Qian Yanling

Embryonic electronics: State of the art and future perspective

This paper gives out an overview of embryonic electronics (Embryonics) which provides an entirely new approach to design highly robust integrated circuits inspired by the embryonic development of living beings. After a general introduction of research concerning Embryonics, we investigate the current status of Embryonics, discuss the key technical issues and the challenges i. e. We are facing to develop practical and large-scale Embryonics. In addition, we indicate the areas of interest for future research.

Survey on evolvable hardware and embryonic hardware

Bio-inspired hardware is a circuit with self-organizing, self-healing and adaptive ability referring the concepts in biology. This paper provides a new method to improve the reliability of electronic equipment. Evolvable hardware and embryonic hardware are the two research branches of bio-inspired hardware. The basic connotations of evolvable hardware and embryonic hardware are introduced, and the differences between them are analyzed. The research statuses, key technologies and challenges of evolvable hardware and embryonic hardware are discussed. Finally, the possible development trends of bio-inspired hardware are given.

Modeling and Simulation of Complex Maintenance System Dynamics

Equipment maintenance system is naturally a complex dynamical system. The effective maintenance management must be based on the knowledge of the systems intrinsic dynamics. This paper analyzes the basic structure and elements of maintenance system for complex multi-components equipment. The maintenance system is considered as a dynamic system whose behavior is influenced by its structures feedback and interaction, and available resources. Building the dynamical model with Simulink, we show some results about the maintenance systems nonlinear dynamics, which are never given by stochastic process methods. The model can be used for understanding and determining maintenance system behaviors, towards which operational adjustments of maintenance infrastructure, precise prediction of maintenance requirements and timely supply of maintenance resources can be made in a more informed way.

Multi-objective mapping for network-on-chip based on bio-inspired optimization algorithms

The network-on-chip (NoC) mapping confronts a balance between energy consumption, area and the performance of system-on-chip (SoC) in order to achieve optimum performance. In this article, we want to solve a two-objective optimization problem in terms of NoC energy consumption and communication latency by optimizing distribution of link load. Traditional heuristic approaches such as genetic algorithm are likely to trap into local optimal solutions. We present a new application specific multi-objective mapping algorithm based on bio-inspired optimization algorithms combining membrane computing and conventional genetic algorithms. Then this approach is applied to two real applications with different numbers of cores. Experimental results demonstrate that the represented mapping is a feasible way to obtain better NoC architecture in term of energy consumption, latency and traffic balance than mapping based on genetic algorithm.