Real-time Field Research Articles

Parameter Estimation of Cellular Communication Systems Models in Computational MATLAB Environment: A Systematic Solver-based Numerical Optimization Approaches

Model-based parameter estimation, identification, and optimisation play a dominant role in many aspects of physical and operational processes in applied sciences, engineering, and other related disciplines. The intricate task involves engaging and fitting the most appropriate parametric model with nonlinear or linear features to experimental field datasets priori to selecting the best optimisation algorithm with the best configuration. Thus, the task is usually geared towards solving a clear optimsation problem. In this paper, a systematic-stepwise approach has been employed to review and benchmark six numerical-based optimization algorithms in MATLAB computational Environment. The algorithms include the Gradient Descent (GRA), Levenberg-Marguardt (LEM), Quasi-Newton (QAN), Gauss-Newton (GUN), Nelda-Meald (NEM), and Trust-Region-Dogleg (TRD). This has been accomplished by engaging them to solve an intricate radio frequency propagation modelling and parametric estimation in connection with practical spatial signal data. The spatial signal data were obtained via real-time field drive test conducted around six eNodeBs transmitters, with case studies taken from different terrains where 4G LTE transmitters are operational. Accordingly, three criteria in connection with rate of convergence Results show that the approximate hessian-based QAN algorithm, followed by the LEM algorithm yielded the best results in optimizing and estimating the RF propagation models parameters. The resultant approach and output of this paper will be of countless assets in assisting the end-users to select the most preferable optimization algorithm to handle their respective intricate problems.

Tailoring bistability in optical tweezers with vortex beams and spherical aberration

We demonstrate a bistable optical trap by tightly focusing a vortex laser beam. The optical potential has the form of a Mexican hat with an additional minimum at the center. The bistable trapping corresponds to a nonequilibrium steady state, where the microsphere continually hops, due to thermal activation, between an axial equilibrium state and an orbital state driven by the optical torque. We develop a theoretical model for the optical force field, based entirely on experimentally accessible parameters, combining a Debye-type nonparaxial description of the focused vortex beam with Mie scattering by the microsphere. The theoretical prediction that the microsphere and the annular laser focal spot should have comparable sizes is confirmed experimentally by taking different values for the topological charge of the vortex beam. Spherical aberration introduced by refraction at the interface between the glass slide and the sample is considered and allows us to finetune between axial, bistable, and orbital states as the sample is shifted with respect to the objective focal plane. We find overall agreement between theory and experiment for a rather broad range of topological charges. Our results open the way for applications in stochastic thermodynamics, as they establish a control parameter—the height of the objective focal plane with respect to the glass slide—that allows us to shape the optical force field in real time and in a controllable way. Published by the American Physical Society 2024

An Artificial Neural Network-Based Data-Driven Embedded Controller Design for a Pneumatic Artificial Muscle-Actuated Pressing Unit

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed.

Test Power Reduction through Reordering Algorithm Implementation and Advancements in BIST Architecture

Due to the reduction in transistor size, test engineers face a severe issue in power minimization. VLSI circuit's power dissipation has become a vital concern due to the switching activity of the Circuit Under Test (CUT). Switching activity is a significant factor in determining power consumption for CMOS VLSI circuits. In this paper, we formulate a distance estimation technique and reordering using the Tversky index. Here, the distance between the test vectors is calculated using the Tversky index with the formation of an adjacency matrix, and reordering is performed. The experimental results of the proposed method have been compared to different distance measures and existing techniques. It shows that the switching activity is reduced from 52.5% to 21.5%, and the average power reduction is from 42% to 14.6% when the distance is estimated on reordering using the Tversky index measure. Thus, the Tversky index distance measure performs better than the Hamming, gray, and Linear feedback shift register (LFSR) methods. Switching activity estimation for complex ISCAS circuits has been performed and compared with existing methods for superior performance. We have also developed a Built in Self-Test (BIST) for the C17 benchmark circuit, which uses the Tversky counter as a test pattern generator using the TANNER EDA tool. Thus, an efficient deterministic BIST for the C17 benchmark circuit was designed to detect all the stuck-at faults, and the same procedure can be used to create BIST for all CUT and precomputed test sets for real-time field testing.

Advancing sustainable agriculture: Evaluation of Poly (lactic acid) (PLA) based mulch films and identification of biodegrading microorganisms among soil microbiota

Non-biodegradable polyolefin based plastic mulch residues in agricultural fields after the end of a crop cycle have raised several concerns as an environmental pollutant in recent years. This study explores the potential of Poly (lactic acid) (PLA) and Poly (butylene adipate-co-terephthalate) (PBAT) based compostable films reactively blended with compatibilizers and chain extenders as a promising solution to environmental challenges associated with traditional plastic mulch films. Epoxidized soybean oil (ESO) and Epoxy-functionalized styrene acrylic copolymer (ESA) have been used as reactive compatibilizers and chain extenders respectively. In-depth analysis of the mechanical, thermal, and barrier properties of the developed films, revealed that the PLA/PBAT blend films at 75:25 weight ratio in the presence of 5 phr ESO and 0.5 phr ESA exhibit improved performance characteristics for application as mulch films. Furthermore, the films were subjected to 360-h UV exposure to gauge their stability under prolonged exposure, specifically investigating changes in the carbonyl index. Additionally, a rigorous real-time field trial of the mulch films spanning eight months with various crops was carried out to understand their performance in practical agricultural settings. The study also involved the identification of microorganisms responsible for the degradation of the developed mulch films employing 16S rRNA sequencing.

A photothermal colorimetric dual-signal sensor for Staphylococcus aureus in food based on CuS nanoparticles

Staphylococcus aureus (S. aureus) is a common foodborne pathogenic organism, and rapid detection with high sensitivity and targeted killing are essential for controlling its spread. Therefore, a sandwich structure of magnetic bead-target bacteria-copper sulfide was constructed in this paper. The photothermal conversion function and peroxidase (POD) activity of CuS NPs were utilized to establish a dual sensing platform for temperature and color rendering, that is, after capturing target bacteria, light was converted to heat signal by infrared irradiation. Moreover, the specific detection of S. aureus was realized by detecting the temperature change of the complex and the oxidation of TMB by nano-enzymes of residual CuS in the supernatant to form oxTMB blue product, which formed the change of UV (ultraviolet) − Visible spectroscopy. Based on the high specificity binding and strong signal amplification of the method, the linear range of the photothermal specific probe combined with MBs-S. aureus-CuS was 10–106 CFU/mL, and the detection limit was 1.407 CFU/mL. The linear range of the color specific probe was 10–106 CFU/mL, R2 = 0.9853, and the detection limit was 1.15 CFU/mL. The photothermal efficiency of MBs-S. aureus-CuS system can reach 62.84 %, which has excellent photothermal conversion effect, and the temperature rises to about 70 °C within 7 min, which can directly kill bacteria in the detection. In addition, the portable infrared thermal imager is applied to realize the real-time field detection of S. aureus. The study was significant, because exploratory experiments were conducted to conceive a proof-of-concept based on multiple bacterial sensors that could easily be adapted to a variety of fields where bacteria pose a threat.

Machine learning-assisted sparse observation assimilation for real-time aerodynamic field perception

Machine learning-assisted sparse observation assimilation for real-time aerodynamic field perception

Virtual sensing for real-time strain field estimation and its verification on a laboratory-scale jacket structure under water waves

This study aims to achieve real-time estimation of the full-field strain distribution in a structure by signals measured from several strain gauges attached to the structure. Our virtual sensing procedure is developed based on finite element formulation and employs the mode superposition approach. To verify the feasibility of the proposed procedure, numerical and experimental tests are conducted on a laboratory-scale offshore jacket structure subjected to water waves. Key aspects addressed in this study include the selection of displacement modes and the division of strain signals. The experiments are performed in an ocean basin, and comprehensive explanations are provided for the jacket prototype design, implementations, experimental setup, and wave loading conditions. The performance of the proposed virtual sensing procedure is thoroughly assessed through various evaluation measures, enhancing the understanding of its capabilities and limitations in practical applications.

Application of a portable ion chromatograph for real-time field analysis of nitrite and nitrate in soils and soil pore waters

Real-time monitoring of nitrite and nitrate is crucial for maintaining soil health and promoting plant growth. In this study, a portable ion-chromatograph (IC, Aquamonitrix) analyser, coupled with a field-applicable ultrasonic-assisted extraction method, was utilised for in-field determination of nitrate and nitrite in soils. This is the first application of this type of analyser to soil nutrients. On-site analysis of soil from a local sports field showed 94.8 ± 4.3 μg g−1 nitrate, with LODs of 32.0 μg g−1 for nitrate and 5.4 μg g−1 for nitrite. The results were in close agreement with those obtained using a conventional lab-based IC. Relative standard deviations (%RSDs) for soil analysis using Aquamonitrix were consistently below 10%. The obtained average recoveries of samples spiked with nitrite were 100% and 104% for the portable IC and conventional IC, respectively. Furthermore, to assess the suitability of portable IC for samples with high organic matter content, various natural organic fertilisers were extracted and analysed. The results showed 16.2 ± 0.7 μg g−1 nitrite and 28.5 ± 5.6 μg g−1 nitrate in sheep manure samples with LODs of 2.0 μg g−1 for nitrite and 12.0 μg g−1 for nitrate. The portable IC system was further demonstrated via real-time on-site analysis of soil pore-water acquired using a portable battery-based ceramic pore-water sampler. A continuous increase in nitrate concentration over time was observed (from 80 to 148 μg mL−1) in the soil pore-water in a vegetable garden four days after heavy rain. Unlike conventionally sampled natural waters, 7-day storage of the studied pore water samples revealed no changes in nitrate concentrations. An average of 558 ± 51 μg mL−1 nitrate was detected in the soil pore-water samples analysed on a spinach farm, immediately after irrigation.

Non-destructive testing of reinforced concrete structures using sub-terahertz reflected waves

Terahertz (THz) and sub-terahertz (sub-THz) waves are unexplored waves between the infrared and microwave ranges. This range features unique characteristics of both light resolution and electromagnetic wave transmission. Sub-THz waves are part of attractive new diagnostic methods for inner objects because they are safer than normal non-destructive inspection methods involving high energy, such as X-rays. In this study, a novel non-contact and non-destructive inspection method for reinforced concrete (RC) structures using sub-THz reflection imaging is proposed to inspect the deterioration of inaccessible damaged RC structures. The results of this study confirmed that the proposed sub-THz imaging method can detect cracks and voids in concrete, as well as in metals inside or behind the concrete, based on differences in reflectance. In addition, the camera-based measurement system makes real-time on-site field measurements possible.

A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications. This review paper discusses several modern algorithms for real-time or near real-time TMS E-field modeling and their advantages and limitations. The reviewed methods include techniques such as basis representation techniques and deep neural-network-based methods. This paper also provides a review of software tools that can integrate E-field modeling with navigated TMS, including a recent software for real-time navigated E-field mapping based on deep neural-network models.

A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.

To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration. Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset. The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively. A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion. The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity.

Mummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult. Using real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate. Experimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16. These features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification.

Real-time stress field reconstruction method using online monitoring parameters for thick-walled power plant components based on gappy proper orthogonal decomposition

The three-dimensional stress distribution information of in-service components is of great significance for the intelligent operation and maintenance of power plants. A real-time stress field reconstruction method based on the gappy proper orthogonal decomposition (POD) and prediction function is proposed for condition monitoring of high-temperature pressured thick-walled components. Based on the “snapshots” method, the standard POD orthogonal basis is extracted from the high-fidelity data obtained from finite element analysis under various hot start load conditions to capture the main features of the stress fields. The complete stress field is reconstructed by the linear combination of the standard POD basis, and the modal coefficients are calculated using the discrete node stresses through the gappy POD procedure. Since only the online monitoring parameters of steam are available in practical operations, the prediction function is used to establish the mapping relationship between the monitoring parameters and node stress. The discrete nodes are determined by the trial-and-error method. The small global relative errors between the reconstructed stress fields and numerical solutions indicate the high accuracy of the proposed method. The spatial error of the entire stress field and the temporal error of the maximum stress were also evaluated. The computation time of the reconstruction process is significantly shorter than the sampling time step. The performance of gappy POD on different numbers of snapshots and modes was also presented.

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches.

Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

Visualization of Multiphase Reactive Flow and Mass Transfer in Functionalized Microfluidic Porous Media.

Multiphase reactive flow in porous media is an important research topic in many natural and industrial processes. In the present work, photolithography is adopted to fabricate multicomponent mineral porous media in a microchannel, microfluidics experiments are conducted to capture the multiphase reactive flow, methyl violet 2B is employed to visualize the real-time concentration field of the acid solution and a sophisticated image processing method is developed to obtain the quantitative results of the distribution of different phases. With the advanced methods, experiments are conducted with different acid concentration and inlet velocity in different porous structures with different phenomena captured. Under a low acid concentration, the reaction will be single phase. In the gaseous cases with higher acid concentration, preferential flow paths with faster flow and reaction are formed by the multiphase hydrodynamic instabilities. In the experiments with different inlet velocities, it is observed that a higher inlet velocity will lead to a faster reaction but less gas bubbles generated. In contrast, more gas bubbles would be generated and block the flow and reaction under a lower inlet velocity. Finally, in heterogeneous structures, fractures or cavities would significantly redirect the flow and promote the formation of preferential flow path nearby.

Frequency-importance gaussian splatting for real-time lightweight radiance field rendering

Frequency-importance gaussian splatting for real-time lightweight radiance field rendering

Fast reconstruction of boiler numerical physical field based on proper orthogonal decomposition and conditional deep convolutional generative adversarial networks

ABSTRACT Obtaining real-time physical field data inside the boiler furnace is crucial for combustion diagnostics and optimization. In this work, we propose a fast reconstruction technique for the physical fields in a tangentially-fired coal boiler. The method combines the Proper Orthogonal Decomposition (POD) and Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) based on the boiler physical field obtained by computational fluid dynamics (CFD). Firstly, CFD simulations are performed using data from the boiler Distributed Control System (DCS). After verifying the CFD results, we expanded the simulation to 105 operational condition cases sample sets. Then, the physical fields of the sample set undergo POD to determine the appropriate number of POD bases, thereby ensuring the accuracy of the reconstruction. Finally, the cDCGAN method is employed to establish the correlation between the POD bases and the operational conditions of the boiler. Results show that the POD-cDCGAN method achieves rapid and accurate reconstruction of temperature and velocity fields, with mean relative error (MRE) below 1.5% and root relative squared error (RRSE) below 5.7%. The reconstruction time is reduced to 10.57s, significantly faster than direct CFD methods. The POD-cDCGAN method offers an efficient approach for online operational diagnosis and digital twin construction of boiler equipment.

Motion Error Mechanism Analysis and Simulation Verification of Ocean Electric Field Sensor

The electric field measurement based on Unmanned Underwater Vehicle (UUV) can monitor and evaluate real-time electric field of underwater targets in motion, which has the ability to adapt to harsh marine environments. However, electric field sensors generate new errors during underwater motion, and there is still absence of studies on the error theory and simulation. To address this, the error generation mechanism of Ag/AgCl electrodes induced by the flow field was analyzed in this paper. A coupled mathematical model of flow-chemical-electric multi-physical field was established for the first time, and finite element simulation analysis was carried out. The results show that the change value of electrode surface potential under the influence of flow rate is basically in agreement with the empirical formulae. R-squared value of the two fits reaches up to 0.968, and RMSE value is less than 0.003, confirming the robustness of the model and simulations. The multi-physical field coupling model and simulation method proposed in this paper can provide theoretical and methodological support for the design of electric field sensors and motion error correction.

A real-time field bus architecture for multi-smart-motor servo system

The multi-motor servo system (MMSS) is an electro-mechanical system widely used in various fields, including electric vehicles, robotics, and industrial machinery. Depending on the application, the number of motors in the system can range from several dozens to tens of thousands, which imposes additional communication demands. Thus, ensuring synchronization and control precision of the system requires addressing the challenge of guaranteeing the performance and reliability of communication among motors in the MMSS. In this paper, we design a smart servo motor (SSM) to upgrade the system to the multi-smart-motor servo system (MSMSS) based on a distributed real-time field bus architecture, namely, Multi-Motor Bus (MMB) architecture. The proposed MMB architecture is lightweight and stable, providing real-time support for Control Area Network connections to a central user computer and inter-integrated circuit connections to SSM units. This MMB architecture facilitates the synchronization of command transmission across SSMs and ensures the consistency of motors in the MSMSS. Additionally, a serial experiments to examine 3 key system performance and reliability characteristics are conducted, including command transmission time, transmission jitters, and rotation consistency. The analysis of these characteristics demonstrates the system’s potential and feasibility to be applicable in industry.