Traditional Linear Interpolation Research Articles

2D/3D Expression Generation Using Advanced Learning Techniques and the Emotion Wheel

Facial expression analysis is a critical component in numerous applications, ranging from human- computer interaction to digital character animation. Despite the availability of extensive datasets, most focus predominantly on basic emotions, limiting the expressiveness and applicability of models trained on them. This article introduces a novel approach to generating compound emotional expressions by leveraging the Emotion Wheel, a principle that captures the complex interrelations between basic emotions. Our method integrates the EMOCA (Emotion Driven Monocular Face Capture and Animation) [1] framework, which enhances 3D facial reconstruction by incorporating emotion recognition, stacking models [2, 54] with a sophisticated expression blending algorithm to synthesize nuanced 2D and 3D facial animations. Utilizing the VKIST dataset, which includes high-resolution facial images of Vietnamese individuals, we build a comprehensive database of emotion parameters. Through principal component analysis (PCA) and correlation-driven blending, our approach not only enhances the realism of generated facial expressions but also preserves the subtle nuances that are characteristic of human emotions. Experimental results demonstrate that our method consistently outperforms traditional linear interpolation techniques, producing more distinct and recognizable blended expressions. A user study further validates the naturalness and quality of the generated expressions, with average ratings indicating a strong preference for the proposed method over existing approaches. These findings suggest significant potential for improving emotional expressiveness in both static and dynamic digital character representations.

Multivariate time series anomaly detection: Missing data handling and feature collaborative analysis in robot joint data

The efficient operation of industrial robots relies on reliable anomaly detection systems, but the problem of missing data caused by sensor failures, data transmission errors, and system maintenance or updates in the industrial field poses a serious challenge to their accuracy and may lead to delays in recognizing critical faults, which in turn threatens production safety and causes economic losses. To address this problem, this paper constructs an Anomaly Detection Method with Combined Data Imputation and Feature Collaboration (AD-CIFC), which firstly employs a comprehensive imputation strategy to impute the missing data in order to better adapt to the natural flow of time series and complex patterns; then two graph attention mechanisms are proposed to be used in parallel to capture the relationship between nodes more comprehensively by introducing synergies, thus effectively utilizing the correlation between features to make the model more sensitive; finally, two collaborative learning models are proposed to be used in parallel to more comprehensively take into account the strong correlation between features in the data, and a collaborative loss function computation method is proposed, thus improving the overall detection accuracy and discrimination ability. Through anomaly detection experiments on industrial robots with different levels of missing joint data, it is demonstrated that the AD-CIFC model demonstrates a performance that matches or even outperforms other mainstream anomaly detection models in complete fault data tests, while the model performance using the comprehensive imputation strategy to impute in the joints is about 25 % higher on average compared to the model performance of traditional linear interpolation methods, and meanwhile, in the case of missing data at the minor to severe levels, the model still demonstrates robustness beyond traditional methods, which is very suitable for industrial robot production lines.

A High-Precision Planar NURBS Interpolation System Based on Segmentation Method for Industrial Robot

NURBS curve parameter interpolation is extensively employed in precision trajectory tasks for industrial robots due to its smoother performance compared to traditional linear or circular interpolation methods. The trajectory planning systems for industrial robots necessitate four essential functional modules: first, the spline curve discretization technique ensuring chord error compliance; second, the contour scanning technique for determining the maximum feasible feed rate for multi-constraint and multi-segment paths; third, the technique for achieving a smooth feed rate profile; and fourth, the continuous curve parameter interpolation technique. Therefore, this paper proposes a high-precision planar NURBS interpolation system for industrial robots. Firstly, a segmentation method for NURBS curves based on a closed-loop chord error constraint is proposed, which segments the original global NURBS curve into a collection of Bezier curves that strictly meet the chord error constraint. Secondly, a bidirectional scanning technique is presented to meet the joint space constraint, establishing an analytical mapping between the tool tip kinematic constraint and the joint kinematic constraint. Then, based on the traditional S-shaped feed rate profile, an adaptive algorithm with a displacement constraint is introduced, considering the real-time speed adjustment requirements of robots. Finally, a compensation interpolation strategy based on arc length parameterization is adopted to solve the accumulated error problem in parameter interpolation. The effectiveness of and potential for enhancing the quality of planar machining of the proposed planar NURBS interpolation system for industrial robots are validated through simulations and experiments. The results demonstrate the system’s applicability and accuracy, and its ability to improve planar machining quality.

Improvement Analysis of a Height‐Deviation Compensation‐Based Linear Interpolation Method for Multi‐Station Regional Troposphere

AbstractIn network real‐time kinematic positioning of multi‐reference station, the spatial and temporal distribution of tropospheric delay is affected by both horizontal and elevation. The traditional modeling strategy of regional troposphere takes more consideration of the horizontal factor, and the incomplete consideration of the elevation factor will lead to the problem of reduced modeling accuracy, especially in the face of the scene with large regional height deviation. Based on the traditional linear interpolation method (LIM), a simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposed. The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height deviation region are significantly improved. The method was verified based on six experimental subnets with large height deviations from a provincial continuously operating GNSS reference stations network in central China. The results showed that: For GPS satellite modeling, compared with the traditional LIM method, the average modeling accuracy improvement rate of HCLIM method is (84.5%, 75.5%, 59.3%, 26.7%) in the elevation angle range of (10–30°/30–40°/40–50°/50–90°). For BDS satellite, the average modeling accuracy improvement rate of HCLIM method in the above four elevation angles is (83.3%, 70%, 50%, 23.5%). For the positioning performance of user RTK, The horizontal positioning accuracy and RTK fixing rate were similar under the two methods, while HCLIM method showed only slight improvement. However, in the U direction, LIM method showed obvious systematic bias, while HCLIM method showed consistent positioning accuracy, which was improved to 82.8% compared with LIM method.

Radio map construction based on BERT for fingerprint-based indoor positioning system

Due to the heavy workload of RSS collection, the instability of WLAN signal strength and the disappearance of signals caused by complex indoor environments, the construction of radio map for wireless local area network (WLAN) fingerprint-based indoor positioning system is time-consuming and laborious. In order to rapidly deploy indoor WLAN positioning system, the bidirectional encoder representation from transformers (BERT) model is used to fill the missing signal in radio map and quickly build radio map. The radio map is imported into the BERT model in the form of natural language text, and the missing signal is filled by the BERT model. Since the number of input data in BERT model cannot exceed 512 words, the structure of BERT model is not suitable for WLAN signals with large data volume. Therefore, we redefine the model structure based on the original BERT model and fill in the missing signals in the radio map in parallel. In addition, the loss function is redefined. Except that each segment has a loss function, the weighted average value of all segment loss functions is defined as the total loss function. The experimental results show that the BERT model is better than the traditional linear interpolation method, compressed sensing algorithm and matrix completion algorithm in filling the missing signals in the fingerprint database, and the probability of error within 2 m reaches about 94%.

Automatic segmentation of tunnel lining defects based on multiscale attention and context information enhancement

To address the dual challenges of complex environmental interference and multiscale targets in deep learning-based tunnel lining defect identification, a novel segmentation algorithm, named Tunnel Lining Defect Segmentation with Multiscale attention and Context information enhancement (MC-TLD), is proposed. In MC-TLD, a context-enhanced feature encoder is designed to compensate the deficiency of Convolutional Neural Network to extract global context information fully, thus reducing the misdetection and omission caused by complex environmental interference. In addition, an atrous spatial pyramid pooling module with multiscale attention is designed to improve the feature extraction capability of the network for different scales of defects. Finally, the traditional linear interpolation upsampling method in the feature decoding module is replaced by parameter learnable DUpsampling to output more accurate pixel prediction results. The experimental results show that MC-TLD has higher segmentation accuracy than other comparison models, which is suitable for the task of tunnel lining multiscale defect identification in complex environments.

Transient analyses of wave propagations in nonhomogeneous media employing the novel finite element method with the appropriate enrichment function

An enriched finite element approach with appropriate enrichment function is proposed for the transient analyses of wave propagations in nonhomogeneous media. In present method, the traditional linear nodal interpolation functions for low order finite elements are enriched by the augmented enrichment function. For the purpose of improving the computation accuracy and reducing the numerical dispersion error, the polynomial bases and the trigonometric functions are utilized to form the enrichment functions. In addition, a simple and direct approach is introduced with the aim to tackle the possible linear dependence of the system equations. The numerical results show that the accuracy of the obtained numerical results can be markedly improved by the proposed method in comparison with the standard finite elements. What's more, it can be found that the calculated solutions from the proposed method with the appropriate temporal discretization scheme are able to monotonically converge to the exact solution in transient wave analysis. In consequence, it is possible to consistently increase the solution accuracy by directly decreasing temporal discretization step size. This valuable and important property enable the present approach to be particularly suitable to tackle the wave propagations in nonhomogeneous media.

A Conditional Generative Adversarial Network for Weather Radar Beam Blockage Correction

Missing or low-quality data regions usually happen to weather radars. One of the most common situations is beam blockage or partial beam blockage. Therefore, correction of weather radar observations that are partially or fully blocked is an indispensable step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments such as the western United States. In this article, we propose a deep learning framework based on generative adversarial networks (GANs) for restoring partial beam blockage regions in polarimetric radar observations using local and global contextual information. Due to the diverse precipitation types, blockage conditions, and ground information in different areas, two radars deployed in two different regions characterized by different precipitation types are used to demonstrate the proposed methodology. Both are S-band operational Weather Surveillance Radar – 1988 Doppler (WSR-88D): KFWS located in Fort Worth, northern Texas, and KDAX located in Davis, northern California. For training the GAN model, this article simulates the partial beam blockage situations by manually cropping observation sectors of both KDAX and KFWS radar data. The trained models were tested using independent precipitation events in Texas and California to demonstrate the model effectiveness in inpainting “missing" data. In addition, this paper cross-tested the data with different precipitation features to examine the generalization capacity of the beam blockage correction models. The beam blockage correction performance is also compared with a traditional linear interpolation approach. The results show that for both domains the continuity of precipitation observations is greatly improved after applying the deep learning-based inpainting approach. For the KFWS test data, some visible discrepancies exist between the results from models trained based on convective and stratiform precipitation events in Texas and California, respectively, yet both models outperform the traditional interpolation method. For the KDAX test data, both the model trained using the KFWS data from convective precipitation events in Texas and the model trained using KDAX data from stratiform precipitation events in California render a similar performance. Although ground truth is not available for the real blocked radar data, the repaired observations demonstrated a great potential for improved quantitative applications.

A high-precision non-uniform rational B-spline interpolator based on S-shaped feedrate scheduling

Parametric interpolation has been widely used in robot control because of its advantages over traditional linear or circular interpolation. The NURBS interpolation based on S-shaped feedrate scheduling can realize the smooth continuous motion of robots, but it also faces two challenges. One challenge is that when the NURBS curve is split into multiple blocks, if one or more blocks are too short, the final feedrate profile is often discontinuous. The other challenge is that the NURBS curve cannot be obtained entirely due to calculation errors. In this paper, a high-precision NURBS interpolator based on S-shaped feedrate scheduling is proposed. In this interpolator, a bidirectional velocity scanning scheme is developed to effectively handle the too short split blocks of the NURBS curve. Meanwhile, an optimization algorithm is proposed to ensure the precision of NURBS curves. The feasibility and advantages of the proposed method are verified through case studies.

Real-Time NURBS Interpolation under Multiple Constraints.

NURBS interpolation is superior to traditional linear or circular interpolation in terms of code size, surface quality, and machining efficiency. However, with the increasing demands for high-accuracy and efficient machining, NURBS interpolation has faced a growing number of challenges. Many researchers are actively involved in this field with great interest. Due to the special form of NURBS curve, there is a nonlinear relationship between its curve and arc length; feed fluctuations and mechanical shocks which are caused during the interpolation process will seriously affect the surface accuracy and quality of machined parts. To solve these problems, a real-time NURBS interpolation is proposed under multiple constraints (RNIC) in this paper. First, the formulas of the constrained feedrate under geometric errors, kinematic constraints, drive constraints, and contour errors are given. Then, the two stages for the proposed interpolation are established. The former stage is offline preprocessing stage, which aims to quickly find feedrate sensitive areas (FSAs), while the latter online stage is the real-time interpolation, which is responsible for smoothing the velocity. In the preprocessing stage, we utilized FSA scan module and feedrate adjustment module to detect the FSAs and adjust the feedrate at the start/end of each subsegment by a bidirectional scanning algorithm. Each segment contains acceleration and deceleration (some contains uniform speed) stages, which can be well matched with the processing process of acceleration and deceleration. Finally, according to the proposed method and the adaptive speed adjustment method, the simulation of a “butterfly-shaped” NURBS curve using the S-shaped ACC/DEC algorithm is carried out, which verifies the reliability and effectiveness of the proposed algorithm.

Robust Image-Based Water-Level Estimation Using Single-Camera Monitoring

This study proposes an automatic image water-level measurement system using a single camera. The inverse perspective mapping (IPM) technology is used to rectify an image to improve deviations because the optical axis of the camera and staff gauge are not orthogonal, causing a perspective distortion in the real environment. The initial position of the current watermark is first determined from the staff gauge’s image by applying basic digital image-processing technology. The system uses the image histogram of staff gauge’s region of interest to provide a reference region for determining accurate water levels from the preliminary watermark position. Furthermore, the proposed system can correctly and automatically determine the current water-level elevation by establishing the control points to convert the pixel distance to the real-world distance as well as through the setting of the reference water-level elevation. The images are shifted because of camera vibration, which will also shift the waterline coordinates. To prevent the image shift due to camera vibration from interfering with the accuracy of water-level measurement, we used the normalized cross correlation (NCC) technology with the proposed camera-vibration calibration scheme for removing the impact. The proposed distance calibration (DISCAL) module further optimizes the results of the images rectified using the IPM technology. The suggested automatic water-level measurement system can improve the traditional linear interpolation approach by >90%, and the deviation value of the real water level is < 0.5 cm, which is ideal for water-level measurement in confined spaces according to experimental results.

Continuous color model as a tool to improve speleothem age model development

Because they can archive a variety of geochemical proxies and be precisely and accurately dated with the U-Th decay series chronometer, stalagmites are widely used for paleoclimate reconstructions. However, limitations in the use of this chronometer arise because U-Th dating is analytically time consuming, expensive, and requires a relatively large sample size. These limitations restrict the number of absolute dates usually obtained, which can result in significant uncertainties in the age model and inhibit the ability to archive high resolution records of environmental variability, particularly in those stalagmites where there are variations in growth rate not constrained by U-Th dates. Here, we explore the relationship between stalagmite color and growth rate. Consequently, we evaluate the use of a simple, practically non-destructive approach to model the age-depth relationship of stalagmites using the sample color to provide a continuous record of growth rate. The method was developed by comparing high-resolution color images with pre-determined U-Th dates along the growth axes of seven stalagmites. The obtained results suggest that prior to dating, a color-derived, continuous growth rate model may be used to identify important changes in growth rate which may aid in the determination of the most efficacious locations for U-Th dating. Further, continuous color-derived interpolations between U-Th derived dates may be superior to traditional linear interpolation methods. Such an approach has the potential to greatly improve a researcher’s ability to efficiently choose sampling locations for more precise, albeit laborious and costly, U-Th dating.

Modified Interpolation Method of Multi-Reference Station Tropospheric Delay Considering the Influence of Height Difference

In network real-time kinematic (NRTK) positioning, atmospheric delay information is critical for generating virtual observations at a virtual reference station (VRS). The traditional linear interpolation method (LIM) is widely used to obtain the atmospheric delay correction. However, even though the conventional LIM is robust in the horizontal direction of the atmospheric error, it ignores the influence of the vertical direction, especially for the tropospheric error. If the height difference between the reference stations and the rover is large and, subsequently, tropospheric error and height are strongly correlated, the performance of the traditional method is degraded for tropospheric delay interpolation at the VRS. Therefore, considering the height difference between the reference stations and the rover, a modified linear interpolation method (MLIM) is proposed to be applied to a conventional single Delaunay triangulated network (DTN). The systematic error of the double-differenced (DD) tropospheric delay in the vertical direction is corrected first. The LIM method is then applied to interpolate the DD tropospheric delay at the VRS. In order to verify the performance of the proposed method, we used two datasets from the American NOAA continuously operating reference stations (CORS) network with significant height differences for experiments and analysis. Results show that the DD tropospheric delay interpolation accuracy obtained by the modified method is improved by 84.1% and 69.6% on average in the two experiments compared to the conventional method. This improvement is significant, especially for low elevation satellites. In rover positioning analysis, the traditional LIM has a noticeable systematic deviation in the up component. Compared to the conventional method, the positioning accuracy of the MLIM method is improved in the horizontal and vertical directions, especially in the up component. The accuracy of the up component is reduced from tens of centimeters to a few centimeters and demonstrates better positioning stability.

Tests and Finite Element Simulation of Yield Anisotropy and Tension-Compression Strength Difference of an Extruded ZK60 Mg Alloy

Multiroute yield experiments were carried out on extruded ZK60 magnesium alloy samples under 200 °C and strain rate of 10−2 s−1 by a thermomechanical simulator. A hypoelastoplastic large deformation constitutive relationship was employed to simulate large deformation yield subjected to uniaxial loading, biaxial loading, loading-reverse loading, and orthogonal loading in the finite element (FE) software with user subroutine. The results showed the following: (1) As the accumulative plastic strain increased, the convex yield surface in the 2D stress space gradually expanded or shrank, rotated, and distorted from the approximate ellipse at low accumulative plastic strain. At the same accumulative plastic strain, uniaxial tensile or compressive yield values along different directions were not the same. In addition, the tensile yield value differed considerably from the compressive one. The large deformation yield of ZK60 magnesium alloy showed significant anisotropy, tension-compression strength difference, and evolution effect. (2) FE simulations based on the hypoelastoplastic constitutive relationship could accurately capture the strongly evolving asymmetric yield behavior under complex loading routes. The stress-strain relationships and hardening rates were in better accordance with the experimental results and reflected the yield behavior more realistically compared to simulations without the evolution effect or with traditional linear interpolation. Deformation at low temperature and high strain rate is important to save process costs and improve processing efficiency, but it may cause evolving asymmetric yield during large deformation. It is believed that the simulation approach used herein is reliable for the prediction and optimization of severe plastic deformation processes of hexagonal close-packed (HCP) alloys.

New algorithm to calculate the cutter location points (CL) optimizing the chordal tolerance for 2 ½ axis milling of free form surfaces

Even though the high order polynomial can be used to describe a free form tool path for CNC milling, traditional linear interpolation (G01) is the most used today. As investigated in a previous work, different CAM systems calculate different points (CC) to mill the same surface under the same tolerance and cutting conditions. The current work presents a new approach to generate NC codes optimizing the use of the chordal tolerance for 2½ milling. The experimental results shown that the NC codes generated by the proposed method reduced in about 60% the milling time by reducing the CC points, without losing the accuracy.

A Preliminary Study on the Use of the Linear Regression Method for Multigroup Cross-Section Interpretation

Computational modeling and simulations are widely used for evaluation of the performance and safety features of innovative nuclear reactor designs. Multigroup-based deterministic neutronics codes are often employed in these reactor design calculations because they can provide fast predictions of the neutron flux distribution and other neutronics characteristic parameters. Nevertheless, providing accurate multigroup cross sections for deterministic codes is an onerous job, which makes establishing an exhaustive cross-section library computationally prohibitive. Partly because of these reasons, multigroup neutron cross sections are normally stored only at certainty state points in the data library of these deterministic codes, and linear interpolation methodology is commonly utilized to estimate the cross sections at unknown states. However, the applicability of linear interpolation is limited, and the precision of its results is moderate. In this paper, we discuss a preliminary feasibility study that we performed on providing more precise multigroup cross sections for deterministic neutronics codes by using the linear regression methodology. Compared to the traditional linear interpolation method, the linear regression approach principally showed improved computational efficiency considering the use of more data in the cross-section library, and constructed hypothesis functions for the responses of interest with a higher order of accuracy. In this study, a case study on Lightbridge Corporation’s metallic fuel element was carried out to demonstrate the feasibility and advantages of linear regression in multigroup cross-section interpretation. A reference cross-section library was established through calculations conducted with the Monte Carlo neutronic code Serpent. Because of the preliminary nature of this feasibility study, only the macroscopic total cross section is considered. Linear interpolation and linear regression were both used to estimate cross sections at unknown states based on the data available in the library. By comparing the performance of both methodologies, we demonstrated that the linear regression methodology achieved wider applicability and better precision in cross-section interpretation. Moreover, the linear regression process was finished within 15 s using a single processor core, which indicated that the additional computational burden brought by the implementation of linear regression methodology in the task was acceptable.

Dynamic Mapping of Along-Track Ocean Altimetry: Performance from Real Observations

AbstractThe dynamic optimal interpolation (DOI) method merges altimetric sea surface height (SSH) data into maps that are continuous in time and space. Unlike the traditional linear optimal interpolation (LOI) method, DOI has the advantage of considering a nonlinear temporal propagation of the SSH field. DOI has been successfully applied to along-track pseudo-observations in observing system simulation experiments (OSSEs), demonstrating a reduction in interpolation error in highly turbulent regions compared to LOI mapping. In the present study, we further extend the validation of the DOI method by an observing system experiment (OSE). We applied and validated the DOI approach with real nadir-altimetric observations in four regional configurations. Overall, the qualitative and quantitative assessments of these realistic SSH maps confirm the higher level of performance of the DOI approach in turbulent regions. It is more of a challenge to outperform the conventional LOI mapping in coastal and low-energy regions. Validations against LOI maps distributed by the Copernicus Marine Environment Monitoring Service indicate a 10%–15% increase in average performance and an improved resolution limit toward shorter wavelengths. The DOI method also shows improved mesoscale mapping of intense jets and fronts and reveals new eddies with smoother trajectories.

Phong deformation

Physics-based simulations of deforming tetrahedral meshes are widely used to animate detailed embedded geometry. Unfortunately most practitioners still use linear interpolation (or other low-order schemes) on tetrahedra, which can produce undesirable visual artifacts, e.g., faceting and shading artifacts, that necessitate increasing the simulation's spatial resolution and, unfortunately, cost. In this paper, we propose Phong Deformation , a simple, robust and practical vertex-based quadratic interpolation scheme that, while still only C 0 continuous like linear interpolation, greatly reduces visual artifacts for embedded geometry. The method first averages element-based linear deformation models to vertices, then barycentrically interpolates the vertex models while also averaging with the traditional linear interpolation model. The method is a fast, robust, and easily implemented replacement for linear interpolation that produces visually better results for embedded deformation with irregular tetrahedral meshes.

Combination of two methodologies, artificial neural network and linear interpolation, to gap-fill daily nitrous oxide flux measurements

Continuous N2O flux acquisition is crucial to enrich our knowledge of the complex mechanisms underlying the annual greenhouse gas budget and to refine their estimation. N2O flux measurement methodologies at high temporal resolution, i.e. micro-meteorology methodologies, are still scarce and may exacerbate the lack of important data, especially during the night if the required turbulent conditions are not met. The static and automated chamber methodologies also lead to numerous gaps in a time series due to low sampling frequency, hardware malfunctions, chambers removal during field operations or filtering of low-quality measurements. There is a strong need to define a generic and realistic N2O flux gap-filling methodology, especially since there is no consensus on the methodology to be used.In this study, we investigated the effect of using either the traditional linear interpolation methodology alone, either an Artificial Neural Networks (ANN) methodology alone or the combination of both on gap-filled daily N2O flux dynamics and annual budget. All three methodologies were tested on daily N2O flux time series measured with automated chambers over 5 years from 2012 to 2016 on a southwestern France crop site following a wheat – maize rotation.On average over the studied period, the results showed better statistical scores using the ANN methodology alone than using the linear interpolation methodology alone, with R² and RMSE of 0.84 and 12.4 gN ha−1 d−1 and of 0.68 and 17.4 gN ha−1 d−1, respectively. However, whereas the use of ANN methodology reproduced well high measured N2O fluxes, it induced overestimation on low measured N2O fluxes where the use of the linear interpolation methodology was relevant. To overcome that issue and to take advantages of both methodologies we propose a new one which mixes both. On average, using the mixed methodology did not increase statistical scores compared to the ANN one, with a R² and a RMSE of 0.84 and 12.4 gN ha−1 d−1 respectively for both, but for periods with low measured N2O fluxes using the mixed methodology improved the statistical scores and the observed daily flux dynamic.

Replicating Advanced Detection using Low Ping Frequency Probe Vehicle Trajectory Data to Optimize Signal Progression

Probe vehicle trajectory data has the potential to transform the current practice of traffic signal optimization. Current scalable trajectory data is limited in both the penetration rate and the ping frequency, or the length of time between vehicle waypoints. This paper introduces a methodology to create binary vehicle trajectories which can be used in a neural network to predict when vehicles will arrive at a virtual detector. The methodology allows for vehicles with ping frequencies of up to 60 s to be utilized for the optimization of offsets at signalized intersections. A nine-signal corridor in west Michigan was used to test the proposed methodology. The neural network was compared to traditional linear interpolation strategies and found to improve the root mean squared error of the arrival times by up to 6.18 s. Using the virtual detector data stacked over time to optimize the offsets of the corridor resulted in 77% of the benefit of an offset optimization performed with continuously collected high resolution signal controller data. In the era of big data, this alternative approach can assist with the large-scale implementation of traffic signal performance measures for improved operations.