Adjacent Time Research Articles

Research on registration method for enface image using multi-feature fusion

Objective.The purpose of this work is to accurately and quickly register the Optical coherence tomography (OCT) projection (enface) images at adjacent time points, and to solve the problem of interference caused by CNV lesions on the registration features.Approach.In this work, a multi-feature registration strategy was proposed, in which a combined feature (com-feature) containing 3D information, intersection information and SURF feature was designed. Firstly, the coordinates of all feature points were extracted as combined features, and then these feature coordinates were added to the initial vascular coordinate set simplified by the Douglas-Peucker algorithm as the point set for registration. Finally, the coherent point drift registration algorithm was used to register the enface coordinate point sets of adjacent time series.Main results.The newly designed features significantly improve the success rate of global registration of vascular networks in enface images, while the simplification step greatly improves the registration speed on the basis of preserving vascular features. The MSE, DSC and time complexity of the proposed method are 0.07993, 0.9693 and 42.7016 s, respectively.Significance.CNV is a serious retinal disease in ophthalmology. The registration of OCT enface images at adjacent time points can timely monitor the progress of the disease and assist doctors in making diagnoses. The proposed method not only improves the accuracy of OCT enface image registration, but also significantly reduces the time complexity. It has good registration results in clinical routine and provides a more efficient method for clinical diagnosis and treatment.

A Spatial Interpolation Method for Meteorological Data Based on a Hybrid Kriging and Machine Learning Approach

ABSTRACTConventional spatial interpolation methods for meteorological data are usually based on linear interpolation. However, with the improvements in the temporal and spatial resolution of observational data, local neighbouring stations are susceptible to the influence of underlying surface changes and high terrain gradients. Moreover, for interpolation at a single time point, the inability to extract continuous change information effectively from adjacent times limits the interpolation performance. In this paper, an improved hybrid deep learning‐kriging method is proposed that combines a graph neural networks (GNNs) prediction model with the kriging interpolation algorithm. The GNNs considers dynamic changes over time and combines spatial and temporal information to estimate (interpolate) meteorological data at target weather stations using reanalysis data as input. The experimental results show that the hybrid method exhibits good performance in interpolating station data in complex terrain areas and under uneven surface conditions. The interpolation effectiveness of this method is markedly improved compared to that of traditional kriging methods. Moreover, when applied to station‐to‐grid interpolation, the hybrid method still provides better interpolation results than those of kriging methods. Therefore, this research provides a new method and perspective for meteorological data interpolation.

Single-Source-Point Detection for DOA Estimation Using Angle Correlation Between Adjacent Time–Frequency Points

Single-Source-Point Detection for DOA Estimation Using Angle Correlation Between Adjacent Time–Frequency Points

Controlling Factors of Chorus Spectral Gaps

AbstractThe present study compares a single‐band chorus wave against a banded chorus wave observed by Van Allen Probes at adjacent times, and demonstrates that the single‐band chorus wave is associated with an anisotropic electron population over a broad energy range, while the banded chorus wave is accompanied by an electron phase space density plateau and an electron anisotropy reduction around Landau resonant energies. We further compare banded chorus waves with different spectral gap widths, and show that a wider spectral gap is associated with electron isotropization extending to higher energies with respect to the equatorial Landau resonant energy. We suggest that early generated chorus waves isotropize electrons via Landau resonant acceleration, and the waves that propagate to higher latitudes isotropize electrons at higher energies. The isotropization extending to higher energies leads to a larger spectral gap of new chorus waves after electrons bounce back to the equator.

TimeNorm: a novel normalization method for time course microbiome data.

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis.

Scatterer spacing based on Gabor atoms matched from harmonic ultrasound echoes to improve assessment of microwave‐induced thermal lesions

AbstractBackgroundMicrowave ablation (MWA) is a minimally invasive alternative for the treatment of unresectable liver tumors. To verify the effectiveness and safety of MWA, it is critical to measure the temperature variation and assess the regions of the microwave‐induced thermal lesions.PurposeRecent studies have indicated that the locations of optimally matched Gabor atoms (LOMGA) from ultrasound radiofrequency (RF) echo signals allow accurate and stable scatterer spacing estimation. Herein, a harmonic‐based LOMGA method is proposed to estimate the scatterer spacing for improving the assessment of microwave‐induced thermal lesions.MethodsThe mean scatterer spacing (MSS) is estimated via the LOMGA method incorporating the selection of concise atoms from separated second‐harmonic RF echo signals with the pulse‐inversion algorithm for thermal lesion evaluation. In vitro experiments, 10 fresh porcine liver samples were ablated at different time nodes during the ablation period, and 200 sets of second‐harmonic and fundamental RF echo signals were randomly selected from the regions of interest in the coagulated liver samples for MSS estimation. The means and standard deviations of the MSSs, as well as the linear regression for the mean MSSs, were calculated from fundamental and second‐harmonic signals for comparison and evaluation, the receiver operating characteristic (ROC) curves for the 200 sets of fundamental‐based and harmonic‐based MSS estimates from the 10 liver samples at five pairs of adjacent time nodes were calculated, and one‐way analysis of variance (ANOVA) tests were performed for the five pairs of adjacent time nodes. The fundamental and harmonic‐based p‐values in the ANOVA tests and the areas under the ROC curves (AUCs) were calculated to statistically analyze the differences in the MSSs between adjacent time nodes.ResultsThe harmonic‐based increments in the intensity variation and coherent components were larger than the fundamental‐based increments with the increasing ablation time. The harmonic‐based MSSs from the 10 liver samples at five pairs of adjacent time nodes were found to be highly statistically significant (p < 0.01). Thus, the harmonic‐based MSSs had greater variations. Compared with the fundamental‐based results, for the five preset ST values, the average increment in the harmonic‐based mean slopes was 69.22% and the average decrement in the mean standard deviations was 11.67% for the linear‐fitting MSS results, and the results were statistically significant (p < 0.05).ConclusionHarmonic‐based MSSs are more sensitive and robust to variations in coagulated tissues, which is advantageous for the assessment of microwave‐induced thermal lesions.

The calculation method of blood flow pressure based on four-dimensional flow magnetic resonance imaging and deep learning

Utilizing artificial intelligence methods for blood flow pressure estimation can significantly enhance the computational speed of blood flow pressure. However, current related research can only calculate the blood flow pressure parameters of vessels with different geometric shapes under fixed boundary conditions, thus fail to achieve transient flow field calculation and consider the hemodynamic differences formed by patients' varying physiological and pathological conditions. In view of this, this study proposes a method for relative pressure estimation based on four-dimensional flow magnetic resonance imaging (4D flow MRI) of patient blood flow and deep learning. 4D flow MRI was used to obtain the patient's blood flow velocity gradient data, and feature engineering processing is performed on the sampled data. Then, a novel neural network was proposed to acquire the characteristic relationship between velocity gradient and pressure gradient in the vicinity of the point to be measured and within adjacent sampling time periods, thereby achieving the calculation of the relative pressure in the vicinity of the point to be measured. Statistical analysis was performed to evaluate the efficacy of the method, comparing it with computational fluid dynamics methods and catheter pressure measurement techniques. The accuracy of the proposed method exceeded 96%, while computational efficiency was improved by several tens of times, and no manual setting of physiological parameters was required. Furthermore, the results were compared with clinical catheter-measured pressure results, r2 = 0.9053, indicating a significant consistency between the two methods. Compared to previous research, the method proposed in this study can take the blood flow velocity conditions of different patients at different times as input features via 4D flow MRI, thus enabling the calculation of pressure in transient flow fields, which significantly improved computational efficiency and reduced costs while maintaining a high level of calculation accuracy. This provides new direction for future research on machine learning prediction of blood flow pressure.

Nonprobabilistic time-dependent reliability analysis for uncertain structures under interval process loads

In this paper, a novel nonprobabilistic analysis framework is proposed to evaluate the time-dependent reliability of uncertain structures under time-varying loads. Firstly, a novel uncertainty propagation method is developed by combining interval process integration and surrogate-based interval analysis and the correlation coefficient between responses of adjacent time steps is further analyzed. Subsequently, the nonprobabilistic time-dependent reliability is analyzed base on the first-passage theory and the established interval model. Unlike existing nonprobabilistic methods that consider time-invariant external loads, the proposed method applies an interval process to describe time-varying external loads, thereby offering a broader range of applicability. Compared to existing nonprobabilistic methods that consider time-varying loads, the proposed method establishes a more refined nonprobabilistic time-dependent reliability model based on the first passage theory, achieving higher accuracy. The effectiveness and superiority of the proposed method are validated through a numerical example and an engineering application.

Parking Lot Traffic Prediction Based on Fusion of Multifaceted Spatio-Temporal Features.

With the rapid growth of population and vehicles, issues such as traffic congestion are becoming increasingly apparent. Parking guidance and information (PGI) systems are becoming more critical, with one of the most important tasks being the prediction of traffic flow in parking lots. Predicting parking traffic can effectively improve parking efficiency and alleviate traffic congestion, traffic accidents, and other problems. However, due to the complex characteristics of parking spatio-temporal data, high levels of noise, and the intricate influence of external factors, there are three challenges to predicting parking traffic in a city effectively: (1) how to better model the nonlinear, asymmetric, and complex spatial relationships among parking lots; (2) how to model the temporal autocorrelation of parking flow more accurately for each parking lot, whether periodic or aperiodic; and (3) how to model the correlation between external influences, such as holiday weekends, POIs (points of interest), and weather factors. In this context, this paper proposes a parking lot traffic prediction model based on the fusion of multifaceted spatio-temporal features (MFF-STGCN). The model consists of a feature embedding module, a spatio-temporal attention mechanism module, and a spatio-temporal convolution module. The feature embedding module embeds external features such as weekend holidays, geographic POIs, and weather features into the time series, the spatio-temporal attention mechanism module captures the dynamic spatio-temporal correlation of parking traffic, and the spatio-temporal convolution module captures the spatio-temporal features by using graph convolution and gated recursion units. Finally, the outputs of adjacent time series, daily series, and weekly series are weighted and fused to obtain the final prediction results, thus predicting the parking lot traffic flow more accurately and effectively. Results on real datasets demonstrate that the proposed model enhances prediction performance.

Data-driven hierarchical learning approach for multi-point servo control of Pan–Tilt–Zoom cameras

Pan–Tilt–Zoom (PTZ) cameras, with their significant features of free rotation and zoom, are widely used in areas such as border security, ecological conservation, emergency management, and the military. PTZ cameras can achieve automatic monitoring of a selected area through multiple servo operations, known as multi-point servo control. However, due to the deficiencies in the servo control Software Development Kit (SDK), hardware wear, and interference from complex external environments, the multi-point servo control process generates significant errors. This paper proposes a precise multi-point servo control framework based on Deep Reinforcement Learning (DRL) to address this issue. The complexity of real-world environments necessitates a reward function design that fully considers various factors, for which we propose the directional gravity reward function. Due to the instability during the training process, prolonged trial-and-error interactions between the agent and the equipment can cause irreversible damage to the devices. This framework employs a phased training approach, where the agent learns sequentially from offline, off-policy, and on-policy data, reducing direct interaction with the equipment while enhancing the agent’s overall performance. Additionally, real-time and accurate device status can be obtained by performing feature matching on images from adjacent time frames, which is crucial for the system’s operation. Evaluation results indicate that our proposed precise multi-point servo control framework significantly outperforms other methods in 4-point servo control tasks in both virtual and real-world scenarios. Additionally, the operational process fully considers safety issues.

ConvTrans-CL: Ocean time series temperature data anomaly detection based context contrast learning

Ocean temperature data anomaly detection is instrumental in monitoring environmental changes and implementing measures to alleviate adverse consequences. This holds immense importance for marine environmental observation and scientific inquiry. However, existing anomaly detection methods encounter significant challenges in extracting features from data, which severely affects the performance of anomaly detection. Existing models have limitations in capturing the local contextual distribution features and high stochastic distribution trends of ocean temperature data. Therefore, this paper introduces the ConvTrans-CL model, which integrates the Transformer encoder with causal convolution and employs a contrastive learning approach. Causal convolution extracts the distributional features of short-time subsequences within a sliding window and integrates them into the self-attention mechanism, enabling the model to focus on the localized features of the data. Contrastive learning efficiently captures the long-range dependencies of time-series data by distinguishing between pairs of subsequences with adjacent time intervals and pairs of non-adjacent subsequences. This enables the model to capture the trend of high stochasticity in the distribution of the temperature data. Finally, we select temperature data from two sea areas that are susceptible to multiple environmental factors for experiments and compare the feature extraction and anomaly detection capabilities of ConvTrans-CL with other methods, confirming the superior performance of our method.

Characterization of Acoustic Emissions from Concrete Based on Energy Activity Coefficient

Single-stage compression loading experiments were carried out on concrete specimens of various strengths to explore the characteristic parameters of the acoustic emission signal and its damage evolution law in the concrete damage process. These specimens were monitored in real time with acoustic emission and DIC instruments during the loading process, and internal pores and slices were scanned with CT scanning instruments after compression. The acoustic emission phenomenon was expressed using the energy activity coefficient, and the law relating to the phenomenon was summarized. The results show that when the peak and mean values in the first adjacent time domain grow rapidly, the specimen produces a large crack and enters the stage of rapid crack development, which can be taken as an indication of the impending damage to the specimen. The energy activity coefficient reflects the damage development intensity as follows: the smaller the energy activity coefficient, the more the cracks developed; the faster the speed, the larger the deformation. With an increase in the load level, the energy activity coefficient gradually tends to stabilize, and the specimen enters the stage of rapid crack development. However, when the energy activity coefficient suddenly increases again, the specimen is destabilized and destroyed. Therefore, the energy activity coefficient responds to the degree of congenital defects in the specimen. As the load increases, the energy activity coefficient is more stable, and the defects are smaller; in contrast, the energy activity coefficient drastically oscillating indicates that the material is very defective.

Gait rhythm analysis as a new continuous scale for cerebellar ataxia: Power law and lognormal components represent the ataxic gait quantity

We estimated the severity of cerebellar ataxia by analyzing gait rhythm. We measured the step times in patients with pure cerebellar ataxia and healthy controls and then analyzed the distribution of the ratios of adjacent times. Gait rhythm displayed the best adaptation when expressed as the sum of the power law and lognormal distributions in both groups, and the groups could be distinguished by the exponent of the power law distribution, reflecting the fractal property of gait rhythm. Gait rhythm might reflect different features of impairment in patients with cerebellar ataxia, making it a useful continuous scale for cerebellar ataxia.

Long-Term Detection of Glycemic Glucose/Hypoglycemia by Microfluidic Sweat Monitoring Patch.

A microfluidic sweat monitoring patch that collects human sweat for a long time is designed to achieve the effect of detecting the rise and fall of human sweat glucose over a long period of time by increasing the use time of a single patch. Five collection pools, four serpentine channels, and two different valves are provided. Among them, the three-dimensional valve has a large burst pressure as a balance between the internal and external air pressures of the patch. The bursting pressure of the two-dimensional diverter valve is smaller than that of the three-dimensional gas valve, and its role is to control the flow direction of the liquid. Through plasma hydrophilic treatment of different durations, the optimal hydrophilic duration is obtained. The embedded chromogenic disc detects the sweat glucose value at two adjacent time intervals and compares the information of the human body to increase or reduce glucose. The patch has good flexibility and can fit well with human skin, and because polydimethylsiloxane (PDMS) has good light transmission, it reduces the measurement error caused by the color-taking process and makes the detection results more accurate.

A novel equivalent reciprocal convex combination technique to sampled‐data master–slave synchronization for chaotic Lur'e systems with time‐varying delays

AbstractFor a class of master–slave (M‐S) systems in chaotic Lur'e systems with time‐varying delays, a sampled‐data synchronization controller is designed, and a new synchronization stability condition is proposed in the form of linear matrix inequality. A novel Lyapunov‐Krasovskii functional (LKF) is constructed by using the looped‐functional method, and the positive definiteness condition of LKF including sampled‐data parts is exchanged with a looping condition by constructing a functional, which should be equal at adjacent sampling times. The M‐S synchronization condition is obtained utilizing the equivalent reciprocal convex combination approach combined with Bessel‐Legendre integral inequality to estimate the LKF derivative. Different from previous methods, due to fully utilizing both nonlinearity and state information at the sampling time via the integral of error systems from the sampling time to current one, the M‐S synchronization condition is less conservative, and obtained sampled‐data controller possesses a longer sampling period. Finally, two numerical examples verify the superiority and validity of the approach.

Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification.

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.

A State-Interactive MAC Layer TDMA Protocol Based on Smart Antennas

Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay.

Discovery of gene regulation mechanisms associated with uniconazole-induced cold tolerance in banana using integrated transcriptome and metabolome analysis

BackgroundThe gibberellic acid (GA) inhibitor, uniconazole, is a plant growth regulator commonly used in banana cultivation to promote dwarfing but also enhances the cold resistance in plants. However, the mechanism of this induced cold resistance remains unclear.ResultsWe confirmed that uniconazole induced cold tolerance in bananas and that the activities of Superoxide dismutase and Peroxidase were increased in the uniconazole-treated bananas under cold stress when compared with the control groups. The transcriptome and metabolome of bananas treated with or without uniconazole were analyzed at different time points under cold stress. Compared to the control group, differentially expressed genes (DEGs) between adjacent time points in each uniconazole-treated group were enriched in plant-pathogen interactions, MAPK signaling pathway, and plant hormone signal transduction, which were closely related to stimulus-functional responses. Furthermore, the differentially abundant metabolites (DAMs) between adjacent time points were enriched in flavone and flavonol biosynthesis and linoleic acid metabolism pathways in the uniconazole-treated group than those in the control group. Temporal analysis of DEGs and DAMs in uniconazole-treated and control groups during cold stress showed that the different expression patterns in the two groups were enriched in the linoleic acid metabolism pathway. In addition to strengthening the antioxidant system and complex hormonal changes caused by GA inhibition, an enhanced linoleic acid metabolism can protect cell membrane stability, which may also be an important part of the cold resistance mechanism of uniconazole treatment in banana plants.ConclusionsThis study provides information for understanding the mechanisms underlying inducible cold resistance in banana, which will benefit the production of this economically important crop.

Managing parking reservations to mitigate congestion in office-oriented garages

ABSTRACT To alleviate the congestion caused by long parking lines in office-oriented garages, we explore the parking reservation management strategy and focus on the optimization of arrival patterns. A k-means algorithm based on dynamic time warping (DTW) is initially applied to extract the representative arrival patterns of parking vehicles. Based on these patterns, a bi-objective arrival optimization model is then developed to decrease traffic delay time and reduce individual discomfort due to arrival time shifts. The Pareto solution set is obtained, suggesting a potentially optimal balance between traffic delay and individual discomfort. The further analysis indicates that, strategically redistributing a small percentage of vehicle arrivals can mitigate congestion under certain conditions. For small-, medium-, and large-sized garages, shifting 9.01%, 6.90% and 11.07%, respectively, of the arrival demand to either of the adjacent time windows can reduce traffic delay time by 32.74%, 49.01% and 63.27%, respectively.

Development of a screening system of gene sets for estimating the time of early skeletal muscle injury based on second-generation sequencing technology.

The present study is aimed to address the challenge of wound age estimation in forensic science by identifying reliable genetic markers using low-cost and high-precision second-generation sequencing technology. A total of 54 Sprague-Dawley rats were randomly assigned to a control group or injury groups, with injury groups being further divided into time points (4h, 8h, 12h, 16h, 20h, 24h, 28h, and 32h after injury, n = 6) to establish rat skeletal muscle contusion models. Gene expression data were obtained using second-generation sequencing technology, and differential gene expression analysis, weighted gene co-expression network analysis (WGCNA) and time-dependent expression trend analysis were performed. A total of six sets of biomarkers were obtained: differentially expressed genes at adjacent time points (127 genes), co-expressed genes most associated with wound age (213 genes), hub genes exhibiting time-dependent expression (264 genes), and sets of transcription factors (TF) corresponding to the above sets of genes (74, 87, and 99 genes, respectively). Then, random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were constructed for wound age estimation from the above gene sets. The results estimated by transcription factors were all superior to the corresponding hub genes, with the transcription factor group of WGCNA performed the best, with average accuracy rates of 96% for three models' internal testing, and 91.7% for the highest external validation. This study demonstrates the advantages of the indicator screening system based on second-generation sequencing technology and transcription factor level for wound age estimation.