Optimal Array Research Articles

Enhancing Sensitivity of Atomic Microwave Receivers Based on Optimal Laser Arrays

Enhancing Sensitivity of Atomic Microwave Receivers Based on Optimal Laser Arrays

Large-Scale Sparse Antenna Array Optimization for RCS Reduction With an AM-FCSN

Large-Scale Sparse Antenna Array Optimization for RCS Reduction With an AM-FCSN

Quantum dots array: an approach to multipixel devices

Abstract Graphene has been proven to be an excellent material for high-frequency electromagnetic radiation detection. Here, we are reporting the graphene quantum dots (GQDs) devices designed as multi-parallel arrays of the dots (200 nm in diameter) between the source and drain electrodes. These state-of-the-art devices provide a novel concept of tuning the total device area and impedance while maintaining superior performance. The GQDs devices have been fabricated on silicon oxide substrate and analyzed for their transport properties. The multi-parallel array of GQDs on SiO2/Si substrates has depicted the possibility of tuning the activation energy depending on the back gate bias and the number of parallelly arranged GQDs while keeping the temperature dependence of resistance higher than 75 MΩ K−1 and 5 GΩ K−1 (for 2-dots and 8-dots devices). The results presented here pave the way for further optimization and realization of chip-scale arrays of GQDs-based multipixel devices, enhancing the applications in imaging and magneto-optical spectroscopy.

Distributed planning method for detection array of UAV formation with bearing-only sensor network

Abstract Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

Computational Design of a Thermal Applicator for Brain Hyperthermia Controlled by Capacitor Positioning in Loop Coils

Hyperthermia is a treatment that applies heat to damage or kill cancer cells and can be also used for drug deliveries. It is important to apply the heat into the specific area in order to target the cancer tissue and avoid damaging healthy tissue. For this reason, the development of heat applicators that have the capability to deliver the heat to the target area is vital. In this study, we present an optimization of an array coil for brain hyperthermia that can be used in combination with MRI, such that the heat can be delivered to the cancer area. The array coils were based on optimizing loop coils by varying the capacitor's position along the perimeter. The optimization was performed using electromagnetic simulations, by computing the electric field (E) and temperature inside of the brain and targeting tumor tissues for focus temperature application. The coils were compared with a general-use symmetric coil array for head heating. The optimization of the coil array was able to focus electric field and make temperature rise at the cancer areas. The temperature in Tumor 1 before and after standard and the proposed method optimization was 43.6°C, 48.3°C, and 42.5°C and for Tumor 2 the temperatures were 44.2°C, 43.1°C, and 42.9°C, respectively. Although the standard optimization method exhibits higher temperatures, it also had higher temperatures outside the tumors area. We demonstrated the optimization of array coils with different capacitor positions to obtain focused heating temperatures.

Computational fluid dynamical analysis of a Savonius vertical axis wind rotor array

Savonius vertical axis wind turbine (VAWT) arrays are being studied for urban and semi-urban wind energy harvesting. With limited ground space in these areas, optimizing energy extraction through various array configurations is crucial. This study presents a computational fluid dynamics (CFD) analysis of a four-rotor Savonius VAWT array. Various unit array configurations, based on wake analysis of an isolated Savonius rotors, are evaluated through detailed CFD study. The parameters of interest in this study are the coefficient of power, Cpi (i= 1, 2, 3, or 4 is the rotor number) at different typical values of the tip-speed ratio, λ=ΩD/2U∞ (where Ω is the angular speed of the rotor, D is the diameter of the rotor, and U∞ is the free stream air velocity) of the constituent rotors of the unit array, rotational orientation as well as the average value of the coefficient of power, Cpa=(∑i=14Cpi)/4. It is observed that at certain configurations of the four-unit array, it is possible to have a power enhancement, Ψ=Cpa/Cp0>1 up to 1.34 (34% gain), where Cp0 is the coefficient of power of an isolated Savonius rotor at the same λ. The observed enhancement is explained using velocity and pressure fields around the unit array. Comparative analysis with previous studies highlights new approaches for designing high-performance Savonius arrays and suggests directions for CFD-based optimization of larger arrays.

Constructions of optimal 2-level orthogonal arrays with repeated rows

Constructions of optimal 2-level orthogonal arrays with repeated rows

Improved Sensor System Detecting via Joint Transfer Learning and Array Optimization Design Based on Multiple Phase Feature Extraction

Improved Sensor System Detecting via Joint Transfer Learning and Array Optimization Design Based on Multiple Phase Feature Extraction

Array Optimization Based on Improved Sparrow Search Algorithm Model

Array Optimization Based on Improved Sparrow Search Algorithm Model

Thermal Optimization of Edge-Emitting Lasers Arrays

This paper presents a novel approach to address the issue of uneven temperature distribution in one-dimensional laser arrays, specifically in gallium nitride edge-emitting lasers emitting green light of 540 nm. The results were obtained using heat flow numerical analysis, which included an optimization method specifically developed for this type of array. It was demonstrated that thermal optimization of a one-dimensional edge-emitting laser array can be achieved by adjusting the placement of the emitters within the array and the size of the top gold contact, without changing the overall dimensions of the device. The proposed design alterations ensure an even temperature distribution across the array without the need for a complex and expensive cooling systems. The proposed optimization method can be applied to arrays made from various material systems, including nitrides, arsenides, and phosphides.

An optimised delay multiply and sum ultrasonic imaging algorithm based on sparse arrays and parallel computing

ABSTRACT The Total Focusing Method (TFM) focuses pixels using the Delay and Sum (DAS) beamforming technique, which relies solely on the temporal information of the full matrix capturing dataset while ignoring its spatial information, and the image resolution and contrast achievable with TFM are limited. In this work, a parallel sparse delay multiply and sum (PSDMAS) focusing imaging algorithm based on sparse arrays and parallel computing is proposed to improve contrast resolution and imaging efficiency. A sparse array optimisation method is applied to reduce the amount of data. A ratio of main-lobe width and side-lobe peak was constructed as the fitness function and a genetic algorithm was used to find the optimal solution for the array arrangement. Delay Multiply and Sum (DMAS) was employed to enhance the spatial coherence and suppress the clutter artefacts. Parallel computing strategies were implemented to improve imaging efficiency. To validate the effectiveness of the algorithm, we processed the full matrix data collected from simulations and experiments using PSDMAS, the imaging results of the PSDMAS provided a considerable improvement in Array Performance Indicator (API), and better lateral spatial resolution was also achieved. The computation time of PSDMAS was reduced by 99.9% compared to conventional DMAS.

A Universal Method for Fingerprinting Multiplexed Bacteria: Evolving Pruned Sensor Arrays via Machine Learning-Driven Combinatorial Group-Specificity Strategy.

Array-based sensing technology holds immense potential for discerning the intricacies of biological systems. Nevertheless, developing a universal strategy for simultaneous identification of diverse types of multianalytes and meeting the diagnostic needs of a range of multiclassified clinical diseases poses substantial challenges. Herein, we introduce a combination method for constructing sensor arrays by assembling two types of group-specific elements. Such a method enables the rapid generation of a library of 100 sensing units, each with dual bacterial targeting capabilities. By employing a three-step screening strategy optimized by machine learning algorithms, various optimal five-element arrays were rapidly obtained for diverse clinical infectious models. Moreover, the pruned arrays successfully identified disparate mixing ratios and quantitative detection of clinically prevalent bacterial strains. Optimized through nine multiclassification algorithms, the top-performing multilayer perceptron (MLP) model demonstrated impressive recognition capabilities, achieving 100% accuracy for diagnosing clinical urinary tract infection (UTI) and 99.4% accuracy for clinical sepsis detection in the test models we collected. Such a combinatorial library construction and screening process should be standard and provides insights into successfully generating powerful high-recognition sensor elements and configuring them into highly discriminative mini-sensor arrays.

Interference mitigation method for LEO satellites with optimal dynamic switching array elements in co‐frequency beams

AbstractSatellite communication has rapidly advanced, from navigation to broadband Internet and direct mobile connectivity. Driven by low Earth orbit satellites, these networks are merging with terrestrial mobile networks, forming integrated space–air–ground systems. However, the growing scale of satellite constellations has intensified intra‐system interference, particularly among ground cells utilizing co‐frequency beams. A mitigation method based on switching array elements is proposed, optimizing their state by considering beamwidth and maximum sidelobe level. Simulations show that the proposed algorithm effectively reduces beam coverage widening and sidelobes, mitigating the interference between co‐frequency beams.

Optimal Dual-Polarized Planar Arrays for Massive Capacity Over Point-to-Point MIMO Channels

Optimal Dual-Polarized Planar Arrays for Massive Capacity Over Point-to-Point MIMO Channels

Array Optimization for a Wave Energy Converter with Adaptive Resonance Using Dual Bayesian Optimization

A novel Dual Bayesian optimization strategy is formed for an array of wave energy converters with adaptive resonance to maximize the annual performance through the energy conversion processes from irregular waves to electricity. A wave energy converter with adaptive resonance changes the natural frequency of power take-off dynamics for varying irregular waves, resulting in the maximum annual energy production. The first step of the two-step Dual Bayesian optimization determines the geometric layout of the array, which maximizes the first energy conversion to the total array excitation for irregular waves occurring annually. The second step optimizes the operational parameters of individual wave energy converters in the optimized array to maximize the power generation in varying sea states through simultaneous conversion to mechanical and electrical energy. The coupled hydrodynamics are solved in the frequency domain, and the power performance is evaluated by solving the Cummins’ equation in the time domain extended for multiple floating bodies, each strongly coupled with nonlinear power take-off dynamics. The proposed method is applied to a surface-riding wave energy converter, already optimized for single unit operation at individual sea states. Investigating two array layouts, linear and random, the optimized arrays after Step 1 increase the excitation spectral area by up to 40% relative to the single unit operation, indicating the synergy enhancing the first energy conversion. Subsequently, the dual-optimized linear layout attained a q-factor up to 1.13 in commonly occurring sea states, achieving improved average power generation in 60% of the evaluated sea states. The performance of the random layout exhibited the average power fluctuating along the wave spectra with a peak q-factor of 1.07. The individual adaptive resonance is confirmed in the optimized arrays, such that each surface-riding wave energy converter of both layouts adaptively resonates with the peak of the wave excitation spectra, maximizing the power generation for the different irregular waves.

Intelligent sniffer: A chip-based portable e-nose for accurate and fast essence evaluation

Electronic nose (e-nose) is capable of identifying gases of different odors and concentrations. However, accurate odor evaluation of complicated volatile compounds (e.g., from essence) at human perception level remains challenging, especially in a fast and cheap way. This study presents a portable e-nose designed to evaluate different essence samples and their mixtures that outperforms human olfaction. Given the complexity and similarity of ingredients among different essence types, six sensors were integrated on a miniaturized chip after the sensor array optimization from 18 gas-sensing materials with varying doping, film thicknesses, operated at different temperatures. A pattern recognition model based on Long-short term memory network and convolutional neural network was developed to automatically extract sensors’ dynamic response features to determine essence type and concentration. Our e-nose exhibited high sensitivity and repeatability to analyze low-concentration essence volatiles better than human olfactory tests. The classification accuracy for four similar textile essence and their mixtures could reach up to 96.9 %. And the R2 and RMSE for relative concentration achieved 0.98 and 0.068 %, respectively. Furthermore, only the first 60 s signals are needed for the sample analysis which significantly reduced the sample testing time compared to the traditional base-peak-base test method (over 200 s). This research provides a favorable opportunity for essence fragrance prediction in various applications such as food, textile, cosmetics, etc.

Layout optimization and Performance of Large Array of imaging atmospheric Cherenkov Telescope (LACT)

Abstract Large Array of imaging atmospheric Cherenkov Telescope (LACT) is an array of 32 Cherenkov telescopes with 6-meter diameter mirrors to be constructed at the LHAASO site. In this work, we present a study on the layout optimization and performance analysis of LACT. We investigate two observation modes: large zenith angle observations for ultra-high energy events and small zenith angle observations for lower energy thresholds. For large zenith angles (60°), simulations show that an 8-telescope subarray can achieve an effective area of $3 ~\rm km^2$ and excellent angular resolution. For small zenith angles, we optimize the layout of 4-telescope cells and the full 32-telescope array. The threshold of the full array is about $200~\rm GeV$, which is particularly crucial for studying transient phenomena, including gamma-ray bursts (GRBs) and active galactic nuclei (AGNs). This study provides important guidance for the final LACT layout design and performance estimates under different observational conditions, demonstrating LACT's potential for deep observations of ultra-high energy \gray sources and morphological studies of PeVatrons, as well as time-domain \gray astronomy.

Taguchi L8 Orthogonal Array and ANOVA-Based Optimization of Injection Molding Parameters for Reducing Warpage in Set-Top Box Components

Taguchi L8 Orthogonal Array and ANOVA-Based Optimization of Injection Molding Parameters for Reducing Warpage in Set-Top Box Components

Enhanced microwave absorption of sandwich panels with magnetized carbon fiber corrugated array reinforced PMI foam core

AbstractIntegrating periodic array structures made of metal wires or conductive inks into the foam core of electromagnetic (EM) wave absorbing sandwich panels can significantly improve broadband absorption performance. However, the complex fabrication process and poor corrosion resistance of these materials limit their practical applications. This work innovatively introduces magnetized carbon fiber (CF) into PMI foam, simultaneously achieving broadband absorption and lightweight characteristics of the sandwich structure through the design of array structure and fiber bundle geometry. Simulation analysis compared the EM absorption performance of sandwich panels with carbonyl iron powder (CIP)/CF tape and helical twisted CIP/CF rope corrugated arrays, determining the optimal array structure parameters, which were then experimentally validated. The experimental results align with the simulations, showing that CIP/CF rope corrugated arrays with an amplitude of 10 mm, a cycle length of 15 mm, and an array spacing of 15 mm provide optimal absorption performance, with a reflection loss below −10 dB across the 9–18 GHz frequency range and a maximum absorption of −33.9 dB at 17 GHz. Finally, the absorption mechanism of these sandwich structures is discussed, highlighting how the synergistic effects between the electromagnetic properties and structural morphology of CIP/CF enhance the absorption performance of the EM absorbing sandwich panel.

A Quantitative Array Optimization Method for the Electronic Nose System Based on Edge Computing and MEMS Sensors

A Quantitative Array Optimization Method for the Electronic Nose System Based on Edge Computing and MEMS Sensors