بازسازی وفقی سیگنال اسپارس متغیر با زمان در حوز? compressive sensing

اثر بربرین در تنظیم آستروسیتهای Gfap+ ناحیه هیپوکمپ موشهای صحرایی دیابتی شده با استرپتوزوتوسین

Background: Diabetes mellitus increases the risk of central nervous system (CNS) disorders such as stroke, seizures, dementia, and cognitive impairment. Berberine, a natural isoquinolne alkaloid, is reported to exhibit beneficial effect in various neurodegenerative and neuropsychiatric disorders. Moreover astrocytes are proving critical for normal CNS function, and alterations in their activity...

اثر بربرین در تنظیم آستروسیتهای Gfap+ ناحیه هیپوکمپ موشهای صحرایی دیابتی شده با استرپتوزوتوسین

Background: Diabetes mellitus increases the risk of central nervous system (CNS) disorders such as stroke, seizures, dementia, and cognitive impairment. Berberine, a natural isoquinolne alkaloid, is reported to exhibit beneficial effect in various neurodegenerative and neuropsychiatric disorders. Moreover astrocytes are proving critical for normal CNS function, and alterations in their activity...

Compressive sensing

Michael B. Wakin is the Ben L. Fryrear Associate Professor in the Department of Electrical Engineering and Computer Science at the Colorado School of Mines (CSM). Dr. Wakin received a B.S. in electrical engineering and a B.A. in mathematics in 2000 (summa cum laude), an M.S. in electrical engineering in 2002, and a Ph.D. in electrical engineering in 2007, all from Rice University. He was an NSF...

Compressive Sensing

Compressive sensing is a new type of sampling theory, which predicts that sparse signals and images can be reconstructed from what was previously believed to be incomplete information. As a main feature, efficient algorithms such as l1-minimization can be used for recovery. The theory has many potential applications in signal processing and imaging. This chapter gives an introduction and overvi...

Compressive Sensing

Amortized Variational Compressive Sensing

The goal of statistical compressive sensing is to efficiently acquire and reconstruct high-dimensional signals with much fewer measurements, given access to a finite set of training signals from the underlying domain being sensed. We present a novel algorithmic framework based on autoencoders that jointly learns the acquisition (a.k.a. encoding) and recovery (a.k.a. decoding) functions while im...