Topology (electrical circuits)

About: Topology (electrical circuits) is a research topic. Over the lifetime, 33316 publications have been published within this topic receiving 397651 citations. The topic is also known as: topology.

Power laws and the AS-level Internet topology

Abstract: In this paper, we study and characterize the topology of the Internet at the autonomous system (AS) level. First, we show that the topology can be described efficiently with power laws. The elegance and simplicity of the power laws provide a novel perspective into the seemingly uncontrolled Internet structure. Second, we show that power laws have appeared consistently over the last five years. We also observe that the power laws hold even in the most recent and more complete topology with correlation coefficient above 99% for the degree-based power law. In addition, we study the evolution of the power-law exponents over the five-year interval and observe a variation for the degree-based power law of less than 10%. Third, we provide relationships between the exponents and other topological metrics.

Design Techniques for Fully Integrated Switched-Capacitor DC-DC Converters

Abstract: This paper describes design techniques to maximize the efficiency and power density of fully integrated switched-capacitor (SC) DC-DC converters. Circuit design methods are proposed to enable simplified gate drivers while supporting multiple topologies (and hence output voltages). These methods are verified by a proof-of-concept converter prototype implemented in 0.374 mm2 of a 32 nm SOI process. The 32-phase interleaved converter can be configured into three topologies to support output voltages of 0.5 V-1.2 V from a 2 V input supply, and achieves 79.76% efficiency at an output power density of 0.86 W/mm2 .

Robustness can evolve gradually in complex regulatory gene networks with varying topology.

Abstract: The topology of cellular circuits (the who-interacts-with-whom) is key to understand their robustness to both mutations and noise. The reason is that many biochemical parameters driving circuit behavior vary extensively and are thus not fine-tuned. Existing work in this area asks to what extent the function of any one given circuit is robust. But is high robustness truly remarkable, or would it be expected for many circuits of similar topology? And how can high robustness come about through gradual Darwinian evolution that changes circuit topology gradually, one interaction at a time? We here ask these questions for a model of transcriptional regulation networks, in which we explore millions of different network topologies. Robustness to mutations and noise are correlated in these networks. They show a skewed distribution, with a very small number of networks being vastly more robust than the rest. All networks that attain a given gene expression state can be organized into a graph whose nodes are networks that differ in their topology. Remarkably, this graph is connected and can be easily traversed by gradual changes of network topologies. Thus, robustness is an evolvable property. This connectedness and evolvability of robust networks may be a general organizational principle of biological networks. In addition, it exists also for RNA and protein structures, and may thus be a general organizational principle of all biological systems.

Design and analysis of an MST-based topology control algorithm

Abstract: In this paper, we present a minimum spanning tree (MST)-based algorithm, called local minimum spanning tree (LMST), for topology control in wireless multihop networks. In this algorithm, each node builds its LMST independently and only keeps on-tree nodes that are one-hop away as its neighbors in the final topology. We analytically prove several important properties of LMST: 1) the topology derived under LMST preserves the network connectivity; 2) the node degree of any node in the resulting topology is bounded by 6; and 3) the topology can be transformed into one with bidirectional links (without impairing the network connectivity) after removal of all unidirectional links. Simulation results show that LMST can increase the network capacity as well as reduce the energy consumption.