Limited Dimensions Research Articles

AE-BEGAN based Synthetic Data Augmentation for Sample-Limited High-Dimensional Problems with Application to NIR Spectral Data

Synthetic data augmentation holds substantial research and application value in scenarios characterized by limited samples and high dimensions. It enhances the analytical ability and efficiency of spectral analysis models. This paper proposes Autoencoder-Combined Boundary Equilibrium Generative Adversarial Networks (AE-BEGAN) as a new method for augmenting synthetic data in scenarios with limited samples and high dimensions, with a specific emphasis on near-infrared (NIR) spectral data. The spectral data first undergoes preprocessing procedures that encompass advanced noise reduction algorithms and techniques for removing abnormal samples, guaranteeing elimination of unwanted disturbances and outliers. Then, the pre-processed data is utilized to train the AE-BEGAN model, which generates augmented synthetic samples. Finally, real NIR spectral data obtained from lubricant samples exhibiting different water contents were employed to validate and test the performance of the model. The experimental results demonstrate that the AE-BEGAN model outperforms other GANs in generating synthetic data of high quality and diversity, as quantified by two evaluation metrics, α-Precision and β-Recall with scores of approximately 0.86 and 0.28, respectively. The application case study confirms that the AE-BEGAN model exhibits the capability to generate derived NIR spectra and expand the number of spectra in scenarios with limited samples and high dimensions.

Portable botanical biofilters to address internal combustion engine exhaust exposure

Previous research has identified the capacity of active botanical biofiltration to provide significant benefits for indoor air quality, however existing systems are large and require integration into the building. Thus, a “Portable Botanical Biofilter” (PBB) has been developed, however, its pollutant filtration capacity is unknown and may be constrained due to the limited dimensions of practical portable systems. This study thus aimed to quantify a PBB's removal efficiency under controlled conditions for CO2, PM (Total Suspended Particles (TSP) along with particle size distribution) and total volatile organic compounds (TVOCs). Testing was conducted in a purpose-built 15.6 m3 chamber at two light intensities, regular lighting (79.4 μmol∙m−2 s−1) as commercially supplied to the system and with elevated supplementary lighting (167 μmol∙m−2 s−1). CO2 removal testing was conducted with the plant species Peperomia obtusifolia, Gibasis pellucida, and a no plant control. TVOC and PM removal testing was conducted with systems containing the known pollutant removing plants: Gibasis pellucida, Spathiphyllum wallisii and mixed modules containing Peperomia obtusifolia and Nematanthus glabra at the two lighting levels. All treatments removed CO2 significantly faster than the control, with supplementary lighting allowing CO2 removal at a significantly faster rate than regular lighting (0.232–0.673 g h−1). However, CO2 net removal rates were likely to be insufficient to have valuable effects in a realistically sized room. TSP removal differed significantly between treatments for plant species, but not between light levels. The system removed particles of all sizes, with promising results for the smallest particle size (PM0.3), with highly effective reduction in comparison to the control (up to ∼72%). We conclude that in its delivered, prototype state, the PBB was unlikely to be an effective air purifying device for the removal of indoor CO2, however promising results for the removal of TVOCs and PM were recorded. To improve both CO2 and other pollutant removal, further improvements to lighting, system sealing, and module design are required, along with further testing to allow comparison with other systems.

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network.

Radar high-resolution range profile (HRRP) provides geometric and structural information of target, which is important for radar automatic target recognition (RATR). However, due to the limited information dimension of HRRP, achieving accurate target recognition is challenging in applications. In recent years, with the rapid development of radar components and signal processing technology, the acquisition and use of target multi-frequency and polarization scattering information has become a significant way to improve target recognition performance. Meanwhile, deep learning inspired by the human brain has shown great promise in pattern recognition applications. In this paper, a Multi-channel Fusion Feature Extraction Network (MFFE-Net) inspired by the human brain is proposed for dual-band polarimetric HRRP, aiming at addressing the challenges faced in HRRP target recognition. In the proposed network, inspired by the human brain's multi-dimensional information interaction, the similarity and difference features of dual-frequency HRRP are first extracted to realize the interactive fusion of frequency features. Then, inspired by the human brain's selective attention mechanism, the interactive weights are obtained for multi-polarization features and multi-scale representation, enabling feature aggregation and multi-scale fusion. Finally, inspired by the human brain's hierarchical learning mechanism, the layer-by-layer feature extraction and fusion with residual connections are designed to enhance the separability of features. Experiments on simulated and measured datasets verify the accurate recognition capability of MFFE-Net, and ablative studies are conducted to confirm the effectiveness of components of network for recognition.

Radiostrontium determination by combination of automated Sr isolation and “on-column” Cherenkov detection using chromatographic model

A novel analytical solution of non-linear chromatography in case of parabolic isotherm for frontal analysis was obtained by combination of Cole-Hopf and Laplace transform. It was used for simulation of strontium capturing on chromatographic column with aim to improve quantitative determination of low-level 90Sr activities. From the experimentally determined breakthrough curves, the retention factor and the number of theoretical plates were calculated using the Glueckauf and Wenzel relations and by fitting the breakthrough curves for the linear isotherm using MatLab. These were used to simulate the breakthrough curves using a parabolic isotherm solution where the non-linear term of the isotherm was taken as a small negative deviation of the retention factor. On the base of theoretical prediction and experimental data, procedure for automated capturing of strontium on chromatographic column with specific dimension and off line „on-column“ Cherenkov detection on commercial ultra low-level liquid scintillation counter was developed. It was shown that analytical solution for parabolic isotherm in comparison with solution for linear isotherm gives better prediction of mass of captured Sr on column filled with small amount of Sr resin and SuperLig®620 in case of elevated Sr concentration, even when non-linear effect is not obvious. The solution also makes it possible to predict the mass of resin required for strontium isolation at 100% yield under given conditions. Considering the limited dimensions of the column, and consequently small mass of the resin in them, it resulted in the low efficiency of the columns, which, however, did not affect the yield in real conditions of isolation. The results have shown that the yields achieved after isolation on SuperLig®620 from real samples are 100%. In addition, it is shown that captured 90Sr can be detected through 90Y ingrowth, on column filled with strontium specific resin, with Cherenkov detection efficiency of at least 50%. The efficiency may be enhanced to 60%, depending on parameters which can affect detection efficiency change (type of column, resin type, surrounding solution, etc.). The developed procedures enable quantitative determination of 90Sr in natural water samples with MDAC below 12 mBq l−1 and solid (soil and vegetation) samples with MDAC below 6 Bq kg−1 within 2–3 days. The proposed solution may easily be implemented in radiochemical laboratories where this type of analysis is routinely done within environmental monitoring or other purposes.

Efficiency of ferromagnetic shielding of superconducting coils of high-speed maglev crew

Background: Magnetic levitation transport with combined traction, suspension and guidance systems based on superconducting coils (SCC) allows reaching speeds of up to 500600 km/h with a very significant (up to 150200 mm) air gap, which is an important factor in ensuring the safety of high-speed transportation. However, SCC are a source of strong external magnetic fields, which, in conditions of limited crew dimensions, can have a harmful effect on both passengers and on-board auxiliary equipment.
 Aim: to analyze the external magnetic fields of the SCC systems for traction, suspension and direction of maglev vehicles and the effectiveness of ferromagnetic screens as a means of ensuring the electromagnetic safety of passengers and the electromagnetic compatibility of the SCC with other onboard equipment.
 Materials and methods: to achieve this goal, methods of analytical and numerical modeling of the magnetic fields of the SCC using modern software packages were used. As a prototype of the combined magnetic system of traction, suspension and direction, the system of the MLX-L0 maglev vehicle, which is undergoing pilot commercial operation at the Yamanashi test line (Japan), was adopted.
 Results: it is shown that the value of the external magnetic fields of the SCC of the traction and guiding systems in the passenger cabin of the prototype crew exceeds the maximum permissible levels established by domestic and foreign regulatory documents, both without shielding and with passive shielding with flat steel sheets.
 Conclusion: passive shielding of the SCC with steel sheets, including multilayer ones, does not provide the required reduction in the level of external magnetic fields for a given vehicle dimensions.

The Effect of Collector Azimuth on Inter-Row Shading in Photovoltaic Fields—A Comprehensive Point of View

Rooftop buildings, mountain terrains, rivers, and lake beds may be oriented at different azimuths other than due south (in the northern hemisphere), on which photovoltaic (PV) systems may be installed. The present study presents a comprehensive point of view on the relation between the inter-row spacing and the inter-row shading in multiple-row collectors of PV fields; it analytically formulates the inter-row spacing and the associated shading losses for collectors deployed at any azimuth on horizontal, sloped-planed, and oriented toward the south and north, and is supported by the numerical results. Several criteria for the inter-row spacing are applied in the study; among them is a newly introduced criterion appropriate for the collectors deployed at a given azimuth. This study indicates that shorter spacing results in increased shading; minimum shading is obtained for collectors oriented toward the equator; inter-row spacing for maintenance purposes is used in limited dimensions of the PV fields and, usually, in optimal PV system designs; less shading occurs for systems deployed on sloped planes oriented toward the south; less shading is obtained for collectors based on the new criterion, where collectors are deployed at azimuth equal to the solar azimuth, and larger spacing is required for collectors deployed on northern slopes.

Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis

Highway traffic state data collected from a network of sensors can be considered as a high-dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven method–anti-circulant dynamic mode decomposition with sparsity-promoting (circDMDsp)–to study the dynamics of highway traffic speed data. Particularly, circDMDsp addresses several issues that hinder the application of existing DMD models: limited spatial dimension, presence of both recurrent and non-recurrent patterns, high level of noise, and known mode stability. The proposed circDMDsp framework allows us to numerically extract spatial–temporal coherent structures with physical meanings/interpretations: the dynamic modes reflect coherent spatial bases, and the corresponding temporal patterns capture the temporal oscillation/evolution of these dynamic modes. Our result based on Seattle highway loop detector data showcases that traffic speed data is governed by a set of periodic components, e.g., mean pattern, daily pattern, and weekly pattern, and each of them has a unique spatial structure. The spatiotemporal patterns can also be used to recover/denoise observed data and predict future values at any timestamp by extrapolating the temporal Vandermonde matrix. Our experiments also demonstrate that the proposed circDMDsp framework is more accurate and robust in data reconstruction and prediction than other DMD-based models. The code for the algorithm and experiment is available at https://github.com/mcgill-smart-transport/circDMDsp.

PCR209 Comparing the Value of Daily Measurement of Wellbeing for Individuals with Alcohol Use Disorder Versus Reimbursement-Based Timing

Centers for Medicare and Medicaid Services (CMS) require patients to be evaluated using setting-specific patient assessment instruments for clinical assessment, payment, and quality assurance. This research examines how wellbeing is assessed in skilled nursing facilities (SNFs) and the role of measurement timing in understanding dimensions of disability, functional limitations, and wellbeing among individuals with alcohol use disorder (AUD). SNFs are central to caregiving and provide short- and long-term skilled nursing care.

Conceptual art and its role in awareness campaigns

Conceptual or conceptual art considers the idea to be the machine that produces art, and the plan becomes by using artistic tools such as line, space, color, simile and etaphor in the service of the idea communicating with the recipient in an nteresting and non-traditional way and can attract the recipient and provoke his thinking and support the message contained in the design of the idea, so conceptual art played a role Important in these campaigns, through the idea, the art was able to transform the advertisement into a visual image with two dimensions that attracts the mind and arouses attention. Accordingly, the structure of the study was established in four chapters, in which the first chapter (the methodological framework of the research) came from the research problem, which is summarized in the following question :What are the basic steps required for the success of awareness campaigns?The importance of the research and the need for it, and the aim of the research, were determined by defining the intellectual dimension of conceptual art, the objective and temporal limits of research, and the spatial limits, then defining and defining the terms. The role of conceptual art in educating the community) and then came the third chapter (research procedures) and the researcher identified the research community consisting of approximately (17) works of art.

P257 UNIQUE UNEXPECTED COURSE OF A S–ICD LEAD: DIAGNOSTIC CHALLENGES AND SAFE CLINICAL AND INTERVENTIONAL MANAGEMENT

Abstract In this report we present a case of subcutaneous cardioverter defibrillator (S–ICD) implantation complicated by pneumothorax (PNX), in the absence of alteration of electrical parameters nor lung parenchyma involvement. After implantation in secondary prevention (1995) of an abdominal transvenous defibrillator (TV–ICD) via the left subclavian vein, a replacement (2009) with a new contralateral TV–ICD system, and the withdrawal of the proximal edge of the right ventricular (RV) lead in the abdominal region, a 78–years–old man presented in our center for pocket erosion. The trans–esophageal echocardiography was suspect for vegetations, while PET/CT and blood cultures were negative. Transvenous extraction of the right–sided ICD system was performed (the original RV lead was left in place due to his complex course), then the patient underwent implantation of a S–ICD with the intermuscular 2–incision technique. In the end of this well tolerated procedure the electrical parameters were optimal, the defibrillation test was effective and the position of the S–ICD was judged correct on the chest X–ray. Few hours later, the patient complained for local pain and progressive respiratory failure. A chest CT showed a PNX at the lateral insertion of the lead, which reached the pleural space, along the left scissure (Fig.1). During lead repositioning, programmed with stand–by thoracic surgery, the lead was easily disconnected from the generator through the lateral incision, pulled–back through the subxiphoid one and tunneled in the correct plane without complications (Fig.2). Serial chest CT confirmed PNX resolution. The patient was discharged in 8 days. At the 1–month follow–up visit electrical parameters were stable, in absence of infective complications. In literature there are few cases of PNX, so that it’s challenging to define their risk factors. We assume that, despite a correct implantation technique, a barrel chest configuration and the aging of the fascia favored the wrong tunnelling. In conclusion, PNX is a rare and unexpected complication of S–ICD implantation and can go unnoticed when of limited dimensions, in the absence of clinical and radiological signs nor alteration of electrical parameters. Fluoroscopic and CT imaging is recommended for diagnosis. Thoracic surgery is useful during repositioning of the lead, which can also be performed by simple traction. CT imaging is of utmost importance for planning such a procedure and predicting its complexity.

Complex Refractive Index Extraction for Ultrathin Molybdenum Oxides Using Micro‐Photonic Integrated Circuit Chips

AbstractThe emerging 2D nanomaterials with unique optical properties are promising for next‐generation miniatured on‐chip devices. One of the prerequisites is to precisely measure their optical parameters during their implementation. However, the inherent features of 2D layers, including limited lateral dimensions and ultra‐small thicknesses, are not favorable to the conventional characterization techniques applied in the bulk system, especially for optical complex refractive indices measurement. Here, this work proposes a silicon photonics‐enabled platform to evaluate the complex refractive indices of ultrathin 2D materials in a facile and reliable manner. Ultrathin molybdenum oxides (MoOx) with multiple stoichiometric states are selected as the target 2D material to provide sufficient complexity of the system for investigation. Upon the integration of ultrathin MoOx, the silicon photonic chip, in the form of a Mach‐Zehnder interferometer, exhibits wavelength shifts which are used for calculating the optical complex refractive indices. Compared with the theoretical calculation, the deviation is as low as 1% and generally less than 5%. This work demonstrates a highly accurate and reliable approach for measuring the complex refractive index of 2D films, possibly assisting future advances in 2D materials‐enabled optical and photonic applications.

Managing decline: Devaluation and just transition at Diablo Canyon nuclear power plant

This paper examines the shifting fortunes of the Diablo Canyon Nuclear Power Plant in California to better understand how asset owners are dealing with an increasingly-significant problem in changing energy landscapes: the devaluation of large fixed capital assets. In 2016, Pacific Gas and Electric Company announced a Joint Proposal to retire the Diablo Canyon plant by 2025, the result of a negotiated compromise with labor unions, environmental and community groups. Heralded by many commentators as a model for “just transitions” in the energy sector, this agreement has lately been called into question as new subsidies and political coalitions have emerged to support for the plant's continued operation. This paper investigates the political and economic conditions for the Joint Proposal as an example of “negotiated devaluation,” aiming to understand why and how this strategy took shape, and what lessons it offers for other transition processes. Linking literatures on just transitions and devaluation in the energy sector, we show how negotiated devaluation may offer an emerging strategy for owners of energy assets to manage decline in a changing energy landscape. The case also demonstrates the political contingency of these coalitions and the transitions they subtend, and the limited dimensions of justice that might be affirmed in them.

Sparse-to-dense coarse-to-fine depth estimation for colonoscopy

Colonoscopy, as the golden standard for screening colon cancer and diseases, offers considerable benefits to patients. However, it also imposes challenges on diagnosis and potential surgery due to the narrow observation perspective and limited perception dimension. Dense depth estimation can overcome the above limitations and offer doctors straightforward 3D visual feedback. To this end, we propose a novel sparse-to-dense coarse-to-fine depth estimation solution for colonoscopic scenes based on the direct SLAM algorithm. The highlight of our solution is that we utilize the scattered 3D points obtained from SLAM to generate accurate and dense depth in full resolution. This is done by a deep learning (DL)-based depth completion network and a reconstruction system. The depth completion network effectively extracts texture, geometry, and structure features from sparse depth along with RGB data to recover the dense depth map. The reconstruction system further updates the dense depth map using a photometric error-based optimization and a mesh modeling approach to reconstruct a more accurate 3D model of colons with detailed surface texture. We show the effectiveness and accuracy of our depth estimation method on near photo-realistic challenging colon datasets. Experiments demonstrate that the strategy of sparse-to-dense coarse-to-fine can significantly improve the performance of depth estimation and smoothly fuse direct SLAM and DL-based depth estimation into a complete dense reconstruction system.

Design Methodology of a Multifunctional Screw-Type Energy Converter

This paper examines the method of designing a non-standard electric machine – a three-phase induction motor with a hollow solid rotor, on the surface of which the turns of the screw are located. Such an unusual design makes it possible to turn the main disadvantage of induction machines with a solid rotor, namely the heating of the rotor due to the effect of eddy currents, into an advantage. The heat from the rotor is transferred to the bulk material, which is mixed by the screw, for drying and reducing the moisture content. At the same time, only one device is used to perform three functions - mixing, drying, transportation of bulk material, which, due to the specified functional features, was called a multifunctional energy converter (MEC). The MFEC design method differs from conventional machines, because it takes into account the peculiarities of determining the parameters of a number of typical methods: an induction motor with a squirrel-cage rotor, an induction motor with a solid internal rotor, and an inductor. In the previous publications of the authors, the complex methodology of designing an induction motor with an external solid rotor was considered in detail, however, in view of the additional theoretical and experimental studies conducted, it needs to be clarified and adjusted. In addition, in this paper, the beginning of the design, the determination of the initial data and the main dimensions of the MFEC is performed in a different way. In particular, the overall dimensions of the MFEC are determined not by the sum of power spent on heating and mixing the material and internal losses in an induction machine (considering the efficiency and power factor), but by the required performance of the unit and the limit dimensions of the installation area. The paper proposes a new approach to determining the dimensions of the stator slot, considering the necessary area for the placement of conductors and the current density in the winding. This paper is one of several publications that aim to reveal the features of design and mathematical modeling of such an atypical class of electric machines as an induction motor with an external hollow solid rotor.

Using Multidimensional Data to Analyze Freeway Real-Time Traffic Crash Precursors Based on XGBoost-SHAP Algorithm

The traditional freeway safety studies with “poststatic” thinking basically use cross-sectional data or panel data, which find it difficult to figure out real-time traffic crash risk factors. With the development of information collection technology, it is possible to obtain high-resolution traffic flow data currently, which provide a data basis for the dynamic traffic safety research towards freeways. This research aims at accurately identifying the real-time traffic crash precursors on freeways and addressing the shortcomings of conventional dynamic traffic safety research with the thinking of limited factor dimensions. In this research, dimensional data were applied as input model variables, the input dataset includes traffic crash data and the matched dynamic traffic flow data, and weather information and road characteristics were also considered to figure out the interaction effects between these dimensional factors. The XGBoost (eXtreme Gradient Boosting) was carried out to identify the dynamic crash-prone variables and the SHAP (SHapley Additive exPlanations) interpreter was introduced to interpret the XGBoost model, as well as the visualization of the influence of each eigenvalue on the traffic crash was realized. The results indicate that, in addition to traffic flow variables, road, weather, and temporal characteristics also have an impact on the traffic crash risk, and there is an interaction between each feature. The results of this research can provide the theoretical basis for freeway real-time traffic crash prediction and safety control.

Boundaries Determination in Potential Field Anomaly Utilizing Analytical Signal Filtering and its Vertical Derivative in Qeshm Island SE Iran

The analytical signal method and its derivatives can be explicitly calculated and assessed for analyzing and estimating the boundary of geological structures, such as faults or dikes, from potential field anomalies. Gravity field of the east of Qeshm Island and its north coast from Sarkhun to Latidan in SE Iran has been measured in one-kilometer regular network. The regional anomaly effects are deducted from the Bouguer anomaly, where it can get the residual anomalies which is the considered dataset used in this study. The Bouguer range is -297 to -330 milligals (mGal) applying filters and modeling. It was revealed that there are five negative anomalies caused by salt penetration in the region. Regional anomalies expectedly have a low spatial frequency and long wavelengths, while the residual anomalies associated with shallow sub-surface potentials have high spatial frequencies and short-wave wavelengths.Two different fault trends were observed comparing the artificial model using MATLAB coding, the real one, and the filtered residual anomaly map of the Qeshm region. Also, another fault trend is suggested in the east height of Oman, which is a possible place of salt penetration. Due to the limited dimensions (120 * 100 km) and the information obtained from the existing drillings, the depth of the anomaly resources can be examined up to 8 km. Due to the gravitational condition between the two domes of Qeshm saline and the bridge located in the Hara saline area, it is suggested to be considered as an area for further oil exploration routines.

Assessment of cation exchange as conditioning processes of water chemistry in freshwater lenses.

Freshwater lenses are groundwater sources of limited dimensions that can be usually found in a variety of climates worldwide. These aquifers' quality is important for socioeconomic development, being cation exchange one of the most important geochemical processes that can change the water geochemistry. This study aims to assess the cation exchange processes which determine the chemistry of freshwater lenses in a multilayer aquifer type, considering the center-east of the Pampean Region (Argentina) as a case study. Water samples were taken from the freshwater lenses at different depths to analyze major ions in the laboratory. In addition, geological profiles were made along with the extraction of sediment samples for XRD and laboratory tests to analyze the cation exchange capacity. The results show that water stored in the lenses has a vertical facies variation from Ca-HCO3 to Na-HCO3 . According to the laboratory results, the change of water facies mainly occurs in the clayey sediments which divide the carbonate bioclastic material above and the loessic sediment below, being cation exchange the most important process.

Low-power continuous-wave all-optical magnetic switching in ferromagnetic nanoarrays

All-optical magnetic switching promises ultrafast magnetization control without a magnetic field. Existing schemes typically require power-hungry femtosecond-pulsed lasers and complex magnetic materials. Here, we demonstrate deterministic, all-optical magnetic switching in simple ferromagnetic nanomagnets (Ni81Fe19, Ni50Fe50) with sub-diffraction limit dimensions using a focused low-power, linearly polarized continuous-wave laser. Isolated nanomagnets are switched across a range of dimensions, laser wavelengths, and powers. All square-geometry artificial spin ice vertex configurations are written at low powers (2.74 mW). Usually, switching with linearly polarized light is symmetry forbidden; here, the laser spot has a similar size to the nanomagnets, producing an absorption distribution that depends on the nanoisland-spot displacement. We attribute the deterministic switching to the transient dynamics of this asymmetric absorption. No switching is observed in Co or Ni nanostructures, suggesting the multi-species nature of NiFe plays a role. These results usher in inexpensive, low-power, optically controlled devices with impact across data storage, neuromorphic computation, and reconfigurable magnonics.

5D Opto‐Magnetization Endowed by Physics‐Enhanced Deep Learning

In the era of big data, all‐optical control of the magnetization is recognized as an alternative scheme that boosts the accelerating advance of multifunctional integrated opto‐magnetization devices with high‐density capacity. The light‐induced magnetizations demonstrated so far are devoted to steering their spatial orientations and structures by engineering the complicated phase, amplitude, and polarization modulations of incident wavefronts, which, however, confront low efficiency, weak flexibility, and limited dimension. To tackle these issues efficaciously, a novel strategy is proposed to first achieve 5D opto‐magnetization composed of 3D spatial location, vectorial orientation as well as magnitude. This relies on physics‐enhanced deep learning incorporating multilayer perceptron (MLP) artificial neural network and opto‐magnetization principles. The preeminent magnetization morphology largely expedites the improvement in multi‐dimensional storage. The proposed facile approach is time‐efficient, flexible, and accurate to attain the prescribed magnetization. Moreover, the presenting findings and proposed route are not only applied for magnetization manipulation, but also applicable to the control of the structured light field.

UAV-Correlated MIMO Channels: 3-D Geometrical-Based Polarized Model and Capacity Analysis

The unmanned aerial vehicle (UAV) communication with the multiple-input–multiple-output (MIMO) system has attracted lots of attention to improve spectral efficiency and channel capacity. The large antenna spacing is often required to reduce the spatial correlation between subchannels. However, this requirement cannot be well satisfied in practical scenarios due to the limited dimension of UAV platforms. Therefore, polarization diversity is a promising approach for UAV MIMO systems. In this work, we propose a three-dimensional (3-D) geometrical-based polarized model for UAV-correlated MIMO channels. By utilizing the geometrical theory of polarization, we describe the channel depolarization caused by the terrestrial scattering environment based on the multicylinder geometrical model and then acquire the channel polarization function and polarized channel impulse response (CIR). Furthermore, we investigate the impact of the key factors, such as the UAV tilt rotation, cross-polarization, and limited antenna spacing on the UAV MIMO channel capacity. To validate the proposed UAV channel model, we compare the spatial correlations of the numerical simulation and UAV field measurement results for the co- and cross-polarized channels, and the close agreements between them are observed. This work can provide useful guidance and support for the design and performance evaluation of UAV communication networks.