognition Using Solid Harmonic Wavelet Scattering and Homotopy Dictionary Learning." Entropy 24, no. 11 (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
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Martín del Campo, Gustavo, Yuriy Shkvarko, Andreas Reigber, and Matteo Nannini. "TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach." Remote Sensing 10, no. 11 (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
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Manokhin, Mikhail, Paul Chollet, and Patricia Desgreys. "Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications." Sensors 24, no. 3 (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
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Sun, Chengfa, Hui Cui, Weidong Zhou, Weiwei Nie, Xiuying Wang, and Qi Yuan. "Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning." International Journal of Neural Systems 29, no. 10 (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 desi
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Davis, Anthony B., Alexander Marshak, Robert F. Cahalan, and Warren J. Wiscombe. "Interactions: Solar and Laser Beams in Stratus Clouds, Fractals & Multifractals in Climate & Remote-Sensing Studies." Fractals 05, supp02 (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 s
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Dissertations / Theses on the topic "Non-uniform wavelet sampling"

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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
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Manokhin, Mikhail. "Towards flexible and low-power wireless smart sensors : reconfigurable analog-to-feature converter for healthcare applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT049.

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La croissance démographique et le vieillissement actuels augmentent le taux de maladies chroniques, principale cause mondiale de décès. Les réseaux corporels sans fil (WBANs), composés de capteurs intelligents portables ou implantables, sont la principale solution pour les systèmes de soins de santé proactifs réduisant le fardeau de ces maladies. Or, ces réseaux sont limités en termes de consommation d'énergie et de débit de données, surtout dans le cas des capteurs biopotentiels et inertiels avec une acquisition continue des signaux. La réduction de la quantité de données collectées et envoyé
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Book chapters on the topic "Non-uniform wavelet sampling"

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

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Conference papers on the topic "Non-uniform wavelet sampling"

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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, and 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, and 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, and 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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