Spatial Sampling Method Research Articles

Minimum Spatial Sampling Method Under Spherical Test Zone in MIMO OTA Testing

Minimum Spatial Sampling Method Under Spherical Test Zone in MIMO OTA Testing

Graph spatial sampling

We develop lagged Metropolis–Hastings walk for sampling from simple undirected graphs according to given stationary sampling probabilities. It is explained how the technique can be applied together with designed graphs for sampling of units‐in‐space. Compared with the existing spatial sampling methods, which chiefly focus on the sample spatial balance regardless of the associated outcomes of interest, the proposed graph spatial sampling method can considerably improve the efficiency because the graph can be designed to take into account the anticipated spatial distribution of the outcome of interest.

Assessing the accuracy of remote sensing data products: A multi-granular spatial sampling method

Over the past two decades, remote sensing data have demonstrated significant potential across various fields. Accuracy assessment, as a crucial component, plays a vital role in ensuring the effective application of remote sensing data products. To assess the accuracy of remote sensing data products, this paper proposed a multi-granular spatial sampling method (MG-SSM). In the proposed MG-SSM, the assessed remote sensing data products were stratified into different layers based on spatial heterogeneity, quantified by calculating the aggregation index. Subsequently, multi-granular sampling units were defined for each layer based on spatial correlation, quantified using Moran’s I and Z-score. Finally, the accuracy of remote sensing data products was assessed by comparing it with the reference data. Using the classification results of Landsat 9 OLI/TIRS with a 30-m spatial resolution as assessed data, and the classification results of Sentinel-2 MSI with a 10-m spatial resolution as reference data, and employing overall accuracy (OA) and the Kappa coefficient as assessment metrics, the performance of MG-SSM was compared against other sampling methods, including SRS, ST, SY, SSS, and GLCM. The performances of SRS, ST, SY, SSS, GLCM, and MG-SSM were 74.41%, 75.39%, 76.57%, 78.47%, 76.77%, and 67.36% in terms of OA, and 0.6, 0.61, 0.64, 0.66, 0.64, and 0.46 in terms of the Kappa coefficient, respectively. The results indicated that MG-SSM exhibited the lowest values for overall accuracy and the Kappa coefficient, aligning with the objective of assessing the accuracy of remote sensing data products. This outcome is attributed to MG-SSM’s strategy of distributing more sample points in areas with lower aggregation of land-cover classes, which are more prone to classification errors. Collectively, the proposed MG-SSM enhanced the representativeness and reduced the information redundancy of the sample points, making it an efficient sampling method for assessing the accuracy of remote sensing data products.

Spatially varying defocus map estimation from a single image based on spatial aliasing sampling method.

In current optical systems, defocus blur is inevitable due to the constrained depth of field. However, it is difficult to accurately identify the defocus amount at each pixel position as the point spread function changes spatially. In this paper, we introduce a histogram-invariant spatial aliasing sampling method for reconstructing all-in-focus images, which addresses the challenge of insufficient pixel-level annotated samples, and subsequently introduces a high-resolution network for estimating spatially varying defocus maps from a single image. The accuracy of the proposed method is evaluated on various synthetic and real data. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods for defocus map estimation significantly.

Model parameter influence on probabilistic flood risk analysis

Flood risk models are critical for effective evidence-based flood management interventions. Risk models typically analyse flood hazards, elements (e.g., buildings) exposed, and their susceptibility to monetary losses. These model parameters are inherently uncertain and their interactions within a risk model create an uncertain loss outcome. This study constructed an object-level probabilistic flood risk model for Westport (New Zealand) to analyse parameter uncertainty and sensitivities for residential building monetary loss prediction. Several model parameter combinations were assessed: 1) water depth spatial sampling method, 2) replacement valuation method, and 3) univariable and multivariable vulnerability model. Monetary loss uncertainty was analysed using test models that varied parameter combinations. Sensitivity to parameter uncertainty distributions were assessed using a global sensitivity analysis based on Sobol's method. Monetary loss estimates ranged between ± NZD 1 million to ± NZD 6 million (90 % confidence interval) for test models by varying parameter combinations. Loss estimates ranged from 2 % to 100 % across the analysed test models. Replacement valuation and vulnerability models were the primary drivers of loss uncertainty. Sobol first-order indices confirmed vulnerability models had the largest parameter influence on uncertainty while total-order indices showed replacement value has a larger overall contribution. The study outcomes emphasise flood risk modellers' need to investigate model uncertainties to inform accuracy improvement solutions.

A Synthesizing Semantic Characteristics Lung Nodules Classification Method Based on 3D Convolutional Neural Network.

Early detection is crucial for the survival and recovery of lung cancer patients. Computer-aided diagnosis system can assist in the early diagnosis of lung cancer by providing decision support. While deep learning methods are increasingly being applied to tasks such as CAD (Computer-aided diagnosis system), these models lack interpretability. In this paper, we propose a convolutional neural network model that combines semantic characteristics (SCCNN) to predict whether a given pulmonary nodule is malignant. The model synthesizes the advantages of multi-view, multi-task and attention modules in order to fully simulate the actual diagnostic process of radiologists. The 3D (three dimensional) multi-view samples of lung nodules are extracted by spatial sampling method. Meanwhile, semantic characteristics commonly used in radiology reports are used as an auxiliary task and serve to explain how the model interprets. The introduction of the attention module in the feature fusion stage improves the classification of lung nodules as benign or malignant. Our experimental results using the LIDC-IDRI (Lung Image Database Consortium and Image Database Resource Initiative) show that this study achieves 95.45% accuracy and 97.26% ROC (Receiver Operating Characteristic) curve area. The results show that the method we proposed not only realize the classification of benign and malignant compared to standard 3D CNN approaches but can also be used to intuitively explain how the model makes predictions, which can assist clinical diagnosis.

Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data.

The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer-Douglas-Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.

Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation

The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring system. In this paper, we used Sequential Gaussian Conditional Simulation (SGCS) to obtain the biomass of four typical forest types in Shangri-La, Yunnan Province, China. In addition, we adopted a geostatistical sampling method for sample point layout and optimization to achieve the purpose of improving sampling efficiency and accuracy, and compared with the traditional sampling method. The main results showed that (1) the Gaussian model, exponential model, and spherical model were used to analyze the variogram of the four typical forests biomass, among which the exponential model had the best fitting effect (R2 = 0.571, RSS = 0.019). The range of the exponential model was 8700 m, and the nugget coefficient (C0/(C0 + C)) was 11.67%, which showed that the exponential model could be used to analyze the variogram of forest biomass. (2) The coefficient of variation (CV) based on 323 biomass field plots was 0.706, and the CV based on SGCS was 0.366. In addition, the Overall Estimate Consistency (OEC) of the simulation result was 0.871, which can be used for comparative analysis of traditional and spatial sampling. (3) Based on the result of SGCS, with 95% reliability, the sample size of traditional equidistant sampling (ES) was 191, and the sampling accuracy was 95.16%. But, the spatial sampling method based on the variation scale needed 92 samples, and the sampling accuracy was 93.12%. On the premise of satisfying sampling accuracy, spatial sampling efficiency was better than traditional ES. (4) The accuracy of stratified sampling (SS) of four typical forest areas based on 191 samples was 97.46%. However, the sampling accuracy of the biomass variance stratified space based on the SGCS was 93.89%, and the sample size was 52. Under the premise of satisfying the sampling accuracy, the sampling efficiency was obviously better than the traditional SS. Therefore, we can obtain the conclusion that the spatial sampling method is superior to the traditional sampling method, as it can reduce sampling costs and solve the problem of sample redundancy in traditional sampling, improving the sampling efficiency and accuracy, which can be used for sampling estimation of forest biomass.

3D urban landscape optimization: From the perspective of heat flux-microclimate relations

Three-dimensional urban landscapes significantly alter surface energy dynamics and local climates. However, it remains unclear how heat fluxes impact the microclimate at the neighborhood scale in dense megacities. Combined spatial gradient sampling method and multi scenarios simulations based on the ENVI-met model, we aimed to explore the relations between heat fluxes and microclimate in Beijing, China. The results showed that (1) The sensible heat flux/latent heat flux had the largest explanatory power to wind speed at night (39% vs. 39%), followed by air temperature at midday (35% vs. 24%). The explanatory power improved under dual-factor regression analysis, especially for the wind speed at night (67%). (2) As the sensible heat flux/latent heat flux increased by 10 W/m2 at midday, the air temperature increased by 0.024°C and 0.017°C, respectively. (3) As the sensible heat flux/latent heat flux increased by 10 W/m2 at night, the wind speed increased by 0.21 m/s and 0.76 m/s, respectively. For the neighborhoods with high sensible heat flux and low latent heat flux, both decreasing the three-dimensional fractal of buildings and greening the plants can lead to more comfortable thermal environment.

An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation

In this paper, we are concerned with microwave subsurface imaging achieved by inverting the linearized scattering operator arising from the Born approximation. In particular, we consider the important question of reducing the required data to achieve imaging. This can help to reduce the radar system’s cost and complexity and mitigate the imaging algorithm’s computational burden and the needed storage resources. To cope with these issues, in the framework of a multi-monostatic/multi-frequency configuration, we introduce a new spatial sampling scheme, named the warping method, that allows for a significant reduction in spatial measurements compared to other literature approaches. The basic idea is to introduce some variable transformations that “warp” the measurement space so that the reconstruction point-spread function obtained by adjoint inversion is recast as a Fourier-like transformation, which provides insights into how to achieve the sampling. In our previous contributions, we focused on presenting and checking the theoretical background with simple numerical examples. In this contribution, we briefly review the key components of the warping method and present its experimental validation by considering a realistic subsurface scattering scenario for the case of a buried water pipe. Essentially, we show that the latter succeeds in reducing the number of data compared to other approaches in the literature, without significantly affecting the reconstruction results.

3D multi-view squeeze-and-excitation convolutional neural network for lung nodule classification.

Early screening is crucial to improve the survival rate and recovery rate of lung cancer patients. Computer-aided diagnosis system (CAD) is a powerful tool to assist clinicians in early diagnosis. Lung nodules are characterized by spatial heterogeneity. However, many attempts use the two-dimensional multi-view (MV) framework to learn and simply integrate multiple view features. These methods suffer from the problems of not capturing the spatial characteristics effectively and ignoring the variability of multiple views. In this paper, we propose a three-dimensional MV convolutional neural network (3D MVCNN) framework and embed the squeeze-and-excitation (SE) module in it to further address the variability of each view in the MV framework. First, the 3D multiple view samples of lung nodules are extracted by the spatial sampling method, and a 3D CNN is established to extract 3D abstract features. Second, build a 3D MVCNN framework according to the 3D multiple view samples and 3D CNN. This framework can learn more features of different views of lung nodules, taking into account the characteristics of spatial heterogeneity of lung nodules. Finally, to further address the variability of each view in the MV framework, a 3D MVSECNN model is constructed by introducing a SE module in the feature fusion stage. For training and testing purposes we used independent subsets of the public LIDC-IDRI dataset. For the LIDC-IDRI dataset, this study achieved 96.04% accuracy and 98.59% sensitivity in the binary classification, and 87.76% accuracy in the ternary classification, which was higher than other state-of-the-art studies. The consistency score of 0.948 between the model predictions and pathological diagnosis was significantly higher than that between the clinician's annotations and pathological diagnosis. The results show that our proposed method can effectively learn the spatial heterogeneity of nodules and solve the problem of multiple view variability. Moreover, the consistency analysis indicates that our method can provide clinicians with more accurate results of benign-malignant lung nodule classification for auxiliary diagnosis, which is important for assisting clinicians in clinical diagnosis.

Phosphorus storage and allocation in vegetation on the Tibetan Plateau

Phosphorus (P) is critical for vegetation growth and ecosystem productivity. P distribution and allocation patterns of plants in natural communities are associated with their resource allocation strategies. However, owing to the lack of systematic research, the spatial variations of vegetation P on the Tibetan Plateau (TP) remain unclear. Based on the 0.5° standardized spatial grid sampling method and survey data obtained from plant organs (leaf, branch, trunk, and root) in 2,040 plant communities, we analyzed the distribution of vegetation P contents (g kg−1) and densities (g P m−2) on the TP. The results indicate that the vegetation P contents and densities differed significantly among the plant organs on the TP. Active organs contained higher P contents but lower P densities. Radiation dominated vegetation P storage and allocation on the TP. Strong radiation had a negative effect on vegetation P accumulation and caused more P to be allocated to the underground parts. The TP vegetation stored approximately 3.64 Tg of P, of which approximately 1.68, 1.28, 0.68, and 0.01 Tg of P were stored in forests, shrublands, grasslands, and deserts, respectively. Based on a large survey dataset, we determined the spatial distributions of vegetation P and constructed a P density distribution map for TP vegetation, which can be used for ecosystem modeling.

An extended 3-compartment model for describing step change experiments in pharmaceutical twin-screw feeders at different refill regimes

Residence time distributions (RTDs) are a valuable tool for product tracking in the unit operations of a continuous line for manufacturing pharmaceutical oral solid dosage (OSD) and the integrated system itself. The first unit operation in such a continuous line in which extended intermixing can occur, is typically a feeder. The RTD of a feeder can be obtained by performing tracer experiments with a tracer material. A physical interpretation can be given to the observed tracer concentration responses by fitting a tanks-in-series (TIS) or compartmental model to it. Consequently, the internal mixing behaviour inside the feeder hopper can be rationalized. However, typically, a constant volume is assumed for the tanks or compartments in these models. This has led to several publications where the experimental set-up does not violate the constant volume assumption, i.e. one performs refills at a high hopper fill level. Here, we step away from this assumption and develop a set of differential equations for a 3-compartment model in order to account for a non-constant volume of the compartments. Moreover, the model distinguishes between a bypass trajectory formed by the agitator inside the feeder and an inner mixing volume, in which the tracer concentration lags on the tracer concentration in the bypass volume. This compartmentalization was inspired by the results obtained in a previous study using a spatial sampling method to assess the tracer concentration throughout the feeder hopper for different experimental runtimes. The developed model successfully describes the step responses for different refill regimes: the standard smooth first order plus dead time response (FOPDT) for a high refill regime and the more complex step response, including dips in the rising phase of the curve, for the low refill regime. As a consequence, a more thorough understanding of the complex mixing behaviour inside the feeder is obtained, which allows for an improved traceability. Next to that, the model delivers enhanced knowledge on the interaction between the residence time and the refill regime. The developed model was fitted to a data set, containing step change experiments for different pharmaceutical materials (Tablettose 80 (T80), Microcelac 100 (MCL), and Avicel PH101 (MCC)), different mass flow rates, and refill regimes. The experimentally observed phenomena could be reliably described by the proposed model. The model showed an improved transferability compared to typical TIS models.

Spatial sampling design optimization of monitoring network for terrestrial ecosystem in China

The rapid socioeconomic development leads to the deterioration of ecological environment. Ecosystem assessment has been conducted worldwide, e.g. the Millennium Ecosystem Assessment to assess consequences of ecosystem change for human well-being. To enhance ecosystem assessment in China, this study proposes the design of a monitoring network for the terrestrial ecosystem consisting of core stations and localized points. With focus on ecosystem services of NPP, water conservation, soil retention and sandstorm prevention, core stations of the monitoring network for observing all four services are first selected by assessing and improving spatial representativeness in ecoregions of forest, grassland and desert ecosystems. Then a spatial sampling method is applied to choose localized points for observing each specific service. Eventually expert's knowledge is used to make final decisions of added stations and points by utilizing existing networks and considering factors such as topography, spatial coverage. Combining both aforementioned approaches and experts knowledge, 60 core stations and 176 localized points are finally determined for the monitoring network. For the forest ecosystem, 39 core stations are decided with 31 selected from existing networks and eight newly added core stations improve spatial representativeness by 51.58 %, 68.11 % and 75.55 % in Temperate grasslands, Temperate desert and Alpine vegetation in Tibet Plateau respectively. For the grassland and desert ecosystem, 21 core stations are chosen with 18 from existing networks and three newly added core stations improve the representativeness by 21.60 % and 44.88 % in Tibet alpine grassland and Grassland in southern mountain areas respectively. Priorities in the implementation phase should be given to instruments installation for monitoring all four services in core stations from existing networks and setting up new stations in regions where representativeness are significantly improved.

A Novel Method of 3D Multipoint Geostatistical Inversion Using 2D Training Images

Abstract The seismic inversion method combined with multipoint geostatistics theory has begun to receive attention, but the acquisition accuracy and calculation efficiency of 3D training image still need more optimization. This paper presents a novel method of 3D multipoint geostatistical inversion based on 2D training images directly. The 2D training image was scanned by the data template to acquire the multipoint statistical probability in 2D direction. The probability fusion method is used to fuse the 2D multipoint probability into 3D multipoint probability. The rock facies types and patterns of the simulated points are obtained by random sampling. On this basis, the elastic parameters are extracted from the statistical rock physics model, and the seismic records are convoluted. Then, the synthetic records and the actual records were compared under a given threshold. If the error exceeds the given threshold, the iterative adaptive spatial sampling method will be used to repeat the process above-mentioned, so as to ensure that the error is below the threshold. Because the 2D training image is easy to obtain and evaluate, the demand problem of 3D training image is solved. The 2D training image scanning, probability storage and access are more convenient, and the adaptive spatial sampling method is more efficient than the reject sampling, so as to ensure the operation efficiency. The model from the Stanford Center for Reservoir Forecasting is selected to test the effectiveness of this newly designed method.

Design and Analysis of Intelligent Agricultural Monitoring System Based on Biological Intelligence Optimization Algorithm

The traditional optimization method has insufficient intelligence, slow operation speed, and some problems in the calculation of optimal parameters when facing the relationship between too large sample size and complex thread. The biological intelligence optimization algorithm is based on the genetic and evolutionary mechanism of the genetic system. The smart agricultural system is the application of new IOT technology in the field of smart agriculture, mainly including real-time monitoring, wireless monitoring, and remote image and analysis functions. Through this topic, it is concluded that (1) when the parameter is set to μ = 0.02, β = 0.99, the deviation of the optimal value = 1.74, the deviation of the average value = 3.86, the standard deviation of the experimental value = 3.81, the performance evaluation = 2.68, and the maximum number of peaks = 4. It can give full play to the advantages of various algorithms and learn from each other's strengths. (2) The sNIOA algorithm is the best. Compared with the NIOA algorithm, the accuracy of N is increased by 50% and the accuracy of L = 8 is increased by 20%. Compared with the NGA algorithm, the error of I is reduced by 23.5% and the offset of M is reduced. NPSO algorithm pm performance is improved by 20%, and pmk peak value is reduced by 20%. The domestic research on smart agriculture has experienced explosive growth, and the research has been carried out from the concept of smart agriculture, related technologies, constraints, industrial chain, etc., to provide theoretical guidance for the development of smart agriculture. The worst algorithm parameter is the NIOA model whose offset increases little, the performance decreases by 20%, and the peak value becomes worse. (3) The smart agriculture project uses the latest Internet of Things and cloud computing technology, and based on the analysis of big data and artificial intelligence technology, a new service form is proposed, that is, a cloud, network, and platform composite service system to establish a regional closed-loop ecological chain integrating agricultural production, processing, and marketing. (4) In the biological genetic algorithm model, the recall rate of Cp is low, the ROC curve fluctuates greatly, the specificity AEa is poor, and the sensitivity is not high. Using the integrated technology integrating GIS technology, RS technology, spatial statistics, mathematical models, and other methods, based on the differences of various temporal and spatial scales and their monitoring methods, combined with regression model and spatial sampling method, quantitative analysis was performed, and its influencing factors were analyzed. The comparison shows that the optimal F1 score of biological intelligence optimization parameters is up to 23% higher, the accuracy rate Ao is increased by 20%, and the accuracy rate is high.

An Energy-Efficient Wearable Functional Near-infrared Spectroscopy System Employing Dual-level Adaptive Sampling Technique.

Functional near-infrared spectroscopy (fNIRS) is a powerful medical imaging tool in brain science and psychology, it can also be employed in brain-computer interface (BCI) due to its noninvasive and artifact-less-sensitive characteristics. Conventional ways to detect large-area brain activity using near-infrared (NIR) technology are based on Time-division or Frequency-division modulation technique, which traverses all physical sensory channels in a specific period. To achieve higher imaging resolution or brain-tasks classification accuracy, the NIRS system require higher density and more channels, which conflict with the limited battery capacity. Inspired by the functional atlas of the human brain, this paper proposes a spatial adaptive sampling (SAS) method. It can change the active channel pattern of the fNIRS system to match with the real-time brain activity, to increase the energy efficiency without significant reduction on the brain imaging quality or the accuracy of brain activity classification. Therefore, the number of the averaging enabled channels will be dramatically reduced in practice. To verify the proposed SAS technique, a wearable and flexible NIRS system has been implemented, in which each channel of light-emitting diode (LED) drive circuits and photodiode (PD) detection circuits can be power gated independently. Brain task experiments have been conducted to validate the proposed method, the power consumption of the LED drive module is reduced by 46.58% compared to that without SAS technology while maintaining an average brain imaging PSNR (Peak Signal to Noise Ratio) of 35 dB. The brain-task classification accuracy is 80.47%, which has a 2.67% reduction compared to that without the SAS technique.

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making.

Application of EEG Signal Recognition Method Based on Duffing Equation in Psychological Stress Analysis

Based on the study of the feature extraction algorithm based on the multiple empirical mode decomposition of the Duffing equation, this paper proposes a corresponding improved algorithm, completes the identification and analysis of the psychological pressure dimension space under the audiovisual induction method, and designs two typical psychological types of music and pictures. Based on the stress induction experiment, an audiovisual-induced psychological stress recognition system based on EEG (electroencephalogram) signals was built. Aiming at the problem that the spatial uniform sampling method cannot well reflect the dynamic characteristics of the multivariate EEG signal, based on the Duffing equation, a nonuniform sampling algorithm that adaptively selects the projection direction is proposed. At present, the use of the Duffing equation to detect weak unknown signals is to select a set of fixed parameters. Analysis of these two aspects to determine the parameters of the system is based on the parameter analysis of the Duffing equation oscillator. Due to the sensitivity of the Duffing equation to the initial value, the choice of parameters has a great influence on the detection effect. In response to this situation, the relationship between the parameters and initial values of the Duffing equation is analyzed. From the relationship between the parameters and the initial values, the influence of different parameters on the detection effect is analyzed to verify the superiority of the current equation parameters. First, the multichannel EEG signal is nonuniformly sampled multiempirical modal decomposition, and an effective intrinsic modal function is selected to extract the mental stress EEG characteristics. Experimental results show that the EEG signal recognition algorithm based on the Duffing equation effectively extracts EEG signal features and improves the classification accuracy of mental stress EEG signals.

Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.