Minimum Error Estimation Research Articles

Quantitative Research on Generalized Linear Modeling of SEU and Test Programs Based on Small Sample Data

Complex integrated circuits (ICs) have complex functions and various working modes, which have many factors affecting the performance of a single event effect. The single event effect performance of complex ICs is highly program-dependent and the single event sensitivity of a typical operating mode is generally used to represent the single event performance of the circuits. Traditional evaluation methods fail to consider the cross effects of multiple factors and the comprehensive effects of each factor on the single event soft error cross section. In order to solve this problem, a new quantitative study method of single event error cross section based on a generalized linear model for different test programs is proposed. The laser test data is divided into two groups: a training set and a validation set. The former is used for model construction and parameter estimation based on five methods, such as the generalized linear model and Ensemble, while the latter is used for quantitative evaluation and validation of a single event soft error cross section of the model. In terms of percentage error, the minimum mean estimation error on the validation set is 13.93%. Therefore, it has a high accuracy to evaluate the single event soft error cross section of circuits under different testing programs based on the generalized linear model, which provides a new idea for the evaluation of a single event effect on complex ICs.

Adaptive estimation of PEMFC stack model parameters - An experimental verification

The growing popularity of using proton-exchange membrane fuel cells (PEMFCs) stacks in stationary, portable, and transportation applications is driving researchers to develop proper dynamic models for PEMFCs. These models are used to accurately capture the electrical characteristics and runtime performance. This work proposes a well-known equivalent circuit model of a battery, to be modified and used as a model for a PEMFC stacks voltage-current characteristics. This model is modified by finding suitable functions to model the open circuit voltage and the series resistance, required to model the electrical performance of a 200-W PEMFC stack. The paper also shows that the existing adaptive parameters estimation (APE) technique for Li-ion battery parameters estimation is also able to estimate parameters of the PEMFC stack's model. The model parameters are estimated using the APE technique that requires only five experiments. The model is validated experimentally under different load conditions for a 200-W PEMFC stack supplied from a hydrogen cylinder (voltage error −0.2 V to 0.5 V), and a 30-W PEMFC stack supplied from a fuel stick (voltage error −0.2 V–0.4 V). The results show that the parameters estimation methodology works well across PEMFC stacks of different sizes with different input fuel intake configurations, with a minimal terminal voltage estimation error in the order of millivolts. Open circuit voltage measurements (OCV) show that the OCV curve starts at a little lower than 31 V, declines slowly to around 30 V for a normalized hydrogen flow rate of 0.6, after which there is a sudden linear decline in OCV was observed. Most of the data has absolute estimation error less than 0.1 V. In fact, the terminal voltage estimation error across all tests, with different current discharge profiles, lies between −0.2 and 0.2 V only. Also, 95.84% of the error samples lie between ±0.1% error.

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon.

This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0–30 cm) soil samples were collected from the covers 1330 km2 land and analyzed for SOC, lime, clay and sand content and soil reaction included in the estimation models. Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors (ME: -0.028, 45 MAE: 0.042, RMSE: 0.066) for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.

Effect of Noise Covariance Matrices on State of Charge Estimation Using Extended Kalman Filter

The electrification of the transport sector increases the demand for a battery management system that allows the Li-ion battery to work in a sophisticated way to reach its maximum. The main focus of the battery management system is the estimation of the battery’s state of charge (SOC) which is an indicator to determine the driving range of an electric vehicle. However, battery modeling has a direct influence on SOC estimation. In this article, the equivalent circuit model (ECM) and extended Kalman filter (EKF) algorithm are combined for SOC estimation of the Li-ion battery. Since the performance of Kalman filter algorithms strictly depends on noise covariance matrices, incorrect values reduce the accuracy and make divergence in state estimation. Therefore, it is motivated to analyze the impact of process noise and measurement noise covariance matrix values on SOC estimation using Kalman filter algorithms. Ten different combinations of noise covariance matrices are employed to analyze the effect of these matrices on SOC estimation. As a result, the tuning of the filter is critical for improvement in state estimation regardless of the type of the battery model and filtering technique. The matrix values are chosen to take into account the convergence rate and estimation error. To validate the effectiveness of the chosen noise covariance matrices, the simulation results of the ECM based EKF estimation method is superimposed with the discharge curves available in the data sheet. The simulation results show minimal estimation error and a good convergence rate for tuned EKF.

Bell pepper yield estimation using time series unmanned air vehicle multispectral vegetation indexes and canopy volume

Accurate and timely crop yield estimation prior to harvest is important for agricultural management, agricultural economy, and food security. In many cases, the farmers estimate the yield visually. Further, several crop simulation models have been developed to estimate yield accurately. However, these are not used efficiently because of their requirements for enormous amounts of data and their inability to show the spatial differences of yield in the field. Recently, the rapid development of unmanned air vehicle (UAV) technologies has shown great potential to estimate crop yield accurately and show the spatial heterogeneity in the field. We estimate the bell pepper yield with time series, high-resolution UAV multispectral images. To do so, canopy volume and five different spectral vegetation indices used widely were calculated. Seven UAV flight missions were conducted between June and August of 2019. Various linear regression models were developed to estimate the bell pepper yield based on the canopy volume values and vegetation indices. The results showed that the bell pepper canopy volume fit the yield best with the minimum estimation error [coefficient of determination ( R2 ) = 0.93 and root mean square error ( RMSE ) = 2.30 tons ha − 1]. In addition, a significant correlation was found between the enhanced vegetation index and bell pepper yield (R2 = 0.87 and RMSE = 3.16 tons ha − 1).

Estimation of Respiratory Nasal Pressure and Flow Rate Signals Using Different Respiratory Sound Features

BackgroundRespiratory sounds are associated with the flow rate, nasal flow pressure, and physical characteristics of airways. In this study, we aimed to develop the flow rate and nasal flow pressure estimation models for the clinical application, and find out the optimal feature set for estimation to achieve the optimal model performance. MethodsRespiratory sounds and flow rate were acquired from nine healthy volunteers. Respiratory sounds and nasal flow pressure were acquired from twenty-three healthy volunteers. Four types of respiratory sound features were extracted for flow rate and nasal flow pressure estimation using different estimation models. Effects of estimations using these features were evaluated using Bland-Altman analysis, estimation error, and respiratory sound feature calculation time. Besides, expiratory and inspiratory phases divided estimation errors were compared with united estimation errors. ResultsThe personalized logarithm model was selected as the optimal flow rate estimation model. Respiratory nasal flow pressure estimation based on this model was also performed. For the four different respiratory sound features, there is no statistically significant difference in flow rate and pressure estimation errors. LogEnvelope was, therefore, chosen as the optimal feature because of the lowest computational cost. In addition, for any type of respiratory sound feature, no statistically significant difference was observed between divided and united estimation errors (flow rate and pressure). ConclusionRespiratory flow rate and nasal flow pressure can be estimated accurately using respiratory sound features. Expiratory and inspiratory phases united estimation using respiratory sounds is a more reasonable estimation method than divided estimation. LogEnvelope can be used for this united respiratory flow rate and nasal flow pressure estimation with minimum computational cost and acceptable estimation error.

Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting

Studies have demonstrated that changes in the climate affect wind power forecasting under different weather conditions. Theoretically, accurate prediction of both wind power output and weather changes using statistics-based prediction models is difficult. In practice, traditional machine learning models can perform long-term wind power forecasting with a mean absolute percentage error (MAPE) of 10% to 17%, which does not meet the engineering requirements for our renewable energy project. Deep learning networks (DLNs) have been employed to obtain the correlations between meteorological features and power generation using a multilayer neural convolutional architecture with gradient descent algorithms to minimize estimation errors. This has wide applicability to the field of wind power forecasting. Therefore, this study aimed at the long-term (24–72-h ahead) prediction of wind power with an MAPE of less than 10% by using the Temporal Convolutional Network (TCN) algorithm of DLNs. In our experiment, we performed TCN model pretraining using historical weather data and the power generation outputs of a wind turbine from a Scada wind power plant in Turkey. The experimental results indicated an MAPE of 5.13% for 72-h wind power prediction, which is adequate within the constraints of our project. Finally, we compared the performance of four DLN-based prediction models for power forecasting, namely, the TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models. We validated that the TCN outperforms the other three models for wind power prediction in terms of data input volume, stability of error reduction, and forecast accuracy.

High-Precision Demodulation Method for Surface Acoustic Wave Strain Sensor Based on Hybrid Changing Area Niche Genetic Algorithm

The echo signal energy of surface acoustic wave (SAW) strain sensor is weak, resulting in low demodulation accuracy. In order to improve the measurement result, a SAW sensor demodulation method by combining hybrid changing area niche genetic algorithm and MUSIC algorithm was proposed. The proposed method was based on the MUSIC algorithm to construct the power spectrum function of SAW echo signal. A hybrid changing area niche genetic algorithm (CANGA) and particle swarm optimization (PSO) algorithm was developed to improve the spectrum peak search accuracy and reduce the amount calculations of MUSIC algorithm. The power spectrum function of the MUSIC algorithm was used as the fitness function of the hybrid algorithm. The echo frequency was estimated by the variable value corresponding to the optimal solution. The proposed hybrid PSO-CANGA has the characteristics of strong global search ability and fast convergence speed to improve the performance of MUSIC algorithm. The numerical comparisons were performed. The results showed that the hybrid PSO-CANGA gives fastest convergence and the highest estimation accuracy compared to original PSO and NGA. In addition, compared with traditional spectrum estimation algorithms, the proposed algorithm had a minimum estimation error with standard deviation of 0.27KHz. The proposed demodulation method was used to SAW strain system. The nonlinearity of the strain measurement was improved, with a nonlinearity of 0.55%. Simulation and experiment results verified the effectiveness of the proposed algorithm for the demodulation of the SAW strain sensor system. It helps to improve the accuracy of SAW strain measurement.

Fundamental bounds on the precision of iSCAT, COBRI and dark-field microscopy for 3D localization and mass photometry

Interferometric imaging is an emerging technique for particle tracking and mass photometry. Mass or position are estimated from weak signals, coherently scattered from nanoparticles or single molecules, and interfered with a co-propagating reference. In this work, we perform a statistical analysis and derive lower bounds on the measurement precision of the parameters of interest from shot-noise limited images. This is done by computing the classical Cramér–Rao bound (CRB) for localization and mass estimation, using a precise vectorial model of interferometric imaging techniques. We then derive fundamental bounds valid for any imaging system, based on the quantum Cramér–Rao formalism. This approach enables a rigorous and quantitative comparison of common techniques such as interferometric scattering microscopy (iSCAT), coherent brightfield microscopy, and dark-field microscopy. In particular, we demonstrate that the light collection geometry in iSCAT greatly increases the axial position sensitivity, and that the Quantum CRB for mass estimation yields a minimum relative estimation error of , where N is the number of collected scattered photons.

Invariant Extended Kalman Filtering for Underwater Navigation

Recent advances in the utilization of Lie Groups for robotic localization have led to dramatic increases in the accuracy of estimation and uncertainty characterization. One of the novel methods, the Invariant Extended Kalman Filter (InEKF) extends the Extended Kalman Filter (EKF) by leveraging the fact that some error dynamics defined on matrix Lie Groups satisfy a log-linear differential equation. Utilization of these observations result in linearization with minimal approximation error, no dependence on current state estimates, and excellent convergence and accuracy properties. In this letter we show that the primary sensors used for underwater localization, inertial measurement units (IMUs) and doppler velocity logs (DVLs) meet the requirements of the InEKF. Furthermore, we show that singleton measurements, such as depth, can also be used in the InEKF update with minor modifications, thus expanding the set of measurements usable in an InEKF. We compare convergence, accuracy and timing results of the InEKF to a quaternion-based EKF using a Monte Carlo simulation and show notable improvements in long-term localization and much faster convergence with negligible difference in computation time.

Impacts of channel fluctuations on least-squares channel estimation in underwater acoustic communications.

The time-varying multipath introduces major distortions to transmissions in the underwater acoustic communication channel. Channel estimation is often used as one of the central steps to address such distortions in high-rate communication receivers. The focus of this paper is to quantify the impacts of the channel fluctuations on the performance of the least-squares channel estimator. A metric, channel variation ratio (CVR), is defined to describe the rate of fluctuations in the channel impulse responses. Equations are derived to reveal the direct relationships between the CVR and channel estimation performance, which is measured by the channel estimation mean squared error (MSE) and signal prediction error (SPE). The equations show that both the MSE and SPE increase linearly with the CVR. The MSE and SPE metrics both have an error floor for time-varying impulse responses, even with zero ambient noise. It is confirmed that an optimum estimated channel length, achieving the minimum estimation error, exists for time-varying impulse responses. The truncation effects in the channel estimation are also investigated. Experimental data are used to validate the findings.

A High-Accuracy Method for Calibration of Nonorthogonal Angles in Dual-Axis Rotational Inertial Navigation System

The dual-axis rotational inertial navigation system (RINS) can compensate the errors of inertial sensors by rotating the inertial measurement unit (IMU), thereby improving the accuracy of the inertial navigation system. However, in practical applications, nonorthogonal angles exist between the rotating axes, resulting in a decrease in navigation accuracy. In this paper, the error model of dual-axis RINS is established, which includes all nonorthogonal angles. Based on the nonorthogonal angle error model, the transformation matrix between the frames is derived, and the necessity of nonorthogonal angle calibration is discussed. The observability of the system is analyzed, and the degree of observability obtained indicates that the proposed nonorthogonal angles are observable. Then, a new nonorthogonal angle calibration method is proposed, which uses the dual-axis rotation mechanism. The proposed calibration method can calibrate all nonorthogonal angles with high accuracy, and the minimum estimation error is less than 10%. Simulations and experiments prove the effectiveness of the proposed error model and the calibration method, and demonstrate that the navigation accuracy can be significantly improved compared with the previous method.

An algorithm for the robust estimation of the COVID-19 pandemic’s population by considering undetected individuals.

Due to the current COVID-19 pandemic, much effort has been put on studying the spread of infectious diseases to propose more adequate health politics. The most effective surveillance system consists of doing massive tests. Nonetheless, many countries cannot afford this class of health campaigns due to limited resources. Thus, a transmission model is a viable alternative to study the dynamics of the pandemic. The most used are the Susceptible, Infected and Removed type models (SIR). In this study, we tackle the population estimation problem of the A-SIR model, which takes into account asymptomatic or undetected individuals. By means of an algebraic differential approach, we design a model-free (no copy system) reduced-order estimation algorithm (observer) to determine the different non-measured population groups. We study two types of estimation algorithms: Proportional and Proportional-Integral. Both shown fast convergence speed, as well as a minimal estimation error. Additionally, we introduce random fluctuations in our analysis to represent changes in the external conditions and which result in poor measurements. The numerical results reveal that both model-free estimators are robust despite the presence of these fluctuations. As a point of reference, we apply the classical Luenberger type observer to our estimation problem and compare the results. Finally, we consider real data of infected individuals in Mexico City, reported from February 2020 to March 2021, and estimate the non-measured populations. Our work’s main goal is to proportionate a simple and therefore, an accessible methodology to estimate the behavior of the COVID-19 pandemic from the available data, such that the competent authorities can propose more adequate health politics.

Effects of DEM resolutions on soil erosion prediction using Chinese Soil Loss Equation

Slope length and slope steepness are the two topographic factors (L and S) used in the Universal Soil Loss Equation (USLE) for soil erosion prediction. These factors are usually extracted from the digital elevation models (DEM). We hypothesized that different DEM resolutions and sources influence extracted topographic factors and therefore soil loss estimation. The objective of this study was to quantify the effect of different DEM resolutions on the topographic factor calculation and consequently soil loss prediction on different geomorphological types in China and United States. We selected three typical landforms of plains, hills and mountains in both countries. We used 1) open source DEMs of 3-, 10-, 30-, and 90-m in the United States, and 2) DEMs generated with 1:10,000 and 1:50,000 topographic maps in China to extract topographic factors using a LS software tool. The Chinese Soil Loss Equation (CSLE) was used to calculate average annual soil loss. The results showed that different DEM resolutions had different effects on the extracted L and S factors. Slope length and the L factor were overestimated as DEM resolutions coarsened, and the trend of the overestimation in different landforms was the same. Slope steepness and the S factor were underestimated as DEM resolution decreased in all landforms. However, the LS factor as a product of L and S factors, which reflects relative soil loss if the other factors are held constant, are less sensitive to DEM resolutions than either L or S factor alone due to partial error cancelation. Thus, the LS factor is not as greatly affected by DEM resolutions as either L or S factor alone. The relative soil loss errors are similar for all DEMs, and “good results for wrong reasons” are obtained with coarser DEMs. Overall results indicate that except for the 90 m DEM for certain occasion, all DEMs may be used to estimate soil loss for all types of terrains for practical application, as far as the relative soil loss errors are concerned. However, in the range of 3–90 m resolutions, for the sake of good practice and solid scientific support, higher resolution DEMs if available ought to be used to minimize estimation errors of L and S factors and consequently to increase confidence of soil loss prediction.

Tunable blood oxygenation in the vascular anatomy of a semi-anthropomorphic photoacoustic breast phantom.

.Significance: Recovering accurate oxygenation estimations in the breast with quantitative photoacoustic tomography (QPAT) is not straightforward. Accurate light fluence models are required, but the unknown ground truth of the breast makes it difficult to validate them. Phantoms are often used for the validation, but most reported phantoms have a simple architecture. Fluence models developed in these simplistic objects are not accurate for application on the complex tissues of the breast.Aim: We present a sophisticated breast phantom platform for photoacoustic (PA) and ultrasound (US) imaging in general, and specifically for QPAT. The breast phantom is semi-anthropomorphic in distribution of optical and acoustic properties and contains wall-less channels with blood.Approach: 3D printing approaches are used to develop the solid 3D breast phantom from custom polyvinyl chloride plastisol formulations and additives for replicating the tissue optical and acoustic properties. A flow circuit was developed to flush the channels with bovine blood with a controlled oxygen saturation level. To showcase the phantom’s functionality, PA measurements were performed on the phantom with two oxygenation levels. Image reconstructions with and without fluence compensation from Monte Carlo simulations were analyzed for the accuracy of oxygen saturation estimations.Results: We present design aspects of the phantom, demonstrate how it is developed, and present its breast-like appearance in PA and US imaging. The oxygen saturations were estimated in two regions of interest with and without using the fluence models. The fluence compensation positively influenced the estimations in all cases and confirmed that highly accurate fluence models are required to minimize estimation errors.Conclusions: This phantom allows studies to be performed in PA in carefully controlled laboratory settings to validate approaches to recover both qualitative and quantitative features sought after in in-vivo studies. We believe that testing with phantoms of this complexity can streamline the transition of new PA technologies from the laboratory to studies in the clinic.

Optimal function approximation with ReLU neural networks

In this paper, we consider the optimal approximations of univariate functions with feed-forward ReLU neural networks. We attempt to answer the following questions. For given function and network, what is the minimal possible approximation error? How fast does the optimal approximation error decrease with network size? Is optimal approximation attainable by current network training techniques? Theoretically, we introduce necessary and sufficient conditions for optimal approximations of convex functions. We give lower and upper bounds of optimal approximation errors, and approximation rate that measures how fast approximation error decreases with network size. ReLU network architectures are presented to generate optimal approximations. We then propose an algorithm to compute optimal approximations and prove its convergence. We conduct experiments to validate its effectiveness and compare with other approaches. We also demonstrate that the theoretical limit of approximation errors is not attained by ReLU networks trained with stochastic gradient descent optimization, which indicates that the expressive power of ReLU networks has not been exploited to its full potential.

Dependently Coupled Principal Component Analysis for Bivariate Inversion Problems.

Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction in various problem domains, including data compression, image processing, visualization, exploratory data analysis, pattern recognition, time-series prediction, and machine learning. Often, data is presented in a correlated paired manner such that there exist observable and correlated unobservable measurements. Unfortunately, traditional PCA techniques generally fail to optimally capture the leverageable correlations between such paired data as it does not yield a maximally correlated basis between the observable and unobservable counterparts. This instead is the objective of Canonical Correlation Analysis (and the more general Partial Least Squares methods); however, such techniques are still symmetric in maximizing correlation (covariance for PLSR) over all choices of the basis for both datasets without differentiating between observable and unobservable variables (except for the regression phase of PLSR). Further, these methods deviate from PCA's formulation objective to minimize approximation error, seeking instead to maximize correlation or covariance. While these are sensible optimization objectives, they are not equivalent to error minimization. We therefore introduce a new method of leveraging PCA between paired datasets in a dependently coupled manner, which is optimal with respect to approximation error during training. We generate a dependently coupled paired basis for which we relax orthogonality constraints in decomposing unreliable unobservable measurements. In doing so, this allows us to optimally capture the variations of the observable data while conditionally minimizing the expected prediction error for the unobservable component. We show preliminary results that demonstrate improved learning of our proposed method compared to that of traditional techniques.

Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

Economical Explicit-Implicit Schemes for Solving Multidimensional Diffusion–Convection Problems

The goal of this work is to construct an efficient parallel numerical solution for a nonstationary diffusion–convection problem on a multiprocessor computer system with distributed memory. Economical explicit-implicit difference schemes and the method of splitting into physical processes are taken as a basis. When using these schemes, it becomes possible to pass to a chain of one-dimensional and two-dimensional difference problems that approximate the original problem in an overall sense. The explicit-implicit difference schemes suggest an explicit approximation in horizontal directions and an implicit approximation with weights in a vertical direction, taking less time to solve the diffusion–convection problem compared to the explicit schemes while retaining an admissible accuracy of the solution. We propose an algorithm for finding an optimal weight that provides a minimum approximation error in the solution of the diffusion–convection problem in a vertical direction for given time grid steps. Computational experiments have been carried out using the three-dimensional model problem of suspension transport in a water environment as an example. The model takes into account the following processes: the advective transport due to the motion of the water environment, the microturbulent diffusion and gravitational settling of suspension particles, and the change in the geometry of the bottom caused by the settling of suspension particles or the rise of bottom sediment particles. The presented parallel approach to the numerical simulation of suspension transport processes will allow one to improve the real-time forecast accuracy manifold and the validity of the engineering decisions being made when creating the objects of coastal infrastructure.

Secure Visible Light Communication Technique Based on Asymmetric Data Encryption for 6G Communication Service

Security in residential and business establishments has become an area of considerable importance. To provide enhanced security using conventional visible light (VL) communication, the characteristics of which allow for easy access by nearby intruders, a new VL communication technique is proposed in this paper. The novel VL communication system is based on a data encryption method using asymmetric encryption, and it is proposed for use in smart indoor services in the 6th generation communication environment. The asymmetric encryption capability is studied by comparison with the conventional technique, the optimum key length for the public or private encryption codes is determined under the criterion of the minimum estimation error. Moreover, the error performance is analyzed with various RSA encryption keys and data lengths to verify the performance of the proposed technique. A real experiment is performed to evaluate the proposed technique using an implemented test bed, and the success rates of the VL communication technique with and without RSA coding are evaluated by a real experiment with a test bed under an indoor laboratory VL channel. We believe it is evident that the VL communication technique based on asymmetric encryption yields superior error performance compared to that without encryption for optimal lengths of the encryption key and data bits.