Resolution Constraints Research Articles

High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation.

In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. However, current one-bit DOA algorithms with atomic norm minimization typically rely on approximating the trace function, which is not the optimal approximation and introduces errors, along with resolution limitations. To date, there have been few studies on how to enhance resolution under the framework of one-bit DOA estimation. This paper aims to improve the resolution constraints of one-bit DOA estimation. The log-det heuristic is applied to approximate and solve the atomic norm minimization problem. In particular, a reweighted binary atomic norm minimization with noise assumption constraints is proposed to achieve high-resolution and robust one-bit DOA estimation. Finally, the alternating direction method of multipliers algorithm is employed to solve the established optimization problem. Simulations are conducted to demonstrate that the proposed algorithm can effectively enhance the resolution.

Long-wavelength, high-resolution microscopy using upconversion in ultra-thin crystals

In this paper, a novel approach based on frequency upconversion in ultra-thin nonlinear crystals is investigated for use in high-resolution infrared (IR) microscopy in the 5–12 µm range, an important domain for biomedical research. Traditional IR imaging encounters spatial resolution constraints due to diffraction, which are addressed via upconversion imaging using ultra-thin crystals. The present work combines a tunable IR quantum cascade laser and a short wavelength mixing laser to circumvent the classical resolution limit dictated by the Rayleigh criterion. A detailed numerical model for small signal upconversion imaging at μm-scale resolution shows good agreement with experimental data. The presented approach opens new avenues for IR applications for label-free biomedical diagnostics and spectral imaging.

Particle image velocimetry in the impeller of a centrifugal pump: Relationship between turbulent flow and energy loss

Turbulent flows play a dominant role in the operation of centrifugal pumps, which find widespread use in industrial settings and various aspects of human life. The dissipation rate of turbulent kinetic energy emerges as a key parameter within these devices, with its local values exerting a significant influence on centrifugal pump performance. Recent advances in particle image velocimetry (PIV) techniques have expanded the ability to analyze complex turbulent flows across a broad spectrum of scales. In this context, this paper aims to deepen our understanding of the turbulent flow field and its correlation with energy loss in centrifugal pump impellers. To achieve this, experiments were conducted using PIV on a transparent pump operating under different conditions. Statistics of the turbulent flow were then obtained from phase-ensemble averages of velocities, vorticity, turbulence production, and local dissipation of turbulent kinetic energy. To overcome the limited spatial resolution constraint of PIV, the large-eddy PIV (LES-PIV) method was employed to estimate the local dissipation rate. In this method, it is assumed that the motion of larger scales is measured by the PIV technique, while the smaller scales (unresolved scales) are modeled by a sub-grid scale model, calculated from the strain rate tensors obtained from the measured fields. Energy losses in the impeller were studied using two methodologies: (i) a conventional method based on power measurements, and (ii) an alternative approach based on the budget of turbulent kinetic energy. Our results reveal that turbulent loss caused by turbulence production is the main source of energy loss in the pump impeller, and it is particularly pronounced in low-flow operating conditions characterized by large-scale structures. On the other hand, in situations where flow rates exceed the best efficiency point (BEP) condition, the predominant flow structures are marked by small-scale features, mainly attributed to local dissipation of turbulence. Our findings clarify the characteristics of energy losses in centrifugal pump impellers and their relationship with the turbulent flow field, and, in addition, providing a methodology for calculating the local turbulent dissipation rate and its limitations when derived from PIV measurements.

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows

The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. These results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.

The Role of Line-Field Confocal Optical Coherence Tomography in Detecting Extramammary Paget Disease Recurrences: A Pilot Diagnostic Study.

Extramammary Paget disease (EMPD) is an uncommon adenocarcinoma of apocrine gland-rich areas, presenting significant diagnostic challenges due to its nonspecific clinical appearance and frequent misidentification as benign, inflammatory skin conditions. Traditional diagnostic methods such as biopsy are invasive and uncomfortable, often required repeatedly due to high recurrence rates. Dermoscopy and non-invasive imaging techniques have been used but provide limited diagnostic accuracy due to their constraints in depth penetration and resolution. Recent advancements in imaging technologies, such as line-field confocal optical coherence tomography (LC-OCT), show promise in enhancing diagnostic precision while minimizing invasive procedures. LC-OCT merges high-resolution imaging with deep penetration capabilities, capturing detailed horizontal and vertical skin images akin to histopathology. This study evaluated the diagnostic performance of LC-OCT in detecting EMPD and its recurrence in 17 clinically suspicious anogenital regions, belonging to six patients. Data were collected prospectively at the patient's bedside by an LC-OCT expert with poor training for EMPD, and, then, reviewed retrospectively by an independent LC-OCT expert with adequate training for EMPD and no concerns about time. The prospective examination yielded 64.7% accuracy (11 true results out of 17 total cases), 71.4% sensitivity (10 true positives out of 14 actual positives), and 33.3% specificity (1 true negative out of 3 actual negatives). The retrospective analysis achieved 94.1% accuracy (16 true results out of 17 total cases), 100% sensitivity (14 true positives out of 14 actual positives), and 66.7% specificity (2 true positives out of 3 actual positives), with the only false positive case being a difficult-to-diagnose concomitant presentation of a lichen sclerosus et atrophicus. Despite the need for specialized training, our results suggest that LC-OCT represents a valuable tool for accurately identifying EMPD and improving its management by reducing unnecessary biopsies. Further studies are needed to standardize its clinical application.

Structural Design and Parameter Optimization of Magnetic Gradient Tensor Measurement System.

Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10-10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers.

Super-Resolution by Localized Plasmonic Structured Illumination Microscopy Using Self-Assembled Nanoparticle Substrates

Structured illumination microscopy (SIM), an advanced super-resolution methodology, transcends the traditional diffraction limit inherent in optical imaging. This technique utilizes standing-wave illumination generated through the interplay of two obliquely incident light waves. The intrinsic resolution constraint of SIM, traditionally pegged at half the wavelength because of the standing wave’s periodicity, has the potential for enhancement by integrating high spatial frequency illumination patterns, particularly when sourced in the near-field of plasmonic nanostructures. The present study introduces and computationally validates a novel, easily fabricated substrate composed of self-assembled gold nanoparticles designed explicitly for generating these high spatial frequency patterns. Addressing the necessity for diverse patterns in reconstructing super-resolution imagery within plasmonic SIM, this research conducted extensive numerical simulations of nanoparticle arrays under varying illumination scenarios. This undertaking affirmed the feasibility of manipulating high-frequency patterns. Super-resolution reconstruction was actualized by applying Blind-SIM techniques, which verified its effectiveness. This innovative approach notably achieved a resolution threshold of 60 nm, markedly exceeding the conventional 150 nm diffraction barrier and surpassing the 75 nm resolution typically observed in standard SIM applications.

Imageless electrocardiographic imaging for atrial electrophysiological characterization: a validation study

Abstract Background The mechanisms responsible for atrial fibrillation (AF) are not yet fully understood and treatment strategies remain suboptimal. Electrocardiographic imaging (ECGi) could provide new insights into the arrhythmogenic substrate’s characterization. Objective To validate a novel imageless ECGi technique against electroanatomical mapping (EAM) systems to evaluate its use in guiding therapeutic decisions. Methods Periprocedural ECGi was implemented on 12 patients submitted for pulmonary vein isolation with EAM arriving in sinus rhythm. As opposed to current methods requiring patient-specific torso-heart geometries from CT/MRI scans, the employed ECGi system computes a cardiac anatomical model from a 3D-camera torso reconstruction. Both ECGi signals and intracavitary recordings were acquired simultaneously during catheter stimulation from a single atrial site at two different pacing cycles and local activation time (LAT) maps were derived. As a result, an ECGi and an EAM map were obtained for both 300 and 600 ms distal coronary sinus pacing for each patient. These were then compared by splitting the left atrial geometries from both mapping methodologies into 7 predefined, clinically relevant regions and identifying the earliest and latest activation sites in each. Results For all 24 LAT maps under consideration, the pacing sites identified by non-invasive ECGi maps were consistent with those determined through EAM as 100% accuracy was obtained in terms of detecting the first activated region. As well as the correct stimulation area, according to the corresponding EAM instance, the earliest ECGi activations were also found in one of its immediate neighbours in 38% of the maps, indicating a broader region of interest. Regarding the latest activation sites, ECGi achieved an accurate localisation in 92% of the activation sequences and, similarly, other neighbouring regions showed the maximum LAT in one third of the studied maps. Conclusions Considering the spatial resolution constraints inherent to its non-invasive nature, imageless ECGi activation patterns are congruent with the gold-standard technique for substrate mapping without the need for prior medical imaging.

Dynamically configured physics-informed neural network in topology optimization applications

The integration of physics-informed neural network (PINN) and topology optimization (TO) is an attractive issue because PINN can avoid the prohibitive data acquisition of solving forward problems compared to traditional machine learning. To enhance the efficiency of this integrated optimization framework, a dynamically configured PINN-based topology optimization (DCPINN-TO) method is proposed in conjunction with an active sampling strategy. The DCPINN comprises two sub-networks with distinct training costs, capable of dynamically adjusting the trainable parameters based on the optimization state of TO. Moreover, the active sampling strategy selectively samples collocations based on the pseudo-densities, which can significantly reduce training costs by decreasing the number of input collocations. Additionally, the Gaussian integral is used to calculate the strain energy of elements to decouple the mapping of the material at the collocations. The proposed method is extended to various scenarios, including those with high resolution, multiple loads, and displacement constraints. Its efficiency and generalization are validated by several illustrative examples. Furthermore, the accuracy of DCPINN versus finite element analysis-based TO (FEA-TO) was also investigated.

Holoscopic Elemental-Image-Based Disparity Estimation Using Multi-Scale, Multi-Window Semi-Global Block Matching

In Holoscopic imaging, a single aperture is used to acquire full-colour spatial images like a fly’s eye by gently altering angles between nearby lenses with a micro-lens array. Due to its simple data collection and visualisation methods, which provide robust and scalable spatial information, and its motion parallax, binocular disparity, and convergence, this technique may be able to overcome traditional 2D imaging issues like depth, scalability, and multi-perspective problems. A novel disparity-map-generating method uses angular information from a single Holoscopic image’s micro-images, or Elemental Images (EIs), to create a scene’s disparity map. Not much research has used EIs instead of Viewpoint Images (VPIs) for disparity estimation. This study investigates whether angular perspective data may replace spatial orthographic data. Using noise reduction and contrast enhancement, EIs with a low resolution and lack of texture are pre-processed to calculate the disparity. The Semi-Global Block Matching (SGBM) technique is used to calculate the disparity between EI pixels. A multi-resolution approach overcomes EIs’ resolution constraints, and a content-aware analysis dynamically modifies the SGBM window size settings to generate disparities across different texture and complexity levels. A background mask and nearby EIs with accurate backgrounds detect and rectify EIs with erroneous backgrounds. Our method generates disparity maps that outperform two state-of-the-art deep learning algorithms and VPIs in real images.

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

Opposite Hydrological Conditions between the Younger Dryas and the 8.2 ka Event Revealed by Stalagmite from Northwest Madagascar in East Africa

As prominent abrupt climatic events during the last deglaciation and the early Holocene, the Younger Dryas (YD) and the 8.2 ka events have been intensely discussed to reveal the relationship between their phases and intensities, and their underlying mechanisms based on massive marine and terrestrial archives. However, the related paleoclimate records with sufficient resolution and/or precise age constraints from the Southern Hemisphere, especially East Africa, are relatively sparse, hindering our comprehensive understanding about the phases of these two events. Here, we provide a precisely dated record of an aragonite-calcite stalagmite covering 11.3–13.5 ka BP from northwest Madagascar to unravel the arid conditions during the YD, in contrast to the pluvial conditions in the 8.2 ka event that has been evidenced before. Changes in austral summer precipitation related to the Intertropical Convergence Zone (ITCZ) have always been interpreted to be the primary means of controlling regional rainfall amounts and thus the δ18O variations in stalagmite. However, ITCZ’s meridional migration alone is not enough to interpret the opposite hydroclimatic conditions during the YD and the 8.2 ka events in northwest Madagascar. The variation in convection intensity within the ITCZ combined with the rainfall dipole mode in East Africa, and the redistribution of the duration of the ITCZ’s presence at different latitudes might be responsible for this phenomenon. In addition, sea surface temperature could play a nonnegligible role.

Estimates of Southern Hemispheric Gravity Wave Momentum Fluxes across Observations, Reanalyses, and Kilometer-Scale Numerical Weather Prediction Model

Abstract Gravity waves (GWs) are among the key drivers of the meridional overturning circulation in the mesosphere and upper stratosphere. Their representation in climate models suffers from insufficient resolution and limited observational constraints on their parameterizations. This obscures assessments of middle atmospheric circulation changes in a changing climate. This study presents a comprehensive analysis of stratospheric GW activity above and downstream of the Andes from 1 to 15 August 2019, with special focus on GW representation ranging from an unprecedented kilometer-scale global forecast model (1.4 km ECMWF IFS), ground-based Rayleigh lidar (CORAL) observations, modern reanalysis (ERA5), to a coarse-resolution climate model (EMAC). Resolved vertical flux of zonal GW momentum (GWMF) is found to be stronger by a factor of at least 2–2.5 in IFS compared to ERA5. Compared to resolved GWMF in IFS, parameterizations in ERA5 and EMAC continue to inaccurately generate excessive GWMF poleward of 60°S, yielding prominent differences between resolved and parameterized GWMFs. A like-to-like validation of GW profiles in IFS and ERA5 reveals similar wave structures. Still, even at ∼1 km resolution, the resolved waves in IFS are weaker than those observed by lidar. Further, GWMF estimates across datasets reveal that temperature-based proxies, based on midfrequency approximations for linear GWs, overestimate GWMF due to simplifications and uncertainties in GW wavelength estimation from data. Overall, the analysis provides GWMF benchmarks for parameterization validation and calls for three-dimensional GW parameterizations, better upper-boundary treatment, and vertical resolution increases commensurate with increases in horizontal resolution in models, for a more realistic GW analysis. Significance Statement Gravity wave–induced momentum forcing forms a key component of the middle atmospheric circulation. However, complete knowledge of gravity waves, their atmospheric effects, and their long-term trends are obscured due to limited global observations, and the inability of current climate models to fully resolve them. This study combines a kilometer-scale forecast model, modern reanalysis, and a coarse-resolution climate model to first compare the resolved and parameterized momentum fluxes by gravity waves generated over the Andes, and then evaluate the fluxes using a state-of-the-art ground-based Rayleigh lidar. Our analysis reveals shortcomings in current model parameterizations of gravity waves in the middle atmosphere and highlights the sensitivity of the estimated flux to the formulation used.

Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)

Abstract. Ice-sheet flow models capable of accurately projecting their future mass balance constitute tools to improve flood risk assessment and assist sea-level rise mitigation associated with enhanced ice discharge. Some processes that need to be captured, such as grounding-line migration, require high spatial resolution (under the kilometer scale). Conventional ice flow models mainly execute on central processing units (CPUs), which feature limited parallel processing capabilities and peak memory bandwidth. This may hinder model scalability and result in long run times, requiring significant computational resources. As an alternative, graphics processing units (GPUs) are ideally suited for high spatial resolution, as the calculations can be performed concurrently by thousands of threads, processing most of the computational domain simultaneously. In this study, we combine a GPU-based approach with the pseudo-transient (PT) method, an accelerated iterative and matrix-free solution strategy, and investigate its performance for finite elements and unstructured meshes with application to two-dimensional (2-D) models of real glaciers at a regional scale. For both the Jakobshavn and Pine Island glacier models, the number of nonlinear PT iterations required to converge a given number of vertices (N) scales in the order of 𝒪(N1.2) or better. We further compare the performance of the PT CUDA C implementation with a standard finite-element CPU-based implementation using the price-to-performance metric. The price of a single Tesla V100 GPU is 1.5 times that of two Intel Xeon Gold 6140 CPUs. We expect a minimum speedup of at least 1.5 times to justify the Tesla V100 GPU price to performance. Our developments result in a GPU-based implementation that achieves this goal with a speedup beyond 1.5 times. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions. The insights gained will benefit efforts to diminish spatial resolution constraints at higher computing performance. The higher computing performance will allow for ensembles of ice-sheet flow simulations to be run at the continental scale and higher resolution, a previously challenging task. The advances will further enable the quantification of model sensitivity to changes in upcoming climate forcings. These findings will significantly benefit process-oriented sea-level-projection studies over the coming decades.

Comparison of Frequency-Domain and Time-Domain Baseline Correction Approaches for Infrared Absorption Spectroscopy of Mixtures Containing Up to 464 Components.

Many baseline correction approaches have been developed to address baseline artifacts observed in measured infrared (IR) absorption spectra during post-processing. These approaches offer distinct advantages and disadvantages, and the choice of which one to employ depends on the complexity of baseline artifacts present in a particular application. In this paper, we compare the performance of two baseline correction approaches: a frequency-domain polynomial fitting approach and a time-domain modified free induction decay approach, under various baseline scenarios, spectral resolutions, and noise levels for mixtures containing up to 464 species. Our results showed that the frequency-domain approach outperformed the time-domain approach by a factor of up to 16 when the baseline was represented by a sine wave with fewer than two cycles over the full spectral range. On the other hand, the time-domain approach performed up to 12 times better when the baseline featured two cycles of a sine wave. Additionally, we observed that the time-domain approach exhibited higher sensitivity to spectral resolution and underperformed when the noise level was high. The findings of this study emphasize the importance of numerically testing a few candidate approaches for a given application, taking into consideration baseline characteristics, as well as the spectral resolution and noise constraints of the application.

Comparing Object-Based and Pixel-Based Machine Learning Models for Tree-Cutting Detection with PlanetScope Satellite Images: Exploring Model Generalization

Despite utilizing various remote sensing datasets, precise tree-cutting detection remains challenging due to spatial and spectral resolution constraints in satellite imagery, complex landscapes, data integration issues, and the need for accurate multi-temporal reference datasets. This study investigates the utilization of PlanetScope (PS) satellite images, along with pixel-based (PBIA) and object-based (OBIA) image analysis, for accurate mapping of forest cover and detection of tree cuttings. Detailed multi-temporal reference datasets were collected based on airborne laser scanning (ALS)-derived canopy height models (CHM) and very high-resolution (VHR) aerial orthomosaics. Reference datasets were used to train three machine learning (ML) models: random forest (RF), support vector machine (SVM), and feed-forward neural network (Nnet) in two forest districts located in Western and Northern Poland. The study also assessed the generalization capabilities of the best model in both local and temporal contexts. Regarding forest cover mapping, the OBIA RF classifier outperformed all other models with an overall accuracy (OA) of 99.27 % and Kappa of 98.18 %, while the PBIA SVM model showed the lowest (OA = 97.18 %, Kappa = 94.35 %). The testing of the model's generalization confirmed the performance of the OBIA RF model, with the Dice Coefficient ranging from 95.86 % to 96.74 %. The methodology's effectiveness in tree-cutting detection was demonstrated, with the detection rate ranging from 96.20 % to 99.39 % for the total number of cuttings, and from 99.45 % to 99.86 % for the total volume. In conclusion, the integration of PS satellite images, spectral-textural features, and generalized ML models proves to be effective for tree-cutting detection.

Seeing Is Believing: Photon Counting Computed Tomography Clearly Images Directional Deep Brain Stimulation Lead Segments and Markers After Implantation

Background and ObjectivesDirectional deep brain stimulation (DBS) electrodes are increasingly used, but conventional computed tomography (CT) is unable to directly image segmented contacts owing to physics-based resolution constraints. Postoperative electrode segment orientation assessment is necessary because of the possibility of significant deviation during or immediately after insertion. Photon-counting detector (PCD) CT is a relatively novel technology that enables high resolution imaging while addressing several limitations intrinsic to CT. We show how PCD CT can enable clear in vivo imaging of DBS electrodes, including segmented contacts and markers for all major lead manufacturers. Materials and MethodsWe describe postoperative imaging and reconstruction protocols we have developed to enable optimal lead visualization. PCD CT images were obtained of directional leads from the three major manufacturers and fused with preoperative 3T magnetic resonance imaging (MRI). Radiation dosimetry also was evaluated and compared with conventional imaging controls. Orientation estimates from directly imaged leads were compared with validated software-based reconstructions (derived from standard CT imaging artifact analysis) to quantify congruence in alignment and directional orientation. ResultsHigh-fidelity images were obtained for 15 patients, clearly indicating the segmented contacts and directional markers both on CT alone and when fused to MRI. Our routine imaging protocol is described. Ionizing radiation doses were significantly lower than with conventional CT. For most leads, the directly imaged lead orientations and depths corresponded closely to those predicted by CT artifact-based reconstructions. However, unlike direct imaging, the software reconstructions were susceptible to 180° error in orientation assessment. ConclusionsHigh-resolution photon-counting CT can very clearly image segmented DBS electrode contacts and directional markers and unambiguously determine lead orientation, with lower radiation than in conventional imaging. This obviates the need for further imaging and may facilitate anatomically tailored directional programming.

MRI‐Compatible, Transparent PEDOT:PSS Neural Implants for the Alleviation of Neuropathic Pain with Motor Cortex Stimulation

AbstractSimultaneous monitoring of electrophysiology and magnetic resonance imaging (MRI) could guide the innovative diagnosis and treatment of various neurodegenerative diseases that are previously impossible. However, this technique is difficult because the existing metal‐based implantable neural interface for electrophysiology is not free from signal distortions from its intrinsic magnetic susceptibility while performing an MRI of the implanted area of the neural interface. Moreover, brain tissue heating from neural implants generated by the radiofrequency field from MRI poses potential hazards for patients. Previous studies with soft polymer‐based electrode arrays provide relatively suitable MRI compatibility but does not guarantee high‐resolution electrophysiological signal acquisition and stimulation performance. Here, MRI compatible, optically transparent flexible implantable device capable of electrophysiological multichannel mapping and electrical stimulation is introduced. Using the device, neuropathic pain (NP) relief with a 30‐channel electrophysiological mapping of the somatosensory area before and after motor cortex stimulation (MCS) in allodynia rats after noxious stimulation is confirmed. Additionally, artifact‐free manganese‐enhanced MRI of dramatic relief of pain‐related region activity by MCS is demonstrated. Furthermore, artifact‐free optogenetics with transgenic mice is also investigated by recording light‐evoked potentials. These results suggest a promising neuro‐prosthetic for analyzing and modulating spatiotemporal neurodynamic without MRI or optical modality resolution constraints.

Three-stage multi-objective optimization approach based on sparsity knowledge for multi-airship earth-observation task scheduling

In recent years, due to the increasing demands for environmental monitoring, disaster warning, urban management, and national defense development, the construction of earth observation platforms has become a strategic requirement for countries in the aerospace sector. Autonomous airships in near space are highly competitive in terms of endurance, power consumption, and coverage area. An important aspect of earth observation is task scheduling, which generates allocation decisions under certain constraints. However, in the current literature, there is insufficient research in this field, which is crucial for human safety. Therefore, it is imperative to establish models to fill the research gap in this area. The novelties and our contributions to the paper are as follows. (i) Based on the consideration of distance, resolution, energy, and time constraints, the multi-airship earth-observation task scheduling (MAEOTS) model is developed to maximize the observation quality and minimize the energy cost. (ii) To solve this model efficiently, a three-stage multi-objective evolutionary algorithm (TSEA) based on sparsity knowledge combined with three indicators (SKTI) is proposed, also called TSEA/SKTI. (iii) Using three types of sparsity knowledge to guide the genetic operator and introducing the fitness evaluation strategy of three indicators for population selection, the competitiveness of populations in diversity and convergence is improved. (iv) A combination of decision variable score and number of tasks is used to initialize the populations. An adaptive strategy is designed to determine the range of operator crossover and mutation. The degree of crowding determined by the sharing function between individuals is used for environmental selection. The experiment was repeated in 4 data sets while comparing with 4 relevant popular evolutionary algorithms. Experimental results show that the proposed algorithm has advantages in terms of effectiveness and diversity of solutions, and demonstrates excellent characteristics in solving the MAEOTS model, exhibiting good time robustness and complexity. An actual case study in Zhuo Zhou City has been conducted to assess the effectiveness and applicability of the proposed model.

Eye-Based Point Rendering for Dynamic Multiview Effects

Eye-based point rendering (EPR) can make multiview effects much more practical by adding eye (camera) buffer resolution efficiencies to improved view-independent rendering (iVIR). We demonstrate this very successfully by applying EPR to dynamic cube-mapped reflections, sometimes achieving nearly 7× speedups over iVIR and traditional multiview rendering (MVR), with nearly equivalent quality. Our application to omnidirectional soft shadows is less successful, demonstrating that EPR is most effective with larger shader loads and tight eye buffer to off-screen (render target) buffer mappings. This is due to EPR's eye buffer resolution constraints limiting points and shading calculations to the sampling rate of the eye's viewport. In a 2.48 million triangle scene with 50 reflective objects (using 300 off-screen views), EPR renders environment maps with a 49.40ms average frame time on an NVIDIA 1080 Ti GPU. In doing so, EPR generates up to 5x fewer points than iVIR, and regularly performs 50× fewer shading calculations than MVR.