Displacement Estimation Research Articles

A lane-level localization method via the lateral displacement estimation model on expressway

The gradual proliferation of high-definition (HD) maps has played a pivotal role in the advancement of intelligent vehicles. However, a considerable number of vehicles still face limitations in harnessing HD maps for high-precision navigation due to the absence of lane-level localization information. To address this challenge, this paper presents a lightweight lane-level localization method based on ubiquitous low-cost on-board vehicle cameras and Global Navigation Satellite System (GNSS) receivers. Initially, the line anchor-based feature refinement network is combined with the DeepSort-based lane tracking algorithm to detect and track the lanes in the image sequences. Subsequently, a lateral displacement estimation (LDE) model is proposed to establish the relationship between the vehicle's lateral displacement value in the ego lane and the slope of the lanes on both sides. Moreover, a lateral motion estimation (LME) method is employed to track the motion of the vehicle across multiple lanes. Finally, a lane-level map matching method is proposed based on the GNSS data and lateral displacement information. In the experimental section, three distinct types of dashcams have been installed in a vehicle, and high-precision positioning equipment is utilized as the ground truth for algorithm validation. The experimental results indicate that the proposed method can be effectively deployed in vehicles, enabling lane-level vehicle positioning on expressway. The code has been open-sourced and can be accessed at https://codeocean.com/capsule/6640813/.

Thin Ice Lithospheres and High Heat Flows on Europa From Large Impact Structure Ring‐Graben

AbstractCraters are probes of planetary surface and interior properties. Here we measure depths, widths, and spacing of circumferential ring‐graben surrounding the two largest multiring impact structures on Europa, Tyre and Callanish. We estimate formation conditions including the ice shell structure. The radial extension necessary to form these graben is thought to be caused by asthenospheric drag of warmer, more ductile ice and/or water flowing toward the excavated center of the crater, under a brittle‐elastic lithospheric lid. Measurements of graben depths from stereo‐photoclinometric digital elevation models result in estimates of displacement, strain, and stress experienced by the ice shell. Graben widths are used to estimate the intersection depth of the bounding normal faults, a quantity related to the brittle‐ductile transition depth that approximates elastic shell thickness during crater collapse. Heat flows at the time of crater formation as well as ice lithosphere and total shell thickness are thus also constrained. Average widths and depths tend to decrease with increasing distance from the structure center, while inter‐graben spacing generally increases. Varied assumptions yield plausible total conductive ice shell thickness estimates between 4–8 and 2.5–5 km for Tyre and Callanish, respectively, and heat flows of ∼70–115 (±30) mW m−2 for realistic thermal conductivities, consistent with other geophysical estimates for Europa. Higher heat flows are consistent with thin (≲10 km), conductive ice shells and impact breaching, or penetration of the stagnant lid for a convecting ice shell. Callanish, geologically younger, formed in a time or region of greater heat flow than Tyre.

Pose and Color-Gamut Guided Generative Adversarial Network for Pedestrian Image Synthesis.

Tremendous transfer requirements in pedestrian reidentification (Re-ID) tasks have greatly promoted the remarkable success in pedestrian image synthesis, to relieve the inconsistency in poses and lighting. However, existing approaches are confined to transferring in a particular domain and are difficult to combine, since pose and color variables locate in two independent domains. To facilitate the research toward conquering this issue, we propose a pose and color-gamut guided generative adversarial network (PC-GAN) that performs joint-domain pedestrian image synthesis conditioned on certain pose and color-gamut through a delicate supervision design. The generator of the network comprises a sequence of cross-domain conversion subnets, where the local displacement estimator, color-gamut transformer, and pose transporter coordinate their learning pace to progressively synthesize images in desired pose and color-gamut. Ablation studies have demonstrated the efficacy and efficiency of the proposed network both qualitatively and quantitatively on Market-1501 and DukeMTMC. Furthermore, the proposed architecture can generate training images for person Re-ID, alleviating the data insufficiency problem.

Integrated force and displacement sensing in an untethered dielectric elastomer actuator with a piezoresistive element

Dielectric elastomer actuators, owing to their fully electrical control and silent operation, are becoming increasingly popular for the development of terrestrial and underwater mobile robots with versatile locomotion capabilities. It is essential to embed the ability to sense their state and external interactions in these robots to facilitate the development of future autonomous capabilities. However, sensorizing dielectric elastomer actuators for untethered robotic applications is challenging due to their use of high voltage and the nonlinear mechanics of the elastomers utilized in them. To address this challenge, we developed a novel technique based on embedded piezoresistive sensing and high voltage feedback to simultaneously estimate the actuator displacement and external force in a fully untethered actuator driven by a miniature low-cost voltage amplifier. A data-driven regression model has been developed to accurately estimate force and displacement from the measured data. Validation tests conducted on three actuators demonstrate promising results. We achieve RMSE values as low as 29.736 mN for force estimation and 0.023 mm for displacement estimation in the zero-voltage condition, where the actuator is subjected to a triangular wave with a mechanical frequency of 0.1 Hz and an amplitude of 3 mm. Additionally, we have realized fully untethered operation by employing a power source, small-size voltage amplifier, microcontroller, and wireless connectivity module embedded in a compact form-factor. This work presents a significant advancement in soft robotics, offering a reliable and cost-effective solution for future autonomous robotic systems based on high-voltage dielectric elastomer actuators.

Method of co-seismic fault displacement estimate based on peak ground acceleration

Based on the results of the seismic hazard survey in Yunnan Province, four exceedance probabilities of peak ground acceleration were converted into earthquake magnitudes using the empirical relationship between epicentral intensity and magnitude. By using logistic regression, earthquake magnitude, source depth, cover layer thickness, and fault type were used as independent variables to calculate the probability of co-seismic displacement for different earthquake magnitudes. Using the magnitude-displacement empirical model in the southwestern region of China, horizontal and vertical displacements were obtained with a 95 % confidence level. The results show that all Holocene faults have the ability to produce surface displacement, but from the perspective of probabilistic hazard, the probability of surface rupture in the short term is low, while the probability of surface rupture for each fault significantly increases over a long time scale. Buildings with different service lives should adopt different avoidance strategies, and buildings with shorter service lives (<50a) can consider not avoiding active faults.

Permanent displacement estimation method based on variational mode decomposition

In the near-field area of strong earthquakes, the information recorded by a strong seismometer includes ground motion information and complex low-frequency noise. This noise results in the presence of an “extra” acceleration component in the record that is independent of ground motion, and these components are integrated into the velocity and displacement time history to cause offset. In order to solve this problem, this paper adopts a baseline correction method based on variational mode decomposition (VMD), which decomposes seismic signals into modal functions of different frequencies and then extracts the low-frequency modal functions. It is worth noting that these low-frequency modal function components contain linear trend terms, a property that allows us to perform baseline corrections efficiently. The results show that the permanent displacement obtained by this method is in good agreement with the coseismic displacement observed by GPS stations nearby. This method provides an efficient approach for estimation permanent displacement which helps revealing the focal mechanism of an earthquake.

Estimation of GPS-observed ocean tide loading displacements with an improved harmonic analysis in the northwest European shelf

Estimation of GPS-observed ocean tide loading displacements with an improved harmonic analysis in the northwest European shelf

Formulation of a similarity law for static and low-velocity collision experiments for beam in gravitational field

Scaled testing offers a financially viable and pragmatic methodology for ascertaining the performance attributes of full-scale civil engineering structures. While the necessity for a similarity law to guide scaled experiments is well-understood, few studies has been directed toward formulating a similarity law that comprehensively addresses both geometrical and mechanical equivalences in experiments where material properties and gravitational factors remain constant between the scaled and full-scale tests. Our prior work has introduced a similarity law tailored for scaled experiments conducted within gravitational fields, utilising materials that bear a resemblance to those used in full-scale structures; however, the scope of applicability was restricted to elastic responses. This study introduces a method for approximating the elasto-plastic displacement in full-scale beams based on data gathered from scaled tests, utilising an integrated approach that combines the principles of similarity and equal-energy laws as formulated by Veletsos et al. Validation of this innovative method was achieved through static three-point bending tests using circular cross-sectional steel beams, with tests conducted at six varying similarity ratios, ranging from 0.26 to 1.00. The empirical results demonstrated that the estimated displacement for the full-scale structure exhibited an error margin of approximately 10 %. When an impact analysis involving a steel ball was carried out on a steel cantilever beam, the residual displacement estimates were found to be significantly imprecise when plastic deformations occurred at multiple locations within the structure. In conclusion, this research contributes to foundational advancements in the field of structural modelling through the introduction of a new methodological approach.

Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography.

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1 -MechSOUL ( L1 -norm-based MechSOUL), which optimize L2 - and L1 -norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1 -MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1 -MechSOUL at https://code.sonography.ai.

Comparative Study of Ultrasound Tissue Motion Tracking Techniques for Effective Breast Ultrasound Elastography

Breast cancer is the most common and deadly cancer in women, where early detection is of the utmost importance as survival rates decrease with the advancement of the disease. Most available methods of breast cancer screening and evaluation lack the ability to effectively differentiate between benign and malignant lesions without a biopsy. Ultrasound elastography (USE) is a cost-effective method that can potentially provide an initial malignancy assessment at the bedside. One of the challenges, however, is the uncertainty of tissue displacement data when performing USE due to out-of-plane movement of the tissue during mechanical stimulation, in addition to the computational efficiency necessary for real-time image reconstruction. This work presents a comparison of four different theoretically sound displacement estimators for their ability in tissue Young’s modulus reconstruction level with an emphasis on quality-to-runtime ratio to determine which estimators are most suitable for real-time USE systems. The methods are known in literature as AM2D, GLUE, OVERWIND, and SOUL methods. The effectiveness of each method was assessed as a stand-alone method or in combination with a strain field enhancement technique known as STREAL, which was recently developed using tissue mechanics-based regularization. The study was performed using radiofrequency US data pertaining to in silico and tissue mimicking phantoms in addition to clinical data. This data was used to generate tissue displacement fields employed to generate axial and lateral strain images before Young’s modulus images were reconstructed. The study indicates that the AM2D displacement estimator, which is an older and computationally less involved method, along with a tissue-mechanics-based image processing algorithm, performs very well, with high CNR, SNR, and preservation of tumor heterogeneity obtained at both strain and stiffness image levels, while its computation run-time is much lower compared to other estimation methods. As such, it can be recommended for incorporation in real-time USE systems.

Harmonic Motion Imaging-Guided Focused Ultrasound Ablation: Comparison of Three Focused Ultrasound Interference Filtering Methods

Harmonic motion imaging (HMI) is an acoustic radiation force-based elasticity imaging technique, which can be used to monitor changes in tissue mechanical properties caused by focused ultrasound (FUS)-induced thermal ablation. In conventional HMI, the amplitude-modulated FUS sequence and imaging pulse are transmitted simultaneously. With this method, the high-amplitude FUS signal must be separated from the imaging data for tissue displacement estimation. Frequency domain notch filtering and bandpass filtering were previously used to filter FUS interference from imaging data. However, FUS interference becomes more pronounced at the increased intensities and durations necessary for thermal ablation, rendering frequency domain filtering challenging. In this study, three methods for FUS interference filtering during HMI-guided FUS ablation (HMIgFUS) were compared. The methods were notch filtering; interleaved HMIgFUS, with interleaved FUS and imaging pulses; and FUS-net, a convolutional neural network-based U-Net autoencoder developed by our group for FUS interference filtering in radiofrequency data. FUS-net was applied here for the first time for the purpose of ablation displacement monitoring. The three filtering methods were tested during 20 HMIgFUS acquisitions in an ex vivo canine liver using a range of peak positive pressures from 11 to 18 MPa and durations ranging from 60 to 180 seconds. The B-mode mean squared errors (MSEs) and displacement amplitude contrast-to-noise ratios (CNRs) of the three methods were calculated and compared. The interleaved method for HMIgFUS was found to be significantly robust in avoiding FUS interference in all tested cases for FUS ablation monitoring, especially cases with high FUS pressure and long durations, as opposed to traditional notch filtering and FUS-net filtering. CNRs obtained from displacement amplitude maps in the interleaved data set were significantly higher in all cases than those obtained from the notch filtered and FUS-net data sets. There was not a significant trend in displacement CNR between the FUS-net and notch filtered data sets. However, B-mode MSE was found to be significantly higher when comparing the FUS-net and interleaved data sets as opposed to the notch filtered and interleaved data sets, suggesting further potential of FUS-net as an FUS interference filtering method. These findings indicate the robustness of interleaved HMIgFUS in avoiding FUS interference during HMIgFUS monitoring and the advantages, limitations and future potential of FUS-net and notch filtering.

Large-time asymptotic behavior for the classical thermoelastic system

In this paper, we study the classical thermoelastic system with Fourier's law of heat conduction in the whole space Rn when n=1,2,3, and obtain asymptotic profiles of its elastic displacement when t is large. We discover optimal growth estimates of the elastic displacement when n=1,2, whose growth rates coincide with those for the free wave model, whereas when n=3 the optimal decay rate is related to the Gaussian kernel. Furthermore, the large-time optimal leading term is firstly introduced by the combination of diffusion-waves, the heat kernel and singular components. We also illustrate a second-order profile of solution by diffusion-waves as a by-product. These results imply that the thermoelastic system has the wave-structure for large t in the one- and two-dimensional cases only.

Dynamic displacement estimation of structures using one-dimensional convolutional neural network

For large infrastructures, dynamic displacement measurement in structures is an essential topic. However, limitations imposed by the installation location of the displacement sensor can lead to measurement difficulties. Accelerometers are characterized by easy installation, good stability and high sensitivity. For this regard, this paper proposes a structural dynamic displacement estimation method based on a one-dimensional convolutional neural network and acceleration data. It models the complex relationship between acceleration signals and dynamic displacement information. In order to verify the reliability of the proposed method, a finite element-based frame structure was created. Accelerations and displacements were collected for each node of the frame model under seismic response. Then, a dynamic displacement estimation dataset is constructed using the acceleration time series signal as features and the displacement signal at a certain moment as target. In addition, a typical neural network was used for a comparative study. The results indicated that the error of the neural network model in the dynamic displacement estimation task was 9.52 times higher than that of the one-dimensional convolutional neural network model. Meanwhile, the proposed modelling scheme has stronger noise immunity. In order to validate the utility of the proposed method, data from a real frame structure was collected. The test results showed that the proposed method has a mean square error of only 5.097 in the real dynamic displacement estimation task, which meets the engineering needs. Afterwards, the outputs of each layer in the dynamic displacement estimation model are visualized to emphasize the displacement calculation process of the convolutional neural network.

Adjusting expected deaths for mortality displacement during the COVID-19 pandemic: a model based counterfactual approach at the level of individuals

BackgroundNear-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed “harvesting” in the literature.MethodsWe present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a “ball and urn” model.ResultsAmong the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy.ConclusionOur results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement – both in its length and prevalence – by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally.

Displacement Estimation of a Skyscraper during Super Typhoon Mangkhut Based on Limited Measurements of Accelerations and Strains

Displacement Estimation of a Skyscraper during Super Typhoon Mangkhut Based on Limited Measurements of Accelerations and Strains

A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction.

In real-time dynamic reconstruction, geometry and motion are the major focuses while appearance is not fully explored, leading to the low-quality appearance of the reconstructed surfaces. In this article, we propose a lightweight lighting model that considers spatially varying lighting conditions caused by self-occlusion. This model estimates per-vertex masks on top of a single Spherical Harmonic (SH) lighting to represent spatially varying lighting conditions without adding too much computation cost. The mask is estimated based on the local geometry of a vertex to model the self-occlusion effect, which is the major reason leading to the spatial variation of lighting. Furthermore, to use this model in dynamic reconstruction, we also improve the motion estimation quality by adding a real-time per-vertex displacement estimation step. Experiments demonstrate that both the reconstructed appearance and the motion are largely improved compared with the current state-of-the-art techniques.

Discovery, Quantitative Recurrence, and Inhibition of Motion-Blur Hysteresis Phenomenon in Visual Tracking Displacement Detection.

Motion blur is common in video tracking and detection, and severe motion blur can lead to failure in tracking and detection. In this work, a motion-blur hysteresis phenomenon (MBHP) was discovered, which has an impact on tracking and detection accuracy as well as image annotation. In order to accurately quantify MBHP, this paper proposes a motion-blur dataset construction method based on a motion-blur operator (MBO) generation method and self-similar object images, and designs APSF, a MBO generation method. The optimized sub-pixel estimation method of the point spread function (SPEPSF) is used to demonstrate the accuracy and robustness of the APSF method, showing the maximum error (ME) of APSF to be smaller than others (reduced by 86%, when motion-blur length > 20, motion-blur angle = 0), and the mean square error (MSE) of APSF to be smaller than others (reduced by 65.67% when motion-blur angle = 0). A fast image matching method based on a fast correlation response coefficient (FAST-PCC) and improved KCF were used with the motion-blur dataset to quantify MBHP. The results show that MBHP exists significantly when the motion blur changes and the error caused by MBHP is close to half of the difference of the motion-blur length between two consecutive frames. A general flow chart of visual tracking displacement detection with error compensation for MBHP was designed, and three methods for calculating compensation values were proposed: compensation values based on inter-frame displacement estimation error, SPEPSF, and no-reference image quality assessment (NR-IQA) indicators. Additionally, the implementation experiments showed that this error can be reduced by more than 96%.

RingVKB: A Ring-Shaped Virtual Keyboard Using Low-Cost IMU

Wearable devices have been important components for ubiquitous computing. However, text input remains challenging on wearables due to the lack of a physical keyboard. In this paper, we propose a novel ring-shaped virtual keyboard system named RingVKB for convenient text input using low-cost IMUs available on any wearables. At the core of RingVKB are two novel designs: 1) A circular keyboard layout with 12 equal sectors, which assembles all common keys on classical keyboards while allowing users to type with only one finger effectively, and 2) an error control algorithm that calculates the relative displacement of keystrokes from the noisy IMU sensor data. The two components, coupled together, enable high-accuracy and efficient text input for ubiquitous scenarios.We implement RingVKB using a small device consisting of a microcontroller and a MEMS sensor, which can be attached to the user's index finger.Experimental results show that RingVKB can effectively improve the relative displacement estimation accuracy, and achieves an overall keystroke recognition accuracy of 93% for 25 key positions. A user study also shows that RingVKB is easy to learn and use. Using only low-cost IMU sensors, RingVKB provides a virtual keyboard solution that can be widely adopted on wearables.

Structural displacement estimation by a hybrid computer vision approach

Structural displacement is an important indicator of structural safety in structural health monitoring (SHM). As one of the vision-based methods, optical flow can provide displacement measurement of pixels in images. However, estimating structural displacement by predicting the optical flow between the current frame and the initial frame may be subject to limited accuracy due to environmental variations. Alternatively, calculating structural velocity by predicting the optical flow of adjacent frames and integrating to obtain displacement may introduce displacement drift attributable to cumulative errors. This study proposes a hybrid structural displacement estimation method to eliminate the effects of environmental variations and cumulative errors. In comparison to the existing methods, the novelty of the proposed method is to effectively integrate deep learning-based dense optical flow and correlation template matching (CM), for achieving both high accuracy and improved robustness. Deep learning-based dense optical flow was used for optical flow prediction between adjacent frames through correlation calculations and iterative updating to obtain the structural velocity. Pyramid-accelerated CM was employed to locate the regions of interest (ROI) of the structure in each frame, and the structural displacement was then estimated by counting temporal changes in these locations. By fusing estimated structural velocity and displacement using the Kalman filter, optimized structural displacement results were obtained, and temporal cumulative errors using dense optical flow could be eliminated. The proposed method was validated in an indoor shaking table test of a three-story reinforced concrete structure, and an outdoor shaking table test of a cold-formed steel wall system. The results indicated that the proposed method reduced the root mean square error of estimated displacement by over 89% compared with dense optical flow and by over 36% compared with CM. Furthermore, the proposed method was able to process 1080p high-definition images at a rate of 5.43 frames per second, indicating its high efficiency for applications.

Assessment of seismic inelastic displacement profiles of steel MRFs by means of modal behavior factors

A new method for the estimation of inelastic displacements of steel moment resisting frames (MRFs) under strong seismic ground motions is presented. The method comes as an extension of previous work of the authors from reinforced concrete to steel MRFs. Key-elements of the present method are the modal behavior (strength reduction) factors, accounting for the first four modes of significance. These factors are expressed in terms of natural period, interstorey drift ratio and/or plastic rotation, soil type and performance level. These factors are multiplied by the corresponding elastic modal displacements to generate the inelastic modal displacement profile. Eventually, the inelastic displacements can be calculated after combining the modal responses through any combination rule (e.g. SRSS). The results of the presented method are compared with that of the well-known equal displacement rule and three others and attested through nonlinear time history analyses.