Realization Of Networks Research Articles

Delayed-feedback oscillators replicate the dynamics of multiplex networks: Wavefront propagation and stochastic resonance

The widespread development and use of neural networks have significantly enriched a wide range of computer algorithms and promise higher speed at lower cost. However, the imitation of neural networks by means of modern computing substrates is highly inefficient, whereas physical realization of large scale networks remains challenging. Fortunately, delayed-feedback oscillators, being much easier to realize experimentally, represent promising candidates for the empirical implementation of neural networks and next generation computing architectures. In the current research, we demonstrate that coupled bistable delayed-feedback oscillators emulate a multilayer network, where one single-layer network is connected to another single-layer network through coupling between replica nodes, i.e. the multiplex network. We show that all the aspects of the multiplexing impact on wavefront propagation and stochastic resonance identified in multilayer networks of bistable oscillators are entirely reproduced in the dynamics of time-delay oscillators. In particular, varying the coupling strength allows suppressing and enhancing the effect of stochastic resonance, as well as controlling the speed and direction of both deterministic and stochastic wavefront propagation. All the considered effects are studied in numerical simulations and confirmed in physical experiments, showing an excellent correspondence and disclosing thereby the robustness of the observed phenomena.

Enforcing exact permutation and rotational symmetries in the application of quantum neural networks on point cloud datasets

Recent developments in the field of quantum machine learning have promoted the idea of incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in this area is the realization of Sn-permutation equivariant quantum neural networks (QNNs) that are equivariant under permutations of input objects. In this paper, we focus on encoding the rotational symmetry of point cloud datasets into the QNN. The key insight of the approach is that all rotationally invariant functions with vector inputs are equivalent to a function with inputs of vector inner products. We provide a structure of the QNN that is exactly invariant to both rotations and permutations, with its efficacy demonstrated numerically in the problems of two-dimensional image classifications and identifying high-energy particle decays, produced by proton-proton collisions, with the SO(1,3) Lorentz symmetry. Published by the American Physical Society 2024

Optical activation function using a metamaterial waveguide for an all-optical neural network.

In this study, we experimentally demonstrated that the nonlinear optical coefficient of the original Si can be enhanced by incorporating a metamaterial structure into an existing silicon waveguide. The two-photon absorption coefficient enhanced by the metamaterial structure was 424 cm/GW, which is 1.2 × 103 times higher than that of Si. Using this metamaterial waveguide-based nonlinear optical activation function, we achieved a high inference accuracy of 98.36% in the handwritten character recognition task, comparable to that obtained with the ReLU function as the activation function. Therefore, our approach can contribute to the realization of more power-efficient and compact all-optical neural networks.

Tensor networks for black hole interiors: non-isometries, quantum extremal surfaces, and wormholes

We use hyperbolic tensor networks to construct a holographic map for black hole interiors that adds a notion of locality to the non-isometric codes proposed by Akers, Engelhardt, Harlow, Penington, and Vardhan. We use tools provided by these networks to study the relationship between non-isometries and quantum extremal surfaces behind the horizon. Furthermore, we introduce a limited notion of dynamics for these interior tensor networks based on the qudit models introduced by Akers et al., and study the evolution of quantum extremal surfaces in an evaporating black hole. We also find a tensor network description of a wormhole connecting the black hole interior to the radiation, providing a mechanism for interior states and operators to be encoded in the radiation after the Page time. As a particular case, we construct a tensor network realization of the backwards-forwards maps recently proposed to incorporate non-trivial effective dynamics in dynamical constructions of these non-isometric black hole codes.

Coupled EMAG-CFD-Thermal analysis of a novel SST system utilizing forced-liquid cooling with biodegradable dielectric fluid

Solid state transformers (SSTs) are at the forefront of emerging technologies, enhancing distribution and transmission networks. These transformers facilitate the development of highly controllable and robust smart grids, seamlessly integrating renewable energy sources and accommodating connections for both alternating current and direct current components/lines at medium or low voltage levels. This research paper introduces an innovative SST incorporating the inductive power transfer (IPT) technology, currently under development within SSTAR, a Horizon Europe project. This novel device operates at a frequency of 50 kHz and provides with an output power of 50 kW. The IPT system is submerged in an ester-based, biodegradable dielectric fluid, providing effective cooling and insulation characteristics. The primary focus of this research is to highlight the guidelines for the optimal design of the forced liquid cooling system of the SST module. The analysis employs a multi-faceted simulation strategy to calculate the cooling aspects of the transformer, utilizing the commercial software ANSYS. This involves 3D coupled Electromagnetic (EMAG) - Computational Fluid Dynamics (CFD) simulations, as well as stand-alone CFD and thermal FEA simulations, ensuring robust cross-verification of the obtained results. This novel SST exhibits improved thermal performance, achieving a hot-spot temperature of 58 °C, the lowest value found in similar research studies. The findings underscore the efficiency and robustness of the proposed design, marking a significant step towards the realization of active and environmentally-friendly electrical networks.

Solute transport characteristics of columnar volumetric contraction networks with mega column structure and aperture variability

Numerical simulations explore for the first time the role of mega columns and aperture variability on particle transport through mature volumetric contraction networks as informed by a unique synthesis of network propagation and maturity. Columnar fracture patterns are generated by updating a series of Voronoi centers to the midpoint of a generated polygon over many iterations, creating 250 network realizations. A DFN simulator solves for fluid flow and tracks conservative particle trajectories within each network. Dominant fracture attributes impacting fluid flow and solute transport in volumetric contraction networks are fracture orientation, density, and aperture/transmissivity. Ensemble plume snapshots generated by networks with equal fracture transmissivity define a baseline-level of dispersion that is solely attributed to network structure and connectivity. Longitudinal and transverse dispersion increase and the center of plume mass becomes delayed relative to the baseline case when fracture transmissivity is varied according to a lognormal distribution. The incorporation of highly-transmissive, large-aperture mega column fractures leads to plume snapshots with a more pronounced leading edge and an order of magnitude faster average breakthrough times. The breakthrough curves contain three peaks reflecting contrasting transport pathways in which particles are: (i) initially placed in mega column fractures and remain in these features until exiting the model domain, (ii) initially placed into small column fractures, incur additional time to migrate and enter a mega column fracture, and remain within those mega columns, and (iii) initially placed in small column fractures and remain in these fractures. Incorporating variability in fracture transmissivity for both small column and mega column fractures disrupts the binary distinction between small column and mega column fracture velocities and leads to dispersed breakthroughs over long time scales with a single peak. These results demonstrate that preferential flow paths emerge in volumetric contraction networks due to contracts in fracture transmissivity, not fracture connectivity as observed in tectonic networks.

Design and Performance Analysis of Compact Printed Ridge Gap Waveguide Phase Shifters for Millimeter-Wave Systems.

This paper introduces compact Printed Ridge Gap Waveguide (PRGW) phase shifters tailored for millimeter-wave applications, with a focus on achieving wide operating bandwidth, and improved matching and phase balance compared to single-layer technology. This study proposes a unique approach to achieve the required phase shift in PRGW technology, which has not been previously explored. This study also introduces a novel analytical approach to calculate the cutoff frequency and propagation constant of the PRGW structure, a method not previously addressed. Furthermore, the utilization of multi-layer PRGW technology enables the realization of multi-layer beamforming networks without crossing, thereby supporting wideband operation in a compact size. The proposed design procedure enables the realization of various phase shift values ranging from 0∘ to 135∘ over a broad frequency bandwidth centered at 30 GHz. A 45-degree phase shifter is fabricated and tested, demonstrating a 10 GHz bandwidth (approximately 33% fractional bandwidth) from 25 GHz to 35 GHz. Throughout the operating bandwidth, the phase balance remains within 45 ± 5∘, with a deep matching level of -20 dB. The proposed phase shifter exhibits desirable characteristics, such as compactness, low loss, and low dispersion, making it a suitable choice for millimeter-wave applications, including beyond 5G (B5G) and 6G wireless communications.

Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO

ABSTRACT The millimetre-wave (mmWave) communication satisfies the demand for high data rates due to the characteristic of wide bandwidth. Using massive multiple-input multiple-output (MIMO) technology, a significant propagation loss of mmWave communication is effectively compensated. However, it is challenging to provide a specialised radio frequency chain for each antenna due to the constrained physical area with closely spaced antennas and prohibitive power consumption in mmWave massive MIMO systems. This paper presents novel approaches for effective channel estimation and hybrid precoding in mmWave communication systems. To address the challenges of channel estimation, a convolutional neural network (CNN) is utilised, and network parameters are optimised using enhanced whale optimization algorithm (EWOA). The proposed CNN-based channel estimation method aims to accurately estimate the channel in mmWave systems with enhanced efficiency and reduced complexity. By training CNN using EWOA optimisation algorithm, the network parameters are fine-tuned to improve accuracy and generalisation capability of channel estimation process. Furthermore, hybrid precoding is achieved using adaptive radial-basis function neural networks (adaptive RBFNNs) which enables efficient precoding while minimising complexity. Moreover, the adaptive RBFNN approach determines the optimal precoding weights based on channel state information, resulting in a improved performance and a reduced computational overhead. The performance analysis is validated using the MATLAB/Simulink software and offers to provide effectual and reliable mmWave communication systems, facilitating the realisation of high-speed and high-capacity wireless networks.

Micrometric thermal electronic nose able to detect and quantify individual gases in a mixture

Recent urbanization and environmental problems urge for networks of sensors that can monitor air quality. Small, inexpensive, and smart sensors are one of the key components enabling the realization of such networks. Chemoresistive sensors are the ideal candidate, but they greatly lack selectivity, and for this reason, they are usually combined in arrays to create electronic noses, whose dimensions, however, make them not miniaturizable and cannot be integrated into portable devices. To overcome this inconvenience, we present a thermal electronic nose consisting of identical resistive sensors working at different temperatures so that the whole device is simple to make and tiny. The device contains two sensor arrays based on tin oxide nanowires decorated with Ag and Pt nanoparticles, respectively. The five sensors in each array are identical, but their response is differentiated by different temperatures locally generated by an on-chip integrated heater. This innovative approach allows the tiny array of five sensors together with the integrated heater to occupy only approximately 50 × 200 μm2 and consume only 120 μW. The tiny and portable device can estimate the concentration of H2 and NH3 in a mixture with a root mean square error of 6.1 ppm and 13.3 ppm, respectively, and it still works well after two months. The performance analysis of the double partial least squares regression used for concentration estimation also allows for feedback on which sensors are the most sensitive to which gas so that the electronic nose can be engineered for specific applications using the most suitable sensors. The size of the thermal electronic nose allows it to be integrated into portable and wearable devices, and its performance makes it suitable for any gas detection application. For example, a smartphone with an integrated sensor could carry out breath analysis and act as medical pre-screening or be used to evaluate the freshness of agri-food products in a rapid and non-invasive way.

Low-frequency Raman active modes of twisted bilayer MoS2

We study the low-frequency Raman active modes of twisted bilayer MoS2 for several twist angles using a force-field approach and a parametrized bond polarizability model. We show that twist angles near high-symmetry stacking configurations exhibit stacking frustration that leads to significant buckling of the moiré superlattice. We find that atomic relaxation due to the twist is of prime importance. The periodic displacement of the Mo atoms shows the realization of a soliton network, and in turn, leads to the emergence of a number of frequency modes not seen in the high-symmetry stacking systems. Some of the modes are only seen in the XZ Raman polarization setup while others are seen in the XY setup. The symmetry of the normal modes, and how this affects the Raman tensors is examined in detail.

Multi-heterointerface integration in nitrogen-doped Ti3C2Tx@Fe3O4 nanocomposites for superior microwave absorption

Engineering a lightweight and thin microwave absorber through the design of multiple heterogeneous interfaces represents a significant challenge. Here, we synthesized a nitrogen (N)-doped Ti3C2Tx@Fe3O4 (N–Ti3C2Tx@Fe3O4) nanocomposite with a sandwich-like structure through a solvothermal approach using hydrazine hydrate as the N donor. The numerous Ti3C2Tx electron transfer pathways of Ti3C2Tx enable the realization of a hierarchical conduction network within N–Ti3C2Tx@Fe3O4. The introduction of defects through N doping and the integration of Fe3O4 with Ti3C2Tx are pivotal for enhancing the dipole polarization. The heterogeneous interfaces in N–Ti3C2Tx@Fe3O4, coupled with Fe3O4 components, substantially boost the electromagnetic wave (EMW) absorption performance of the composite. An optimal N integration and the loading of Fe3O4 in Ti3C2Tx result in an improved impedance matching of N–Ti3C2Tx@Fe3O4. The synergistic effect of the improved impedance matching and the strong EMW attenuation capability endow N–Ti3C2Tx@Fe3O4 with enhanced microwave absorption (MA) properties. Specifically, at a thickness of 1.04 mm, the composite exhibits the effective absorption bandwidth (3.92 GHz; 14.08–18.00 GHz), surpassing that of Ti3C2Tx. At a thickness of 0.98 mm, it exhibits a maximum reflection loss of −40.28 dB at 17.25 GHz, which substantially exceeds that of Ti3C2Tx. Furthermore, it shows a maximum Radar Cross Section reduction of 21.32 dB cm2. A cotton fabric was coated with N–Ti3C2Tx@Fe3O4, and it was found to exhibit enhanced heat conduction and dissipation, at rates 3.83 and 4.64 times greater than those of uncoated cotton, respectively. These results highlight the considerable potential of N–Ti3C2Tx@Fe3O4 nanocomposites for use in far-field applications. It is envisaged that the exceptional MA and thermal management properties of N–Ti3C2Tx@Fe3O4 nanocomposites will provide a novel avenue for crafting lightweight and thin EMW absorbing materials.

Hybrid synaptic structure for spiking neural network realization

Neural networks and neuromorphic computing represent fundamental paradigms as alternative approaches to Von-Neumann-based implementations, advancing in the applications of deep learning and machine vision. Nonetheless, conventional semiconductor circuits encounter challenges in achieving ultra-fast processing speed and low power consumption due to their dissipative properties. Conversely, single flux quantum circuits exhibit inherent spiking behavior, showcasing their characteristics as a promising candidate for spiking neural networks (SNNs). In this work, we present a compact hybrid synapse circuit to mimic the biological interconnect functionality, enabling the weighting operations for excitatory and inhibitory impulses. Additionally, the proposed structure facilitates input accumulation, which is performed before the activation function. In the experiments, our synaptic structure interfaces with a soma circuit fabricated using a commercial Nb process, underscoring its compatibility and supporting its potential for integration into efficient neural network architectures. The weight value on the synapse is configurable by utilizing cryo-CMOS circuits, providing adaptability to the inference networks. We’ve successfully designed, fabricated, and partially tested the JJ-Synapse within our cryocooler system, enabling high-speed inference implementation for SNNs.

Multiplexable all-optical nonlinear activator for optical computing

As an alternative solution to surpass electronic neural networks, optical neural networks (ONNs) offer significant advantages in terms of energy consumption and computing speed. Despite the optical hardware platform could provide an efficient approach to realizing neural network algorithms than traditional hardware, the lack of optical nonlinearity limits the development of ONNs. Here, we proposed and experimentally demonstrated an all-optical nonlinear activator based on the stimulated Brillouin scattering (SBS). Utilizing the exceptional carrier dynamics of SBS, our activator supports two types of nonlinear functions, saturable absorption and rectified linear unit (Relu) models. Moreover, the proposed activator exhibits large dynamic response bandwidth (∼11.24 GHz), low nonlinear threshold (∼2.29 mW), high stability, and wavelength division multiplexing identities. These features have potential advantages for the physical realization of optical nonlinearities. As a proof of concept, we verify the performance of the proposed activator as an ONN nonlinear mapping unit via numerical simulations. Simulation shows that our approach achieves comparable performance to the activation functions commonly used in computers. The proposed approach provides support for the realization of all-optical neural networks.

Modeling the performance and bandwidth of single-atom adiabatic quantum memories

Quantum memories are essential for quantum repeaters, which will form the backbone of the future quantum internet. Such memory can capture a signal state for a controllable amount of time, after which this state can be retrieved. In this work, we theoretically investigated how atomic material and engineering parameters affect the performance and bandwidth of a quantum memory. We have applied a theoretical model for quantum memory operation based on the Lindblad master equation and adiabatic quantum state manipulation. The materials’ properties and their uncertainty are evaluated to determine the performance of Raman-type quantum memories by showcasing two defects in two-dimensional hexagonal boron nitride. We have derived a scheme to calculate the signal bandwidth based on the material parameters as well as the maximum efficiency that can be realized. The bandwidth depends on four factors: the signal photon frequency, the dipole transition moments in the electronic structure, the cavity volume, and the strength of the external control electric field. As our scheme is general and independent of materials, it can be applied to many other quantum materials with a suitable three-level structure. We, therefore, provided a promising route for designing and selecting materials for quantum memories. Our work is, therefore, an important step toward the realization of a large-scale quantum network.

DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal neurons for non-linear computations and signal processing. Dendritic branches facilitate temporal feature detection via synaptic delays that enable coincidence detection (CD) mechanisms. Spiking neural networks highlight the significance of delays for spatio-temporal pattern recognition in feed-forward networks, eliminating the need for recurrent structures. Here, we introduce DenRAM, a novel analog electronic feed-forward spiking neural network with dendritic compartments. Utilizing 130 nm technology integrated with resistive RAM (RRAM), DenRAM incorporates both delays and synaptic weights. By configuring RRAMs to emulate bio-realistic delays and exploiting their heterogeneity, DenRAM mimics synaptic delays and efficiently performs CD for pattern recognition. Hardware-aware simulations on temporal benchmarks show DenRAM’s robustness against hardware noise, and its higher accuracy over recurrent networks. DenRAM advances temporal processing in neuromorphic computing, optimizes memory usage, and marks progress in low-power, real-time signal processing

Account based ticketing (ABT) integration for seamless multi-modal transport systems in smart cities

The digital transformation of urban transportation systems remains a critical challenge in Smart Cities' development. This paper addresses the pervasive issue of inadequate digitalization within public transportation systems. The paper highlights the promise of Account Based Ticketing (ABT) as a pivotal solution for integrating diverse public and private transportation modes allowing users to access different transportation services with a single account, without the need for new physical tickets or cards. ABT approach represents a breakthrough in seamlessly amalgamating various transportation operators' systems, transcending city limits into broader metropolitan spheres, and fostering integration and interoperability across transportation modes operated by disparate entities. Emphasizing beyond-city-scale integration, it explores the potential value of ABT to connect transportation networks extending into metropolitan influences. The proposed approach involves analyzing the implementation and functionality of ABT systems, underlining their capacity to overcome existing barriers in multi-modal transportation. The results anticipate enhanced accessibility, increased ridership, and streamlined user experiences, contributing to the realization of efficient and interconnected urban transport networks. This study not only underscores the value of ABT in revolutionizing contemporary transportation systems but also examines its broader implications for future urban planning and smart city development, highlighting the transformative potential and essential role that ABT plays in shaping the future of integrated transportation and Mobility as a Service in Smart Cities.

Full-function Pavlov associative learning photonic neural networks based on SOA and DFB-SA

Pavlovian associative learning, a form of classical conditioning, has significantly impacted the development of psychology and neuroscience. However, the realization of a prototypical photonic neural network (PNN) for full-function Pavlov associative learning, encompassing both photonic synapses and photonic neurons, has not been achieved to date. In this study, we propose and experimentally demonstrate the first InP-based full-function Pavlov associative learning PNN. The PNN utilizes semiconductor optical amplifiers (SOAs) as photonic synapses and the distributed feedback laser with a saturable absorber (DFB-SA) as the photonic spiking neuron. The connection weights between neurons in the PNN can be dynamically changed based on the fast, time-varying weighting properties of the SOA. The optical output of the SOA can be directly coupled into the DFB-SA laser for nonlinear computation without additional photoelectric conversion. The results indicate that the PNN can successfully perform brain-like computing functions such as associative learning, forgetting, and pattern recall. Furthermore, we analyze the performance of PNN in terms of speed, energy consumption, bandwidth, and cascadability. A computational model of the PNN is derived based on the distributed time-domain coupled traveling wave equations. The numerical results agree well with the experimental findings. The proposed full-function Pavlovian associative learning PNN is expected to play an important role in the development of the field of photonic brain-like neuromorphic computing.

Design of federated learning-based resource management algorithm in fog computing for zero-touch network

The concept of zero-touch networking involves creating networks that are fully autonomous and require minimal human intervention. This approach is increasingly relevant due to the rapid growth of current cloud architectures, which are beginning to reach their limits due to continuous expansion demands from users and within the network core itself. In response, Fog computing, acting as a smart, localized data center closer to network nodes, emerges as a practical solution to the challenges of expansion and upgrading in existing architectures. Fog computing complements cloud technology. However, the realization of zero-touch networks is still in its early stages, and numerous challenges hinder its implementation. One significant challenge is the NP-hard problem related to resource management. This paper introduces an optimal resource management algorithm based on Federated Learning. The effectiveness of this algorithm is evaluated using the iFogSim simulator within the existing cloud-fog architecture. The results demonstrate that the proposed architecture outperforms the current infrastructure in several key aspects of resource management, including system latency, number of resources processed, energy consumption, and bandwidth utilization.

Fracture Network Influence on Rock Damage and Gas Transport following an Underground Explosion

Simulations of rock damage and gas transport following underground explosions that omit preexisting fracture networks in the subsurface cannot fully characterize the influence of geo-structural variability on gas transport. Previous studies do not consider the impact that fracture network structure and variability have on gas seepage. In this study, we develop a sequentially coupled, axi-symmetric model to look at the damage pattern and resulting gas breakthrough curves following an underground explosion given different fracture network realizations. We simulate 0.327 and 0.164 kT chemical explosives with burial depths of 100 m for 90 stochastically generated fracture networks. Gases quickly reach the surface in 30% of the higher yield simulations and 5% of the lower yield simulations. The fast breakthrough can be attributed to the formation of connected pathways between fractures to the surface. The formation of a connected damage pathway to the surface is not clearly correlated with the fracture intensity (P32) in our simulations. Breakthrough curves with slower transport are highly variable depending on the fracture network sample. The variability in the breakthrough behavior indicates that ignoring the influence of fracture networks on rock damage, which strongly influences the hydraulic properties following an underground explosion, will likely lead to a large underestimation of the uncertainty in the gas transport to the surface. This work highlights the need for incorporation of fracture networks into models for accurately predicting gas seepage following underground explosions.

Evaluation of Lifetime Predictions for Future Hydrogen Pipelines by Different Fracture Mechanics‐Based Design Codes

The realization of a hydrogen transport network requires the requalification of existing high‐pressure natural gas pipelines and the design and construction of new hydrogen pipelines. Therefore, appropriate design codes are needed, which consider the detrimental effects of gaseous hydrogen on the mechanical properties and ensure a robust, but not too conservative, lifetime concept. For the assessment of hydrogen pipelines, the ASME B31.12 is understood as a benchmark standard. Recently, the technical rule DVGW G 464 (2023) was published in Germany. Both standards consider a fracture mechanics‐based concept, where the pipeline design lifetime is assessed by the hydrogen‐assisted fatigue crack growth of a pre‐existing defect until a defined lifetime criterion is reached. This article compares both concepts within a use case study, where influencing factors on the lifetime predictions are identified and discussed. Relatively similar results are obtained for typical loading scenarios, whereby the ASME B31.12 predicts more allowable cycles compared to the DVGW G 464 mostly arising from a different assumed crack length. To reduce the conservatism, the focus in the design and assessment of hydrogen pipelines should be placed on a detectable small initial crack size and the measurement of material‐specific crack growth curves resulting in pronounced lifetime enhancements.