Activated carbon from krishnachura fruit (<i>Delonix regia</i>) and castor seed (<i>Ricinus communis L.</i>)

146-151Activated carbons were prepared from the husk of krishnachura (Delonix regia) fruit and the hull of castor (Ricinus communis L. ) seed using zinc chloride and steam-N2 as activating agent. The activating agent steam-N2 was used only for krishnachura whi le ZnCl2 was used both for activating krishnachura and castor samples. It was found that the adsorptive capacity of carbons produced by using ZnCl2 and steam-N2 as activating agent is comparable with that of commercially available samples. The adsorptive capacity was measured by permanganate method and the carbons produced were employed for the decolorization of molasses solution.</span

Recognition of Symbolic Gestures Using Depth Information

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields