Resolution Requirements Research Articles

Open-source complex-geometry 3D fluid dynamics for applications with unpredictable heterogeneous dynamic high-performance-computing loads

We discuss the software implementation of a finite element method, intended for the simulation of complex-geometry 3D flows, using hardware resources effectively, including problems with a priori unknown, heterogeneous, and dynamic parallel computational load. Our fundamental choices of data structures, algorithm, and software design specifically target engineering resolution and accuracy requirements. Some of these choices are: unstructured grids (to explicitly resolve complex 3D geometries), tetrahedra-only computational elements (to enable automatic mesh generation), edge-based finite element scheme (to reduce indirect addressing for increased performance), distributed-memory parallel computing paradigm (to enable large problems), and Charm++ (https://charmplusplus.org) as the runtime system (to effectively use computing resources even in the presence of hardware heterogeneities and dynamic application requirements). We discuss aspects of the implementation that enable exercising unique features of the runtime system, e.g., the single Charm++ programming abstraction, overdecomposition, asynchronous execution, latency-hiding parallel communication and computation, task-parallelism, and dynamic load balancing via object migratability. Multiple test problems are used to verify and validate the numerical solutions and computational performance and scalability to high-performance computing environments are discussed. For maximum transparency and reproducibility, and to encourage future research, development, and use, the full source code, together with regression tests and documentation, is publicly available at https://xyst.cc.

Adaptive task decomposition physics-informed neural networks

As a new approach for solving partial differential equations, physics-informed neural networks (PINNs) have received extensive attention in recent years and have made breakthroughs in various fields. Serving as a neural network-based universal solver, they possess advantages such as high continuity and being meshless. However, an increasing number of studies have pointed out that PINNs may fail to converge to accurate results when dealing with complex tasks. In this paper, we highlight the differences between training PINNs and regular neural network tasks, summarizing three key characteristics of training PINNs. Based on these findings, we propose a strategy of task decomposition and progressive learning, which successfully explains the effectiveness of existing methods for time-dependent problems and reveals their limitations. For time-dependent and time-independent problems, we partition the tasks into physically complete and progressively challenging sub-tasks. The division is based on the resolution requirements for time-independent problems and the coverage of the time domain for time-dependent problems. Through task parameters and the decomposition of the loss terms, we adaptively control the progressive increase in task complexity during the training process. The proposed method overcomes the limitations of existing approaches, incurs no additional computational cost, and is applicable to both time-dependent and time-independent problems, demonstrating higher generality. Furthermore, building upon the concepts of task decomposition and progressive learning, we introduce a stable and efficient minibatch training method for the first time. In a series of numerical experiments on baseline PDE problems, our approaches outperformed other state-of-the-art algorithms.

Varstrometry for Off-nucleus and Dual Sub-kiloparsec Active Galactic Nuclei (VODKA): Very Long Baseline Array Searches for Dual or Off-nucleus Quasars and Small-scale Jets

Dual and off-nucleus active supermassive black holes are expected to be common in the hierarchical structure formation paradigm, but their identification at parsec scales remains a challenge due to strict angular resolution requirements. We conducted a systematic study using the Very Long Baseline Array (VLBA) to examine 23 radio-bright candidate dual and off-nucleus quasars. The targets are selected by a novel astrometric technique (varstrometry) from Gaia, aiming to identify dual or off-nucleus quasars at (sub)kiloparsec scales. Among these quasars, eight exhibit either multiple radio components or significant (>3σ) positional offsets between the VLBA and Gaia positions. The radio emission from the three candidates, which exhibit multiple radio components, is likely to originate from small-scale jets based on their morphology. Among the remaining five candidates with significant VLBA-Gaia offsets, three are identified as potential dual quasars at parsec scales, one is likely attributed to small-scale jets, and the origin of the last candidate remains unclear. We explore alternative explanations for the observed VLBA-Gaia offsets. We find no evidence for optical jets at kiloparsec scales, nor any contamination to Gaia astrometric noise from the host galaxy; misaligned coordinate systems are unlikely to account for our offsets. Our study highlights the promise of the varstrometry technique in discovering candidate dual or off-nucleus quasars and emphasizes the need for further confirmation and investigation to validate and understand these intriguing candidates.

Анализ применения различных типов 3D-принтеров в машиностроении

Relevance. The article discusses the main types of 3D-printers that can be used in mechanical engineering. The analysis of the basic principles of operation, as well as the advantages, disadvantages and features of the use of these types of devices. In the end, the author will consider the possibilities of future research in the field of additive technologies. The purpose of the research. To conduct an in-depth analysis of the use of 3D-printing in mechanical engineering. The purpose of the article is to study the application, advantages and limitations of various types of 3D-printers used in mechanical engineering. It also evaluates factors that influence the choice of printer, such as material compatibility, resolution, print volume and print speed. The study highlights the problems faced when integrating 3D-printing into mechanical engineering. The article concludes with a summary of the main conclusions, a discussion of the implications for management and decision-making, as well as the definition of future research opportunities. The objectives of the study are to identify existing problems in the use of additive technologies in mechanical engineering and to consider possible applications in the future. The results of the study. The study provides an overview of 3D-printing in mechanical engineering, its importance and application. It discusses various types of 3D-printers, their features and capabilities. The article analyzes the use of 3D-printers in mechanical engineering, assesses the advantages, limitations and impact on product quality, production time and economic efficiency. Factors influencing the choice of printer are investigated, such as compatibility of materials and requirements, resolution, print volume, print speed, post-processing and scalability requirements. The study also identifies challenges and future directions of 3D-printing integration, including potential advances in printing technology. In conclusion, the main conclusions are presented, their importance for decision-making in mechanical engineering and recommendations for improving the printer selection process. Future research opportunities are also highlighted.

Method and Experiments for Acquiring High Spatial Resolution Images of Abnormal Rice Canopy by Autonomous Unmanned Aerial Vehicle Field Inspection

The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order to achieve automatic and efficient acquisition of HSR images, based on the capability of high-throughput field inspection of UAV remote sensing and combining the advantages of high-flying efficiency and low-flying resolution, this paper proposes a method of “far-view and close-look” autonomous field inspection by unmanned aerial vehicle (UAV) to acquire HSR images of abnormal areas in the rice canopy. First, the UAV equipped with a multispectral camera flies high to scan the whole field efficiently and obtain multispectral images. Secondly, abnormal areas (namely areas with poor growth) are identified from the multispectral images, and then the geographical locations of identified areas are positioned with a single-image method instead of the most used method of reconstruction, sacrificing part of positioning accuracy for efficiency. Finally, the optimal path for traversing abnormal areas is planned through the nearest-neighbor algorithm, and then the UAV equipped with a visible light camera flies low to capture HSR images of abnormal areas along the planned path, thereby acquiring the “close-look” features of the rice canopy. The experimental results demonstrate that the proposed method can identify abnormal areas, including diseases and pests, lack of seedlings, lodging, etc. The average absolute error (AAE) of single-image positioning is 13.2 cm, which can meet the accuracy requirements of the application in this paper. Additionally, the efficiency is greatly improved compared to reconstruction positioning. The ground sampling distance (GSD) of the acquired HSR image can reach 0.027 cm/pixel, or even smaller, which can meet the resolution requirements of even leaf-scale deep-learning classification. The HSR image can provide high-quality data for subsequent automatic identification of field abnormalities such as diseases and pests, thereby offering technical support for the realization of the UAV-based automatic rice field inspection system. The proposed method can also provide references for the automatic field management of other crops, such as wheat.

Forced-motion simulations of vortex-induced vibrations of wind turbine blades – a study of sensitivities

Abstract. Vortex-induced vibrations on wind turbine blades are a complex phenomenon not predictable by standard engineering models. For this reason, higher-fidelity computational fluid dynamics (CFD) methods are needed. However, the term CFD covers a broad range of fidelities, and this study investigates which choices have to be made when wanting to capture the vortex-induced vibration (VIV) phenomenon to a satisfying degree. The method studied is the so-called forced-motion (FM) approach, where the structural motion is imposed on the CFD blade surface through mode shape assumptions rather than fully coupled two-way fluid–structure interaction. In the study, two independent CFD solvers, EllipSys3D and Ansys CFX, are used and five different turbulence models of varying fidelities are tested. Varying flow scenarios are studied with low to high inclination angles, which determine the component of the flow in the spanwise direction. In all scenarios, the cross-sectional component of the flow is close to perpendicular to the chord of the blade. It is found that the low-inclination-angle and high-inclination-angle scenarios, despite having a difference equivalent to up to only a 30∘ azimuth, have quite different requirements of both grid resolution and turbulence models. For high inclination angles, where the flow has a large spanwise component from the tip towards the root, satisfying results are found from quite affordable grid sizes, and even with unsteady Reynolds-averaged Navier–Stokes (URANS) k–ω turbulence, the result is quite consistent with models resolving more of the turbulent scales. For low inclination, which has a high degree of natural vortex shedding, the picture is the opposite. Here, even for scale-resolving turbulence models, a much finer grid resolution is needed. This allows us to capture the many incoherent vortices, which have a large impact on the coherent vortices, which in turn inject power into the blade or extract power. It is found that a good consistency is seen using different variations of the higher-fidelity hybrid RANS–large eddy simulation (LES) turbulence models, like improved delayed detached eddy simulation (IDDES), stress-blended eddy simulation (SBES) and k–ω scale-adaptive simulation (SAS) models, which agree well for various flow conditions and imposed amplitudes. This study shows that extensive care and consideration are needed when modeling 3D VIVs using CFD, as the flow phenomena, and thereby solver requirements, rapidly change for different scenarios.

Simulating ionization feedback from young massive stars: impact of numerical resolution

ABSTRACT Modelling galaxy formation in hydrodynamic simulations has increasingly adopted various radiative transfer methods to account for photoionization feedback from young massive stars. However, the evolution of H ii regions around stars begins in dense star-forming clouds and spans large dynamical ranges in both space and time, posing severe challenges for numerical simulations in terms of both spatial and temporal resolution that depends strongly on gas density (∝n−1). In this work, we perform a series of idealized H ii region simulations using the moving-mesh radiation-hydrodynamic code arepo-rt to study the effects of numerical resolution. The simulated results match the analytical solutions and the ionization feedback converges only if the Strömgren sphere is resolved by at least 10–100 resolution elements and the size of each time integration step is smaller than 0.1 times the recombination time-scale. Insufficient spatial resolution leads to reduced ionization fraction but enhanced ionized gas mass and momentum feedback from the H ii regions, as well as degrading the multiphase interstellar medium into a diffuse, partially ionized, warm (∼8000 K) gas. On the other hand, insufficient temporal resolution strongly suppresses the effects of ionizing feedback. This is because longer time-steps are not able to resolve the rapid variation of the thermochemistry properties of the gas cells around massive stars, especially when the photon injection and thermochemistry are performed with different cadences. Finally, we provide novel numerical implementations to overcome the above issues when strict resolution requirements are not achievable in practice.

Upscaling peatland mapping with drone-derived imagery: impact of spatial resolution and vegetation characteristics

ABSTRACT Northern peatland functions are strongly associated with vegetation structure and composition. While large-scale monitoring of functions through remotely sensed mapping of vegetation patterns is therefore promising, knowledge on the interdependency between spatial resolution of acquired imagery, spatial vegetation characteristics, and resulting mapping accuracy needs to be improved. We evaluated how classification accuracy of commonly used vegetation mapping units (microforms and plant functional types) was affected by spatial resolution of acquired imagery and several spatial characteristics of the vegetation itself (size, shape, configuration, and diversity). To this end, we collected very high-resolution drone imagery (<0.05 m) from eight Irish peatlands differing in vegetation composition and pattern complexity in September 2021 and 2022. We then resampled this imagery from pixel sizes of 0.027–1 m and classified both mapping units at all unique spatial resolutions. Hereafter, we computed spatial vegetation characteristics for each of the eight classified images at 0.027 m spatial resolution to determine their role in defining minimum spatial resolution requirements for both microforms and plant functional types. We found that overall classification accuracy of microforms and plant functional types was consistently high (>90%) for all studied peatlands until average spatial resolutions were reached of 0.5 m ± 0.2 m and 0.25 m ± 0.1 m, respectively. However, variability within mapping units was considerable, with overall minimum spatial resolution ranging between 0.25 and 0.7 m for microforms and between 0.15 and 0.35 m for plant functional types. Individual classes even varied from 0.05 to 1 m. Furthermore, spatial vegetation characteristics were important drivers of minimum spatial resolution for microforms, but not for plant functional types. Particularly, peatlands with larger, compacter, and more clustered microform patches could be classified with coarser spatial resolution imagery (up to 0.7 m), while peatlands with small, complex, diverse, and more finely distributed patches required higher spatial resolutions (minimally 0.25 m). Based on these findings, we conclude that spatial vegetation characteristics strongly determine minimum required spatial resolution and thus affect upscaling of peatland vegetation mapping beyond the landscape scale by constraining the use of specific remote sensing platforms.

Adaptive Mesh Refinement Strategies for Cost-Effective Eddy-Resolving Transient Simulations of Spray Dryers

The use of adaptive meshing strategies to perform cost-effective transient simulations of spray drying processes is evaluated. These simulations are often computationally expensive, given the large differences between the characteristic times of the central jet and those of the unsteady flow developed at its periphery. Managing the computational cost through the control of the grid resolution by regions is inadequate in many of these applications since the grid resolution requirements change dynamically within the domain. These conditions are related to the unsteady nature of the flow in both the central jet and the flow recirculation zones. Therefore, the application of adaptive mesh refinement (AMR) strategies is recommended. In this paper, general AMR criteria based on relative errors are evaluated by testing three mesh adaptation criteria: velocity gradient, pressure gradient, and vorticity. This evaluation is performed using a low-cost turbulence model with eddy resolution (DDES) in two different types of drying chambers, in which experimental measurements are available. The use of AMR exerts appreciable effects on decreasing computational costs and contributes to the capture of large eddies in critical regions. The present approach provides an appropriate balance between solution accuracy and computational cost. By using a correct AMR configuration, it is possible to obtain results similar to those obtained on a fixed grid but reducing the computational costs by 3 to 5 times.

Development of an Interpolated RANS-LES solver for wall-bounded turbulent fluid flows

Scale-resolving hybrid RANS-LES models are being increasingly used to numerically simulate wall-bounded turbulent flows since they are computationally efficient compared to LES (Large Eddy Simulation) while also being more accurate compared to RANS (Reynolds-Averaged Navier–Stokes) models. Existing hybrid RANS-LES models typically either suffer from modeled stress depletion or they can introduce a sharp jump in the mean Reynolds stress across the RANS-LES zone. We present a novel interpolated RANS-LES (IRL) solver implemented in OpenFOAM, in which the RANS equations (for eddy-viscosity) and LES equations (for filtered velocity) are evolved simultaneously on the same computational grid. Based on the distance from the no-slip wall, the flow domain is demarcated into a near-wall, free-stream, and a “hybrid” region, sandwiched between the near-wall and free-stream regions. Spalart Allmaras (S-A) model has been used to evolve the RANS eddy viscosity, while the Wall Adapting Local Eddy Viscosity (WALE) model is used for evolving the filtered LES velocity. Using a novel scheme based on solving a partial differential equation, the turbulent eddy-viscosity is interpolated between the RANS eddy viscosity in the near wall region and the effective eddy viscosity from the resolved LES fluctuations in the free-stream region. The mean subgrid stress in the LES momentum equation is then corrected in the near-wall and hybrid regions using the interpolated turbulent eddy-viscosity. IRL’s grid resolution requirements are the same as DES’s (Detached Eddy Simulation). Grid convergence and sensitivity studies have been carried out for two canonical flow geometries. For turbulent channel flow simulations, the IRL solver does not display the typical mean stress depletion observed in DES while also predicting resolved Reynolds stresses accurately in the free-stream region. For simulations of flow over backward facing step, the IRL solver can predict the friction factor and coefficient of pressure better than that DES, especially after the reattachment point.

Grid resolution requirement of chemical explosive mode analysis for large eddy simulations of premixed turbulent combustion

The grid resolution requirement for trustworthy Chemical Explosive Mode Analysis (CEMA) in Large Eddy Simulation (LES) of premixed turbulent combustion is proposed. Explicit filtering, to emulate the effect of the LES filter, is applied to one-dimensional laminar flame and three-dimensional planar turbulent flames across a wide range of Karlovitz numbers . The identification of the flame front by CEMA is found relatively insensitive to the cell size (Δ), while the combustion mode identification shows more significant sensitivity. Specifically, increasing Δ falsely enhances the auto-ignition and local extinction modes and suppresses the diffusion-assisted mode. Limited dependence of the CEMA performance on the turbulent combustion regime (Karlovitz number) is observed. A simple grid size criterion for reliable CEMA mode identification in LES is proposed as ; The criterion can be relaxed to in the laminar flame limit. Furthermore, theoretical analysis is conducted on an idealised chemistry-diffusion system. The effects of the filtering process and turbulence on the local combustion mode are demonstrated, which is consistent with the numerical observations. By incorporating turbulent combustion models in CEMA, potential improvement in identifying local combustion modes can be expected.

Downscaling of Oceanic Chlorophyll-a with a Spatiotemporal Fusion Model: A Case Study on the North Coast of the Yellow Sea

Chlorophyll-a concentration (Chl-a) is an important indicator of coastal eutrophication. Remote sensing technology provides a global view of it. However, different types of sensors are subject to design constraints and cannot meet the requirements of high temporal and spatial resolution on nearshore engineering simultaneously. To obtain high-spatiotemporal-resolution images, this study examines the performance of the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) on GOCI and Landsat Chl-a data fusion. Considering the rapidly changing rate and consistency of oceanic Chl-a, the ESTARFM was modified via segmented fitting and numerical conversion. The results show that both fusion models can fuse multiple data advantages to obtain high-spatiotemporal-resolution Chl-a images. Compared with the ESTARFM, the modified solution has a better performance in terms of the root mean square error and correlation coefficient, and its results have better spatial consistency for coastal Chl-a. In addition, the new solution expands the data utilization range of data fusion by reducing the influence of the time interval of original data and realizes better monitoring of nearshore Chl-a changes.

Scatterer-Level Time-Frequency-Frequency Rate Representation for Micro-Motion Identification

Radar micro-motion signatures help to judge the target’s motion state and threat level, which plays a vital role in space situational awareness. Most of the existing micro-motion feature extraction methods derived from time-frequency (TF) representation cannot simultaneously satisfy the requirements of high resolution and multiple component representation, which has limitations on processing intersected multi-component micro-motion signals. Meanwhile, as the micro-motion features extracted from the TF spectrograms only focus on the global characteristics of the targets and ignore the physical properties of micro-motion components, it leads to poor performance in micro-motion discrimination. To address these challenges, we empirically observed a decrease in the probability of intersection between the components within the time-frequency-frequency rate (TFFR) space, where components appeared as separated and non-intersecting spatial trajectories. This observation facilitates the extraction and association of multiple components. Given the differences in modulation laws among various micro-motions in the TFFR space, we introduced a novel micro-motion identification method based on scatterer-level TFFR representation. Our experimental evaluations of different targets and micro-motion types demonstrate the efficacy and robustness of this proposed method. This method not only underscores the separability of signal components but also expands the scope of micro-motion discrimination within the TFFR domain.

Real-time water/fat imaging at 0.55T with spiral out-in-out-in sampling.

To develop an efficient and flexible water/fat separated real-time MRI (RT-MRI) method using spiral out-in-out-in (OIOI) sampling and balanced SSFP (bSSFP) at 0.55T. A bSSFP sequence with golden-angle spiral OIOI readout was developed, capturing three echoes to allow water/fat separation. A low-latency reconstruction that combines all echoes was available for online visualization. An offline reconstruction provided water and fat RT-MRI in two steps: (1) image reconstruction with spatiotemporally constrained reconstruction (STCR) and (2) water/fat separation with hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (HIDEAL). In healthy volunteers, spiral OIOI was acquired in the wrist during a radial-to-ulnar deviation maneuver, in the heart without breath-hold and cardiac gating, and in the lower abdomen during free-breathing for visualizing small bowel motility. We demonstrate successful water/fat separated RT-MRI for all tested applications. In the wrist, resulting images provided clear depiction of ligament gaps and their interactions during the radial-to-ulnar deviation maneuver. In the heart, water/fat RT-MRI depicted epicardial fat, provided improved delineation of epicardial coronary arteries, and provided high blood-myocardial contrast for ventricular function assessment. In the abdomen, water-only RT-MRI captured small bowel mobility clearly with improved water-fat contrast. We have demonstrated a novel and flexible bSSFP spiral OIOI sequence at 0.55T that can provide water/fat separated RT-MRI with a variety of application-specific temporal resolution and spatial resolution requirements.

Image denoising and deringing for fourier single-pixel imaging based on upgraded weighted nuclear norm minimization

Single-pixel imaging techniques have been extensively studied because of the low requirements for the detector resolution and the low equipment cost. However, a large amount of noise and ringing commonly occur in the reconstructed images via previous single-pixel imaging methods at ultra-low sampling rates, which limits the real applications. In this paper, a novel Fourier single-pixel imaging method is proposed to reconstruct high-quality single-pixel imaging images at ultra-low sampling rates, which involves the stages of Fourier single-pixel imaging, block matching, upgraded weight nuclear norm minimization, and aggregation. To decrease the computational burden while important image details, block matching and aggregation are employed for the blocks divided from the low-quality image reconstructed by Fourier single-pixel imaging. To adapt the proposed upgraded weight nuclear norm minimization to different contaminated images, an adaptive scheme for the iterative regularization parameter is proposed to update the traditional weight nuclear norm minimization. Experimental results indicate that the proposed Fourier single-pixel imaging method based on upgraded weight nuclear norm minimization exhibits the capacity to reliably and effectively reconstruct high-quality images for single-pixel imaging at low sampling rates, even at the sampling rate of 1 %. Experimental results also demonstrate that the proposed method performs better image reconstruction than some existing single-pixel imaging.

Temporary Relining in Removable Prosthesis for Corrective and Conditioning Purposes: A Case Report

Objective: To report a clinical case of tissue readjustment by provisional relining in an upper complete denture.&#x0D; Presentation of Case: A 63-year-old male patient, melanoderma, asthmatic and type II diabetic, attended the dental clinic of a Higher Education Institution, complaining of pain when fitting the prosthesis. He reported that he had had a complete upper denture approximately 5 months ago. On clinical examination, hyperplastic lesions were noted in the anterior and middle portions at the bottom of the vestibule of the upper arch. The prosthesis, on the other hand, highlighted peripheral irregularities with a pointed aspect in the areas corresponding to the lesions. Given the conditions presented, it was urgently decided to remove the irregularities of the upper complete denture followed by provisional relining. After performing the technique, the patient was clinically followed up, after 30 days there was a decrease in the lesion.&#x0D; Conclusion: The use of this technique in complete dentures as a provisional action aimed at conditioning the injured tissue and retaining the piece in the alveolar ridge was, therefore, satisfactory, meeting the main requirements for resolution of the case and suitability for subsequent continuity of definitive rehabilitation.

Minimum resolution requirements of digital pathology images for accurate classification.

Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.

UAV Velocity Function Design and Trajectory Planning for Heterogeneous Visual Coverage of Terrestrial Regions

In this paper, we propose a novel approach of designing the velocity function and the trajectory for a UAV to efficiently achieve location-dependent visual coverage. Specifically, the UAV dynamically adjusts its altitude to photograph terrestrial polygons with different image resolution requirements. Unlike prior work that assumes the UAV speed is constant, the proposed approach allows the UAV to change its speed. To minimize the task completion time, we put forward a novel approach that is composed of three algorithmic components. The first component uses an aggressive method for selecting the UAV photographing altitudes, designs the UAV velocity functions, and derives the UAV flying times for all pairs of regions. Based on the UAV flying times rather than the distances, the second component utilizes an auxiliary traveling salesman problem to determine the visited order of terrestrial regions. For each terrestrial region, the third component generates candidate coverage paths and picks up the coverage path based on the UAV flying time. We also derive analytical results on the UAV trajectory length. Large-scale simulation results indicate that the proposed approach outperforms a two-dimensional trajectory planning algorithm and a greedy algorithm for planning a three-dimensional trajectory in terms of the UAV task completion time.

Three-Dimensional Multi-UAV Trajectory Design for Cooperative Video Inspection and Uploading

This paper studies the multiple unmanned aerial vehicles (UAVs) trajectory design for post-disaster cooperative video inspection and information uploading. The locations to be inspected are modeled as points of interest (PoIs) with heterogeneous resolution requirements. The three-dimensional (3D) trajectories for multiple cooperative UAVs are designed to minimize the time for successfully inspecting all the PoIs and maximize the communication time for the video uploading. An UAV inspection model is built to clarify the criteria for successful inspection, which is related to the UAV's 3D positions and velocity. The proposed UAV trajectory design involves two steps. First, the PoIs in proximity are grouped using convex optimization. Then, the multiple UAV trajectories are jointly designed by solving a multiple traveling salesman problem (MTSP). Numerical results show that the proposed design can significantly reduce the mission completion time.

Compact high-resolution spectrometer based on super-prism and local-super-collimation effects of photonic crystal.

We propose a design of the compact high-resolution photonic crystal (PhC) spectrometer with a wide working bandwidth based on both super-prism and local-super-collimation (LSC) effects. The optimizing methods, finding the ideal incident angle and oblique angle of PhC for a wider working bandwidth and ideal incident beam width and PhC size for a certain resolution requirement, are developed. Besides the theoretical work, for the first time, the experiment of such a PhC spectrometer is conducted in the microwave frequency range, and the beam-splitting effects for different frequencies in a wide working bandwidth agree very well with the theoretical predictions. According to the scalability, with the condition to control the deviations in the fabrication processes the design could be extended to optical frequency ranges, e.g., infrared, visible-light, and ultraviolet ranges. The spectrometer in optical frequencies can be implemented on silicon-on-insulator (SOI) chips as a thin-slab structure so that the operating bandwidth can be expanded further through the multi-layer design. Theoretically, the size of the ultra-high-resolution PhC spectrometer in optical frequency ranges based on our design could be two orders smaller than the traditional design.