A Random Sequence Generation Method for Random Demodulation Based Compressive Sampling System
نویسندگان
چکیده
منابع مشابه
A Random Sequence Generation Method for Random Demodulation Based Compressive Sampling System
Random demodulation based compressive sampling technique is a novel approach that it can break through the Shannon sampling theorem for the sparse signal capturing. A major challenge in the random demodulation based sampling system is the random sequence generation. In this paper, we introduce an approach to generate the high-speed random sequence that meets the incoherence of compressive sampl...
متن کاملRandom Projection-Based Anderson-Darling Test for Random Fields
In this paper, we present the Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) goodness of fit statistics for stationary and non-stationary random fields. Namely, we adopt an easy-to-apply method based on a random projection of a Hilbert-valued random field onto the real line R, and then, applying the well-known AD and KS goodness of fit tests. We conclude this paper by studying the behavior o...
متن کاملA Block-Wise random sampling approach: Compressed sensing problem
The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initiali...
متن کاملDBRS: A Density-Based Spatial Clustering Method with Random Sampling
When analyzing spatial databases or other datasets with spatial attributes, one frequently wants to cluster the data according to spatial attributes. In this paper, we describe a novel density-based spatial clustering method called DBRS. The algorithm can identify clusters of widely varying shapes, clusters of varying densities, clusters which depend on non-spatial attributes, and approximate c...
متن کاملImproved Compressive Sensing of Natural Scenes Using Localized Random Sampling
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, loc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Signal Processing, Image Processing and Pattern Recognition
سال: 2015
ISSN: 2005-4254
DOI: 10.14257/ijsip.2015.8.1.11