Recursive Network Research Articles

Exploration of Forecasting the State of Metal Oxide Volt-Sensitive Protectors Based on Improve Recursive Neural Networks

In this paper, a method is put forward to forecast the MOVs state based on the improve recursive neural network. The result indicates that recursive network is more adapted to the state forecast of MOV. Because the running state of MOV is closely related to the system voltage and the environment, the state forecast method affected by multi-factors should be further considered.

An optimised 3D topology for on-chip communications

Increasing system complexity, energy and device reliability, requirement of modular approach, structured layout, effective spatial reuse of resources, scalability and re-programmability have made network-on-chip (NoC) an obvious interconnection design alternative to the ubiquitous bus based on chip communication architecture in system-on-chip. Designing of a topology and its routing scheme plays a vital role in determining performance of any NoC architecture. In recent years, 3D stacked NoC architecture attracts added interest in NoC design as it offers improved performance and shorter global interconnect. In this paper, we have developed a partially, vertically interconnected 3D topology, namely 3D Recursive Network Topology (3D RNT) and prove that the topology has a Hamiltonian connectedness. We have developed deadlock-free routing algorithm for the 3D RNT topology. Also, we compare the performance of the 3D RNT with partially and fully connected 3D mesh topologies (3D PMT and 3D FMT) by conducting suitable experiments. The experiment results show that there is not much deviation in respect of the performance of the 3D RNT on comparing with 3D PMT and 3D FMT even though a number of vertical links are trimmed down to 75%, which is an encouraging outcome as far as design space is concerned.

Social Networks and Peer Effects at Works

This paper extends the standard work effort model by allowing workers to interact through networks. We investigate experimentally whether peer performances and peer contextual effects influence individual performances. Two types of network are considered. Participants in Recursive networks are paired with participants who played previously in isolation. In Simultaneous networks, participants interact in real-time along an undirected line. Mean peer effects are identified in both cases. Individual performances increase with peer performances in the recursive network. In the simultaneous network, endogenous peer effects vary according to gender: they are large for men but not statistically different from zero for women.

Design of a set of One-to-Many Node-Disjoint and Nearly Shortest Paths on Recursive Circulant Networks

The recursive circulant network G(N,d) can be widely used in the design and implementation of parallel processing architectures. It consists of N identical nodes, each node is connected through bidirectional, point-to-point communication channels to different neighbors by jumping d i, where 0≤i≤?logd N??1. In this paper, we investigate the routing of a message on G(2 m ,4), a special kind of RCN, that is key to the performance of this network. On G(2 m ,4) we would like to transmit k packets from a source node to k destination nodes simultaneously along paths on this network, the i th packet will be transmitted along the i th path, where 1≤k≤m?1, 0≤i m ,4) for generating a set of one-to-many node-disjoint and nearly shortest paths, where each path is either shortest or nearly shortest and the total length of these paths is nearly minimum since the path is mainly determined by employing the Hungarian method.

Laplacian spectra of recursive treelike small-world polymer networks: Analytical solutions and applications

A central issue in the study of polymer physics is to understand the relation between the geometrical properties of macromolecules and various dynamics, most of which are encoded in the Laplacian spectra of a related graph describing the macrostructural structure. In this paper, we introduce a family of treelike polymer networks with a parameter, which has the same size as the Vicsek fractals modeling regular hyperbranched polymers. We study some relevant properties of the networks and show that they have an exponentially decaying degree distribution and exhibit the small-world behavior. We then study the Laplacian eigenvalues and their corresponding eigenvectors of the networks under consideration, with both quantities being determined through the recursive relations deduced from the network structure. Using the obtained recursive relations we can find all the eigenvalues and eigenvectors for the networks with any size. Finally, as some applications, we use the eigenvalues to study analytically or semi-analytically three dynamical processes occurring in the networks, including random walks, relaxation dynamics in the framework of generalized Gaussian structure, as well as the fluorescence depolarization under quasiresonant energy transfer. Moreover, we compare the results with those corresponding to Vicsek fractals, and show that the dynamics differ greatly for the two network families, which thus enables us to distinguish between them.

Remote Sensing-Based Fractal Analysis and Scale Dependence Associated with Forest Fragmentation in an Amazon Tri‑National Frontier

In the Amazon, the development and paving of roads connects regions and peoples, and over time can form dense and recursive networks, which often serve as nodes for continued development. These developed areas exhibit robust fractal structures that could potentially link their spatial patterns with deforestation processes. Fractal dimension is commonly used to describe the growth trajectory of such fractal structures and their spatial-filling capacities. Focusing on a tri-national frontier region, we applied a box-counting method to calculate the fractal dimension of the developed areas in the Peruvian state of Madre de Dios, Acre in Brazil, and the department of Pando in Bolivia, from 1986 through 2010. The results indicate that development has expanded in all three regions with declining forest cover over time, but with different patterns and rates in each country. Such differences were summarized within a proposed framework to indicate deforestation progress/level, which can be used to understand and regulate deforestation and its evolution in time. In addition, the role and influence of scale was also assessed, and we found local fractal dimensions are not invariant at different spatial scales and thus concluded such scale-dependent features of fragmentation patterns are here mainly shaped by the road paving.

SitLog: A Programming Language for Service Robot Tasks

In this paper we present SitLog: a declarative situation-oriented logical language for programming situated service robot tasks. The formalism is task and domain independent, and can be used in a wide variety of settings. SitLog can also be seen as a behaviour engineering specification and interpretation formalism to support action selection by autonomous agents during the execution of complex tasks. The language combines the recursive transition network formalism, extended with functions to express dynamic and contextualized task structures, with a functional language to express control and content information. The SitLog interpreter is written in Prolog and SitLog's programs follow closely the Prolog notation, permitting the declarative specification and direct interpretation of complex applications in a modular and compact form. We discuss the structure and representation of service robot tasks in practical settings and how these can be expressed in SitLog. The present framework has been tested in the service robot Golem-II+ using the specification and programming of the typical tasks which require completion in the RoboCup@Home Competition.

Hierarchical Modeling by Recursive Unsupervised Spectral Clustering and Network Extended Importance Measures to Analyze the Reliability Characteristics of Complex Network Systems

The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.

Performance and cost metrics analysis of a 3D NoC topology using network calculus

The packet switching based Network-on-Chip (NoC) is an obvious interconnect design alternative to the shared bus, crossbar or ring based on-chip communication architecture used in System-on-Chips (SoCs). The advent of the three dimensional NoC (3D NoC) architecture attracts added interest as it offers improved performance and shorter global interconnect. In the 3D NoC architecture, topology plays a vital role in determining the performance of the interconnect architecture. The performance and cost metrics of the 3D NoC topology are evaluated by using simulation in general. However, analytical models provide more insights on how the traffic related parameters influence the performance of the topology. In this paper, the traffic related parameters of a 3D NoC topology, namely 3D Recursive Network Topology (3D RNT) are evaluated by using network calculus based methodology and the results of the evaluation are compared against the results produced using simulation.

A Scalable Electrothermal Model for Transient Self-Heating Effects in Trench-Isolated SiGe HBTs

This paper demonstrates a scalable electrothermal model for transient self-heating effects in trench-isolated SiGe heterojunction bipolar transistors (HBTs). The scalability of the thermal model has been investigated by considering pyramidal heat diffusion approximation between the heat source and the thermal ground. Three-dimensional thermal TCAD simulations have been carried out to obtain transient variations of the junction temperature and to extract the thermal impedance in the frequency domain. A recursive thermal network with scalable model parameters has been developed and added at the temperature node of the HBT compact model HiCuM. This network has been verified through numerical simulations and by low-frequency <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$s$</tex></formula> -parameter measurements and found to be in excellent agreement for various device geometries.

Digital IIR Filters Design Using Differential Evolution Algorithm with a Controllable Probabilistic Population Size

Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.

Characterization of self-heating in Si–Ge HBTs with pulse, DC and AC measurements

This paper presents an extensive evaluation of self-heating in microwave Heterojunction Bipolar Transistors (HBTs). In particular, DC measurements, pulse measurements and low frequency s-parameter measurements have been performed. Pulse measurements are described in detail including optimization and characterization of lumped elements. Thermal parameters have been extracted for a recursive electro-thermal network that has been connected to the temperature node of HiCuM L2. Compact model simulations are verified in time and frequency domain and found to be in very good agreement for various device geometries. The accuracy of the presented approach for thermal capacitance (CTH) extraction has been validated with low frequency y-parameters.

Transient electro-thermal characterization of Si–Ge heterojunction bipolar transistors

In this paper, a comprehensive evaluation of the transient self-heating in microwave heterojunction bipolar transistors (HBTs) have been carried out through simulations and measurements. Three dimensional thermal TCAD simulations have been performed to investigate precisely the influence of backend metallization on transient thermal behavior of a submicron SiGe:C BiCMOS technology with fT and fmax of 230GHz and 290GHz, respectively. Transient variation of Collector current caused by self-heating is obtained through pulse measurements. For thermal characterization, different electro-thermal networks have been employed at the temperature node of HiCuM compact model. Thermal parameters have been extracted by means of compact model simulation using a scalable transistor library. It has been shown that, the conventional R–C thermal network is not sufficient to accurately model the transient thermal spreading behavior and therefore a recursive network needs to be used. Recursive network is verified with device simulations as well as measurements and found to be in excellent agreement.

Exact calculations of first-passage quantities on recursive networks

We present general methods to exactly calculate mean first-passage quantities on self-similar networks defined recursively. In particular, we calculate the mean first-passage time and the splitting probabilities associated to a source and one or several targets; averaged quantities over a given set of sources (e.g., same-connectivity nodes) are also derived. The exact estimate of such quantities highlights the dependency of first-passage processes with respect to the source-target distance, which has recently revealed to be a key parameter in characterizing transport in complex media. We explicitly perform calculations for different classes of recursive networks [finitely ramified fractals, scale-free (trans)fractals, nonfractals, mixtures between fractals and nonfractals, nondecimable hierarchical graphs] of arbitrary size. Our approach unifies and significantly extends the available results in the field.

Combinatorial results on the Extended Star graph with Cross Connections

The extended star graph with cross-connections ESC (n, k) is a relatively new interconnection network topology, that has a hierarchical and recursive network that combines the versatility and robustness of star graph architectures. In this paper we have discussed some combinatorial results to find the number of nodes and edges of ESC (n, k).

Recursive quantum repeater networks

Internet-scale quantum repeater networks will be heterogeneous in physical technology, repeater functionality, and management. The classical control necessary to use the network will therefore face similar issues as Internet data transmission. Many scalability and management problems that arose during the development of the Internet might have been solved in a more uniform fashion, improving flexibility and reducing redundant engineering effort. Quantum repeater network development is currently at the stage where we risk similar duplication when separate systems are combined. We propose a unifying framework that can be used with all existing repeater designs. We introduce the notion of a Quantum Recursive Network Architecture, developed from the emerging classical concept of 'recursive networks', extending recursive mechanisms from a focus on data forwarding to a more general distributed computing request framework. Recursion abstracts independent transit networks as single relay nodes, unifies software layering, and virtualizes the addresses of resources to improve information hiding and resource management. Our architecture is useful for building arbitrary distributed states, including fundamental distributed states such as Bell pairs and GHZ, W, and cluster states.

Thermal Impedance Modeling of Si–Ge HBTs From Low-Frequency Small-Signal Measurements

In this letter, the thermal impedance of SiGe heterojunction bipolar transistors has been characterized using low-frequency small-signal measurements. Theoretical works for thermal impedance modeling using different electrothermal networks, developed until date, have been verified with our experimental results. We report for the first time the experimental verification of the electrothermal model for thermal impedance developed by Mnif using a nodal and recursive network. A generalized expression of the frequency-domain thermal impedance has been selected for electrical compact transistor model (HiCuM) improvement, parameter extraction, and electrothermal network verification.

Non-invasive assessment of allometric scaling laws in the human coronary tree

Assessing the geometry of the coronary arteries in a patient can help to better explain coronary artery disease (CAD) development. Allometric scaling functions were successfully applied to describe how essential materials are transported through recursive networks, also observed in the coronary tree. In this work we used skeletonization methods on multislice computed tomography (MSCT) images to render the coronary tree in 3D. Fifty subjects were recruited in two groups: 1) free from plaques and 2) with focal lesions. The left coronary tree was segmented using a custom algorithm with minimum user intervention. Vessels were separated using a stem-crown architecture. Cumulative arterial length ( L) with volume ( V) and vessel stem diameter ( D STEM) with distal L functions were analysed. In the allometric functions L = k v V β and D STEM = k L L γ , no significant differences were found between groups. The scaling exponent range for β was 0.6–0.9 and for γ was 0.1–0.4. Values were not different from other studies in pigs. In a Log–Log scatter plot in all patients, lines were parallel, confirmed with an ANCOVA. In other words, the allometric function stood for all patients. The idea that parameters from diseased hearts are expected to deviate from normal was not revealed in this study. We believe that CAD did not disturb the allometric relations due to the focal nature of the lesions and the absence of diffuse CAD. Further combination of MSCT with 3D morphological extraction algorithms can help to overcome the qualitative analysis of a patient vasculature and advance into new clinical quantitative perspectives.

Zero Pearson coefficient for strongly correlated growing trees

We obtained Pearson's coefficient of strongly correlated recursive networks growing by preferential attachment of every new vertex by m edges. We found that the Pearson coefficient is exactly zero in the infinite network limit for the recursive trees (m=1). If the number of connections of new vertices exceeds one (m>1), then the Pearson coefficient in the infinite networks equals zero only when the degree distribution exponent gamma does not exceed 4. We calculated the Pearson coefficient for finite networks and observed a slow power-law-like approach to an infinite network limit. Our findings indicate that Pearson's coefficient strongly depends on size and details of networks, which makes this characteristic virtually useless for quantitative comparison of different networks.

Classification and biomarker identification using gene network modules and support vector machines.

BackgroundClassification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)ResultsInitially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form n clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.ConclusionMore than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.The Matlab version of SVM-RNE can be downloaded from