Splines in Compressed Sensing
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[1] H. Nyquist,et al. Certain Topics in Telegraph Transmission Theory , 1928, Transactions of the American Institute of Electrical Engineers.
[2] H. Andrews,et al. Hadamard transform image coding , 1969 .
[3] A. Pinkus. n-Widths in Approximation Theory , 1985 .
[4] Robert H. Walden,et al. Analog-to-digital converter survey and analysis , 1999, IEEE J. Sel. Areas Commun..
[5] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[6] Thierry Blu,et al. The fractional spline wavelet transform: definition end implementation , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[7] Michael Unser,et al. Splines: A perfect fit for signal processing , 2000, 2000 10th European Signal Processing Conference.
[8] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[9] Michael Elad,et al. A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.
[10] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[11] R. Fensli,et al. A wireless ECG system for continuous event recording and communication to a clinical alarm station , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[12] Michael Unser. Cardinal exponential splines: part II - think analog, act digital , 2005, IEEE Transactions on Signal Processing.
[13] Thierry Blu,et al. Cardinal exponential splines: part I - theory and filtering algorithms , 2005, IEEE Transactions on Signal Processing.
[14] W. Wajszczuk,et al. The recording of d.c. and very low frequency components of ECG signals from epicardial leads , 1966, Medical and biological engineering.
[15] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[16] D. Donoho. For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .
[17] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[18] Emmanuel J. Candès,et al. Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions , 2004, Found. Comput. Math..
[19] J. Romberg,et al. Sparse Signal Recovery via l1 Minimization , 2006, 2006 40th Annual Conference on Information Sciences and Systems.
[20] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[21] Thierry Blu,et al. Self-Similarity: Part II—Optimal Estimation of Fractal Processes , 2007, IEEE Transactions on Signal Processing.
[22] Thierry Blu,et al. Self-Similarity: Part I—Splines and Operators , 2007, IEEE Transactions on Signal Processing.
[23] Manuel Blanco-Velasco,et al. Compressed sensing based method for ECG compression , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[24] Falgun Thakkar,et al. ECG signal compression using Compressive Sensing and wavelet transform , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[25] Vladimir Stojanovic,et al. Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors , 2012, IEEE Journal of Solid-State Circuits.
[26] Kurt Bryan,et al. Making Do with Less: An Introduction to Compressed Sensing , 2013, SIAM Rev..
[27] Zhu Yu,et al. Compressed sensing in array signal processing based on modulated wideband converter , 2014, 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS).
[28] Yehia Massoud,et al. Accurate and efficient modeling of random demodulation based compressive sensing systems with a general filter , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).
[29] Amar Rouane,et al. Simple and Efficient Compressed Sensing Encoder for Wireless Body Area Network , 2014, IEEE Transactions on Instrumentation and Measurement.
[30] Riccardo Rovatti,et al. A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing? , 2015, IEEE Signal Processing Letters.
[31] Luca Benini,et al. Long-Term ECG monitoring with zeroing Compressed Sensing approach , 2015, 2015 Nordic Circuits and Systems Conference (NORCAS): NORCHIP & International Symposium on System-on-Chip (SoC).
[32] Luca Benini,et al. An ultra-low power dual-mode ECG monitor for healthcare and wellness , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[33] Yu Cao,et al. Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits , 2015, 2015 33rd IEEE International Conference on Computer Design (ICCD).
[34] Aziza I. Hussein,et al. Compression of ECG Signal Based on Compressive Sensing and the Extraction of Significant Features , 2015 .
[35] K. A. Narayanankutty,et al. Exponential-Splines in Compressed Sensing ECG Reconstruction , 2016 .
[36] Swagatam Das,et al. Artificial Intelligence and Evolutionary Computations in Engineering Systems , 2016 .