Spatial Correlation Matrix Research Articles

TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port

Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data.

Global POD Modes From PIV Asynchronous Patches

A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (overlapping) spatial locations. When performing robotic volumetric Particle Image Velocimetry (PIV) (Jux et al., 2018), the large-scale mean velocity fields are estimated merging several measurements performed at different (adjacent) locations covered by the robot sequence. The proposed methodology leverages the definition of POD modes as eigenvectors of the spatial correlation matrix to obtain also large-scale modes. Performing measurements over overlapping (50-75%) regions allows to approximate the correlation matrix in the two adjacent domains. When applied over a sequence of views, this method has the potential to deliver full-domain POD modes spanning the volume covered by the robot sequence, even if different regions are covered asynchronously. This methodology is particularly well-suited for applications that seek to investigate large-scale flow structures, whenever the dynamic spatial range (DSR) of the measurement system does not allow to capture the whole domain at once. The methodology is validated using a 2D experimental dataset of a turbulent boundary layer, where patches are artificially created from splitting the PIV measurements, later used as ground truth to assess the results. Furthermore, we apply the technique to a 3D robotic volumetric PIV experiment of the flow around a wall-mounted cube.

Spatio-temporal adaptive algorithm using Hamming code based on 3D-MIMO wireless channel model

Space-time coding is an effective method for constructing communication systems while saving bandwidth, taking into account the shortage of frequency resources and power in fading channels, which is implemented based on the advantages of using adaptive antenna arrays. Currently, WiMAX radio access technology has become widespread; its use is justified in conditions of direct visibility between the base and mobile stations, but in a city with dense buildings and in the presence of organized interference, its use leads to an increase in the likelihood of bit errors. At the same time, the combination of OFDM (orthogonal frequency division multiplexing) and MIMO (spatial coding) technologies based on adaptive antenna arrays significantly reduces the probability of bit errors, and therefore increases the throughput of the communication system. The use of adaptive space-time devices at the outputs of antenna arrays makes it possible to select a signal path with maximum power and with a minimum error in receiving OFDM pilot subcarriers, even in the absence of direct visibility between the base and mobile stations and in conditions of active interference. In addition, the use of a 3D model of the communication channel will allow us to evaluate the effectiveness of the communication system model in real operating conditions. The goal of the work is to solve the problem of additionally increasing noise immunity in communication systems in conditions of numerous reflections caused by dense buildings in urban environments, using a combination of Hamming code, OFDM and an algorithm for adaptive spatio-temporal signal processing in receivers such as SISO (Singl Input – Singl Output), and MIMO (Multiply Input – Multiply Output), built on adaptive antenna arrays. The work calculated the weight coefficients of the adaptation algorithm using the theory of eigenvalues and eigenvectors of the spatial correlation matrix of signals at the outputs of the adaptive antenna arrays. Modeling of algorithms and a 3D model of the communication channel in Matlab using the Hamming code was carried out, as a result, an increase in the efficiency of error correction for the Hamming code was demonstrated, as well as a significant reduction in the probability of code errors and an increase in the performance of the communication system when using an adaptive algorithm. At the same time, the article uses the term “multipath” channel in contrast to the generally accepted, but incorrect “multibeam” channel, since multibeam can be a characteristic of an antenna system. The presented results confirm that when processing signals in a receiving system in a multipath channel with dense urban development, the use of adaptive spatiotemporal algorithms provides increased noise immunity and radio communication capacity, especially for image transmission when using unmanned aerial vehicles.

Evolution and Influencing Factors of Spatial Correlation Network of Construction Carbon Emission in China from the Perspective of Whole Life Cycle

Based on the whole life cycle perspective, the carbon emissions of the provincial construction industry in China from 2011 to 2019 were calculated from the production, construction, operation, and demolition stages of building materials. A spatial correlation network matrix of the carbon emissions in the construction industry was constructed by using the modified gravity model, and the structural characteristics of the correlation network were described by introducing social network analysis. Through the quadratic assignment program, the spatial correlation matrix of carbon emissions in the construction industry and its influencing factors were regressed and analyzed. The conclusions were as follows:① the spatial correlation network of carbon emissions in China's construction industry clearly existed. The network density and network correlation numbers were gradually rising, and the network tightness and stability were gradually improving. ② Shanghai, Tianjin, Beijing, and Jiangsu had a higher degree centrality and closeness centrality, which are the core and dominant positions of the spatial correlation network of carbon emissions in the construction industry. Zhejiang replaced Shanghai in the top four from 2013 to 2018, and the betweenness centrality of each province had unbalanced characteristics. ③ Beijing, Tianjin, Jiangsu, Inner Mongolia, Shanghai, and Shandong were "net beneficiaries" blocks, receiving the carbon emissions from other regions. Four provinces, Guangdong, Chongqing, Fujian, and Shandong, belonged to the "broker" sector, achieving a dynamic balance between the production and consumption sides of building carbon emissions. The remaining 20 provinces played a "net spillovers" role, actively sending carbon emissions from the construction industry to other provinces. The correlation between blocks was much greater than the correlation relationship within the blocks. ④ Industrial structure, urban population, spatial adjacency, consumption level, and construction industry process structure had a significant influence on the spatial correlation of carbon emissions in the construction industry. The greater the inter-provincial differences in industrial structure, urban population, spatial adjacency, and consumption level, the greater the similarity of inter-provincial construction industry process structure, and the stronger the spatial correlation and spatial spillover of the construction industry carbon emissions. Finally, according to the evolution characteristics and influencing factors of the spatial correlation network of building carbon emissions, relevant countermeasures and suggestions were provided for the collaborative carbon reduction development of the construction industry region.

Research on the characteristics and influencing factors of the spatial correlation network of cultivated land utilization ecological efficiency in the upper reaches of the Yangtze River, China.

Researching the structural characteristics of the spatial correlation network of cultivated land utilization ecological efficiency is of great significance to China's food security and agricultural green and low-carbon development. Taking 47 cities (autonomous prefectures) in the upper reaches of the Yangtze River as the research object, the ecological efficiency of cultivated land utilization from 2010 to 2020 was measured based on the unexpected output model (Super SBM), and the spatial correlation matrix was constructed using the revised gravity model. The structural characteristics of the spatial correlation network were analyzed using the social network model (SNA), and finally, the factors affecting the spatial correlation network of cultivated land utilization ecological efficiency in the upper reaches of the Yangtze River were analyzed through the quadratic assignment procedure (QAP) model. The results show that: (1) the ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River has been increasing year by year, but the overall level is low, and there is a large gap among provinces. Sichuan Province has the highest average value of 0.605, and Yunnan Province has the lowest average value of 0.359. (2) The ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River has broken through the provincial boundaries and has formed an obvious spatial correlation network, but the overall density is low, and the network is still relatively loose, needing further development and improvement. Chengdu, Yibin, Luzhou, and other cities are located in the center of the network and have formed four cohesive subgroups. (3)The differences in the level of agricultural economic development, the rural per capita disposable income, the differences in agricultural mechanization intensity, the regional population differences, and spatial adjacency have an impact on the spatial network of ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River. The difference in the level of agricultural economic development, the rural per capita disposable income, and the differences in agricultural mechanization intensity are negatively correlated, while the regional population differences are positively correlated with spatial adjacency.

Synthetic Simulation of Spatially‐Correlated Streamflows: Weighted‐Modified Fractional Gaussian Noise

AbstractStochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation.

Correlation-based hierarchical clustering of time series with spatial constraints

Correlation-based hierarchical clustering methods for time series typically are based on a suitable dissimilarity matrix derived from pairwise measures of association. Here, this dissimilarity is modified in order to take into account the presence of spatial constraints. This modification exploits the geometric structure of the space of correlation matrices, i.e. their Riemannian manifold. Specifically, the temporal correlation matrix (based on van der Waerden coefficient) is aggregated to the spatial correlation matrix (obtained from a suitable Matérn correlation function) via a geodesic in the Riemannian manifold. Our approach is presented and discussed using simulated and real data, highlighting its main advantages and computational aspects.

Advanced ANN model for DoA estimation in smart textile wearable antenna array subsystem

AbstractArtificial neural networks‐based module for fast direction‐of‐arrival (DoA) estimation of the signal received by multi‐element textile wearable antenna array (TWAA) is proposed in this paper. The developed multilayer perceptron (MLP) DoA module considers the effects of changing the gain of the antenna elements, the distance between the antenna elements and their resonant frequencies during the movement of the TWAA wearer and the crumpling of the textile. The inputs of the MLP DoA module are the elements of the spatial correlation matrix of the signal sampled by the TWAA and the output is the angular position of the signal source in the azimuthal plane. The influence of the number of TWAA elements on the accuracy of the MLP DoA module in DoA estimation under textile crumple conditions is investigated. The performances of the MLP DoA module under increased noise conditions are investigated, as well as its behavior under different degrees of textile crumpling. A comparison was made between the proposed module and the corresponding root MUSIC DoA module in terms of accuracy and speed of DoA estimation.

Research on Spatial Correlation Evolution of Marine Ecological Efficiency Based on Social Network and Spatial Correlation Matrix Model

Most developed countries in the world are maritime powers. This article constructs an ecological efficiency evaluation system based on the characteristics of the ocean itself while taking into account the relationship between land and sea. Based on social network analysis, the relationship between China’s marine ecological efficiency is regarded as a social network system, and the roles and positions played by 11 coastal cities in the network are analyzed from a relational perspective. Next, based on the unexpected super efficiency model to measure the ocean efficiency value in China’s coastal areas, we explore its spatiotemporal evolution characteristics, and measure the ocean ecological efficiency while incorporating ecological environmental pollution as an unexpected output into the evaluation system. Then, the spatial incidence matrix of marine ecological efficiency is calculated through the modified gravity model, and the characteristics of network structure are described with the help of the social network method. In addition, ArcGIS software is used to visualize the spatial evolution process. Finally, QAP regression is used to explore the key factors affecting the spatial correlation network of marine ecological efficiency in China. The results show the following: (1) In terms of time, the marine eco-efficiency of most provinces is not high, and the difference between provinces is obvious, but on the whole, it shows a fluctuating upward trend. (2) From the perspective of space, the overall displacement of the center of marine eco-efficiency in China is large in the north–south direction and small in the east–west direction, and the center of marine eco-efficiency is always concentrated near the Yangtze River Delta. (3) On the whole, the spatial correlation network of China’s marine eco-efficiency is becoming more and more complex. The number of correlation relationships and network density is increasing, and the network framework is gradually maturing. The spatial adjacency matrix, the difference in economic development level, and the difference in population distribution level can significantly promote the formation and development of the spatial correlation network of marine eco-efficiency. However, the differences in the level of opening to the outside world and in the structure of marine industries restrain their spatial networks. The difference between marine science and technology levels is not significant.

Spatial Network Analysis on the Coupling Coordination of Digital Finance and Technological Innovation

For sustainable economic development, it is extremely important to understand how digital finance and technological innovation are coupled and how the spatial coupling network is connected. Based on panel data collected from 31 Chinese provinces between 2011 and 2020, this paper calculates the technological innovation index using the entropy method, and adopts the coupling coordination model to measure the coupling coordination degree of digital finance and technological innovation. Furthermore, this paper utilizes the improved gravity model to determine the spatial correlation matrix and uses the social network analysis (SNA) method to investigate the spatial spillover characteristics of the coupling network. The results demonstrate the following: (1) While the index of digital finance and technological innovation rose and digital finance developed rapidly, the level of technological innovation remained low. (2) There was an improvement in the degree of coupling coordination between digital finance and technological innovation, which was higher in the eastern region and lower in the west. (3) The overall network density and the number of associations increased; meanwhile, the network hierarchy and network efficiency declined, indicating that the spatial structure was strengthened. (4) The centrality of some developed eastern regions, such as Beijing, Shanghai, and Zhejiang, was greater than that of some underdeveloped areas in the midwest and northeast regions. (5) The coupling coordination network can be classified into four types: the “main inflow plate” mainly includes underdeveloped regions in the midwest areas; the “main outflow plate” and “bidirectional spillover plate” primarily include the developed eastern areas; and the “agent plate” mainly includes the central provinces. This research provides a foundation for enhancing the cross-regional coupling coordinated development of digital finance and technological innovation.

Joint Transmissive and Reflective RIS-Aided Secure MIMO Systems Design Under Spatially-Correlated Angular Uncertainty and Coupled PSEs

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

Simultaneous Execution of Dereverberation, Denoising, and Speaker Separation Using a Neural Beamformer for Adapting Robots to Real Environments

It remains challenging for robots to accurately perform sound source localization and speech recognition in a real environment with reverberation, noise, and the voices of multiple speakers. Accordingly, we propose “U-TasNet-Beam,” a speech extraction method for extracting only the target speaker’s voice from all ambient sounds in a real environment. U-TasNet-Beam is a neural beamformer comprising three elements: a neural network for removing reverberation and noise, a second neural network for separating the voices of multiple speakers, and a minimum variance distortionless response (MVDR) beamformer. Experiments with simulated data and recorded data show that the proposed U-TasNet-Beam can improve the accuracy of sound source localization and speech recognition in robots compared to the conventional methods in a noisy, reverberant, and multi-speaker environment. In addition, we propose the spatial correlation matrix loss (SCM loss) as a loss function for the neural network learning the spatial information of the sound. By using the SCM loss, we can improve the speech extraction performance of the neural beamformer.

Research on the Structural Characteristics and Evolutionary Process of China’s Tourism Investment Spatial Correlation Network

The paper uses the revised gravity model to measure the intensity of tourism investment spatial correlation, constructs the spatial correlation matrix of tourism investment, and uses the social network method to analyze the structural characteristics and evolutionary process of tourism investment spatial correlation network based on 31 provinces in China from 2000 to 2016. The findings revealed: (1) The spatial correlation quantity of interprovincial tourism investment continues to grow, with Beijing, Jiangsu, Zhejiang, Shanghai, Shandong, and Guangdong at the top of the list. (2) Overall network density and correlation are rising, and the spatial correlation of interprovincial tourism investment is increasingly close. Network hierarchy and network efficiency are decreasing, and network stability has been enhanced. (3) Degree centrality and closeness centrality of each province have shown a significant increase; Beijing, Shandong, Guangdong, Jiangsu, Zhejiang, and Shanghai are the top six and in the center of the network. Most provinces have improved betweenness centrality, Beijing, Guangdong, Shandong, Liaoning, Shaanxi, and Hunan have a strong betweenness centrality with strong intermediary capacity. (4) The core area mainly includes eastern and central provinces, and the periphery areas mainly include western and northeastern provinces. The network connection density of the core and periphery areas shows an increasing trend, while the network linkage density between the core and periphery areas shows a downward trend.

Learning the ECSI from binary signals for cognitive multicast secure beamforming

This paper investigates cognitive single-group multicast secure beamforming (SGMC-SBF) in multicast scenario where an eavesdropper who acts as a regular user seeks to intercept the multicast service without authorization. This study emphasizes that the transmitter iteratively learns the eavesdropper’s spatial correlation matrix from the accumulated binary feedback on received signal-to-noise-ratio. In each iteration, the transmitter learns the eavesdropper’s spatial correlation matrix based on the historical beamformings and the historical binary feedback information, which is then used to design the optimal beamforming that will be used to learn eavesdropper’s spatial correlation matrix in the next iteration. Without loss of generality, it is assumed that the transmitter knows instantaneous channel state information (CSI) of the legitimate users (LCSI), but not the instantaneous or statistical CSI of the eavesdropper (ECSI). For comparison, we also established the corresponding genie-aided SGMC-SBF with perfect ECSI and two traditional robust schemes with erroneous and statistical ECSI, respectively. The numerical results verify that the proposed cognitive SGMC-SBF are feasible solutions that provide excellent performance.

Characteristics of Channel Eigenvalues and Mutual Coupling Effects for Holographic Reconfigurable Intelligent Surfaces.

As a prospective key technology for the next-generation wireless communications, reconfigurable intelligent surfaces (RISs) have gained tremendous research interest in both the academia and industry in recent years. Only limited knowledge, however, has been obtained about the channel eigenvalue characteristics and spatial degrees of freedom (DoF) of systems containing RISs, especially when mutual coupling (MC) is present between the array elements. In this paper, we focus on the small-scale spatial correlation and eigenvalue properties excluding and including MC effects, for RISs with a quasi-continuous aperture (i.e., holographic RISs). Specifically, asymptotic behaviors of far-field and near-field eigenvalues of the spatial correlation matrix of holographic RISs without MC are first investigated, where the counter-intuitive observation of a lower DoF with more elements is explained by leveraging the power spectrum of the spatial correlation function. Second, a novel metric is proposed to quantify the inter-element correlation or coupling strength in RISs and ordinary antenna arrays. Furthermore, in-depth analysis is performed regarding the MC effects on array gain, effective spatial correlation, and eigenvalue architectures for a variety of element intervals when a holographic RIS works in the radiation and reception mode, respectively. The analysis and numerical results demonstrate that a considerable amount of the eigenvalues of the spatial correlation matrix correspond to evanescent waves that are promising for near-field communication and sensing. More importantly, holographic RISs can potentially reach an array gain conspicuously larger than conventional arrays by exploiting MC, and MC has discrepant impacts on the effective spatial correlation and eigenvalue structures at the transmitter and receiver.

Robust comprehensive PV hosting capacity assessment model for active distribution networks with spatiotemporal correlation

Variate but similar microclimate in different buses of a distribution system usually leads to correlated photovoltaic (PV) outputs. Such correlation may reduce the PV output uncertainty and enhance the hosting capacity of active distribution networks (ADNs). To fully elucidate the hosting capacity of geographically dispersed PV, this paper proposes a novel robust comprehensive PV capacity assessment model that considers both the spatiotemporal correlation of PV output and active distribution network management (ADNM) techniques. In the proposed model, the historical PV output data of the vicinity region are employed to generate the empirical spatial and temporal correlation matrix and ellipsoidal uncertainty sets for arbitrary PV site pairs. The uncertainty of PV outputs is addressed by a two-stage robust optimization. Concerning distribution network characteristics, the proposed capacity assessment model employs a convex conic quadratic format of AC power flow equations that are transformed into second-order cone programming. A historical PV output dataset from Suzhou China and a 59-bus rural distribution system in an adjacent city was used to demonstrate the effectiveness of the proposed PV hosting capacity assessment methodology. The hosting capacity results indicate that both spatial and temporal correlations can enhance the PV hosting capacity. Considering both the spatial and temporal leads to a significant increase in PV hosting capacity. To obtain an accurate PV hosting capacity, both spatial and temporal features should be simultaneously considered.

Channel Modeling and Channel Estimation for Holographic Massive MIMO With Planar Arrays

In a realistic wireless environment, the multi-antenna channel usually exhibits spatially correlated fading. This is more emphasized when a large number of antennas is densely deployed, known as holographic massive MIMO (multiple-input multiple-output). In the first part of this letter, we develop a channel model for holographic massive MIMO by considering both non-isotropic scattering and directive antennas. With a large number of antennas, it is difficult to obtain full knowledge of the spatial correlation matrix. In this case, channel estimation is conventionally done using the least-squares (LS) estimator that requires no prior information of the channel statistics or array geometry. In the second part of this letter, we propose a novel channel estimation scheme that exploits the array geometry to identify a subspace of reduced rank that covers the eigenspace of any spatial correlation matrix. The proposed estimator outperforms the LS estimator, without using any user-specific channel statistics.

Electromagnetic Interference in RIS-Aided Communications

The prospects of using a reconfigurable intelligent surface (RIS) to aid wireless communication systems have recently received much attention. Among the different use cases, the most popular one is where each element of the RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. In prior literature, this setup has been analyzed by neglecting the electromagnetic interference, consisting of the inevitable incoming waves from external sources. In this letter, we provide a physically meaningful model for the electromagnetic interference that can be used as a baseline when evaluating RIS-aided communications. The model is used to show that electromagnetic interference has a non-negligible impact on communication performance, especially when the size of the RIS grows large. When the direct link is present (though with a relatively weak gain), the RIS can even reduce the communication performance. Importantly, it turns out that the SNR grows quadratically with the number of RIS elements only when the spatial correlation matrix of the electromagnetic interference is asymptotically orthogonal to that of the effective channel (including RIS phase-shifts) towards the intended receiver. Otherwise, the SNR only increases linearly.

Spectral indices for estimating total dissolved solids in freshwater wetlands using semi-empirical models. A case study of Guartinaja and Momil wetlands

ABSTRACT Water is one of the most important and abundant resources on earth; therefore, determining easier and more accurate methods of measuring water quality has become challenging in recent years. Images of the Sentinel-2 multispectral (MSI) sensor and field measurements for three seasons and 39 spectral indices were evaluated to identify those that can be used as patterns for the generation of semi-empirical bio-optical models to predict the total dissolved solids (TDS) in surface water of freshwater wetlands. The Guartinaja and Momil wetlands, located in the Wetland Complex of Bajo Sinú, Northern Colombia, were used as references in the field. The individual bands of the Sentinel 2 MSI sensor and the spectral indices derived from these bands were tested (and will be referred to in this paper as spectral indices). They were classified as follows: 10 individual spectral bands of the MSI sensor of the Sentinel 2 satellite; 11 indices reported by the literature to determine water, vegetation, or soils; 11 indices reported to estimate salinity; and 7 to estimate total dissolved solids and proposed in this research. The indices were evaluated based on three exclusive conditions: 1) the correlation between the TDS values measured in the field and indices spectral values >0.7 for at least two sampling seasons; 2) for the three seasons, the regression models derived from the indices have a determination coefficient >0.7; and 3) the spatial correlation matrix between the images derived from the models is >0.8 for at least two out of the three analysis seasons. We achieved important results: 1) seven indices (B3, Brightness-3, SI-3, SI-5, Ferdous-2020, TDS-1, and TDS5) met the three conditions proposed above and therefore, they were preliminarily defined as patterns for estimating TDS in the selected wetlands; 2) Additionally, this research provided two new efficient indices for calculating TDS through bio-optical models: TDS-1 and TDS-5. The validation of the regression models derived from these indices indicated a high accuracy of the prediction surfaces, expressed in values <10% of the normalized root-mean-square deviation. The results of this study contribute to the determination of semi-empirical bio-optical models for predicting optically inactive water quality parameters, which are generally predicted with empirical models whose use is spatiotemporally limited. Additionally, the research contributions that are carried out to improve the methods of evaluating water quality from remote sensing products become investigations that positively impact the evaluation of water resources, especially in geographical locations in which there is no with sufficient financial resources for on-site sampling.

Fast DoA estimation of the signal received by textile wearable antenna array based on ANN model

MLP_DoA module, being an integral part of the smart TWAA DoA subsystem, intended for fast DoA estimation is proposed. Multilayer perceptron network is used to create the MLP_DoA module that provides a radio gateway location in azimuthal plane at its output when a spatial correlation matrix, found by receiving the radio gateway signal using two-element textile wearable antenna array, is on its input. MLP_DoA network training with monitoring the generalization capabilities on the validation set of samples is applied. The accuracy of the proposed modeling approach is compared to the classical approach in MLP_DoA module training previously developed by the authors. Comparison of the presented ANN model with the root MUSIC algorithm in terms of accuracy and program execution time is also done.