Preparation and characterization of novel bio-composites based on functionalized zeolite and nano-hydroxyapatite for a potential application in bone treatment

Journal of Porous Materials - This work was aiming to design novel bio-composite (BC) materials based on microwave-prepared AuNPs-loaded Zeolite-A (ZA-Au) and nano-hydroxyapatite (HA)....     

Multi-facial patches aggregation network for facial expression recognition and facial regions contributions to emotion display

Multimedia Tools and Applications - In this paper, an approach for Facial Expressions Recognition (FER) based on a multi-facial patches (MFP) aggregation network is proposed. Deep features are...     

K-nearest neighbor finding using MaxNearestDist

Similarity searching often reduces to finding the k nearest neighbors to a query object. Finding the k nearest neighbors is achieved by applying either a depth- first or a best-first algorithm to the search hierarchy containing the data. These algorithms are generally applicable to any index based on hierarchical clustering. The idea is that the data is partitioned into clusters which are aggregated to form other clusters, with the total aggregation being represented as a tree. These algorithms have traditionally used a lower bound corresponding to the minimum distance at which a nearest neighbor can be found (termed MinDist) to prune the search process by avoiding the processing of some of the clusters as well as individual objects when they can be shown to be farther from the query object q than all of the current k nearest neighbors of q. An alternative pruning technique that uses an upper bound corresponding to the maximum possible distance at which a nearest neighbor is guaranteed to be found (termed MaxNearestDist) is described. The MaxNearestDist upper bound is adapted to enable its use for finding the k nearest neighbors instead of just the nearest neighbor (i.e., k=1) as in its previous uses. Both the depth-first and best-first k-nearest neighbor algorithms are modified to use MaxNearestDist, which is shown to enhance both algorithms by overcoming their shortcomings. In particular, for the depth-first algorithm, the number of clusters in the search hierarchy that must be examined is not increased thereby potentially lowering its execution time, while for the best-first algorithm, the number of clusters in the search hierarchy that must be retained in the priority queue used to control the ordering of processing of the clusters is also not increased, thereby potentially lowering its storage requirements.     

Weighted link quality and forward progress coupled with modified RTS/CTS for beaconless packet forwarding protocol (B-PFP) in VANETs

Telecommunication Systems - The Internet Engineering Task Force recommends the use of a group of well-defined protocols to support Internet of ThingsLow-Power Low-Rate Networks (LLNs). These...     

Driers based on tall oil—A review

The metal soaps of tall oil were introduced as paint driers during World War II. Paint industry experience reveals that these oxidation-polymerization catalysts are generally equivalent in performance to naphthenate driers. Physical properties, manufacturing procedures and application of tallate driers in typical paint systems are reviewed. Presented at the AOCS Meeting, New Orleans, 1964.     

Reducing Resource Usage Based on User Hints and Policies

Multimedia applications often have performance requirements that make these applications computing resource intense; e.g., the number of video frames displayed to the user must be about 25 frames per second. A user hint is an indication of the interest that a user has in an application. Examples of user hints include a screen saver being invoked or covering a window with another window. In the case that the user is running video, the occurrence of these hints imply that the user is no longer viewing the video. However, the resource usage of the application has not changed. This paper describes an architecture that make use of user hints to reduce the resource consumption of an application. The emphasis is on network traffic and CPU usage. Experimental results are presented.