Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Publication
Articles 1 - 2 of 2
Full-Text Articles in Social and Behavioral Sciences
Competitive Adsorption Of Metals On Cabbage Waste From Multi-Metal Solutions, M Hossain, H Ngo, W Guo, L Nghiem, F Hai, S Vigneswaran, Thanh Vinh Nguyen
Competitive Adsorption Of Metals On Cabbage Waste From Multi-Metal Solutions, M Hossain, H Ngo, W Guo, L Nghiem, F Hai, S Vigneswaran, Thanh Vinh Nguyen
Faisal I Hai
This study assessed the adsorption capacity of the agro-waste ‘cabbage’ as a biosorbent in single, binary, ternary and quaternary sorption systems with Cu(II), Pb(II), Zn(II) and Cd(II) ions. Dried and ground powder of cabbage waste (CW) was used for the sorption of metals ions. Carboxylic, hydroxyl, and amine groups in cabbage waste were found to be the key functional groups for metal sorption. The adsorption isotherms obtained could be well fitted to both the mono- and multi-metal models. In the competitive adsorption systems, cabbage waste adsorbed larger amount of Pb(II) than the other three metals. However, the presence of the …
Depth Image Super-Resolution Using Multi-Dictionary Sparse Representation, Haoheng Zheng, Abdesselam Bouzerdoum, Son Lam Phung
Depth Image Super-Resolution Using Multi-Dictionary Sparse Representation, Haoheng Zheng, Abdesselam Bouzerdoum, Son Lam Phung
Haoheng Zheng
In this paper, we propose a new depth super-resolution technique based on multiple dictionary learning. A novel dictionary selection method using basis pursuit is proposed to generate multiple dictionaries adaptively. A sparse representation of each low-resolution input patch is derived based on the learned dictionaries, and then used to reconstruct the corresponding high-resolution patch. Experimental results are presented which show that the proposed multi-dictionary scheme outperforms existing depth super-resolution methods.