In this paper, a line driver for HomePlug AV powerline communication system has been described. The proposed line driver includes a damping factor control (DFC) network which suppressed the open loop high frequency peaking effect to improve the stability for the various characteristic impedance of powerline. The line driver was fabricated in TSMC 0.18-μm CMOS technology and occupied 0.195 mm2active area. When operating with a power line and a coupling unit including a 1:4 turn ratio transformer, the line driver achieves 60 MHz bandwidth and 52.14 dBc in-band MTPR, with 5.15 dBm output signal power and 4.44 peak-to-average ratio (PAR), while 2.5 V supply voltage.

The inference of genetic regulatory networks from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred model is to obtain the expressions quantitatively comprehending every detail and principle of biological systems. This study introduces a multiobjective optimization approach to infer a realizable S-system structure for genetic regulatory networks. The work of inference is to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. Hybrid differential evolution is applied to solve the epsiv-constrained problem, which is converted from the multiobjective optimization problem, for minimizing the interaction measure with subject to the expectation constraints for the concentration and slope error criteria. This approach could avoid assigning a suitable penalty weight for sum of magnitude of kinetic orders for the penalty problem in order to prune the model structure.

MOTIVATION: The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks. RESULTS: We introduce an interactive inference algorithm to infer a realizable S-system structure for biochemical networks. The inference problem is formulated as a multiobjective optimization problem to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. The multiobjective optimization problem is solved by the epsilon-constraint method to minimize the interaction measure subject to the expectation constraints for the concentration and slope error criteria. The theorems serve to guarantee the minimum solution for the epsilon-constrained problem to achieve the minimum interaction network for the inference problem. The approach could avoid assigning a penalty weight for sums of magnitude of kinetic orders.