Incorporation Of Noise Research Articles

Adaptive-propagating heterophilous graph convolutional network

Graph convolutional networks have significant advantages in dealing with graph-structured data, but most existing methods usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real-world graph-structured data. Due to the propagation mechanism of graph convolutional networks, it is challenging to prevent the interference aggregation from nodes of different classes, which may result in the incorporation of noise and irrelevant data in the outcome, ultimately decreasing the performance of the model. In this paper, we propose a new framework to address this issue on heterophilous graph-structured data. The proposed method comprises two main components. On one hand, the homophily of the graph-structured data is modeled so that the method can adaptively adjust the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information. On the other hand, the implicit node interaction is captured through the learned feature space, which is then fused with the original interaction to aggregate sufficient intra-class knowledge. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method against current state-of-the-art approaches. 22Code to reproduce our experiments is available at https://github.com/rebridger/APHGCN.

Flocking and swarming in a multi-agent dynamical system.

Over the past few decades, the research community has been interested in the study of multi-agent systems and their emerging collective dynamics. These systems are all around us in nature, such as bacterial colonies, fish schools, and bird flocks, as well as in technology, such as microswimmers and robotics, to name a few. Flocking and swarming are two key components of the collective behaviors of multi-agent systems. In flocking, the agents coordinate their direction of motion, but in swarming, they congregate in space to organize their spatial position. We propose a minimal mathematical model of a locally interacting multi-agent system where the agents simultaneously swarm in space and exhibit flocking behavior. Various cluster structures are found depending on the interaction range. When the coupling strength value exceeds a crucial threshold, flocking behavior is observed. We do in-depth simulations and report the findings by changing the other parameters and with the incorporation of noise.

Joint Probabilities Approach to Quantum Games with Noise.

A joint probability formalism for quantum games with noise is proposed, inspired by the formalism of non-factorizable probabilities that connects the joint probabilities to quantum games with noise. Using this connection, we show that the joint probabilities are non-factorizable; thus, noise does not generically destroy entanglement. This formalism was applied to the Prisoner's Dilemma, the Chicken Game, and the Battle of the Sexes, where noise is coupled through a single parameter μ. We find that for all the games except for the Battle of the Sexes, the Nash inequalities are maintained up to a threshold value of the noise. Beyond the threshold value, the inequalities no longer hold for quantum and classical strategies. For the Battle of the sexes, the Nash inequalities always hold, no matter the noise strength. This is due to the symmetry and anti-symmetry of the parameters that determine the joint probabilities for that game. Finally, we propose a new correlation measure for the games with classical and quantum strategies, where we obtain that the incorporation of noise, when we have quantum strategies, does not affect entanglement, but classical strategies result in behavior that approximates quantum games with quantum strategies without the need to saturate the system with the maximum value of noise. In this manner, these correlations can be understood as entanglement for our game approach.

Fault analysis of photovoltaic based DC microgrid using deep learning randomized neural network

The implementation of solar photovoltaic (PV) based Direct Current (DC) microgrid is limited due to lack of well-defined protection standards. These low voltage DC distribution networks are facing implementation challenges especially under multi-distributed generations subjected to various events such as PV arc faults, load switching faults, PV partial shading, change in PV irradiance and DC cable faults, etc. Thus a new deep learning neural network model is proposed for the accurate fault classification and distance calculation for an effective monitoring and protection coordination of photovoltaic based DC microgrid. The proposed model is the combination of an Adaptive variational mode decomposition (AVMD) and deep minimum variance random vector functional link network (DRVFLN). The optimal parameters of AVMD are selected using chaotic sine–cosine firefly algorithm (CSCFA) and efficient weighted kurtosis index (EWKU). Further, a novel non-iterative DRVFLN network is applied for accurate fault classification and location. In the DRVFLN direct connections are present from preceding layers to the forward layers of the network like random vector functional link network. These connections help to reduce the model complexity and also regularize the randomization. Further, the denoising criterion is also introduced in this network where the uncorrupted input can be recovered from the corrupted versions by using the autoencoder to obtained better results than the traditional networks. The performances of various models are evaluated using some performance index such as overall accuracy and sensitivity. Results conclude that the proposed AVMD-DRVFLN model classified the faults with an accuracy and sensitivity of 100% for all the events. The model performance is also tested and validated against the unwanted noise by considering different signal-to-noise ratio to ensure the robustness of the proposed model. Results conclude that the proposed model correctly classified the faults against such incorporation of noise. Similarly, the performances of various models for the estimation of fault distance are validated by computing the relative error. Results conclude that the proposed AVMD-DRVFLN model produces promising result in terms of relative error(which is less than 5%) for all the events. Comparisons with some existing models like extreme learning machine, support vector machine, random vector functional link network and deep extreme learning machine are included to validate the efficacy of the proposed AVMD-DRVFLN model. Results conclude that proposed method outperforms all the other methods. The effectiveness of the proposed AVMD-DRVFLN model for fault classification and distance estimation is established through rigorous case studies in MATLAB augmented by some real time test results.

Analysis and circuit sizing performance of a differential amplifier using HPSO algorithm

This paper presents an analysis of thermal noise of differential amplifier and automated sizing procedure with thermal noise incorporation, in addition to various design specifications, as constraints in the design process. Human behaviour-based particle swarm optimisation (HPSO), a swarm intelligence (SI)-based optimisation algorithm is used to perform the sizing task to obtain optimal value of design variables value subject to a satisfying set of constraints, with the main objective of designing a low-noise amplifier with minimum circuit area. The presented design procedure gives an option of considering both width and length of MOS transistor as design variables, which in turn can tune trade-off circuit performance parameters. The computational analysis is performed in MATLAB and CADENCE tool with UMC 180 nm parameters technology is used to validate the presented design procedure. Further, the performance of the purposed automated design methodology is compared with previous design methodology to check its efficiency in terms of speed, time and robustness.

Stochastic Resonance in Graphene Bilayer Optical Nanoreceivers

Graphene, a 2-D sheet of carbon atoms, is believed to have diverse application areas ranging from medicine to communications. A novel application is using graphene as a photodetector in optical communications due to its superior optical and electrical properties such as wide and tunable absorption frequency range and high electron mobility. Noise, which is especially significant in nanoscale communications, is mostly seen as an adversary. Stochastic resonance (SR) is the performance enhancement of a system due to incorporation of noise. It is shown that the excess noise in nanocommunications can be used to improve the performance of a graphene bilayer photodetector system with hard threshold decoder, when received signals are subthreshold. SR arises due to the nonlinear nature of the hard decoder. First, the SR effect due to the background ambient noise and intentional light noise is analyzed. An approximate inverse signal-to-noise ratio expression is derived, which maximizes the mutual information. The effect of frequency on the mutual information is also investigated, and it is shown that the higher frequencies are more preferable for noise limited regimes. Later, the case with the intentional noise added to the top gate is investigated. It is shown that significant mutual information improvements are achieved for subthreshold signals, due to the multiplicative stochastic terms arising from the nonlinear graphene bilayer characteristics, i.e., the exponential dependence of photocurrent on the gate voltages. All the analytical results are verified with extensive simulations.

Stochastic Steepest Descent Optimization of Multiple-Objective Mobile Sensor Coverage

We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the scheduling decision is effected by a simple constant-time coin toss operation only. We apply our method to the scheduling of a mobile sensor's coverage time among a set of points of interest (PoIs). The coverage algorithm is guided by a Markov chain, wherein the sensor at PoI i decides to go to the next PoI j with transition probability pij. We use steepest descent to compute the transition probabilities for optimal tradeoff among different performance goals with regard to the distributions of per-PoI coverage times and exposure times and the entropy and energy efficiency of sensor movement. For computational efficiency, we show how we can optimally adapt the step size in steepest descent to achieve fast convergence. However, we found that the structure of our problem is complex, because there may exist surprisingly many local optima in the solution space, causing basic steepest descent to easily get stuck at a local optimum. To solve the problem, we show how proper incorporation of noise in the search process can get us out of the local optima with high probability. We provide simulation results to verify the accuracy of our analysis and show that our method can converge to the globally optimal control parameters under different assigned weights to the performance goals and different initial parameters.

Timing-driven MultiChip Module routing algorithm with crosstalk consideration

This paper presents a timing-driven MultiChip Module (MCM) routing algorithm considering crosstalk, which maximizes routing density while minimizing vias and total wire length. The routing algorithm allows a more global solution as well as the incorporation of more accurate crosstalk modeling. In addition, various time domain characteristics of MCM are analyzed in this contribution. A deembedding technique for the S-parameter calculation is presented and functions for the time-domain signals are investigated in order to decrease the computation time. Routing results show that the proposed algorithm consistently produces the better results than other previously proposed routers while offering flexibility for future incorporation of noise and delay constraints.

Senders, Receivers and Deceivers: How Liar Codes Put Noise Back on the Diagram of Transmission

Senders, Receivers and Deceivers: How Liar Codes Put Noise Back on the Diagram of Transmission

Existence of Dendritic Crystal Growth with Stochastic Perturbations

Summary. We prove the first mathematical existence result for a model of dendritic crystal growth with thermal fluctuations. The incorporation of noise is widely believed to be important in solidification processes. Our result produces an evolving crystal shape and a temperature field satisfying the Gibbs-Thomson condition at the crystal interface and a heat equation with a driving force in the form of a spatially correlated white noise. We work in the regime of infinite mobility, using a sharp interface model with a smooth and elliptic anisotropic surface energy. Our approach permits the crystal to undergo topological changes. A time discretization scheme is used to approximate the evolution. We combine techniques from geometric measure theory and stochastic calculus to handle the singular geometries and take advantage of the cancellation properties of the white noise.