In this paper, a robust feature level fusion of four directional vector's (i.e. horizontal, vertical, forward and backward diagonal) based approach is proposed. For each image sequence, GEI (gait energy image) is firstly constructed. Next, GGEI (gradient gait energy image) is obtained based on neighborhood gradient computation. Then, variations in all four directions are used as discriminative gait features. Finally, SVM (support vector machine) classifier is effectively incorporated into this work for the classification procedures. The proposed method is tested on considerably largest multi view dataset CASIA - B (Chinese Academy of Sciences) and promising results were obtained. The prominent gait pragmatic conditions such as multi view and different walking speed conditions are effectively addressed in this work by considering whole, even, odd and multiple's of three numbering frames from each individual sequence.

Background: Gait uniquely distinguishes the persons by using their physiological and psychological state. For this reason, human gait recognition at a distance recently gained wider interest from research community. In recent days, the concept of fusion based biometric systems has attracted many researchers and it is proved that gait can be valuable solution when all other biometric sources failed. However gait is still open research area. Method: The review explains the observations with the help of available data and scientific arguments of the experts in their documented works are also taken for our study. Findings: This paper provides a comprehensive survey of current developments on gait recognition approaches and emphasizes on three major phases involved in gait recognition system, namely representation, dimensionality reduction and classification. We also discuss in detail the pragmatic conditions of gait and efficient utilization of standard datasets in order to use gait recognition systems in masses. Also we highlighted the prominent guidelines to internal and external gait challenges. Further we provide the motivations for fusion of physiological and psychological biometric sources to accomplish practical scenarios. Application/Improvement: The study concludes that all previous attempts are restricted to only few external gait variations and evaluated on limited data set. However, there is no single attempt which addresses most of the external gait variations. Hence there is a scope for further exploration and evaluation.

Background: One of the key considerations in the topological design of a network is the fault tolerance and survivability of the network topology. A deterministic measure for fault tolerance proposed by computer scientist and researchers is the k-node connectivity number of the network graph. Fault may happen at the physical level or at the software level. Method: A few heuristics and methods are proposed in the literature for the design of k-node connected network topological structures. The methods are analyzed on the parameters such as link optimality, computational efforts, repeated searching of nodes, adopting node numbering techniques, mathematical modelling, generality of the methods etc. Findings: This article portrays the comprehensive study and investigation of existing works and current developments on the design of k-node connected topological design of computer communication network structures, applicable for both wired and wireless network structures. This paper also highlights on the various design methods applicable for specific cases and also compares the existing and current methods, for the design parameters considered. Applications/Improvement: The study concludes by stating that the previous methods and strategies are restricted only to a few network design parameters. However there is no single method which addresses most of the issues, hence there is a scope for future exploration design methods.

Human gait recognition is an emerging research topic in the biometrics research field. It has recently gained a wider interest from machine vision research community because of its rich amount of merits. In this paper, a robust energy blocks based approach is proposed. For each silhouette sequence, gait energy image (GEI) is generated. Then it is split into three blocks, namely lower legs, upper-half and head. Further, Radon transform is applied to three energy blocks separately. Then, standard deviation is used to capture the variation in radial axis angle. Finally, support vector machine classifier (SVM) is effectively used for the classification procedures. The more prominent gait covariates such as multi views, backpack, carrying, least number of frames, clothing and different walking speed conditions are effectively addressed in this work by choosing sequential, even, odd and multiple's of three numbering frames for each sequence. Extensive experiments are conducted on four considerably large, publicly available standard datasets and the promising results are obtained.