Connectivity Features Research Articles

Design of an Integrated Arrhythmia Detection Model using Connectivity Features and Multivariate Time Series Classification

Cardiac arrhythmia, characterized by irregular heart rhythms, represents a widespread concern within the realm of cardiology. It encompasses a range of rhythm irregularities, with some being benign and others carrying substantial health risks. Therefore, the timely detection of arrhythmia holds considerable importance. Existing methods to detect arrhythmia mainly utilize either the traditional machine learning classifiers like SVM, and random forest or the recent deep learning-based models like CNN, LSTM, and RNN for the classification while few other methods use the classical signal processing-based transforms to extract the discriminating features. This paper proposes a novel integrated approach to classify the ECG signals for arrhythmia detection. Unlike existing methods, it considers the multivariate time series nature of the input along with the interrelationships among different ECG leads. The approach utilizes multivariate time series features extracted using ROCKET (RandOM Convolutional KErnal Transform) and introduces new connectivity-based features such as correlation and coherence for improved ECG signal classification. The state-of-the-art classification performance of the proposed integrated model on the PTB-XL PhysioNet dataset attested to the efficacy of the same.

Effects of rhythmic visual cues on cortical activation and functional connectivity features during stepping: an fNIRS study.

Rhythmic visual cues (RVCs) may influence gait initiation by modulating cognition resources. However, it is unknown how RVCs modulate cognitive resources allocation during gait movements. This study focused on investigating the effects of RVCs on cortical hemodynamic response features during stepping to evaluate the changes of cognitive resources. We recorded cerebral hemoglobin concentration changes of 14 channels in 17 healthy subjects using functional near-infrared spectroscopy (fNIRS) during stepping tasks under exposure to RVCs and non-rhythmic visual cues (NRVCs). We reported mean oxygenated hemoglobin (HbO) concentration changes, β-values, and functional connectivity (FC) between channels. The results showed that, the RVC conditions revealed lower HbO responses compared to the NRVC conditions during the preparation and early stepping. Correspondingly, the β-values reflected that RVCs elicited lower hemodynamic responses than NRVCs, and there was a decreasing trend in stimulus-evoked cortical activation as the task progressed. However, the FC between channels were stronger under RVCs than under NRVCs during the stepping progress, and there were more significant differences in FC during the early stepping. In conclusion, there were lower cognitive demand and stronger FC under RVC conditions than NRVC conditions, which indicated higher efficiency of cognitive resources allocation during stepping tasks. This study may provide a new insight for further understanding the mechanism on how RVCs alleviate freezing of gait.

Relation of connectome topology to brain volume across 103 mammalian species.

The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.

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Industrial revolution 4.0 allows the integration of digital technology with human life through the Internet of Things. One form of IoT application is in kitchen devices. In this case, kitchen safety is the main concern. In this research, an IoT device was developed that can detect gas leaks in the kitchen. The success of previous studies using gas sensors Arduino Uno and MQ2, MQ3 and MQ5 proved to be able to detect gas leaks, but there are still some deficiencies in these devices such as not being connected to mobile devices and efforts to reduce gas levels. Therefore this study adds a feature to monitor room temperature and humidity using the DHT11 sensor, as well as a remote connection feature via a smartphone. In addition, an automatic fan feature is also added to suck up excess gas levels. So that this research is expected to facilitate the process of monitoring the kitchen and reduce the risk of gas leaks which are harmful to health and safety.

Electrophysiological effects of kappa-opioid analgesic, RU-1205, using machine learning methods

The study is focused to the investigation of a new kappa-opioid agonist RU-1205, which exhibits an analgesic effect without causing dysphoric or aversive actions. It is assumed that this effects may be due to its functional selectivity, or the presence of an additional mechanism of action that involves blocking p38 mitogen-activated protein kinase (MAPK).The aim of the study was an experimental identification of RU-1205 mechanisms of action associated with the inhibition of MAPK p38 and functional selectivity for kappa opioid receptors.Materials and methods. The LFP activity was recorded in the male rats weighing 260–280 g (n=62) and implanted with chronic cortical and deep electrodes, after the intracerebroventricular administration of the well-studied reference substances: the selective kappa-opioid agonist U-50488 100 μg; the MAPK p38 blocker SB203580 1 μg; and the investigational compound RU-1205 at 350 μg. The weighted phase lag index (WPLI) was calculated. Subsequently, machine learning methods were employed to reduce the dimensionality and extract connectivity features using the principal component analysis method, then a signal classification was performed (models based on Gaussian processes). Using the local patch-clamp technique in the “whole-cell” configuration, the spike activity of pyramidal neurons in the basolateral amygdala was studied. Neurons were identified by their accommodation properties. After local perfusion of the test compounds, 3 dose-response curves were obtained for: (1) U-50488 at concentrations ranging from 0.001 to 10 μM; (2) combinations of U-50488 (0.001–10 μM) and RU-1205 (10 μM); and (3) the combinations of U-50488 (0.01–10 μM) and RU-1205 (100 μM).Results. The developed models made it possible to classify the compound RU-1205 as a “non-inhibitor” of MAPK p38 with a high probability. The results obtained were confirmed in patch-clamp experiments on acute brain slices where it was demonstrated that U-50488 statistically significantly increases the spike activity of pyramidal neurons of the basolateral amygdala (p <0.05), and RU-1205 interacts with U-50488, competitively suppressing its effect on the spike activity of neurons.Conclusion. The findings suggest that compound RU-1205 displays properties consistent with a functional kappa agonist activity and does not have a significant effect on MAPK p38. The study demonstrates the possibility of integrating electrophysiological measurements and advanced data analysis methods for a deep understanding of drug action and underscores the potential for further research in this area.

Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset.

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.

Empirical performance analysis and ML-based modeling of 5G non-standalone networks

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models.

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.

Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application. In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied. The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%. The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Background:The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).Material and methodsWe analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.ResultsThe AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.ConclusionsOur results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Impact of beyond-line-of-sight connectivity on the capacity and stability of mixed traffic flow: An analytical and numerical investigation

With the aid of wireless vehicle-to-vehicle communication, connected and automated vehicles (CAVs) are able to access the motion information of vehicles that are beyond-line-of-sight in mixed traffic environment. However, few attention has been paid to the influence of this feature on the mixed traffic flow characteristics. To fill this gap, this paper attempts to unveil the effect of beyond-line-of-sight connectivity on capacity and stability of mixed traffic flow. Specifically, we first formulate the distribution of various driving modes in mixed traffic based on the Markov model. Then, the analytical model of the capacity for mixed traffic is developed with proper time headway settings. Furthermore, the extensively implemented linear stability analysis procedure is extended to accommodate the beyond-line-of-sight connectivity feature. The analytical results show that (1) the proportion of CAVs in coordinated driving with beyond-line-of-sight reaches its peak at the half of penetration rate and lowest platoon clustering intensity for CAVs; (2) the capacity and stability performance are promoted with introducing the coordinated driving mode, and the increment becomes larger with the increase of communication range limit. (3) introducing coordinated driving can be an alternative to shifting the penetration rate of CAVs to improve the stability and capacity of mixed traffic in some cases.

Conservation implications of high gene flow and lack of pronounced spatial genetic structure in elephants supported by contiguous suitable habitat in north‐western India

AbstractThe western Terai Arc Landscape (wTAL) in Uttarakhand, India, marks the range limit for the Asian elephant in north‐western India. This region has been impacted by land‐use changes and infrastructure expansion for the last seven decades. To evaluate the impact of habitat deterioration on the population structure of elephants in the region, we characterized their genetic diversity and local genetic structure using mitochondrial (D loop) and nuclear DNA (microsatellites; n = 15) markers. We used tissue samples of 114 elephants from five different sub‐populations, collected between 2005 and 2014. The genetic variation was moderate (HO = 0.49–0.55) compared with other Indian elephant populations. Two mtDNA haplotypes were identified without strong spatial patterns across wTAL. Bayesian individual‐based clustering algorithm identified two genetic clusters (K = 2) with high admixture (50% at Q < 0.7) and no spatial adherence. Though K = 1 was not supported by the Bayesian algorithm, multivariate analysis and sibship patterns did not indicate genetic differentiation. The lack of spatial genetic structuring suggests high levels of gene flow, indicating that this population is still panmictic. This suggests that the life history traits of elephants as well as the ecological features of this landscape influence genetic connectivity. However, ongoing land use changes necessitate regular genetic monitoring in wTAL to identify incipient structuring caused by anthropogenic barriers to movement.

Navigating Geopolitical Alliances: Understanding Azerbaijan’s Relationship with Israel over Iran

This article examines the complex relationships between Iran, Azerbaijan, and Israel and attempts to provide an explanation for Azerbaijan’s greater inclination toward Israel than Iran. It analyzes Iran’s view of the relationship between Israel and Azerbaijan in the larger South Caucasus context, taking into account historical background, present events, and future predictions. Using a wide range of geopolitical analyses and sources, the study aims to understand the manifold variables that impact Iran’s attitude toward Azerbaijan’s alignment with Israel. From a theoretical perspective, the article explores the strategic features of this triangular connection by means of a neorealist paradigm. It applies game theory in order to assess the many strategic alternatives accessible to Iran, Azerbaijan, and Israel, emphasizing the reasons for Azerbaijan’s inclination towards Israel. The study improves our knowledge of the complicated dynamics underlying Azerbaijan’s preference for Israel over Iran, as well as the broader ramifications for the South Caucasus region, by analyzing the interactions between the variables at play. It seeks to highlight the historical, geopolitical, and strategic factors that contribute to Azerbaijan’s alignment with Israel over Iran, as well as the effect of this alignment on the geopolitical dynamics of the South Caucasus region.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

Automatic Detection of Electric Motorcycle Based on Improved YOLOv5s Network

Electric motorcycles are widely used due to their economic, portable, and easy‐to‐use characteristics. Power batteries are the primary power source of electric motorcycles. Electric motorcycles are usually pushed into elevators and parked at home or in enclosed corridor spaces for charging, which may pose serious safety hazards due to using inferior or expired batteries. The traditional manual management method is limited by human resources, making it difficult to manage and monitor such behavior. Automated detection of electric motorcycles based on artificial intelligence technology is an effective solution. Considering that common monitoring systems typically have limited data processing capabilities, this study proposes an electric motorcycle detection model based on improved You Only Look Once version 5s (YOLOv5s). Firstly, we develop the model by adding a transformer encoder module to the backbone of classical YOLOv5s. Next, the Bidirectional Feature Pyramid Network (BiFPN) is used for cross‐scale connectivity and multiscale feature fusion. Finally, the Coordinate Attention module (CA) is added to improve the representation capacity of the target features and enhance the detection accuracy. The results of comparative experiments and ablation experiments verified the effective performance of the proposed model, which attained a mean average precision of 81.2%. Compared to classical models like faster R‐CNN and YOLOv5, this methodology achieves higher performance with fewer parameters and computational complexity, meeting real‐time requirements.

NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification.

Goal: The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. Methods: we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we use the graph attention layer to learn the node features and gets the node information of different nodes for ASD classification. Results: Compared with other models, the NF-GAT has significant advantages in terms of classification results. Conclusions: NF-GAT can be effectively used for ASD classification.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

A study on integrated development approaches for bus stations based on cluster analysis

Integrated development of bus stations is proposed in response to the city’s priority development policy for public transportation. This approach aims to address the challenges of limited station land and increased financial pressure on bus operations by effectively utilizing urban land resources. Therefore, the selection of integrated development methods for bus stations becomes crucial. This study examines the characteristics of 36 bus station sites in Xi’an, considering both their intrinsic features and external environmental attributes. Using diverse data collection methods and analysis techniques, including Python, AcrGIS, and the Gaode Maps Open Platform, the research focuses on four dimensions: Points of Interest (POI), location characteristics, station attributes, and station connectivity features. By identifying 24 key clustering factors through Z-score standardization, Principal Component Analysis (PCA), elbow method, and K-means++ clustering, the 36 bus stations are categorized into five types: residential community development, commercial development, office building development, integrated development, and not recommended for development. These classification results provide guidance for future research on integrated development methods for bus stations.

Design of an Integrated Myocardial Infarction Detection Model Using ECG Connectivity Features and Multivariate Time Series Classification

Design of an Integrated Myocardial Infarction Detection Model Using ECG Connectivity Features and Multivariate Time Series Classification

Longitudinal prognosis of Parkinson’s outcomes using causal connectivity

Despite the prevalence of Parkinson’s disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson’s disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1*1*A summary of all acronyms used in this paper is presented in Appendix A. Supplementary Data. and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

Super-resolution reconstruction algorithm for dim and blurred traffic sign images in complex environments

<abstract> <p>In poor lighting and rainy and foggy bad weather environments, road traffic signs are blurred and have low recognition, etc. A super-resolution reconstruction algorithm for complex lighting and bad weather traffic sign images was proposed. First, a novel attention residual module was designed to incorporate an aggregated feature attention mechanism on the jump connection side of the base residual module so that the deep network can obtain richer detail information; second, a cross-layer jump connection feature fusion mechanism was adopted to enhance the flow of information across layers as well as to prevent the problem of gradient disappearance of the deep network to enhance the reconstruction of the edge detail information; and lastly, a positive-inverse dual-channel sub-pixel convolutional up-sampling method was designed to reconstruct super-resolution images to obtain better pixel and spatial information expression. The evaluation model was trained on the Chinese traffic sign dataset in a natural scene, and when the scaling factor is 4, the average values of PSNR and SSIM are improved by 0.031 when compared with the latest release of the deep learning-based super-resolution reconstruction algorithm for single-frame images, MICU (Multi-level Information Compensation and U-net), the average values of PSNR and SSIM are improved by 0.031 dB and 0.083, and the actual test average reaches 20.946 dB and 0.656. The experimental results show that the reconstructed image quality of this paper's algorithm is better than the mainstream algorithms of comparison in terms of objective indexes and subjective feelings. The super-resolution reconstructed image has a higher peak signal-to-noise ratio and perceptual similarity. It can provide certain technical support for the research of safe driving assistive devices in natural scenes under multi-temporal varying illumination conditions and bad weather.</p> </abstract>