Topology Noise Research Articles

Fixed‐time Synchronization of Stochastic Kuramoto‐Oscillator Networks With Switching Topology Under Dynamic Event‐triggered Scheme

AbstractThis paper proposes a dynamic event‐triggered fixed‐time control scheme for the phase synchronization of Kuramoto networks with switching topology and stochastic coupling noise. To deal with the problems of switching topology and stochastic coupling noise, a multilayered control structure with a specially designed noise reduction layer is developed. The effectiveness is proved based on stochastic differential theory. To save communication resources and reduce control frequency, a dynamic event‐triggered mechanism is proposed. By introducing a dynamic variable into the triggering condition, the triggered instants are further reduced compared to the traditional static event‐triggered method. The synchronizing criteria and parameter conditions are established with stochastic Lyapunov stability theory. The analysis of Zeno behavior is also given. Finally, numerical simulation experiments are conducted to demonstrate the effectiveness of the proposed dynamic event‐triggered control scheme.

Dispersive optical solitons with DWDM topology and multiplicative white noise

The primary objective of this research is to investigate and analyze the intricate dynamics exhibited by dispersive optical soliton solutions within dispersion-flattened fibers while considering the impact of white noise. By examining this phenomenon, the study aims to gain a comprehensive understanding of the behavior and characteristics of these solitons, which are essential in the field of optical communication. A wide range of soliton solutions is obtained by the extended auxiliary equation procedure and the unified Riccati equation scheme, which are two powerful methods employed in the field of nonlinear dynamics and soliton theory to obtain a diverse array of soliton solutions. These techniques enable researchers to explore and analyze a wide range of complex nonlinear phenomena in various physical systems. This wide range of soliton solutions contributes to a deeper understanding of nonlinear phenomena and provides valuable insights into the fundamental properties and applications of solitons in different areas of science and engineering, including optics, fluid dynamics, plasma physics, and condensed matter physics. White noise only affects the phase part of the solitons and does not spread to other components. Such conclusions are applicable only under the specific transformation and the special simplification employed in deriving the solutions in the paper. The existence of white noise does not change the soliton amplitudes, widths, or velocities in any component. This paper is the first to report an essential observation concerning dispersion-flattened fibers, which has never been reported before.

Information-transfer characteristics in network motifs

Information processing in biological systems is realized by the appropriate transmission of information flows over complex networks, such as gene regulatory, signal transduction, and neural networks. These information flows are affected by the input-signal characteristics and structural properties of network systems, such as the network topology, regulation rules, and intrinsic and environmental noise. Many biological networks frequently include several typical patterns called network motifs, which are considered to play important roles in biological functions. However, their information-theoretic properties, particularly the dependence of the information flows in each network on the input signal, remain poorly understood. In our previous study [Mori and Okada, Phys. Rev. Res. 2, 043432 (2020)], we developed a graphical expansion method to describe transfer entropy (TE), a measure of information flow, in Boolean networks in terms of multiple information pathways. There, the input signal was limited to a simple case, and the effect of the input-signal characteristics on TE was not clarified. In this paper, we improve our method to render it applicable to Boolean networks that receive input signals with arbitrary stochastic characteristics. Our formula expresses how TE is determined by the input-signal characteristics, the assignment of Boolean functions, and the noise magnitude. We find that, in both positive and negative feedback loops, TE hardly depends on the signal timescale. In contrast, coherent and incoherent feedforward loops show low- and high-pass filtering properties, respectively, for a time-varying signal, which is consistent with previous reports. The emergence of either low- or high-pass filtering is determined by the Fourier components of the Boolean functions on specific pathways transmitting information flows. Thus our formula reveals the mechanism of information transfer in network motifs and provides insights into the origin of information processing in biological networks.

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module.

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

Bayesian Topology Learning and noise removal from network data

Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.

Effect of Surface Roughness Geometry on Boundary-Layer Transition and Far-Field Noise

Surface roughness elements are often used to force laminar to turbulent transition in aerodynamic and aeroacoustic wind-tunnel experiments. The statistical features and spectral content of the pressure fluctuations in the resulting turbulent boundary layer at the trailing edge can affect far-field noise. To elucidate this dependence, boundary-layer transition induced by randomly distributed roughness elements and a zigzag strip of the same height over a NACA 0012 airfoil is investigated experimentally. The effects of roughness geometry on the near-field flow topology, transition location, and far-field noise are addressed in the common experimental setting for the first time. For a fixed roughness height, distributed roughness elements are less effective in forcing transition than the zigzag strip at low freestream velocity (). As increases, the transition front for the distributed roughness elements moves closer to the roughness location, reaching the same or even further upstream locations compared to the transition onset in the presence of the zigzag strip. The far-field noise depends on the transition location. For , a higher noise level is measured for the distributed roughness elements with respect to the zigzag strip. In contrast, for , the earlier onset of transition with the distributed surface roughness leads to a lower noise level than that with the zigzag strip. The data confirm that an adequate characterization of the boundary-layer transition is necessary when measuring the far-field noise during wind-tunnel experiments.

Wireless sensor networks localization based on graph embedding with polynomial mapping

Localization of unknown nodes in wireless sensor networks, especially for new coming nodes, is an important area and attracts considerable research interests because many applications need to locate the source of incoming measurements as precise as possible. In this paper, in order to estimate the geographic locations of nodes in the wireless sensor networks where most sensors are without an effective self-positioning functionality, a new graph embedding method is presented based on polynomial mapping. The algorithm is used to compute an explicit subspace mapping function between the signal space and the physical space by a small amount of labeled data and a large amount of unlabeled data. To alleviate the inaccurate measurement in the complicated environment and obtain the high dimensional localization data, we view the wireless sensor nodes as a group of distributed devices and use the geodesic distance to measure the dissimilarity between every two sensor nodes. Then employing the polynomial mapping algorithm, the relative locations of sensor nodes are determined and aligned to physical locations by using coordinate transformation with sufficient anchors. In addition, the physical location of a new coming unknown node is easily obtained by the sparse preserving ability of the polynomial embedding manifold. At last, compared with several existing approaches, the performances of the presented algorithm are analyzed under various network topology, communication range and signal noise. The simulation results show the high efficiency of the proposed algorithm in terms of location estimation error.

Tackling the flip ambiguity in wireless sensor network localization and beyond

There have been significant advances in range-based numerical methods for sensor network localizations over the past decade. However, there remain a few challenges to be resolved to satisfaction. Those issues include, for example, the flip ambiguity, high level of noises in distance measurements, and irregular topology of the concerning network. Each or a combination of them often severely degrades the otherwise good performance of existing methods. Integrating the connectivity constraints is an effective way to deal with those issues. However, there are too many of such constraints, especially in a large and sparse network. This presents a challenging computational problem to existing methods. In this paper, we propose a convex optimization model based on the Euclidean Distance Matrix (EDM). In our model, the connectivity constraints can be simply represented as lower and upper bounds on the elements of EDM, resulting in a standard 3-block quadratic conic programming, which can be efficiently solved by a recently proposed 3-block alternating direction method of multipliers. Numerical experiments show that the EDM model effectively eliminates the flip ambiguity and retains robustness in terms of being resistance to irregular wireless sensor network topology and high noise levels.

Active and Passive Combined Mixer for Low Flicker Noise and Low dc Offset

A direct-conversion mixer with low flicker noise and low dc offset is presented for WLAN applications. The design consists of a folded-type active mixer with a resonant inductor in cascade with a source-driven passive mixer, operating at half the conventional LO frequency. Because of this unique topology, neither direct nor indirect flicker noise of the active mixer appears at the mixer output. Moreover, the dc offset voltage from the LO self-mixing is lowered, and the burden of designing a high-frequency VCO is relieved. The measurement results show a noise figure of 12.2 dB at 10 kHz, a conversion gain of 11.4 dB, and an IIP3 of $-10.5 ~{\rm dBm}$ while the mixer consumes 8.7 mW for a 1 V supply voltage. The design is implemented in a Dongbu Hitek 0.11 $\mu{\rm m}$ CMOS process with a chip area of 1.3 $\,\times\,$ 0.98 ${\rm mm}^{2}$ .

An Approach of Similarity Analysis for Non-Rigid Models Based on Diffusion Map with Thin-Plate Spline

Isometric transformation with geodesic distance is widely used in similarity analysis or 3D retrieval of non-rigid objects. However, geodesic distance is sensitive to topology noise and can be problematic in cases where objects topology is changed locally. In order to solve the problem, a new method based on diffusion map was proposed. We used diffusion map to compute embedded models and utilized thin-plate spline (TPS) to match the embedded models to obtain the similarity error of original objects. The method was tested on some examples, the results show that the new method is suitable to similarity analysis or 3D retrieval of non-rigid objects, moreover, our method is robust to the deformed objects with topology noise.

Local voting protocol for decentralized load balancing of network with switched topology and noise in measurements

The applicability of local voting protocol for the nonstationary problem of load balancing in a decentralized network with switched topology and measurement noise is investigated. The obtained theoretical results are illustrated by simulation models. The system is considered with and without the redistribution of tasks. It is shown that an adaptive multiagent strategy with the redistribution of tasks among the neighbors is better for handling the distribution of tasks than a strategy in which tasks are sent to random nodes and are not redistributed after arrival.

Transcriptional Regulation: Effects of Promoter Proximal Pausing on Speed, Synchrony and Reliability

Recent whole genome polymerase binding assays in the Drosophila embryo have shown that a substantial proportion of uninduced genes have pre-assembled RNA polymerase-II transcription initiation complex (PIC) bound to their promoters. These constitute a subset of promoter proximally paused genes for which mRNA elongation instead of promoter access is regulated. This difference can be described as a rearrangement of the regulatory topology to control the downstream transcriptional process of elongation rather than the upstream transcriptional initiation event. It has been shown experimentally that genes with the former mode of regulation tend to induce faster and more synchronously, and that promoter-proximal pausing is observed mainly in metazoans, in accord with a posited impact on synchrony. However, it has not been shown whether or not it is the change in the regulated step per se that is causal. We investigate this question by proposing and analyzing a continuous-time Markov chain model of PIC assembly regulated at one of two steps: initial polymerase association with DNA, or release from a paused, transcribing state. Our analysis demonstrates that, over a wide range of physical parameters, increased speed and synchrony are functional consequences of elongation control. Further, we make new predictions about the effect of elongation regulation on the consistent control of total transcript number between cells. We also identify which elements in the transcription induction pathway are most sensitive to molecular noise and thus possibly the most evolutionarily constrained. Our methods produce symbolic expressions for quantities of interest with reasonable computational effort and they can be used to explore the interplay between interaction topology and molecular noise in a broader class of biochemical networks. We provide general-purpose code implementing these methods.

Semi-supervised Laplacian regularized least squares algorithm for localization in wireless sensor networks

In this paper, we propose a new approach for localization in wireless sensor networks based on semi-supervised Laplacian regularized least squares algorithm. We consider two kinds of localization data: signal strength and pair-wise distance between nodes. When nodes are close within their physical location space, their localization data vectors should be similar. We first propose a solution using the alignment criterion to learn an appropriate kernel function in terms of the similarities between anchors, and the kernel function is used to measure the similarity between pair-wise sensor nodes in the networks. We then propose a semi-supervised learning algorithm based upon manifold regularization to obtain the locations of the non-anchors. We evaluate our algorithm under various network topology, transmission range and signal noise, and analyze its performance. We also compare our approach with several existing approaches, and demonstrate the high efficiency of our proposed algorithm in terms of location estimation error.

Designing a Low-phase-noise, Low-power-dissipation CMOS-Ring VCO according to a New Figure of Merit

Since the conventional figure of merit (FOM) neglects the flicker-noise effect in a voltage-controlled oscillator (VCO), we devised a new FOM that considers the flicker-noise effect and, thus, allows phase noise (which is dominated by flicker noise) of different VCOs to be compared. Using the proposed FOM, we analyze the phase noise and power dissipation in an inverter-based ring oscillator, since it has a simple topology and few noise sources. From this analysis, we draw three conclusions: first, varying load capacitance changes oscillation frequency only without changing proposed FOM; second, to improve the proposed FOM, large transistor length should be chosen, third, comparing FOM of a VCO and FOM calculated from target specifications of phase noise and power dissipation shows that the designed VCO meets target specifications. This second and third conclusion is useful for easily designing an actual inverter-based quadrature-ring VCO. A prototype inverter-based quadrature-ring VCO was fabricated by using 0.18-μm CMOS technology with an active die area of 0.018 mm2. It consumes 2.61 mW from a 1.8-V power supply. Moreover, it has an oscillation frequency of 338.5 MHz, phase noise of -110 dBc/Hz at 1-MHz offset frequency, and proposed FOM of 160.4 dB (at 0.1-MHz offset frequency).

Combinatorial Optimization of the piecewise constant Mumford-Shah functional with application to scalar/vector valued and volumetric image segmentation

Front propagation models represent an important category of image segmentation techniques in the current literature. These models are normally formulated in a continuous level sets framework and optimized using gradient descent methods. Such formulations result in very slow algorithms that get easily stuck in local solutions and are highly sensitive to initialization. In this paper, we reformulate one of the most influential front propagation models, the Chan–Vese model, in the discrete domain. The graph representability and submodularity of the discrete energy function is established and then max-flow/min-cut approach is applied to perform the optimization of the discrete energy function. Our results show that this formulation is much more robust than the level sets formulation. Our approach is not sensitive to initialization and provides much faster solutions than level sets. The results also depict that our segmentation approach is robust to topology changes, noise and ill-defined edges, i.e., it preserves all the advantages associated with level sets methods.

Hybrid stochastic simplifications for multiscale gene networks

BackgroundStochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models.ResultsWe propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples.ConclusionHybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

Recursive self-organizing network models

Self-organizing models constitute valuable tools for data visualization, clustering, and data mining. Here, we focus on extensions of basic vector-based models by recursive computation in such a way that sequential and tree-structured data can be processed directly. The aim of this article is to give a unified review of important models recently proposed in literature, to investigate fundamental mathematical properties of these models, and to compare the approaches by experiments. We first review several models proposed in literature from a unifying perspective, thereby making use of an underlying general framework which also includes supervised recurrent and recursive models as special cases. We shortly discuss how the models can be related to different neuron lattices. Then, we investigate theoretical properties of the models in detail: we explicitly formalize how structures are internally stored in different context models and which similarity measures are induced by the recursive mapping onto the structures. We assess the representational capabilities of the models, and we shortly discuss the issues of topology preservation and noise tolerance. The models are compared in an experiment with time series data. Finally, we add an experiment for one context model for tree-structured data to demonstrate the capability to process complex structures.

Classification of radar targets using synthetic neural networks

Radar target classification performance of neural networks is evaluated. Time-domain and frequency-domain target features are considered. The sensitivity of the neural network algorithm to changes in network topology and training noise level is examined. The problem of classifying radar targets at unknown aspect angles is considered. The performance of the neural network algorithms is compared with that of decision-theoretic classifiers. Neural networks can be effectively used as radar target classification algorithms with an expected performance within 10 dB (worst case) of the optimum classifier. >