本文選題：香農(nóng)采樣定理 + 奈奎斯特率��； 參考：《華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版)》2014年10期

【摘要】：在伯努利循環(huán)矩陣的基礎(chǔ)上,對(duì)其獨(dú)立元素中隨機(jī)地引入零元,形成超稀疏三元循環(huán)矩陣,與伯努利-循環(huán)矩陣相比,其隨機(jī)獨(dú)立變?cè)獋€(gè)數(shù)和矩陣非零元數(shù)目顯著減少,從而有利于信息的傳輸和存儲(chǔ).數(shù)值實(shí)驗(yàn)結(jié)果表明:提出的測(cè)量矩陣重建效果略優(yōu)于伯努利矩陣和伯努利循環(huán)矩陣的重建效果,并在絕大多數(shù)情形下重建時(shí)間可以降低到原來(lái)的10%~40%,加快了后端信號(hào)重建的速度,有利于壓縮感知理論的實(shí)用化.
[Abstract]:On the basis of Bernoulli cyclic matrix, zero element is randomly introduced into its independent element to form super-sparse ternary cyclic matrix. Compared with Bernoulli cyclic matrix, the number of random independent variables and the number of nonzero elements of matrix are significantly reduced. This is beneficial to the transmission and storage of information. The numerical results show that the reconstruction effect of the proposed measurement matrix is slightly better than that of the Bernoulli matrix and the Bernoulli cyclic matrix, and the reconstruction time can be reduced to 100.40% of the original reconstruction time in most cases, which accelerates the speed of the back-end signal reconstruction. It is beneficial to the practical application of the theory of compressed perception.
【作者單位】： 安徽大學(xué)計(jì)算智能與信號(hào)處理教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：NSFC-廣東聯(lián)合基金資助項(xiàng)目(U1201255) 國(guó)家自然科學(xué)基金資助項(xiàng)目(61201396,61301296,61377006) 安徽大學(xué)博士科研啟動(dòng)經(jīng)費(fèi)資助項(xiàng)目(33190218)
【分類號(hào)】：TN911.7
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本文編號(hào)：2005069