Dynamic Mapping Research Articles

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).

Influence of core–shell intermetallic on dynamic recrystallization and processing maps of novel Al-Cu-Ni composite through Arrhenius and artificial neural network (ANN) models

Influence of core–shell intermetallic on dynamic recrystallization and processing maps of novel Al-Cu-Ni composite through Arrhenius and artificial neural network (ANN) models

Mobile Robot Motion Behavior Based on Hopfield Neural Network Dynamics Under Electromagnetic Radiation

The nonlinear characteristic of activation function is a critical aspect that affects the performance of artificial neural networks (ANNs). Despite its importance, the impact of neuronal nonlinearity variations on the dynamics of ANNs has not been thoroughly studied in previous research. This paper aims to fill this gap by exploring the influence of nonlinearity changes in activation function due to memristive electromagnetic induction on the dynamical characteristics of ANNs. We first propose a memristor model with sinusoidal conductance function. Then, the effects on the biological neuron under the electromagnetic induction are simulated by replacing the gradient of hyperbolic tangent activation function with the conductance of the proposed memristor. Furthermore, a Hopfield neural network (HNN) is constructed by taking the nonlinearity changes of activation function into consideration and its dynamical behaviors are analyzed through Lyapunov exponent spectra, bifurcation diagram, biparameter-based dynamic maps and phase portraits. The numerical simulation results show complex dynamical behaviors such as single-scroll chaotic spiral attractor, double-scroll chaotic attractors and initial-value-dependent offset boosted attractors, demonstrating that nonlinearity changes of activation function induced by the electromagnetic induction have a significant impact on the dynamics of HNN. To further verify our findings, we implement the proposed HNN in hardware and find that the results are in line with our numerical simulations. Finally, we apply the chaotic HNN in the mobile robot path planning task, which achieves higher coverage rate than the random number-based random walk method.

Investigating Egocentric Tuning in Hippocampal CA1 Neurons.

Navigation requires integrating sensory information with a stable schema to create a dynamic map of an animal's position using egocentric and allocentric coordinate systems. In the hippocampus, place cells encode allocentric space, but their firing rates may also exhibit directional tuning within egocentric or allocentric reference frames. We compared experimental and simulated data to assess the prevalence of tuning to egocentric bearing (EB) among hippocampal cells in rats foraging in an open field. Using established procedures, we confirmed egocentric modulation of place cell activity in recorded data; however, simulated data revealed a high false-positive rate (FPR). When we accounted for false positives by comparing with shuffled data that retain correlations between the animal's direction and position, only a very low number of hippocampal neurons appeared modulated by EB. Our study highlights biases affecting FPRs and provides insights into the challenges of identifying egocentric modulation in hippocampal neurons.

Risk Assessment and Motion Planning for MAVs in Dynamic Uncertain Environments

Risk assessment to quantify the danger associated with a planned trajectory is critical for micro aerial vehicles (MAVs) navigating in dynamic uncertain environments. Existing works usually perform risk assessment by reasoning the occupancy status of the MAV’s surrounding space which only incorporates the position information of the MAV and the obstacles in the environment. In this paper, we further consider the MAV’s motion direction in risk assessment to reflect the fact that the obstacles in front of the MAV pose a higher risk while those behind pose a lower risk. In particular, we rely on a particle-based dynamic map which consists of a large number of particles to represent the local environment. The risk is defined to evaluate the safety level of a subspace in the map during some time interval and assessed by reasoning the occurrence of particles in the subspace. Those particles around the MAV are assigned different weights taking into account their relative positions to the MAV and its motion direction. We then incorporate the proposed risk assessment method into MAV motion planning by minimizing both the path length and the associated risk to achieve safer navigation. We compared our method with several state-of-the-art approaches in PX4+Gazebo simulations and real-world experiments. The results show that our method can achieve a 15% higher collision avoidance rate and a 20% lower flight risk in various environments with static and dynamic obstacles.

A Data-Driven Multi-Step Flood Inundation Forecast System

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

In vivo targeted-imaging of mitochondrial acidification in an aristolochic acid I-induced nephrotoxicity mouse model by a fluorescent/photoacoustic bimodal probe

Aristolochic acid I (AAI), a natural compound in aristolochia type Chinese medicinal herb, is generally acknowledged to have nephrotoxicity, which may be associated with mitophagy. Mitophagy is a cellular process with important functions that drive AAI-induced renal injury. Mitochondrial pH is currently measured by fluorescent probes in cell culture, but existing probes do not allow for in situ imaging of AAI-induced mitophagy in vivo. We developed a ratiometric fluorescent/PA dual-modal probe with a silicon rhodamine fluorophore and a pH-sensitive hemicyanine dye covalently linked via a short chain to obtain a FRET type probe. The probe was used to measure AAI-mediated mitochondrial acidification in live cells and in vivo. The Förster resonance energy transfer (FRET)-mediated ratiometric and bimodal method can efficiently eliminate signal variability associated with the commonly used one-emission and single detection mode by ratiometric two channels of the donor and acceptor. The probe has good water-solubility and low molecular weight with two positively charged, facilitating its precise targeting into renal mitochondria, where the fluorescent/PA changes in response to mitochondrial acidification, enabling dynamic and semi-quantitative mapping of subtle changes in mitochondrial pH in AAI-induced nephrotoxicity mouse model for the first time. Also, the joint use of L-carnitine could mitigate the mitophagy in AAI-induced nephrotoxicity.

Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer.

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital-model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.

Soliton and lump and travelling wave solutions of the (3 + 1) dimensional KPB like equation with analysis of chaotic behaviors

Nonlinear evolution equations (NLEEs) have a wide range of applications in various fields, including physics, engineering, biology, economics, and more. These equations are used to describe complex systems that change over time, where the state or behavior of the system not only depends on the current situation, but may also be related to the past. A deep understanding of the solutions to NLEEs is of great significance for predicting and controlling the behavior of complex systems. The (3 + 1)-dimensional Kadomtsev-Petviashvili-Boussinesq-like (KPB-like) equation represents a partial differential equation that describes nonlinear wave phenomena in multi-dimensional spaces. This model is commonly employed in fields such as fluid dynamics, plasma physics, and nonlinear optics to study wave behaviors. This paper investigates the KPB-like equations, which have meaningful physical implications in describing nonlinear wave phenomena. Hirota bilinear method is employed to derive the bilinear form for the KPB-like equation and 1-soliton, 2-soliton, lump, and travelling wave solutions are studied to this kind of nonlinear model. In addition, the chaotic behaviors of soliton and lump solutions are explored via applying the Duffing chaotic system. To have a better understanding of dynamic structures, 3D, line, density and contour map plots of begotten results are displayed. Our results indicate the directness and effectiveness of the applied method for analyzing high-dimensional differential equations that arise in nonlinear science.

The Discursive Evolution of Human Rights Law: Empirical Insights from a Computational Analysis of 180,000 UN Recommendations

Abstract Building on an independent database of 180,000 UN recommendations and a novel computational method, we present the most comprehensive study of human rights (HR) debates to date. We develop a unique empirical model that measures topical density of discourse. This innovative instrument measures the discursive activity of UN HR bodies through a machine-learning textual analysis of their outputs, offering a dynamic map of evolving trends in human rights, both over time (diachronically) and across different mechanisms (synchronically) within the UN HR ecosystem. Leveraging this comprehensive dataset and sophisticated computational methodologies, we identify which protected groups are central to different mechanisms’ attention and highlight the major human rights issues that have witnessed significant changes in attention. Our research presents significant findings on the density of UN HR discourse and its implications for two major debates in the field of HR law—HR proliferation and the structural critique of UN HR bodies.

Web-based visualization of static and dynamic HD road traffic map layers

Web-based visualization of static and dynamic HD road traffic map layers

Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment

Objective.We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.Approach.Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.Main results.Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.Significance.The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.

AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU). But, the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption; to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled "block partition" schemes are proposed. However, these methods are coarse-grained or static and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model [long short-term memory (LSTM), combined with the dynamic-fill scheme] generates remarkable mapping performance on the small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms.

Toward a dynamic national transportation noise map: Modeling temporal variability of spectral traffic noise emission levels.

The National Transportation Noise Map predicts time-averaged road traffic noise across the continental United States (CONUS) based on annual average daily traffic counts. However, traffic noise can vary greatly with time. This paper outlines a method for predicting nationwide hourly varying source traffic sound emissions called the Vehicular Reduced-Order Observation-based Model (VROOM). The method incorporates three models that predict temporal variability of traffic volume, predict temporal variability of different traffic classes, and use Traffic Noise Model (TNM) 3.0 equations to give traffic noise emission levels based on vehicle numbers and class mix. Location-specific features are used to predict average class mix across CONUS. VROOM then incorporates dynamic traffic class mix data to obtain dynamic traffic class mix. TNM 3.0 equations then give estimated equivalent sound level emission spectra near roads with up to hourly resolution. Important temporal traffic noise characteristics are modeled, including diurnal traffic patterns, rush hours in urban locations, and weekly and yearly variation. Examples of the temporal variability are depicted and possible types of uncertainties are identified. Altogether, VROOM can be used to map national transportation noise with temporal and spectral variability.

InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.

Comprehensive Mapping and Dynamic Monitoring of the Temporal Branch of the Facial Nerve in Complex Cranial Reconstruction Surgery and Revision Cranioplasty.

Facial nerve (FN) injury is a significant risk during complex cranial reconstruction surgeries, especially in revision cases where normal anatomy is distorted. The authors introduce a technique to mitigate FN injury, including preoperative FN mapping, intraoperative FN mapping, and continuous FN monitoring. Preoperative mapping uses a handheld ball-tip stimulator to elicit compound muscle action potentials (CMAP) in the frontalis muscle. Needle electrodes are placed above the orbital margin to record responses, starting at the stylomastoid foramen and extending until the entire temporal branch is mapped. Intraoperatively, continuous mapping is achieved using an electrified insulated suction device, allowing dynamic mapping during soft tissue dissection. Continuous monitoring involves placing stimulation electrodes near the stylomastoid foramen and recording CMAP responses from the frontalis and orbicularis oculi/orbicularis oris muscles. The authors tested this technique in 5 patients. The authors successfully mapped the temporal branch of the FN with isolated frontalis CMAP recordings in all patients. Intraoperative FN mapping and continuous monitoring were successfully performed in all cases. Two patients experienced complete cessation of FN CMAPs, which resumed upon loosening the tension on the myocutaneous flap held by fishhooks. One patient showed a 39% decrement in frontalis CMAP that did not recover. Despite these issues, all patients had intact FN function postoperatively and at follow-up. Our technique shows promise in mitigating FN injury during complex cranial reconstruction and cranioplasty revision surgery. Further research with a larger cohort is needed to confirm efficacy and statistical significance.

Spatiotemporal Modeling of Aedes aegypti Risk: Enhancing Dengue Virus Control through Meteorological and Remote Sensing Data in French Guiana.

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities.