Processing Simulation Research Articles

Long-range four-body interactions in structured nonlinear photonic waveguides

Multiphoton dynamics beyond linear optical materials are of significant fundamental and technological importance in quantum information processing. However, it remains largely unexplored in nonlinear waveguide QED. In this work, we theoretically propose a structured nonlinear waveguide in the presence of staggered photon-photon interactions, which supports two branches of gaped bands for doublons (i.e., spatially bound-photon-pair states). In contrast to linear waveguide QED systems, we identify two important contributions to its dynamical evolution, i.e., single-photon bound states (SPBSs) and doublon bound states (DBSs). Most remarkably, the nonlinear waveguide can mediate the long-range four-body interactions between two emitter pairs, even in the presence of disturbance from SPBSs. By appropriately designing system's parameters, we can achieve high-fidelity four-body Rabi oscillations mediated only by virtual doublons in DBSs. Our findings pave the way for applying structured nonlinear waveguide QED in multi-body quantum information processing and quantum simulations among remote sites. Published by the American Physical Society 2024

Green building interior design based on digital image processing and thermal environment simulation

With the increasingly severe global climate change and energy crisis, this study aims to explore effective strategies for green building interior design, especially in terms of thermal environment control, through digital image processing and thermal environment simulation. The study simulates indoor scenes based on image processing technology, using methods such as image acquisition, localization, and object detection to improve the accuracy of scene reconstruction, and conducts in-depth analysis of detection effects, laying the foundation for subsequent thermal environment simulations. In the design of the thermal energy environment control system, the calculation of indoor cooling load was studied and compared with existing thermal environment models to determine the impact of user activities on the indoor thermal environment. By establishing a thermal energy environment control model and implementing precise environmental regulation for different usage scenarios, indoor comfort and energy efficiency have been significantly improved. The experimental results show that the use of digital image processing simulation technology can effectively improve the accuracy and flexibility of indoor thermal environment control in green buildings, optimize energy efficiency, emphasize the importance of thermal factors in interior design, and provide theoretical basis and practical guidance for future building design practices.

FPGA-Based Sparse Matrix Multiplication Accelerators: From State-of-the-Art to Future Opportunities

Sparse matrix multiplication (SpMM) plays a critical role in high-performance computing applications, such as deep learning, image processing, and physical simulation. Field-Programmable Gate Arrays (FPGAs), with their configurable hardware resources, can be tailored to accelerate SpMMs. There has been considerable research on deploying sparse matrix multipliers across various FPGA platforms. However, the FPGA-based design of sparse matrix multipliers still presents numerous challenges. Therefore, it is necessary to summarize and organize the current work to provide a reference for further research. This article first introduces the computational method of SpMM and categorizes the different challenges of FPGA deployment. Following this, we introduce and analyze a variety of state-of-the-art FPGA-based accelerators tailored for SpMMs. In addition, a comparative analysis of these accelerators is performed, examining metrics including compression rate, throughput, and resource utilization. Finally, we propose potential research directions and challenges for further study of FPGA-based SpMM accelerators.

Frequency-domain sound field from the perspective of band-limited functions.

A model that approximates the sound field well is useful in various fields, such as acoustic signal processing and numerical simulation. We have proposed an effective model in which the wideband instantaneous sound field is regarded as an element of a spherically band-limited function space, using the reproducing kernel of that space. In this paper, the frequency-domain sound field is regarded as an element of some band-limited function space, and a representation of the field as a linear combination of the reproducing kernel in that space is proposed. This model has the strongest representational capacity of all function systems when we know only the sound pressure information at arbitrary positions. The proposed model can be considered a generalization of the existing three-dimensional sound field model using the reproducing kernel of the solution space of the Helmholtz equation to the spatial dimension. One of the advantages of capturing the frequency-domain sound field in this way is the simplicity achieved for the estimation formula of the wavenumber spectrum. Two numerical simulations were conducted to validate the proposed methods.

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete element model (DEM) and BF parameters to provide a reference for the simulation of BF processing operationsand the development of related equipment. The average particle size of the 5 types of BFs ranges from 0.136 mm to 0.293 mm, and the small particle size of BF particles causes to the number of BF particles in bamboo processing equipment to reach tens of millions or even billions. When conventional methods are used for simulation, ordinary computers cannot provide the required computing power. To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. Using particle scaling theory and dimensional analysis methods, the average particle size of the BF was increased to 1 mm, and the main discrete element contact parameters of the five types of BF to be tested were used as input layers. The injection method and sidewall collapse method were used to obtain the angle of repose (AR) as the output layer. Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. The relative error between the simulated AR obtained through calibration parameters and the physical experimental values is less than 2.3%. Through BF parameter validity verification, the simulated maximum compression displacement and compression ratio after stabilization were 34.81 mm and 0.477, which were close to the actual experimental results of 34.77 mm and 0.461, respectively, verifying the accuracy of the neural network prediction model. The research results provide a reference for the simulation of BF processing operations and the development of related equipment.

Fast thermal simulation of WAAM processing: toward manufacturing strategy evaluation

Managing the quality of parts produced by the Wire Arc Additive Manufacturing (WAAM) process presents a significant challenge, particularly due to the complexity of thermal control. Effective thermal management is crucial for minimizing defects, making fast and accurate thermal simulations essential for testing and optimizing various manufacturing strategies. This article proposes a rapid simulation that decouples the calculation of heat conduction from convection and radiation. The proposed simulation is described and validated against experimental data. The influences of spatial and temporal discretization are examined. In conclusion, this developed approach provides a fast and efficient simulation of a manufacturing strategy for improvement.

Comparative exploration on EEG signal filtering using window control methods

Electroencephalography (EEG) is used to monitor brain activity. The brain signals consist of different frequency band signals delta, theta, alpha, beta, and gamma waves. The signals are affected by external noise which reduces the quality of the EEG signal due to which it becomes difficult to do further processing of EEG signals like feature extraction or extraction of meaningful features from EEG signal. Therefore, it becomes important to filter the noise from the EEG signal before feature extraction or classification of the EEG signal. The research article presents an overview of different types of windowing filter techniques like Rectangular, Bartlett, Hamming, Hanning, and Kaiser windows applied for finite impulse response (FIR) behavior which is used for EEG signal processing for different brain waves processed in different frequency bands. The comparative analysis is carried out in terms of the response time of brain frequency bands for different windowing filter techniques using the MATLAB 2023 signal processing simulation tool. The novelty of the work lies in estimating minimum latency and appropriate filter selection for various typical EEG waves, since the EEG signals are pre-supposed in the hardware chip design, noise elimination is the first step in high-performance computing applications. The Bartlett window band stop has an optimal response time of 12.666 s for delta waves, a highpass filter with a response time of 16.187 s for theta waves, a bandpass with a response time of 13.122 s for alpha waves, a highpass filter with a response time of 17.866 s for beta waves, and a highpass filter with a response time of 13.797 s for gamma waves. The Barlett window FIR filter is well-suited for EEG applications.

Quantitative analysis of pore-size influence on granite residual soil permeability using CT scanning

In the field of soil hydrology, pore structure is a key factor affecting soil permeability. This paper uses micro-CT scanning technology to visualize and quantitatively analyze the impact of different pore sizes on the permeability of granite residual soil through scanning, image processing, pore removal, and permeability simulation. The results show that granite residual soil samples have a well-developed connected pore structure, with connected pore diameters ranging from 25-400 μm, while isolated pores are mostly smaller than 50 μm. Medium-sized pores bridge small and large pores, forming an important component of pore connectivity. Pore size significantly affects permeability; the removal of medium-sized pores greatly reduces the connectivity, fractal dimension, and permeability of the remaining pores, indicating that pores in the 100–300 μm range contribute the most to permeability. In contrast, pores smaller than 50 μm and larger than 300 μm have a minimal impact on overall permeability. Effective porosity and fractal dimension are crucial parameters affecting permeability, with a significant linear correlation between the two. This study supply crucial data that delineates the relationship between the structure of soil pores and their permeability, which is invaluable for developing improvement strategies aimed at reducing soil permeability.

Stress Analysis of Radial Compression Disk Based on MATLAB

Elastic mechanics is an important branch of solid mechanics, mainly studies the deformation and internal force of elastic objects under the action of external forces and other external factors, also known as elastic theory. The study object of elasticity is elastomer. The detailed theoretical structure helps us solve more practical engineering problems. We used Matlab software in the data simulation for visualization processing. On the basis of the study of elasticity, this paper explores the stress distribution of the uniform ideal elastic body to the radial compression disk model, and observes the change of the stress of the radial compression disk when the external pressure changes gradually. The expressions of the corresponding stress components , and are obtained by using the theory of normal concentrated force on the boundary of a half plane body. The stress solutions considering the thickness t of the disk are derived. Finally, the drawing software in Matlab software is used to visualize the theoretical derivation results in order to intuitively feel the change of stress. After the data simulation, the data simulation results are compared with the stress analysis results derived from the theoretical deduction, and the conclusion is drawn.

Abstract Cyclic Functional Relation and Taxonomies of Cyclic Signals Mathematical Models: Construction, Definitions and Properties

This work is devoted to the procedure of the construction of an abstract cyclic functional relation, which summarizes and extends the known results for a cyclically correlated random process and a cyclic (cyclically distributed) random process to the case of arbitrary cyclic functional relations. Two alternative definitions of the abstract cyclic functional relation are given, and the fundamental properties of its cyclic and phase structures are presented. The theorem on the invariance of cyclicity attributes of an abstract cyclic functional relation to shifts of its argument, and which are determined by the rhythm function of this functional relation, is formulated and proved. This theorem gives the sufficient and necessary conditions that the rhythm function of an abstract cyclic functional relation must satisfy. By specifying the range of values and attributes of the cyclicity of an abstract cyclic functional relation, the definitions of important classes of cyclic functional relations are formulated. A deductive approach to building a wide system of taxonomies of classes of deterministic, stochastic, fuzzy and interval cyclic functional relations as potential mathematical models of cyclic signals is demonstrated. A comparative analysis of an abstract cyclic functional relation with the known mathematical models of cyclic signals was carried out. The results obtained in the article significantly expand and systematize the mathematical tools of the description of cyclic signals and are the basis for the development of effective model-based technologies for processing and computer simulation of signals with a cyclic space-time structure.

Retraction Note: Simulation of image optical processing based on artificial intelligence in the motion adaptive adjustment system of aerobics athletes

Retraction Note: Simulation of image optical processing based on artificial intelligence in the motion adaptive adjustment system of aerobics athletes

Mathematical modeling and numerical simulation of supercritical processing of drug nanoparticles optimization for green processing: AI analysis.

In recent decades, unfavorable solubility of novel therapeutic agents is considered as an important challenge in pharmaceutical industry. Supercritical carbon dioxide (SCCO2) is known as a green, cost-effective, high-performance, and promising solvent to develop the low solubility of drugs with the aim of enhancing their therapeutic effects. The prominent objective of this study is to improve and modify disparate predictive models through artificial intelligence (AI) to estimate the optimized value of the Oxaprozin solubility in SCCO2 system. In this paper, three different models were selected to develop models on a solubility dataset. Pressure (bar) and temperature (K) are the two inputs for each vector, and each vector has one output (solubility). Selected models include NU-SVM, Linear-SVM, and Decision Tree (DT). Models were optimized through hyper-parameters and assessed applying standard metrics. Considering R-squared metric, NU-SVM, Linear-SVM, and DT have scores of 0.994, 0.854, and 0.950, respectively. Also, they have RMSE error rates of 3.0982E-05, 1.5024E-04, and 1.1680E-04, respectively. Based on the evaluations made, NU-SVM was considered as the most precise method, and optimal values can be summarized as (T = 336.05 K, P = 400.0 bar, solubility = 0.00127) employing this model. Fig 4.

Conformal Self‐Powered Inertial Displacement Sensor with Geometric Optimization for In Situ Noninvasive Data Acquisition

AbstractThe growing focus on health management and smart technology advancements have propelled the use of wearable sensors in healthcare and human body motion analysis, particularly in preventing work‐related upper limb musculoskeletal disorders (MSDs) through guided exercises. However, most available wearable medical sensors are rigid, bulky, and incapable of in situ recognition of the comprehensive motion of human body. Here, a conformal self‐powered inertial displacement sensor (CSIDS) with geometric optimization for in situ noninvasive inertial data acquisition is proposed. Leveraging template‐assisted processing and COMSOL simulation, the geometric configurations of tribo‐layer materials, specifically focusing on the curvature of semicylindrical protrusions is systematically altered. This enhancement significantly improves the detection accuracy of joint range of motion. The features of shoulder joint bending angles and linear accelerations of the humerus are accurately captured using a deep learning model based on multilayer perceptron (MLP) networks, resulting in an exceptional recognition accuracy of 99.38% and 99.58%. Compared to traditional TENG wearable sensors that can only identify single metrics, CSIDS achieves a more comprehensive health assessment through inertial data detection. This system provides early warning for shoulder joint diseases, prevents MSDs, and extends to smart wearables for comprehensive joint health and ergonomic monitoring.

3D-measurement of particles and particulate assemblies - A review of the paradigm shift in describing anisotropic particles

The goal of seeking advanced solutions to the descriptions of particle shape, packing and tomographic measurement were key areas promoted by Professor Reg Davies. In this paper we review and reflect on the revolution that has taken place over the last 30 years in our ability to describe and measure particle shape going beyond simple shape factors to their real morphologies of complex particles and particulate assemblies. The paper presents a comprehensive review of how shape has been described and some critical analyses in the form of extended tabulations. We show how digital approaches to particle descriptions can be used to predict the properties of particles and assemblies and their use in simulations of particle processing. We note the current status and prospects for the continued development of microtomographic systems to enable the measurement of particle shape in 3D and also the 3D imaging of complex particulate structures to enable property and processing predictions. Examples of these developments and critical appraisal of their utility will be given.

Development and Manufacturing of a Fibre Reinforced Thermoplastic Composite Spar Produced by Oven Vacuum Bagging.

The limited recyclability of fibre-reinforced thermoset composites has fostered the development of alternative thermoplastic-based composites and their manufacturing processes. The most common thermoplastic-based composites are often costly due to their availability in the form of prepreg materials and to the high pressure and temperatures required for their manufacturing. Yet, the manufacturing of economic and recyclable composites, made of semi-preg composite materials using traditional composite manufacturing technologies, has only been proved at a laboratory scale through the manufacturing of flat plates. This work reports the manufacturing of a real structural part, a wing spar section with complex geometry, made of commingled polyamide 12 (PA12) fibres and carbon fibres (CFs) semi-preg and by oven vacuum bagging (OVB). The composite layup was studied using finite element analysis, and processing simulation assisted in the determination of the PA12/CF preform for OVB. Processing of two forms of semi-preg materials was first evaluated and optimised. The material selection for part manufacturing was mainly defined by the materials' processability. The spar section was manufactured in two OVB stages and was then mechanically tested. The mechanical test showed a linear strain response of the prototype up to the maximum load and validated the optimised layup configuration of the composite structure.

An integrated atom array-nanophotonic chip platform with background-free imaging

Arrays of neutral atoms trapped in optical tweezers have emerged as a leading platform for quantum information processing and quantum simulation due to their scalability, reconfigurable connectivity, and high-fidelity operations. Individual atoms are promising candidates for quantum networking due to their capability to emit indistinguishable photons that are entangled with their internal atomic states. Integrating atom arrays with photonic interfaces would enable distributed architectures in which nodes hosting many processing qubits could be efficiently linked together via the distribution of remote entanglement. However, many atom array techniques cease to work in close proximity to photonic interfaces, with atom detection via standard fluorescence imaging presenting a major challenge due to scattering from nearby photonic devices. Here, we demonstrate an architecture that combines atom arrays with up to 64 optical tweezers and a millimeter-scale photonic chip hosting more than 100 nanophotonic cavities. We achieve high-fidelity ( ~ 99.2%), background-free imaging in close proximity to nanofabricated cavities using a multichromatic excitation and detection scheme. The atoms can be imaged while trapped a few hundred nanometers above the dielectric surface, which we verify using Stark shift measurements of the modified trapping potential. Finally, we rearrange atoms into defect-free arrays and load them simultaneously onto the same or multiple devices.

Toward high-fidelity quantum information processing and quantum simulation with spin qubits and phonons

Toward high-fidelity quantum information processing and quantum simulation with spin qubits and phonons

Simulation of femtosecond laser-induced periodic surface structures on fused silica by considering intrapulse and interpulse feedback

The formation of laser-induced periodic surface structures (LIPSSs) on fused silica upon irradiation with plane wave, double pulse, spot processing, and scanning processing (pulse duration tp = 35 fs, center wavelength λ = 800 nm, low repetition rate ≈1 kHz) is studied theoretically with an improved three-dimensional finite-difference time-domain plasma model. The model covers both intrapulse feedback under single shot and interpulse feedback under multi-shots, thus enabling better prediction of transient responses during laser–material interaction and the evolution of the ablated morphology and accumulated defects’ density with more shots. In simulations of a single plane wave, a double pulse can modulate LIPSS periodicity. In simulations of spot processing with Gaussian beam, an increase in the number of shots results in a noticeable ablation pattern where high-spatial-frequency LIPSS surrounds low-spatial-frequency LIPSS at a fluence of 2.8 J/cm2. Moreover, simulations of scanning processing with Gaussian beam showcase the broad applicability of this model, revealing that the orientation of the LIPSS depends on the polarization direction rather than the scanning path. This new model provides a powerful tool to simulate the formation of LIPSS on silica, particularly when temporally modulated laser is involved or predicting the evolution of morphology dependent on the number of shots.

Femtosecond laser-induced dewetting of sub-10-nm nanostructures on silicon in ambient air

To realize nanoscale manufacturing based on laser direct writing technology, objective lenses with high numerical apertures immersed in water or oil are necessary. The use of liquid medium restricts its application in semiconductors. Achieving nanoscale features on silicon by laser direct writing in a low refractive index medium has been a challenge. In this work, a microsphere assisted femtosecond laser far-field induced dewetting approach is proposed. A reduction in the full-width at half-maximum of the focused light spot is realized by modulating tightly focused light through microspheres and achieving a minimum feature size of 9 nm on silicon in ambient air with energy smaller than the ablation threshold. Theoretical analysis and numerical simulation of laser processing are performed based on a two-temperature model. Furthermore, we explored the potential of femtosecond laser-induced dewetting in nanolithography and demonstrated its ability to achieve an arbitrary structure on silicon. Our work enables laser-based far-field sub-10-nm feature etching on a large-scale, providing a novel avenue for nanoscale silicon manufacturing.

Quantum squeezing in a nonlinear mechanical oscillator

Mechanical degrees of freedom are natural candidates for continuous-variable quantum information processing and bosonic quantum simulations. However, these applications require the engineering of squeezing and nonlinearities in the quantum regime. Here we demonstrate squeezing below the zero-point fluctuations of a gigahertz-frequency mechanical resonator coupled to a superconducting qubit. This is achieved by parametrically driving the qubit, which results in an effective two-phonon drive. In addition, we show that the resonator mode inherits a nonlinearity from the off-resonant coupling with the qubit, which can be tuned by controlling the detuning. We, thus, realize a mechanical squeezed Kerr oscillator, in which we demonstrate the preparation of non-Gaussian quantum states of motion with Wigner function negativities and high quantum Fisher information. This shows that our results can also have applications in quantum metrology and sensing.