Visible to the public Biblio

Filters: Keyword is resource optimization  [Clear All Filters]
2020-07-20
Nguyen, Lan K., Tringe, Joseph W., Bosler, Clayton, Brunnenmeyer, David.  2019.  An Algorithmic Approach to Highly Resilient SATCOM. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :89–94.

This paper proposes a generic SATCOM control loop in a generic multivector structure to facilitate predictive analysis for achieving resiliency under time varying circumstances. The control loop provides strategies and actions in the context of game theory to optimize the resources for SATCOM networks. Details of the theoretic game and resources optimization approaches are discussed in the paper.

2020-04-13
Sanchez, Cristian, Martinez-Mosquera, Diana, Navarrete, Rosa.  2019.  Matlab Simulation of Algorithms for Face Detection in Video Surveillance. 2019 International Conference on Information Systems and Software Technologies (ICI2ST). :40–47.
Face detection is an application widely used in video surveillance systems and it is the first step for subsequent applications such as monitoring and recognition. For facial detection, there are a series of algorithms that allow the face to be extracted in a video image, among which are the Viola & Jones waterfall method and the method by geometric models using the Hausdorff distance. In this article, both algorithms are theoretically analyzed and the best one is determined by efficiency and resource optimization. Considering the most common problems in the detection of faces in a video surveillance system, such as the conditions of brightness and the angle of rotation of the face, tests have been carried out in 13 different scenarios with the best theoretically analyzed algorithm and its combination with another algorithm The images obtained, using a digital camera in the 13 scenarios, have been analyzed using Matlab code of the Viola & Jones and Viola & Jones algorithm combined with the Kanade-Lucas-Tomasi algorithm to add the feature of completing the tracking of a single object. This paper presents the detection percentages, false positives and false negatives for each image and for each simulation code, resulting in the scenarios with the most detection problems and the most accurate algorithm in face detection.