Acquisition Of Spatial Information Research Articles

Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology.

The electrophysiological signals recorded by cutaneous electrodes, known as body surface potentials (BSPs), are widely employed biomarkers in medical diagnosis. Despite their widespread application and success in detecting various conditions, the poor spatial resolution of traditional BSP measurements poses a limit to their diagnostic potential. Advancements in the field of bioelectronics have facilitated the creation of compact, high-quality, high-density recording arrays for cutaneous electrophysiology, allowing detailed spatial information acquisition as BSP maps (BSPMs). Currently, the design of electrode arrays for BSP mapping lacks a standardized framework, leading to customizations for each clinical study, limiting comparability, reproducibility, and transferability. This perspective proposes preliminary design guidelines, drawn from existing literature, rooted solely in the physical properties of electrophysiological signals and mathematical principles of signal processing. These guidelines aim to simplify and generalize the optimization process for electrode array design, fostering more effective and applicable clinical research. Moreover, the increased spatial information obtained from BSPMs introduces interpretation challenges. To mitigate this, two strategies are outlined: observational transformations that reconstruct signal sources for intuitive comprehension, and machine learning-driven diagnostics. BSP mapping offers significant advantages in cutaneous electrophysiology with respect to classic electrophysiological recordings and is expected to expand into broader clinical domains in the future.

Realisation of an Application Specific Multispectral Snapshot-Imaging System Based on Multi-Aperture-Technology and Multispectral Machine Learning Loops

Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi aperture imaging, which enables a simultaneous capture (snapshot) of spectrally selective and spatially resolved scene information. There are some limiting factors for the spectral resolution when implementing this imaging principle, e.g., usable sensor resolutions and area, and required spatial scene resolution or optical complexity. Careful analysis is therefore needed for the specification of the multispectral system properties and its realisation. In this work we present a systematic approach for the application-related implementation of this kind of MSI. We focus on spectral system modeling, data analysis, and machine learning to build a universally usable multispectral loop to find the best sensor configuration. The approach presented is demonstrated and tested on the classification of waste, a typical application for multispectral imaging.

BEV-TinySpotter: A novel BEV perception method considering multi-dimensional feature fusion of small target

The BEV perceptual integrity depends on the ability to accurately capture multi-scale targets. However, the detection of multi-scale targets, especially small targets, is often affected by perceptual noise, information sparsity, and target occlusion, which subsequently affecting the accuracy of BEV perception and causing certain hidden danger to driving safety. To address these issues, we propose a novel BEV perception method BEV-TinySpotter (BEV-TS), which introduced two feature detection enhancement modules and one analytical causal reasoning module to highlighting small object features and improving the accuracy and robustness of small object detection. Specifically, we first enhance the feature recognition of small targets using the multi-scale spatial information acquisition module, and introduce the distribution and migration weights of small targets reflected in the time-series information. Then, we jointly employ global pooling and local pooling to differentiate the acquisition paths of the information to improve the sensitivity of capturing information for small targets. Additionally, we design causal inference graphs to post-fuse the recognition results of BEV, which utilizes causal reasoning to construct a state description mechanism for small targets, further enhancing the accuracy of BEV perception from a logical reasoning perspective. Compared with other SOTA BEV methods, the proposed method effectively enhances the ability to capture and infer the status of small targets during BEV perception, thus significantly improves the completeness of driving environment sensing.

Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets

Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. Conventional pose estimation for space targets unfolds in two stages: establishing 2D–3D correspondences using keypoint detection networks and 3D models, followed by pose estimation via the perspective-n-point algorithm. The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. To tackle the aforementioned challenges, we propose an adaptive dual-stream aggregation network (ADSAN), which enables the learning of finer local representations and the acquisition of abundant spatial and semantic information by merging features from both inter-layer and intra-layer perspectives through a multi-grained approach, consolidating features within individual layers and amplifying the interaction of distinct resolution features between layers. Furthermore, our ADSAN implements the selective keypoint focus module (SKFM) algorithm to alleviate problems caused by partial occlusions and viewpoint alterations. This mechanism places greater emphasis on the most challenging keypoints, ensuring the network prioritizes and optimizes its learning around these critical points. Benefiting from the finer and more robust information of space objects extracted by the ADSAN and SKFM, our method surpasses the SOTA method PoET (5.8°, 8.1°/0.0351%, 0.0744%) by 0.5°, 0.9°, and 0.0084%, 0.0354%, achieving 5.3°, 7.2° in rotation angle errors and 0.0267%, 0.0390% in normalized translation errors on the Speed and SwissCube datasets, respectively.

Metabotropic NMDAR Signaling Contributes to Sex Differences in Synaptic Plasticity and Episodic Memory.

NMDA receptor (NMDAR) - mediated calcium influx triggers the induction and initial expression of Long-Term Potentiation (LTP). Here we report that in male rodents ion flux-independent (metabotropic) NMDAR signaling is critical for a third step in the production of enduring LTP, i.e., cytoskeletal changes that stabilize the activity-induced synaptic modifications. Surprisingly, females rely upon estrogen receptor alpha (ERα) for the metabotropic NMDAR operations used by males. Blocking NMDAR channels with MK-801 eliminated LTP expression in hippocampal field CA1 of both sexes but left intact theta burst stimulation (TBS)-induced actin polymerization within dendritic spines. A selective antagonist (Ro25-6981) of the NMDAR GluN2B subunit had minimal effects on synaptic responses but blocked actin polymerization and LTP consolidation in males only. Conversely, an ERα antagonist thoroughly disrupted TBS-induced actin polymerization and LTP in females while having no evident effect in males. In an episodic memory paradigm, Ro25-6981 prevented acquisition of spatial locations by males but not females whereas an ERα antagonist blocked acquisition in females but not males. Sex differences in LTP consolidation were accompanied by pronounced differences in episodic memory in tasks involving a minimal (for learning) cue-sampling. Males did better on acquisition of spatial information whereas females had much higher scores than males on tests for acquisition of the identity of cues (episodic 'what') and the order in which the cues were sampled (episodic 'when'). We propose that sex differences in synaptic processes used to stabilize LTP result in differential encoding of the basic elements of episodic memory.Significance Statement Calcium influx through NMDARs has long been recognized as the initiating event for LTP. Results of the present studies call for a substantial revision to this fundamental observation about learning-related synaptic plasticity. Specifically, we show cytoskeletal mechanisms that consolidate field CA1 LTP and episodic memory are triggered not by NMDAR-mediated calcium but by ion flux-independent (metabotropic) signaling. Males used metabotropic functions of the NMDARs for this purpose whereas females relied upon synaptic estrogen receptors. This unprecedented instance of sex differences in synaptic function was accompanied by surprisingly large male/female differences in the acquisition of the three basic elements of episodic memory.

Application of UAV airborne LiDAR technology in highway engineering construction

Abstract The airborne LiDAR technology of UAVs is an efficient and high-precision spatial information acquisition technology. This paper introduces the airborne LiDAR technology of UAV in detail and studies the application of airborne LiDAR technology of UAV in highway construction based on the sixth section of the Leibokahalo-Yongshan connection line of G4216 Yibin Xinshi to Panzhihua Section. Engineering application practice shows that this technology can solve the difficult problem of obtaining basic geographic information data, and verify the rationality of the scheme and the matching degree with the field environment. In addition, compared with traditional survey technology, the technology has obvious advantages in the construction of highways in complex terrain, which helps to improve the quality of survey and design.

ABDALAMEER. DEVELOPMENT OF AUTONOMOUS MOBILE ROBOTS CONTROL SYSTEMS

The development of an efficient control system for mobile autonomous robots is explored. It delineates key stages in this process, starting with the necessity of precise trajectory planning followed by the creation of a mathematical model for controlling the robotic device. Subsequently, the implementation of navigation algorithms in the management system is emphasized. The importance of conducting comprehensive error analysis to address discrepancies during robot movement is underscored. Post the primary phase of control system creation, evaluating the cost-effectiveness and ensuring workplace safety are deemed crucial management steps. Localization emerges as a pivotal factor enabling the robot's effective autonomous movement relative to its target position. This, in turn, aids in orienting the robot within its external environment. Spatial information acquisition is underscored through the significance of traffic planning, cartography and specialized robotics sensors. Furthermore, the article delves into proprioceptive sensors, detailing their components, such as optical and electrical conductor sensors operating via the Hall effect discovered in 1879. It provides a formula to accurately ascertain conductivity state, involving parameters like hole and electron concentration, electron and hole mobility and elementary charge. In essence, the control system parallels the human brain, requiring a recognition system, action planning and execution functions. Navigation is identified as a critical element facilitating the robot's movement in three-dimensional space. The article integrates technical intricacies about proprioceptive sensors, highlighting their pivotal role within robot control systems.

A multi-scale semantic feature fusion method for remote sensing crop classification

The efficient and accurate acquisition of crop spatial planting structure information is of great significance to ensure national food security. Current crop classification studies have still not achieved optimal results in accurate crop classification identification due to the complex heterogeneous details of clear images and insufficient analysis and utilization of information. This study takes the Yuncheng-Linfen Basin in Shanxi, China as the study area and uses the ReliefF-RFE feature selection algorithm to achieve data dimensionality reduction. Contextual semantic feature aggregation, spatial channel attention mechanism, and hierarchical refinement strategy are used to fuse the multi-scale feature information in the extracted remote sensing images from the top down and bottom up. This study shows that 1) the ReliefF-RFE algorithm can effectively reduce the dimensionality of 224-dimensional multisource features and generate a 31-dimensional optimal feature space subset with strong interpretation to improve the computational efficiency of the model; 2) the multi-scale feature fusion model constructed in this study is better than UNet, ResNet, DeepLabv3+, HRNet and Swin Transformer in classification recognition of winter wheat and corn, with an overall accuracy of 95.76% and 93.44%; 3) A multiple feature fusion strategy designed through ablation experiments was evaluated to evaluate the reasonableness of each module and validate the irreplaceability and superiority of the model for improving crop accuracy in complex and heterogeneous terrain; 4) the multi-scale feature fusion model converts deep abstract features into local detail information by cross-scale contextual aggregation of deep and shallow multi-scale features, it obtains good extraction effect on fragmented small plots, and the reduction of local fine details of features is significantly better than other models. The multi-scale feature fusion classification model constructed in this study fully considers the interpretability of the input features and fuses the semantic features at different scales to eliminate the “semantic gap”, which shows that the model has strong stability and robustness and provides a reference for fast and accurate mapping of Sentinel-2 images of crops in large areas.

Time division multiplexing based multi-spectral semantic camera for LiDAR applications

The recent progress in the development of measurement systems for autonomous recognition had a substantial impact on emerging technology in numerous fields, especially robotics and automotive applications. In particular, time-of-flight (TOF) based light detection and ranging (LiDAR) systems enable to map the surrounding environmental information over long distances and with high accuracy. The combination of advanced LiDAR with an artificial intelligence platform allows enhanced object recognition and classification, which however still suffers from limitations of inaccuracy and misidentification. Recently, multi-spectral LiDAR systems have been employed to increase the object recognition performance by additionally providing material information in the short-wave infrared (SWIR) range where the reflection spectrum characteristics are typically very sensitive to material properties. However, previous multi-spectral LiDAR systems utilized band-pass filters or complex dispersive optical systems and even required multiple photodetectors, adding complexity and cost. In this work, we propose a time-division-multiplexing (TDM) based multi-spectral LiDAR system for semantic object inference by the simultaneous acquisition of spatial and spectral information. By utilizing the TDM method, we enable the simultaneous acquisition of spatial and spectral information as well as a TOF based distance map with minimized optical loss using only a single photodetector. Our LiDAR system utilizes nanosecond pulses of five different wavelengths in the SWIR range to acquire sufficient material information in addition to 3D spatial information. To demonstrate the recognition performance, we map the multi-spectral image from a human hand, a mannequin hand, a fabric gloved hand, a nitrile gloved hand, and a printed human hand onto an RGB-color encoded image, which clearly visualizes spectral differences as RGB color depending on the material while having a similar shape. Additionally, the classification performance of the multi-spectral image is demonstrated with a convolution neural network (CNN) model using the full multi-spectral data set. Our work presents a compact novel spectroscopic LiDAR system, which provides increased recognition performance and thus a great potential to improve safety and reliability in autonomous driving.

Fine grained analysis method for unmanned aerial vehicle measurement based on laser-based light scattering particle sensing

As an effective particle measurement method, laser-based particle sensors combined with unmanned aerial vehicles (UAVs) can be used for measuring air quality in near ground space. The Sniffer4D Mini2 features portability and real-time acquisition of accurate spatial distribution information on air pollution. Additionally, a new fine-grained analysis method called Co-KNN-DNN has been proposed to assess air quality between flight trajectories, allowing for a more detailed presentation of the continuous distribution of air quality. Therefore, this article introduces an unmanned aerial vehicle measurement fine-grained analysis method based on laser light scattering particle sensors. Firstly, the overall scheme was designed, M30T UAV was selected to carry the portable air quality monitoring equipment, with laser-based laser particulate matter sensor and Mini2, to collect AQI and related attributes of the near-ground layer in the selected research area, to do the necessary processing of the collected data, to build a data set suitable for model input, etc., to train and optimize the model, and to carry out practical application of the model. This article is based on the Co-KNN-DNN model for fine-grained analysis of air quality in spatial dimensions. Three experiments were conducted at different altitudes in the study area to investigate the practical application of fine-grained analysis of near-surface air quality. The experimental results show that the average R-squared value can reach 0.99. Choose to conduct experiments using the M30T UAV equipped with Sniffer4D Mini2 and a laser-based particulate matter sensor. The application research validates the effectiveness and practicality of the Co-KNN-DNN model.

Precise immunofluorescence canceling for highly multiplexed imaging to capture specific cell states

Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.

Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic resonance imaging (MRI). Due to the unpredictable pathological process of brain tumor generation and growth, brain tumor images often show irregular shapes and uneven internal gray levels. Existing neural network-based segmentation methods with an encoding/decoding structure can perform image segmentation to some extent. However, they ignore issues such as differences in multi-modality information, loss of spatial information, and under-utilization of boundary information, thereby limiting the further improvement of segmentation accuracy. This paper proposes a multimodal spatial information enhancement and boundary shape correction method consisting of a modality information extraction (MIE) module, a spatial information enhancement (SIE) module, and a boundary shape correction (BSC) module. The above three modules act on the input, backbone, and loss functions of deep convolutional networks (DCNN), respectively, and compose an end-to-end 3D brain tumor segmentation model. The three proposed modules can solve the low utilization rate of effective modality information, the insufficient spatial information acquisition ability, and the improper segmentation of key boundary positions can be solved. The proposed method was validated on BraTS2017, 2018, and 2019 datasets. Comparative experimental results confirmed the effectiveness and superiority of the proposed method over state-of-the-art segmentation methods.

Abstract 4881: Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics

Abstract Background: Recently, the study of microbiota has emerged as a novel area in cancer research. While next-generation sequencing (NGS) technologies are becoming more prevalent in the area, the acquisition of spatial information in microbiota is still challenging. Despite the possibility of detecting microbial RNAs on Visium (10X genomic, USA), methods depending on predefined host and microbial references cannot reliably capture microbial RNA reads, since Visium inherently lacks the ability to capture RNAs that are missing poly-A tails. Therefore, a reference-independent microbial RNA detection method needs to be developed for more trustworthy research on cancer microbiota on Visium. Methods: Initially, we reconstructed Visium data from a public raw dataset (Project: PRJNA811533, European Nucleotide Archive) derived from colorectal cancer (CRC) and oral squamous cell carcinoma (OSCC). BAM files were obtained by running Space Ranger on a human reference, GRCh38. These BAM files were firstly separated according to spatial barcodes with samtools and xargs. Then, microbial scores for each barcode were obtained using PathSeq. On the other hand, barcode-wise unmapped reads were extracted from the original BAM files simply by remaining unmapped reads, sorting them by spatial barcodes, and counting the number of reads. Then, the unmapped reads originating from host RNA reads were eliminated by regression analysis, remaining the microbial RNA reads. Subsequently, we conducted a comparison of microbial RNA reads from two different techniques. Results: By filtering out the unmapped reads associated with the host, microbial reads could be isolated. While these remaining microbial RNA reads show a significant correlation with the PathSeq scores, the unmapped reads were significantly higher than the PathSeq scores, increasing the statistical power for the downstream analysis with unmapped reads. Additionally, tissue regions with low PathSeq scores and high volumes of non-human unmapped reads were identified. These regions may indicate the presence of microbial sequences or RNA species that were not detected in the PathSeq analysis. Conclusion: The use of unmapped reads enables us to capture a greater number of RNA transcripts compared to PathSeq to indirectly analyze cancer microbiota. This means that we can observe hard-to-identify RNA variants, such as unstable RNAs or new species originating from the interaction between microbial RNAs and host RNAs. Our methods can provide plausible explanations for the existence of microbial RNAs on Visium, and we suggest a reference-independent detection of cancer microbota reads by utilizing unmapped reads. Citation Format: Jeongbin Park, Seo Hye Park, Dongjoo Lee, Jae Eun Lee, Daeseung Lee, Kwon Joong Na, Hongyoon Choi, Hyung-Jun Im. Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4881.

Letrozole delays acquisition of water maze task in female BALB/c mice: Possible involvement of anxiety

Letrozole, an aromatase inhibitor preventing estrogen synthesis from testosterone, is used as an adjuvant therapy in estrogen receptor-positive breast cancer patients. However, like other aromatase inhibitors, it induces many side effects, including impaired cognition. Despite its negative effect in humans, results from animal models are inconsistent and suggest that letrozole can either impair or improve cognition. Here, we studied the effects of chronic letrozole treatment on cognitive behavior of adult female BALB/c mice, a relevant animal model for breast cancer studies, to develop an appropriate animal model aimed at testing therapies to mitigate side effects of letrozole. In Morris water maze, letrozole 0.1 mg/kg impaired reference learning and memory. Interestingly, most of the letrozole 0.1 mg/kg-treated mice were able to learn the new platform position in reversal training and performed similar to control mice in a reversal probe test. Results of the reversal test suggest that letrozole did not completely disrupt spatial navigation, but rather delayed acquisition of spatial information. The delay might be related to increased anxiety as suggested by increased thigmotactic behavior during the reference memory training. The learning impairment was water maze-specific since we did not observe impairment in other spatial tasks such as in Y-maze or object location test. In contrast, the dose of 0.3 mg/kg did not have effect on water maze learning and facilitated locomotor habituation and recognition in novel object recognition test. The current study shows that letrozole dose-dependently modulates behavioral response and that its effects are task-dependent.

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

ABSTRACT Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine.

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

3D muscle networks based on vibrational mechanomyography

Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers. Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG. Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force. Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

Exploring the construction and analysis method of landscape spatial structure based on complex networks

Complex network theory proposes a structural network model that abstracts the elements in a complex system into nodes and the relationship between elements into ties, which proved to be a scientific method to study the structural system of complexities. This article aims to apply the complex network theory to interpret the organization of landscape spatial structure and propose a spatial structure network construction and quantitative analysis method suitable for landscape space. The following two questions are addressed in this research: (1) How to extract the structural elements from the landscape space and realize the construction of the structural network model based on the 3D spatial information? (2) How to quantitatively analyze the organization characteristics of a spatial structure according to the particularity of landscape space? The research content of this article includes the following five aspects, which are 3D spatial information acquisition, structural element extraction, structural element description, structural network model diagramming, and quantitative index selection. Taking Nanjing Lovers Park as an example, the feasibility of this method is verified. The results show that the method proposed can guarantee the accuracy of the landscape spatial structure model, visualize the attributes of structural elements, and achieve a refined analysis of the structure organization.

Offshore Hydrocarbon Exploitation Target Extraction Based on Time-Series Night Light Remote Sensing Images and Machine Learning Models: A Comparison of Six Machine Learning Algorithms and Their Multi-Feature Importance

The continuous acquisition of spatial distribution information for offshore hydrocarbon exploitation (OHE) targets is crucial for the research of marine carbon emission activities. The methodological framework based on time-series night light remote sensing images with a feature increment strategy coupled with machine learning models has become one of the most novel techniques for OHE target extraction in recent years. Its performance is mainly influenced by machine learning models, target features, and regional differences. However, there is still a lack of internal comparative studies on the different influencing factors in this framework. Therefore, based on this framework, we selected four different typical experimental regions within the hydrocarbon basins in the South China Sea to validate the extraction performance of six machine learning models (the classification and regression tree (CART), random forest (RF), artificial neural networks (ANN), support vector machine (SVM), Mahalanobis distance (MaD), and maximum likelihood classification (MLC)) using time-series VIIRS night light remote sensing images. On this basis, the influence of the regional differences and the importance of the multi-features were evaluated and analyzed. The results showed that (1) the RF model performed the best, with an average accuracy of 90.74%, which was much higher than the ANN, CART, SVM, MLC, and MaD. (2) The OHE targets with a lower light radiant intensity as well as a closer spatial location were the main subjects of the omission extraction, while the incorrect extractions were mostly caused by the intensive ship activities. (3) The coefficient of variation was the most important feature that affected the accuracy of the OHE target extraction, with a contribution rate of 26%. This was different from the commonly believed frequency feature in the existing research. In the context of global warming, this study can provide a valuable information reference for studies on OHE target extraction, carbon emission activity monitoring, and carbon emission dynamic assessment.

Network QoS Impact on Spatial Perception through Sensory Substitution in Navigation Systems for Blind and Visually Impaired People

A navigation system for individuals suffering from blindness or visual impairment provides information useful to reach a destination. Although there are different approaches, traditional designs are evolving into distributed systems with low-cost, front-end devices. These devices act as a medium between the user and the environment, encoding the information gathered on the surroundings according to theories on human perceptual and cognitive processes. Ultimately, they are rooted in sensorimotor coupling. The present work searches for temporal constraints due to such human-machine interfaces, which in turn constitute a key design factor for networked solutions. To that end, three tests were conveyed to a group of 25 participants under different delay conditions between motor actions and triggered stimuli. The results show a trade-off between spatial information acquisition and delay degradation, and a learning curve even under impaired sensorimotor coupling.