Two‐graph analysis of pathological equivalent networks

This chapter summarizes the analysis and design of analog integrated circuits (ICs) using the nullor concept and pathological elements. First, the usefulness of the nullor concept and pathological elements is highlighted through modeling the behavior of several active devices, which are used to perform symbolic nodal analysis of analog integrated circuits. Second, the nullator and norator elements are used to develop a binary genetic encoding (BGE) technique for the systematic design of active devices. For instance, we show the computational synthesis of current mirrors (CMs), and we discuss the synthesis of four unity-gain cells, which are combined for the design of mixed-mode amplifiers, such as: current conveyors and current-feedback operational amplifiers. Finally, those active devices are biased and sized (optimized) by applying evolutionary algorithms. Several applications of the active devices, e.g. to implement filters and oscillators, are described herein, while novel applications are provided in the following chapters.

The use of pathological elements (i.e., nullators, norators, and the voltage mirror-current mirror pair) as universal active elements opens up the possibility to model the behavior of active devices with either differential features or input-output multiples, in order to be used in analysis tasks of linear(ized) analog circuits. This brief tries on the modeling this class of active devices, which is carried out by considering the impedance characteristics along with the behavior equation of each active device, the kind of signal to be processed and by applying the pathological element properties. In order to obtain a more realistic model, not only parasitic elements associated to the input-output terminals of each active device are considered, but the tracking errors of voltage and current differential followers modeled with grounded pathological elements and admittances are also included. Due to that the gain and tracking errors are finites and of limited bandwidth, a two-pole model is used for each of them in order to include their contributions on the symbolic expressions computed. Because the behavior of fully-differential amplifiers is modeled with grounded pathological elements, a standard nodal analysis can be performed. This imply that not only the size of the admittance matrix is smaller than those generated by applying modified nodal analysis (MNA) method, for instance, but also the number of nonzero elements and the generation of cancellation-terms are both reduced. As a result, the computational complexity during the solution of the system of equations is reduced when recursive determinant-expansion techniques are applied. Examples are described and compared with the MNA method, in order to show the usefulness of the proposed models to compute fully-symbolic small-signal characteristics of analog circuits containing fully-differential active devices and/or with input-output multiples.

The nullor, as a network element, is very popular in the areas of modeling, synthesis and analysis of analog circuits. Nullors are pathological or singular elements that posses ideal characteristics and are specified according to the constraints they impose on their terminal voltages and currents. Therefore, in order to compute symbolic expressions of pathological element-base equivalent analog circuits, the nullor properties should be taken into account in the formulation process. Nodal analysis (NA) of these circuits is very popular. However, the recently proposed methods of formulation the nodal admittance (NA) matrix, require many operations on the rows and columns of an initial node admittance matrix (INAM), which has the order (nxn), for the (n+1) node network or a laborious procedure of computation of norator and nullator indexes. This paper presents a new method of formulation of the nodal admittance matrix for linear active circuits with nullors that performs direct construction of the final nodal admittance matrix (FNAM), which has the order (n-k)x(n-k), and uses only classical methods of element stamps. (k is the number of nullators and norators pairs). The viability and the potentiality of the proposed method (of the FNAM) is showcased via application examples. The improved formulation method is compared with other formulation methods, showing that it is simpler.

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.

Successful drug development is a risky and lengthy process that can take over ten years and consume billions of dollars. Target discovery is a critical stage of drug development for the identification of key molecules and pathways that can be targeted by novel therapeutics to find more effective treatments. Due to the rapid development in artificial intelligence and machine learning techniques over the past decade, computational approaches have now emerged as powerful tools to unravel complex interactions within biological systems to identify novel therapeutic targets. In particular, modeling and analysis of intracellular molecular networks play a pivotal role in target discovery by enabling researchers to efficiently and simultaneously navigate massive amounts of biological data to identify potential therapeutic targets. Such technologies can significantly accelerate the prolonged process of development of innovative therapies for complex diseases. Besides highlighting the findings of the recently introduced extreme signaling failures in intracellular molecular networks, here we briefly review various methods for modeling and analysis of intracellular molecular networks and discuss how they can be utilized to predict potential drug targets within such complex signaling systems. Overall, this review emphasizes the significance of modeling and analysis of molecular networks for fast-tracking and rapid discovery of novel therapeutic targets; to pave the way for the development of more effective treatments.

Urban environmental settings influence human psychological states, contributing to varying mental health outcomes. This study examines the relationships between objective environmental features and psychological states at a fine scale. Using a geo-enabled survey tool, we collected data on individuals’ perceptions of their immediate environment within their daily activity space on an urban university campus. The psychological assessment included emotional and affective states such as perceived stress, fatigue, and happiness. Objective environmental properties were derived from high-resolution imagery to analyze the association between environmental settings and psychological responses. The data were analyzed using Spearman’s correlation, moderated multiple regression, and partial correlation networks. Our findings revealed that beneficial psychological states were positively associated with the quantity of natural elements in the immediate environment such as trees, water, and grass. Conversely, negative psychological states were positively associated with barren areas, parking lots, buildings, and artificial surfaces. These relationships were not significantly moderated by gender or ethnicity in our experiment. The interconnections of psychological states show distinct patterns in three different environmental settings, which are a mostly green environment, a mixed environment with green and artificial elements, and a mostly artificial environment. A difference in such interconnections between males and females has been observed. These results highlight the complex interplay between environmental features and mental state networks.

This paper proposes a modification for the symbolic synthesis method of analog circuits using admittance matrix expansion. The modification involves a generalization of the synthesis approach to employ mirror elements (voltage mirrors and current mirrors) in the admittance matrix expansion and ideal description of active elements, rather than using only nullor elements (nullators and norators). Accordingly, more alternative ideal representations, based on nullor-mirror elements, can be realized and a wide range of active elements can be used in the circuit synthesis. Systematic synthesis of the CCII-based generalized impedance converters (GICs) is presented as an application example to illustrate the potential of this generalized approach. Multiple equivalent nullor-mirror realizations for the GIC could be extracted easily, by virtue of using mirror elements in the admittance matrix expansion. Consequently, numerous circuit realizations, spanning various combinations of CCII types, have been generated in a simple and direct way.

This paper proposes a hierarchical symbolic music generation method based on autoregressive modeling of variational autoencoder (VAE)-based bar-wise features. The method represents music as a sequence of low-dimensional bar-level features, enabling efficient modeling of long-term structure while maintaining local coherence. It consists of a VAE-based encoder and decoder for bar-wise feature extraction and composition, and a Transformer-based feature sequence generator conditioned on chord progression. To evaluate global structural coherence, we introduce a new metric, Bar-wise Feature Similarity Distance (BFSD). Experimental results show that the proposed method improves long-term structure compared to baseline models and achieves local naturalness comparable to existing methods. The source code for BFSD is available at https://github.com/KateSawada/barwise_feature_similarity_distance.

This paper focuses on the modeling and analysis of different power distribution network (PDN) structures in 3D TSV ICs, including 3D full wave model and equivalent circuit model. In addition, a simplified equivalent circuit model of stacked PDN is proposed to analysis aspects influencing stacked PDN impedance, such as number of ground TSVs, number of TSV pairs and number of stacked layers. Analyses about how these aspects influencing total capacitance, inductance of stacked PDN and causing resonance in PDN self-impedance (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> ) are made in detail. What's more, power noise in different stacked PDN structures is calculated and analyzed in time domain to evaluate their power integrity features. Analysis in this paper can offer instructions for the design and analysis of on-chip PDN.

This paper extends previous research efforts related to the simulation performance modelling and analysis of satellite communication networks. Specifically, the use of low earth orbit (LEO) satellite networks for personal communications is examined. Six different satellite constellation configurations are investigated in a packet-switched operating environment. Performance metrics examined are the end-to-end packet delay and the utilization of satellite channels in the dynamic environment. Realistic and accurate models of the physical satellite network and its terrestrial transmitters require that numerous operating characteristics and assumptions be specified. These are based on proposed design requirements of commercial systems, such as Iridium. Via the use of simulation, we show the relative delay and utilization performance of differing satellite network architectures. From these simulation models, mathematical metamodels are derived for the system delays. These innovative models are used to predict the delay performance of different network architectures not previously simulated. Comparison of these metamodels with simulation results show that metamodels provide an accurate means for performance prediction. Copyright © 1999 John Wiley & Sons, Ltd.

Video modeling and performance analysis of ATM networks by Kwok-leung Chung thesis 1995 xii, 81 leaves : ill. ; 30 cm Video sources will become one of the major traffic in future ATM networks. The video sources should be transmitted…Read more ›

The nodal method has been widely used for formulating circuit equations in computer-aided network analysis and design programs. However, several limitations exist in this method including the inability to process voltage sources and current-dependent circuit elements in a simple and efficient manner. A modified nodal analysis (MNA) method is proposed here which retains the simplicity and other advantages of nodal analysis while removing its limitations. A simple and effective pivoting scheme is also given. Numerical examples are used to compare the MNA method with the tableau method. Favorable results are observed for the MNA method in terms of the dimension, number of nonzeros, and fill-ins for comparable circuit matrices.

BackgroundMathematical modelling of cellular networks is an integral part of Systems Biology and requires appropriate software tools. An important class of methods in Systems Biology deals with structural or topological (parameter-free) analysis of cellular networks. So far, software tools providing such methods for both mass-flow (metabolic) as well as signal-flow (signalling and regulatory) networks are lacking.ResultsHerein we introduce CellNetAnalyzer, a toolbox for MATLAB facilitating, in an interactive and visual manner, a comprehensive structural analysis of metabolic, signalling and regulatory networks. The particular strengths of CellNetAnalyzer are methods for functional network analysis, i.e. for characterising functional states, for detecting functional dependencies, for identifying intervention strategies, or for giving qualitative predictions on the effects of perturbations. CellNetAnalyzer extends its predecessor FluxAnalyzer (originally developed for metabolic network and pathway analysis) by a new modelling framework for examining signal-flow networks. Two of the novel methods implemented in CellNetAnalyzer are discussed in more detail regarding algorithmic issues and applications: the computation and analysis (i) of shortest positive and shortest negative paths and circuits in interaction graphs and (ii) of minimal intervention sets in logical networks.ConclusionCellNetAnalyzer provides a single suite to perform structural and qualitative analysis of both mass-flow- and signal-flow-based cellular networks in a user-friendly environment. It provides a large toolbox with various, partially unique, functions and algorithms for functional network analysis.CellNetAnalyzer is freely available for academic use.

The fifth-generation (5G) is coming. Quo vadis 5G? What architectures, network topologies and technologies will define 5G? Are methodologies to the analysis, design and optimization of current cellular networks still applicable to 5G? The proposed tutorial is intended to offer a comprehensive and in depth crash course to communication professionals and academics. It is aimed to critically illustrate and discuss essential and enabling transmission technologies, communication protocols and architectures that are expected to make 5G wireless communication networks a reality. More specifically, the present tutorial is focused on illustrating the critical and essential importance of channel and spatial models for an accurate system-level analysis and optimization of 5G networks, which are expected to use different frequency bands compared to state-of-the-art networks and to rely on a much denser deployment of access points and antenna-elements, to a scale that has never been observed in the past.

Advances in exponential random graph ( p*) models applied to a large social network