| 1. |
- Changqing, Ruan, et al.
(författare)
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A green and simple method for preparation of an efficient palladium adsorbent based on cysteine functionalized2,3-dialdehyde cellulose
- 2016
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Ingår i: Cellulose. - : Springer Science and Business Media LLC. - 0969-0239 .- 1572-882X. ; 23:4, s. 2627-2638
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Tidskriftsartikel (refereegranskat)abstract
- A green and efficient adsorbent for adsorption of palladium ions was prepared from 2,3-dialdehyde cellulose (DAC) originating from nanocellulose from the green algae Cladophora. The DAC was functionalized with cysteine via reductive amination in a convenient one-pot procedure to provide the adsorbent. The adsorption properties for adsorbing palladium(II) ions, including capacity, adsorption isotherm and kinetics, were studied. The successful reductive amination of cysteine with 2,3-dialdehyde cellulose was confirmed by FT-IR, elemental analysis and XPS. The adsorbent was characterized by SEM, XRD, gas adsorption and TGA. The adsorbent had a high adsorption capacity (130 mg palladium per gram adsorbent) and enabled fast adsorption of palladium(II) ions from solution (80 % of maximum capacity reached in 2 h). Adsorbent materials suitable for both filters (fibrous) and column matrixes (spherical particles) could be obtained in an efficient manner by controlling the degree of oxidation while producing the DAC material.
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| 2. |
- Changrampadi, Mohamed Hashim, 1987, et al.
(författare)
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Multi-Class Ada-Boost Classification of Object Poses through Visual and Infrared Image Information Fusion
- 2012
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9784990644109 ; , s. 2865-2868
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for pose classification using fusion of visual and thermal infrared(IR) images. We propose a novel tree structure multi-class classification scheme with visual and IR sub-classifiers. These sub-classifiers are different from the conventional one-against-all or one-against-one strategies, where we handle the multi-class problem directly. We propose to use an accuracy score for the fusion of visual and IR subclassifiers. In addition, we propose to use the original Haar features plus an extra one, and a multi-threshold weak learner to obtain weak hypothesis. The experimental results on a visual and IR image dataset containing 3018 face images in three poses show that the proposed classifier achieves high classification rate of 99.50% on the test set. Comparisons are made to a fused one-vs-all method, a classifier with visual band only, and a classifier with IR band only. Results provide further support to the proposed method.
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