Auswahl der wissenschaftlichen Literatur zum Thema „Non-uniform wavelet sampling“
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Zeitschriftenartikel zum Thema "Non-uniform wavelet sampling"
Pelissier, Michael, und Christoph Studer. „Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion“. IEEE Transactions on Circuits and Systems I: Regular Papers 65, Nr. 2 (Februar 2018): 471–84. http://dx.doi.org/10.1109/tcsi.2017.2729779.
Der volle Inhalt der QuelleAldroubi, Akram. „Non-uniform weighted average sampling and reconstruction in shift-invariant and wavelet spaces“. Applied and Computational Harmonic Analysis 13, Nr. 2 (September 2002): 151–61. http://dx.doi.org/10.1016/s1063-5203(02)00503-1.
Der volle Inhalt der QuelleSun, Zongzheng, Yinghui Quan und Zhixing Liu. „A Non-Uniform Interrupted-Sampling Repeater Jamming Method for Intra-Pulse Frequency Agile Radar“. Remote Sensing 15, Nr. 7 (30.03.2023): 1851. http://dx.doi.org/10.3390/rs15071851.
Der volle Inhalt der QuelleDe Vito, Luca, Grazia Iadarola, Francesco Lamonaca, Francesco Picariello, Sergio Rapuano und Ioan Tudosa. „Non-Uniform Wavelet Bandpass Sampling Analog-to-Information Converter: A hardware implementation and its experimental assessment“. Measurement 134 (Februar 2019): 739–49. http://dx.doi.org/10.1016/j.measurement.2018.11.015.
Der volle Inhalt der QuelleChui, Charles K., Yu-Ting Lin und Hau-Tieng Wu. „Real-time dynamics acquisition from irregular samples — With application to anesthesia evaluation“. Analysis and Applications 14, Nr. 04 (27.04.2016): 537–90. http://dx.doi.org/10.1142/s0219530515500165.
Der volle Inhalt der QuelleHe, Yi, Peng Cheng, Shanmin Yang und Jianwei Zhang. „Three-Dimensional Face Recognition Using Solid Harmonic Wavelet Scattering and Homotopy Dictionary Learning“. Entropy 24, Nr. 11 (13.11.2022): 1646. http://dx.doi.org/10.3390/e24111646.
Der volle Inhalt der QuelleMartín del Campo, Gustavo, Yuriy Shkvarko, Andreas Reigber und Matteo Nannini. „TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach“. Remote Sensing 10, Nr. 11 (17.11.2018): 1822. http://dx.doi.org/10.3390/rs10111822.
Der volle Inhalt der QuelleManokhin, Mikhail, Paul Chollet und Patricia Desgreys. „Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications“. Sensors 24, Nr. 3 (03.02.2024): 999. http://dx.doi.org/10.3390/s24030999.
Der volle Inhalt der QuelleSun, Chengfa, Hui Cui, Weidong Zhou, Weiwei Nie, Xiuying Wang und Qi Yuan. „Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning“. International Journal of Neural Systems 29, Nr. 10 (Dezember 2019): 1950021. http://dx.doi.org/10.1142/s0129065719500217.
Der volle Inhalt der QuelleDavis, Anthony B., Alexander Marshak, Robert F. Cahalan und Warren J. Wiscombe. „Interactions: Solar and Laser Beams in Stratus Clouds, Fractals & Multifractals in Climate & Remote-Sensing Studies“. Fractals 05, supp02 (Oktober 1997): 129–66. http://dx.doi.org/10.1142/s0218348x97000875.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleThe 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
Buchteile zum Thema "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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "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.
Der volle Inhalt der QuelleMalhotra, Hari Krishan, und 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.
Der volle Inhalt der QuelleBo, Liu, Zhang Qi, Liu Guofu und 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.
Der volle Inhalt der QuellePelissier, Michael, Gilles Masson, Laurent Ouvry, Luis Felipe Fonseca Dias und 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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