Dynamic Mapping Research Articles

Using spatial video and deep learning for automated mapping of ground-level context in relief camps.

The creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV). We trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math. The initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time. The results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

Playback method for dynamic star map simulation by fusing cosmic background radiation information

Playback method for dynamic star map simulation by fusing cosmic background radiation information

A fuzzy activation function based zeroing neural network for dynamic Arnold Map image cryptography

A fuzzy activation function based zeroing neural network for dynamic Arnold Map image cryptography

High dynamic range mapping for the evaluation of parallel transmit arrays.

Demonstration of a high dynamic-range and high SNR method for acquiring absolute maps from a combination of gradient echo and actual-flip-angle measurements that is especially useful during the construction of parallel-transmit arrays. Low flip angle gradient echo images, acquired when transmitting with each channel individually, are used to compute relative maps. Instead of computing these in a conventional manner, the equivalence of the problem to the ESPIRiT parallel image reconstruction method is used to compute maps with a higher SNR. Absolute maps are generated by calibration against a single actual flip-angle acquisition when transmitting on all channels simultaneously. Depending on the number of receiver channels and the location of the receive elements with respect to the subject being investigated, moderate to high gains in the SNR of the acquired maps can be achieved. The proposed method is especially suited for the acquisition of maps during the construction of transceiver arrays. Compared to the original method, maps with higher SNR can be computed without the need for additional measurements, and maps can also be generated using previously acquired data. Furthermore, easy adoption and fast estimation of receiver channels is possible because of existing highly optimized open-source implementations of ESPIRiT, such as in the BART toolbox.

Geoelectric fields and geomagnetically induced currents during the April 23–24, 2023 geomagnetic storm

We present a study on the dynamical variations of geoelectric fields E during the intense geomagnetic storm of April 23–24, 2023. The storm is caused by the interplanetary counterpart of a coronal mass ejection erupted from the Sun in association with an M1.7 X-ray flare. The GeoElectric Dynamic Mapping (GEDMap) code is applied to develop spatial E maps using magnetometer data from 29 observatories distributed across northern Europe. The code has been applied during local nighttime and morningtime intense substorms which occurred during the storm main phase. The maps show intensifications and strong variability of the E-fields during the substorm events. In particular, the morningtime event is associated with a significant variability in the E-field direction. Using the computed E-fields, we determined geomagnetically induced currents (GICs) at the sub-auroral station Mäntsälä. The modeled GIC values exhibit significant correlation (correlation coefficients r=0.73−0.76\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r=0.73-0.76$$\\end{document}) with the observed values. The performed E-field mapping provides a basis for the further determination of GICs in selected locations not covered by magnetometers and can be useful in understanding the GIC variability during magnetic storms and substorms.

Three-Dimensional Space Chaotic Scrolls Generated by Memristive Hopfield Neural Network and Its Application in Robot Path Planning

Abstract In this paper, a novel three-dimensional space-scroll chaotic attractor is constructed by introducing piece-wise linear memristors into a three-neurons-based Hopfield neural network to simulate the electromagnetic influence on neural dynamics. The dynamical behaviors of the novel memristive Hopfield neural network are discussed by means of numerical methods including the Lyapunov exponent spectrum, bifurcation diagrams, phase plots and dynamic maps. Furthermore, we also investigated different neurons subject to the memristive electromagnetic influence, where the two-dimensional grid-scroll chaotic attractors can be generated by one neuron subjects to the electromagnetic influence and three-dimensional space-scroll chaotic attractors can be generated by all of the neurons subject to the memristive electromagnetic influence. Moreover, we present a three-dimensional space-scroll scroll attractor-based chaotic mobile robot system. Superiority of the proposed space-scroll-based chaotic mobile robot system compared to existing methods has been demonstrated through numerical simulations.

Lean process mining: adopting process mining in lean manufacturing for dynamic process mapping and avoiding waste occurrence in real time

Purpose This paper aims to recommend a method entitled “lean process mining (LPM)” for mapping, analyzing and improving the material/information flows in the value stream of manufacturing processes. Design/methodology/approach The method is developed based on literature review and in-depth explorative research in value stream mapping and process mining approaches. The proposed LPM framework consists of three phases including as-realized process state, improvement strategies and reengineered process state. Hence, firstly, extracts the as-realized model, measures the identified wastes and identifies the sources of wastes. Secondly, implements prediction-recommendation-prevention strategies. Thirdly, reengineers the process model and measures the improved wastes. Findings It presents the applicability of the proposed approach in (1) online observation of manufacturing process behavior and tracing the process deviations dynamically in real time to identify the sources of waste; (2) avoiding defective products occurring during the production and eliminating the relevant derived wastes including wasted material, wasted energy, waste of labor, excess inventory, increased production lead time and wasted operational costs. Originality/value The practical application of LPM is illustrated through implementing it in a real-life manufacturing case. The outcomes prove the remarkable applicability of this method in lean manufacturing to avoid waste occurrence in the value stream.

Dynamic Voltage Mapping of the Post-infarct Ventricular Tachycardia Substrate: A Practical Technique to Help Differentiate Scar from Borderzone Tissue

During catheter ablation of post-infarct ventricular tachycardia (VT), substrate mapping is used when VT is non-inducible or poorly tolerated. Substrate mapping aims to identify regions of slowly conducting myocardium (borderzone) within and surrounding myocardial scar for ablation. Historically, these tissue types have been identified using bipolar voltage mapping, with areas of low bipolar voltage (<0.50 mV) defined as scar, and areas with voltages between 0.50 mV and 1.50 mV as borderzone. In the era of high-density mapping, studies have demonstrated slow conduction within areas of bipolar voltage <0.50 mV, suggesting that this historical cut-off is outdated. While electrophysiologists often adapt voltage cut-offs to account for this, the optimal scar-borderzone threshold is not known. In this review, we discuss dynamic voltage mapping, a novel substrate mapping technique we have developed, which superimposes data from both activation and voltage maps, to help delineate the post-infarct VT circuit through identification of the optimal scar-borderzone voltage threshold.

Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps

AbstractHigh‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.

RGB Mapping: A Dynamic Approach for Flow Pattern Identification and Classification

Thermal-hydraulic modeling and simulation are heavily dependent on the knowledge of flow regimes, which sort multiphase flow patterns into classifications with static boundaries that display characteristic features of flow patterns and phase distributions. Flow regime maps heretofore have been primarily developed mainly for vertical or horizontal pipe orientations by capturing images of flows at varying conditions and assigning them to different flow regimes based on visualization studies. As such, this method is inherently subjective. Furthermore, classifying two-phase flows into regimes with static boundaries is not physical, as two-phase flows undergo gradual changes as they transition from one regime to another. Therefore, such a static approach is not only unphysical but also inaccurate, resulting in the erroneous analysis of two-phase flows. Additionally, subjective observations at such flow conditions are prone to disagreement and inconsistencies in labeling. These difficulties are exacerbated in inclined two-phase flows. In this study, a novel two-phase flow pattern map employing the dynamic red, green, and blue (RGB) color model is presented. The model contains the embedded information of three normalized time-averaged statistics from signals obtained by a dual-ring impedance meter. A database of impedance meter signals from a 25.4-mm-inner-diameter adiabatic air-water inclinable test facility is used to compute statistics for each experimental flow condition. Multiple statistical measures are evaluated, and a principal component analysis is performed for feature selection. By mapping each parameter to intensities of red, green, and blue, a dynamic RGB flow pattern map can be produced in which groups of similar colors are representative of various flow regimes and the dynamic transition characteristics between the flow regimes are characterized physically. Therefore, this new method makes viable the observation of how flow patterns gradually change with respect to angle, as well as the fuzzification or quantification of the uncertainty surrounding experimental transition boundaries.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Statistical estimation of full-sky radio maps from 21cm array visibility data using Gaussian Constrained Realisations

Abstract An important application of next-generation wide-field radio interferometers is making high dynamic range maps of radio emission. Traditional deconvolution methods like CLEAN can give poor recovery of diffuse structure, prompting the development of wide-field alternatives like Direct Optimal Mapping and m-mode analysis. In this paper, we propose an alternative Bayesian method to infer the coefficients of a full-sky spherical harmonic basis for a drift-scan telescope with potentially thousands of baselines, that can precisely encode the uncertainties and correlations between the parameters used to build the recovered image. We use Gaussian Constrained Realisations (GCR) to efficiently draw samples of the spherical harmonic coefficients, despite the very large parameter space and extensive sky-regions of missing data. Each GCR solution provides a complete, statistically-consistent gap-free realisation of a full-sky map conditioned on the available data, even when the interferometer’s field of view is small. Many realisations can be generated and used for further analysis and robust propagation of statistical uncertainties. In this paper, we present the mathematical formalism of the spherical harmonic GCR-method for radio interferometers. We focus on the recovery of diffuse emission as a use case, along with validation of the method against simulations with a known diffuse emission component.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.

Oligotrophic state reduces the time dependence of the observed survival fraction for heavy ion beam-irradiated Saccharomyces cerevisiae and provides new insights into DNA repair.

Heavy ion beam (HIB) irradiation is widely utilized in studies of cosmic rays-induced cellular effects and microbial breeding. Establishing an accurate dose-survival relationship is crucial for selecting the optimal irradiation dose. Typically, after irradiating logarithmic-phase cell suspensions with HIB, the survival fraction (SF) is determined by the ratio of clonal-forming units in irradiated versus control groups. However, our findings indicated that SF measurements were time sensitive. For the Saccharomyces cerevisiae model, the observed SF initially declined and subsequently increased in a eutrophic state; conversely, in an oligotrophic state, it remained relatively stable within 120 minutes. This time effect of SF observations in the eutrophic state can be ascribed to HIB-exposed cells experiencing cell cycle arrest, whereas the control proliferated rapidly, resulting in an over-time disproportionate change in viable cell count. Therefore, an alternative involves irradiating oligotrophic cells, determining SF thereafter, and transferring cells to the eutrophic state to facilitate DNA repair-mutation. Transcriptomic comparisons under these two trophic states yield valuable insights into the DNA damage response. Although DNA repair was postponed in an oligotrophic state, cells proactively mobilized specific repair pathways to advance this process. Effective nutritional supplementation should occur within 120 minutes, beyond this window, a decline in SF indicates an irreversible loss of repair capability. Upon transition to the eutrophic state, S. cerevisiae swiftly adapted and completed the repair. This study helps to minimize time-dependent variability in SF observations and to ensure effective damage repair and mutation in microbial breeding using HIB or other mutagens. It also promotes the understanding of microbial responses to complex environments.IMPORTANCEMutation breeding is a vital means of developing excellent microbial resources. Consequently, understanding the mechanisms through which microorganisms respond to complex environments characterized by mutagens and specific physiological-biochemical states holds significant theoretical and practical values. This study utilized Saccharomyces cerevisiae as a microbial model and highly efficient heavy ion beam (HIB) radiation as a mutagen, it revealed the time dependence of observations of survival fractions (SF) in response to HIB radiation and proposed an alternative to avoid the indeterminacy that this variable brings. Meanwhile, by incorporating an oligotrophic state into the alternative, this study constructed a dynamic map of gene expression during the fast-repair and slow-repair stages. It also highlighted the influence of trophic states on DNA repair. The findings apply to the survival-damage repair-mutation effects of single-celled microorganisms in response to various mutagens and contribute to elucidating the biological mechanisms underlying microbial survival in complex environments.

An enhanced seagull algorithm for multi-threshold image segmentation based on Kapur entropy

Abstract To address the shortcomings of the Seagull Optimization Algorithm (SOA), such as poor distribution of individuals’ positions and low population diversity leading to susceptibility to local optima after the collision avoidance phase, an Enhanced Seagull Algorithm (ESOA) is proposed. Firstly, a chaos-guided mechanism is utilized to replace the original individual positions after collision avoidance with the mean of individual optima and current positions, effectively improving the distribution of the population. Secondly, a circular-topology neighborhood structure with random weights is introduced, allowing interaction between the current individual’s position and the information from its two adjacent optimal positions, thereby improving population clustering and enhancing diversity. Thirdly, a dynamic lens mapping strategy is employed to direct the optimal seagull individuals toward their newly generated mapped points, enhancing their ability to avoid local optima. ESOA is applied to the problem of multi-threshold image segmentation based on Kapur entropy, and experimental results demonstrate its effectiveness in improving segmentation performance.

EFFECT OF PHENOL FORMALDEHYDE RESIN IMPREGNATION ON NANODYNAMIC VISCOELASTICITY OF PINUS MASSONIANA LAMB IN WET STATE

We evaluated the effects of phenol formaldehyde (PF) resin modification on Masson pine (Pinus massoniana Lamb.) wood cell wall in wet states. The penetration degree of PF resin into wood cell was determined using confocal laser scanning microscopy (CLSM). The micromechanical properties of PF-modified wood cell walls in wet state were analyzed by quasi-static nanoindentation and dynamic modulus mapping techniques. Results showed that the PF resin significantly affected the static viscoelasticity and nanodynamic viscoelasticity of wood cell walls in oven-dried and wet states. The cell-wall mechanics increased at a PF resin concentration due to the increased bulking effects, such as decreased crystallinity of cellulose. Furthermore, the microfibrillar angle (MFA) of cell walls was lower than that of the control wood cell wall. The cell-wall mechanics of PF resin-modified sample decreased small than control sample in wet states.

Tactile Simultaneous Localization and Mapping Using Low-Cost, Wearable LiDAR

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired.

High dynamic range image tone mapping based on variational image decomposition and color correction

Tone mapping aims to reproduce high dynamic range (HDR) images on low dynamic range (LDR) display devices. The layer-decomposition-based tone mapping algorithms usually decompose HDR images into base and detail layers and process them accordingly. Since the detail layer is not refined again, these algorithms may suffer from halo artifacts, relatively dull color, and over-enhancement problems. To solve these problems, we introduce the variational image decomposition model and color correction into the field of tone mapping, and propose a tone mapping algorithm that combines the three-part variational image decomposition model with a color correction scheme based on minimum color loss. In contrast to traditional layer-decomposition-based tone mapping algorithms, we employ the three-part decomposition model that decomposes the HDR image into three components: a cartoon component, a texture component, and a high-frequency detail component. The cartoon component is compressed using a gamma function, while the texture and high-frequency detail components are each enhanced using the nonlinear stretching functions. This approach allows for better detail enhancement while preserving the smoothness of the image. In addition, we further use the color correction scheme based on minimum color loss to solve the color problem caused by the layer-decomposition-based tone mapping algorithms. Experimental results show that our proposed tone mapping algorithm can reduce the halo artifacts and preserve the edge details while outperforming some state-of-the-art tone mapping algorithms.

People with HIV exhibit spectrally distinct patterns of rhythmic cortical activity serving cognitive flexibility

Despite effective antiretroviral therapy, cognitive impairment remains prevalent among people with HIV (PWH) and decrements in executive function are particularly prominent. One component of executive function is cognitive flexibility, which integrates a variety of executive functions to dynamically adapt one's behavior in response to changing contextual demands. Though substantial work has illuminated HIV-related aberrations in brain function, it remains unclear how the neural oscillatory dynamics serving cognitive flexibility are affected by HIV-related alterations in neural functioning. Herein, 149 participants (PWH: 74; seronegative controls: 75) between the ages of 29–76 years completed a perceptual feature matching task that probes cognitive flexibility during high-density magnetoencephalography (MEG). Neural responses were decomposed into the time-frequency domain and significant oscillatory responses in the theta (4–8 Hz), alpha (10–16 Hz), and gamma (74–98 Hz) spectral windows were imaged using a beamforming approach. Whole-brain voxel-wise comparisons were then conducted on these dynamic functional maps to identify HIV-related differences in the neural oscillatory dynamics supporting cognitive flexibility. Our findings indicated group differences in alpha oscillatory activity in the cingulo-opercular cortices, and differences in gamma activity were found in the cerebellum. Across all participants, alpha and gamma activity in these regions were associated with performance on the cognitive flexibility task. Further, PWH who had been treated with antiretroviral therapy for a longer duration and those with higher current CD4 counts had alpha responses that more closely resembled those of seronegative controls, suggesting that optimal clinical management of HIV infection is associated with preserved neural dynamics supporting cognitive flexibility.

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy.

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic zone (EZ) while preserving the eloquent brain areas. The development of a reliable and noninvasive localization method that provides clinically useful information for the localization of the EZ is, therefore, crucial to achieving successful surgical outcomes. Electric and magnetic source imaging (ESI and MSI) have been increasingly utilized in the presurgical evaluation of these patients showing promising findings in the delineation of epileptogenic as well as eloquent brain areas. Moreover, the combination of ESI and MSI into a single solution, namely electromagnetic source imaging (EMSI), performed on simultaneous high-density electroencephalography (HD-EEG) and magnetoencephalography (MEG) recordings has shown higher source localization accuracy than either modality alone. Despite these encouraging findings, such techniques are performed in only a few tertiary epilepsy centers, are rarely recorded simultaneously, and are underutilized in pediatric cohorts. This study illustrates the experimental setup for recording simultaneous MEG and HD-EEG data as well as the methodological framework for analyzing these data aiming to localize the irritative zone, the seizure onset zone, and eloquent brain areas in children with DRE. More specifically, the experimental setups are presented for (i) recording and localizing interictal and ictal epileptiform activity during sleep and (ii) recording visual-, motor-, auditory-, and somatosensory-evoked responses and mapping relevant eloquent brain areas (i.e., visual, motor, auditory, and somatosensory) during visuomotor task, as well as auditory and somatosensory stimulations. Detailed steps of the data analysis pipeline are further presented for performing EMSI as well as individual ESI and MSI using equivalent current dipole (ECD) and dynamic statistical parametric mapping (dSPM).