Results 251 to 260 of about 10,846,082 (297)
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Sequential Signals of Recession and Recovery
The Journal of Business, 1982This paper describes a sequential procedure for identifying the beginning and ending dates of business cycle recessions as promptly and accurately as practicable. Its origin lies in a study in progress for the Economic Development Administration (EDA), U.S.
Zarnowitz, Victor, Moore, Geoffrey H
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Window Selection of the Savitzky–Golay Filters for Signal Recovery From Noisy Measurements
IEEE Transactions on Instrumentation and Measurement, 2020The Savitzky–Golay (SG) filtering is a widely used denoising method employed in different applications. The SG filter has two design parameters: the window length and the filter order.
Mohammad Sadeghi, F. Behnia, R. Amiri
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DOA Estimation via Sparse Signal Recovery in 4-D Linear Antenna Arrays With Optimized Time Sequences
IEEE Transactions on Vehicular Technology, 2020A novel direction of arrival (DOA) estimation method for four-dimensional (4-D) linear arrays with optimized time sequences using the sparse signal recovery is proposed in this paper.
Feng Yang +5 more
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Resolution limits in signal recovery
IEEE Transactions on Signal Processing, 1996Resolution analysis for the problem of signal recovery from finitely many linear measurements is the subject of this paper. The classical Rayleigh limit serves only as a lower bound on resolution since it does not assume any recovery strategy and is based only on observed data.
Satya Dharanipragada, K. S. Arun
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Signal Recovery and the Large Sieve
SIAM Journal on Applied Mathematics, 1992Inequalities are developed for the fraction of a bandlimited function’s $L_p $ norm that can be concentrated on any set of small “Nyquist density.” Two applications are mentioned. First, that a bandlimited function corrupted by impulsive noise can be reconstructed perfectly, provided the noise is concentrated on a set of Nyquist density $ < 1/\pi $;
Donoho, D. L., Logan, B. F.
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On uniqueness of sparse signal recovery
Signal Processing, 2018Abstract A basic issue of sparse signal recovery (SSR) is to explore the condition of the uniqueness with regard to the solution of the relevant optimization framework. However, the standard uniqueness conditions, such as spark condition, NSP (null space property), RIP (restricted isometry property) and mutual coherence condition, are with respect to
Xiao-Li Hu +4 more
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Signal recovery in imaging photoplethysmography
Physiological Measurement, 2013Imaging photoplethysmography is an emerging technique for the extraction of biometric information from people using video recordings. The focus is on extracting the cardiac heart rate of the subject by analysing the luminance of the colour video signal and identifying periodic components.
Benjamin D, Holton +3 more
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Signal Recovery From Wavelet Transform Maxima [PDF]
IEEE Transactions on Signal Processing, 1994 This paper presents an iterative algorithm for signal recovery from discrete-time wavelet transform maxima. The signal recovery algorithm is developed by using the method of projections onto convex sets.
A E Cetin, R Ansari
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Learning to invert: Signal recovery via Deep Convolutional Networks
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-
A. Mousavi, Richard Baraniuk
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2009
The literature on the recovery of signals and images is vast (e.g., [23, 110, 112, 257, 391, 439, 791, 795, 933, 934, 937, 945, 956, 1104, 1324, 1494, 1495, 1551]). In this Chapter, the specific problem of recovering lost signal intervals from the remaining known portion of the signal is considered. Signal recovery is also a topic of Chapter 11 on POCS.
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The literature on the recovery of signals and images is vast (e.g., [23, 110, 112, 257, 391, 439, 791, 795, 933, 934, 937, 945, 956, 1104, 1324, 1494, 1495, 1551]). In this Chapter, the specific problem of recovering lost signal intervals from the remaining known portion of the signal is considered. Signal recovery is also a topic of Chapter 11 on POCS.
openaire +1 more source