An iterative scheme for finite horizon model reduction of continuous-time linear time-varying systems

In this paper, we obtain the functional derivatives of a finite horizon error norm between a full-order and a reduced-order continuous-time linear time-varying (LTV) system. Based on the functional derivatives, first-order necessary conditions for optimality of the error norm are derived, and a projection-based iterative scheme for model reduction is proposed. The iterative scheme upon convergence produces reduced-order models satisfying the optimality conditions. Finally, through a numerical example, we demonstrate the better performance of the proposed model reduction scheme in comparison to the finite horizon balanced truncation algorithm for continuous-time LTV systems

Recent Results on Some Word Oriented Stream Ciphers: SNOW 1.0, SNOW 2.0 and SNOW 3G

In this chapter, we have studied three word-oriented stream ciphers SNOW 1.0, SNOW 2.0 and SNOW 3G in a detailed way. The detailed description includes the working principles of each cipher, security vulnerabilities and implementation issues. It also helps us to study the challenges in each cipher. As SNOW 3G is used as a confidentiality and integrity component in 3G, 4G and 5G communications, the after study of this article may instigate the reader to find the fixes from different cryptanalysis and also find a new suitable design in Mobile telephony security

Mobile agents for network management: when and when not

ABSTRACT In order to fully realise the potential of mobile agent technology to address the needs of the network management domain, it is imperative to establish the conditions where mobile agent-based (MA) NMS or SNMP-based NMS should be employed to achieve the optimal performance in term of overhead traffic generated. This paper presents mathematical models to approximate the overhead traffic created by the MA-based NMS and SNMP-based NMS on the production network, based on the complexity of the management task involved. Through our analysis and experimentation, we establish that there is range (of the number of nodes involved) wherein it is advantageous to use mobile agents. Furthermore, we demonstrate through our analytical model that this range can be estimated a priori, thereby facilitating a decision on when to deploy mobile agents and when not to