References Image Compression by Edge Pattern Learning Using Multilayer P Erc E Ptro N S
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Improved neural structures for image compression
The problem considered is the effective compression of image data. Compared to the many methods which allow for high compression factors (mainly based on the DCT), the proposed neural approaches offer, after a suitable training, comparable performance both in terms of perceived image quality and measured SNR. Moreover, they are able to perform in the same step the two main required operations of transformation and selection of the most significant terms, with a correspondingly smaller computational effort.<<ETX>>
Experimental energy barriers to anions transporting through nanofiltration membranes.
Environmentally relevant contaminants fluoride, chloride, nitrate, and nitrite face Arrhenius energy barriers during transport through nanofiltration (NF) membranes. The energy barriers were quantified using crossflow filtration experiments and were in the range of 7-17 kcal·mol(-1), according to ion type and membrane type (Filmtec NF90 and NF270). Fluoride faced a comparatively high energy barrier for both membranes. This can be explained by the strong hydration energy of fluoride rather than other ion properties such as bare ion radius, fully hydrated radius, Stokes radius, diffusion coefficient, or ion charge. The energy barrier for fluoride decreased with pressure, indicating an impact of directional force on energy barriers. The influence of temperature-induced pore radius variability and viscosity on energy barriers was considered. The novel link between energy barriers and ion properties emphasizes the importance of ion hydration and/or partial dehydration mechanisms in determining transport in NF.