Fast Face Recognition Using Eigen Faces

Face is a typical multidimensional structure and needs good computational analysis for recognition. Our approach signifies face recognition as a two-dimensional problem. In this approach, face recognization is done by Principal Component Analysis (PCA). Face images are faced onto a space that encodes best difference among known face images. The face space is created by eigenface methods which are eigenvectors of the set of faces, which may not link to general facial features such as eyes, nose, and lips. The eigenface method uses the PCA for recognition of the images. The system performs by facing pre-extracted face image onto a set of face space that shows significant difference among known face images. Face will be categorized as known or unknown face after imitating it with the present database

Faster Image Zooming using Cubic Spline Interpolation Method

This paper proposes an efficient image interpolation algorithm using cubic spline interpolation method. The proposed interpolation algorithm is done in two steps. In the first step, the area of the image is enlarged by inserting zeros in between every two columns and rows according to the zooming intensity as input by the user. Second step involves the estimation of correct values of those zeros so that after zooming the image does not ruptures with absurd values of the pixel. The cubic spline method is efficiently programmed in MATLAB 7.12 and results were according to the requirement. DOI: 10.17762/ijritcc2321-8169.15010

HARMONIC MAPPINGS WITH THE FIXED ANALYTIC PART

In this article, we consider a class of sense-preserving harmonic mappings whose analytic part is convex in one direction. We prove that functions in this class are close-to-convex for certain values of parameters. Further, we obtain bounds on preSchwarzian derivatives and bounds on the Bloch’s constant. Finally, we obtain coefficient bounds, growth and distortion results

SODIUM FLUORIDE-INDUCED OXIDATIVE STRESS AND HISTOLOGICAL CHANGES IN LIVER OF SWISS ALBINO MICE AND AMELIORATION BY OCIMUM SANCTUM LINN.

Objective: The present study was designed to evaluate hepatotoxicity induced by sodium fluoride (NaF) in Swiss albino mice and amelioration by Ocimum sanctum Linn.Methods: Mice were divided into six groups, Group I received tap water, Group II received low dose of NaF (8 mg/L), Group III high dose of NaF (80 mg/L) in drinking water, Group IV tap water along with 250 mg/kg body weight/day leaf extract of O. sanctum Linn., Group V 8 mg/L NaF in drinking water and 250 mg/kg body weight leaf extract of O. sanctum Linn., and Group VI 80 mg/L NaF in drinking water along with leaf extract of O. sanctum Linn. 250 mg/kg body weight/day for 90 days. On the 91st day, the animals were autopsied and liver tissue samples were taken to assess histopathological changes and oxidative stress by estimating glutathione peroxidase, superoxide dismutase, and catalase.Results: A highly significant decrease in the activity of antioxidant enzymes occurred with the high dose (Group III). Hepatic histopathological architecture exhibited deformities, namely, ballooning, hypertrophy, hepatocellular necrosis, infiltration of mononuclear cells, deformed central vein, sinusoidal dilation, and binucleated cells. Low-dose group (Group II) showed a significant decrease in antioxidant enzyme levels as compared to control group, and histological sections of liver showed dilated sinusoids, infiltration of mononuclear cells, ballooning, and hypertrophy of hepatocytes. Groups IV and V showed no pathological features. Group VI showed less damage to the liver as compared to Group III.Conclusion: The results revealed that the administration of leaf extract of O. sanctum Linn. elicited protection against NaF-induced hepatotoxicity and oxidative stress. It may, therefore, be inferred that fluoride caused hepatotoxicity in Swiss albino mice at the tested dose levels can be ameliorated by O. sanctum Linn

Hierarchical multi-agent reinforcement learning

Consider sending a team of robots to carry out reconnaissance of an indoor environment to check for intruders

ABSTRACT Hierarchical Multi-Agent Reinforcement Learning

In this paper we investigate the use of hierarchical reinforcement learning to speed up the acquisition of cooperative multi-agent tasks. We extend the MAXQ framework to the multi-agent case. Each agent uses the same MAXQ hierarchy to decompose a task into sub-tasks. Learning is decentralized, with each agent learning three interrelated skills: how to perform subtasks, which order to do them in, and how to coordinate with other agents. Coordination skills among agents are learned by using joint actions at the highest level(s) of the hierarchy. The Q nodes at the highest level(s) of the hierarchy are configured to represent the joint task-action space among multiple agents. In this approach, each agent only knows what other agents are doing at the level of sub-tasks, and is unaware of lower level (primitive) actions. This hierarchical approach allows agents to learn coordination faster by sharing information at the level of sub-tasks, rather than attempting to learn coordination taking into account primitive joint state-action values. We apply this hierarchical multi-agent reinforcement learning algorithm to a complex AGV scheduling task and compare its performance and speed with other learning approaches, including flat multi-agent, single agent using MAXQ, selfish multiple agents using MAXQ (where each agent acts independently without communicating with the other agents), as well as several well-known AGV heuristics like ”first come first serve”, ”highest queue first ” and ”nearest station first”. We also compare the tradeoffs in learning speed vs. performance of modeling joint action values at multiple levels in the MAXQ hierarchy. 1