Sparse Computation with PEI

Sparse Computation with PEI

Fast Compressive Sensing Recovery with Transform-based Sampling

We present a fast compression sensing (CS) reconstruction algorithm with computation complexity O(M2), where M denotes the length of a measurement vector Y = φX that is sampled from the signal X of length N via the sampling matrix φ with dimensionality M ×N . Our method has the following characteristics: (1) it is fast due to a closedform solution is derived; (2) it is accurate because signific...

Fast Binary Embedding via Circulant Downsampled Matrix - A Data-Independent Approach

Binary embedding of high-dimensional data aims to produce low-dimensional binary codes while preserving discriminative power. State-of-the-art methods often suffer from high computation and storage costs. We present a simple and fast embedding scheme by first downsampling N -dimensional data into M -dimensional data and then multiplying the data with an M × M circulant matrix. Our method requir...

Fabrication of Crosslinkable Hollow Fiber Membranes for Pervaporation Dehydration

Integrally-skinned asymmetric Polyetherimide/Poly (vinyl alcohol) (PEI/PVA) hollow fber membranes for pervaporation dehydration were fabricated by non-solvent induced phase inversion. PVA inside the PEI matrix could be crosslinked to provide membrane performance stability during long term operation. The effects of different PEI/PVA blend ratio, external coagulant type and...

Fast Reconstruction of SAR Images with Phase Error Using Sparse Representation

In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR has gained a significant research interest. This method offers the advantage of reducing the sampling rate, bu...