Optimal Position of Cameras Design in a 4D Foot Scanner

Tajdari, Farzam; Eijck, Christiaan; Kwa, Felix; Christiaan, Versteegh,; Huysmans, Toon; Song, Yu

doi:10.1115/detc2022-89145

Cited by 6 publications

(6 citation statements)

References 41 publications

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“…In the present study, the PLI (Phase Lag Index) was utilized to investigate the phase synchronization relationships in brain oscillations during a state where the phases of two oscillators were coupled. Phase synchronization refers to the simultaneous occurrence of phases despite their 5 of 16 non-coherence in amplitude [56][57][58]. The foundations and theory of this method have been further examined in the following sections.…”

Section: Estimate Of Correlationmentioning

confidence: 99%

Exploring Temporal Discrimination of Visual Events though EEG-Based Functional Connectivity Analaysis of Brain Regions

Khoonbani,

Ramezanian

2023

Preprint

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To investigate the intricate dynamics of brain activity when interacting with a dynamic environment, it is imperative to continuously generate and update expectations regarding forthcoming events and their corresponding sensory and motor responses. This study aims to explore the interconnectivity in time perception across predictable and unpredictable conditions. The necessary data for this study were acquired from EEG signals, sourced from an existing database that involved an experiment conducted on a group of healthy participants. The individuals were subjected to two distinct conditions, predictable and unpredictable, encompassing various time delays. The functional connectivity between brain regions was estimated using a method known as the phase lag index. This method was employed to discern disparities in time perception between the two conditions. A comprehensive comparison of the two conditions across different delays demonstrated noteworthy variations, particularly in the gamma, beta, and theta frequency bands. The differences between delays were more pronounced in the predictable condition. Subsequently, an in-depth analysis was conducted to scrutinize the dissimilarities between the conditions within each delay. Notably, significant differences were observed across all delays. In the unpredictable condition, an increase in connectivity was detected within the alpha band during the 400-ms delay, specifically between occipital and temporal regions. Moreover, the mean connectivity in the unpredictable condition surpassed that of the predictable condition. In the delta band, distinct connectivity patterns were observed across different delays, involving connections between central and frontal regions. Specifically, a heightened connectivity between central and prefrontal regions was noted during the 83-ms delay. Notably, the right hemisphere of the prefrontal cortex played a vital role in time perception. Furthermore, a decline in connectivity across the delta, theta, and beta bands was observed in both conditions during the longest delay (800 ms) when compared to other delays

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Section: Estimate Of Correlationmentioning

confidence: 99%

Exploring Temporal Discrimination of Visual Events though EEG-Based Functional Connectivity Analaysis of Brain Regions

Khoonbani,

Ramezanian

2023

Preprint

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“…3.2, we have a low-quality time-series 3D point clouds of the spine. Here, we use each of the reconstructed spines in each time interval as a template for the mesh registration method explained in Appendix B, which gives us a mesh morphing 4D (3D + time) [51,52] data of the spine.…”

Section: Volumetric Mesh Generationmentioning

confidence: 99%

Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Tajdari

Shirzadian

et al. 2022

Engineering with Computers

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Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient's initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework. The proposed technique is utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis. The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. Using minimal user input, the snake parameters are calibrated and automatically computed over the dataset, resulting in fast image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point mesh registration technique is then used to establish a mesh morphing approach and creates a 3D atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. Moreover, the proposed bio-informed deep learning network with the modified bone growth model achieves competitive or even superior performance against other state-of-the-art learning-based methods.

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“…Thus, the optimal problem is finding a set of optimal design parameters l i and u i such thatsubject toand with a conditional constraint ofTwo conditional constraints are proposed to lead the optimization process towards the farthest maximum point ( z = 0.46 m) and farthest minimum point ( z = 0.3 m) locations that the end-effector should travel from its initial location ( z 0 = 0.38 m). By applying the algorithm in Lagarias et al (1998) such as Tajdari et al (2022a), the optimal parameters are designed, and for each of the constraints, the minimum values are obtained, as reported in Table 1(b). Thus, the maximum of the optimal values for each of the parameters is selected to make sure that both the constraint conditions are covered, and then u i = 0.11( m ) and l i = 0.404( m ).…”

Section: Equation Of Motionmentioning

confidence: 99%

Adaptive time-delay estimation and control of optimized Stewart robot

Tajdari

2022

Journal of Vibration and Control

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Aiming at a more efficient and accurate performance of parallel manipulators in the existence of complex kinematics and dynamics, a robust generalizable methodology is proposed here for an integrated 6-DOF Stewart platform with rotary time-delayed actuators torque control. The suggested method employs a time-delay linear–quadratic integral regulator with online artificial neural network gain adjustment. The unknown time-delay is estimated through a novel robust adaptive estimator. The global asymptotic stability of the estimator is proved via a Lyapunov function. The controller is developed in MATLAB software and implemented on the robot designed in ADAMS software to ensure that the real-time tracking error of a nonlinear system with an unknown time-delay is kept to a minimum. The sensitivity of the controller to the parameter choices is studied via implementing the controller in ADAMS software and is validated by investigating the performance on a naturalistic fabricated robot. The approach is assessed using simulation and experimental tests to show the feasibility, optimum, and zero-error convergence of the technique developed.

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Optimal Position of Cameras Design in a 4D Foot Scanner

Cited by 6 publications

References 41 publications

Exploring Temporal Discrimination of Visual Events though EEG-Based Functional Connectivity Analaysis of Brain Regions

Exploring Temporal Discrimination of Visual Events though EEG-Based Functional Connectivity Analaysis of Brain Regions

Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Adaptive time-delay estimation and control of optimized Stewart robot

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