Abstract:The uncertainty principle can easily be generalized to cases where the “sets of concentration” are not intervals. Such generalizations are presented for continuous and discrete-time functions, and for...The uncertainty principle can easily be generalized to cases where the “sets of concentration” are not intervals. Such generalizations are presented for continuous and discrete-time functions, and for several measures of “concentration” (e.g., $L_2 $ and $L_1 $ measures). The generalizations explain interesting phenomena in signal recovery problems where there is an interplay of missing data, sparsity, and bandlimiting.Read More
Publication Year: 1989
Publication Date: 1989-06-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 1047
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot