3D Position Research Articles

3D position deployment and performance optimization of mmWave UAV-assisted HetIoT under jamming condition

Heterogeneous Internet of Things (HetIoT) has received widespread attention due to its provision of various convenient services in fields such as smart cities, intelligent transportation, environmental monitoring and security systems. Due to HetIoT inherently demands high data rates, bandwidth, and low latency, the application of millimeter-wave (mmWave) unmanned aerial vehicle (UAV) as emergency aerial base station (ABS) providing services to HetIoT users has become a low-cost and efficient means. However, due to the sensitivity of mmWave to obstacles and jamming, guaranteeing network performance has become a pressing issue. This paper considers a mmWave UAV-assisted HetIoT under jamming conditions, where auxiliary ABSs serve multiple ground users (GUs) who generate a large amount of sensor data. We establish a coverage maximization problem under the constraints of signal-to-interference ratio (SIR) threshold, maximum power of ABSs and maximum number of GUs that the base station can serve, and propose a novel ABS hovering deployment algorithm M-HiAPSO that combines the artificial potential field (APF) method and the improved particle swarm optimization (PSO) algorithm in a hierarchical manner. Specifically, the multi-element APF method is used to characterize the horizontal force between nodes, combined with the improved hierarchical adaptive PSO algorithm to adjust the horizontal position of the ABS to obtain the optimal UAV hovering position and power allocation strategy. Numerical results show that the coverage rate reached 96.2% when the number of iterations was 283, and it can reach up to 99.6%.

The Importance of Ground Control Points In A Photogrammetric Workflow

Abstract This study investigates the optimal distribution and pattern of ground control points (GCPs) in aerial photogrammetric projects. Aerial triangulation (AT), also known as bundle adjustment, is the fundamental step in refining 3D reconstruction models and camera positions, thereby minimizing reprojection errors. The study utilizes data from a national project in Romania, employing high-resolution aerial images acquisition using photogrammetric sensors. The project has rigorous requirements of ground control points (GCP) placement and field measurements using GNSS and geometric leveling techniques. The study employs various scenarios, manipulating the number and distribution of GCPs, to assess their influence on planimetric and altimetric accuracy. Results indicate that the configuration and number of GCPs significantly affect the accuracy of photogrammetric products, such as dense image point clouds, digital surface models, and orthophotos. Moreover, the study underscores the importance of precise GCP determination methods, especially in regions lacking a precise gravimetric geoid model. In scenarios with inadequate GCP coverage the outcomes have inferior quality, emphasizing the critical role of GCPs in ensuring the quality of photogrammetric products. Overall, the research gives a clear view on the best placement patterns of GCPs and their influence on AT process evaluation performed in check points (CHKs).

FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.

A Magnetic Particle Imaging Approach for Minimally Invasive Imaging and Sensing With Implantable Bioelectronic Circuits.

Minimally-invasive and biocompatible implantable bioelectronic circuits are used for long-term monitoring of physiological processes in the body. However, there is a lack of methods that can cheaply and conveniently image the device within the body while simultaneously extracting sensor information. Magnetic Particle Imaging (MPI) with zero background signal, high contrast, and high sensitivity with quantitative images is ideal for this challenge because the magnetic signal is not absorbed with increasing tissue depth and incurs no radiation dose. We show how to easily modify common implantable devices to be imaged by MPI by encapsulating and magnetically-coupling magnetic nanoparticles (SPIOs) to the device circuit. These modified implantable devices not only provide spatial information via MPI, but also couple to our handheld MPI reader to transmit sensor information by modulating harmonic signals from magnetic nanoparticles via switching or frequency-shifting with resistive or capacitive sensors. This paper provides proof-of-concept of an optimized MPI imaging technique for implantable devices to extract spatial information as well as other information transmitted by the implanted circuit (such as biosensing) via encoding in the magnetic particle spectrum. The 4D images present 3D position and a changing color tone in response to a variable biometric. Biophysical sensing via bioelectronic circuits that take advantage of the unique imaging properties of MPI may enable a wide range of minimally invasive applications in biomedicine and diagnosis.

6 degree of freedom positional object tracking for physical prototype digitisation

AbstractUnderpinning much work on the use of Virtual Reality technologies in design prototyping, is the need to reliably track the 3D position of a physical object in real space, then allowing synchronisation with a digital counterpart. With many tracking methods requiring changes to object geometry, this work develops and benchmarks four minimally invasiveness 6 DoF tracking approaches, before discussing their use in a prototyping context. Results show that using AI and point cloud methods, accuracies of 20mm at 20Hz are achievable on low-end hardware with no alterations to the prototype needed.

Marker-based C-arm self-calibration with unknown calibration pattern.

Accurate tomographic reconstructions require the knowledge of the actual acquisition geometry. Many mobile C-arm CT scanners have poorly reproducible acquisition geometries and thus need acquisition-specific calibration procedures. Most of geometric self-calibration methods based on projection data either need prior information or are limited to the estimation of a low number of geometric calibration parameters. Other self-calibration methods generally use a calibration pattern with known geometry and are hardly implementable in practice for clinicalapplications. We present a three-step marker based self-calibration method which does not require the prior knowledge of the calibration pattern and thus enables the use of calibration patterns with arbitrary markerspositions. The first step of the method aims at detecting the set of markers of the calibration pattern in each projection of the CT scan and is performed using the YOLO (You Only Look Once) Convolutional Neural Network. The projected marker trajectories are then estimated by a sequential projection-wise marker association scheme based on the Linear Assignment Problem which uses Kalman filters to predict the markers 2D positions in the projections. The acquisition geometry is finally estimated from the marker trajectories using the Bundle-adjustmentalgorithm. The calibration method has been tested on realistic simulated images of the ICRP (International Commission on Radiological Protection) phantom, using calibration patterns with 10 and 20 markers. The backprojection error was used to evaluate the self-calibration method and exhibited sub-millimeter errors. Real images of two human knees with 10 and 30 markers calibration patterns were then used to perform a qualitative evaluation of the method, which showed a remarkable artifacts reduction and bone structures visibilityimprovement. The proposed calibration method gave promising results that pave the way to patient-specific geometric self-calibrations inclinics.

Multi-type classification and 3D visualization of internal defects in concrete plates based on a hybrid method of deep learning algorithm and bending mode analysis for acoustic vibration signals

In this paper, a hybrid method combining the deep learning algorithm and bending mode analysis of acoustic vibration signals is proposed for the multi-type classification and 3D visualization of internal defects in concrete plates. A novel deep learning model termed fully convolutional network based on principal component analysis and attention-embedded long-short term memory (PCA-ALSTM-FCN) is established. The PCA-ALSTM-FCN model successfully classifies multi-type internal defects, and the two-dimensional (2D) defect contour maps are generated based on the predicted state by the trained model. The defect depths are calculated according to the analytical formula of bending vibration mode of acoustic vibration signals. Combining the 2D defect map and depth information, the three-dimensional (3D) visualization images of defects are obtained. The average recognition accuracy of PCA-ALSTM-FCN model for different types of defects reaches 94.8 %, and the defect depth calculation error range is approximately 10 %–20 %. The experimental results show that the proposed method in this paper can accurately distinguish different types of internal defects and effectively locate the 3D position of defects in concrete.

Effect of ribs in a suddenly expanded flow at sonic Mach number

This study aims to assess the influence of a rib on the base pressure and the flow development in an abruptly expanded duct at sonic Mach number. Initially, the simulations were done to validate the experimental results, keeping all the parameters the same. Accordingly, a duct-of-area ratio of 6.25 was considered for validation. Five ribs of aspect ratios 3:1, 3:2, and 3:3 were used as a first step, and simulations were performed for the same nozzle pressure ratios. Results indicate that for an area ratio of 6.25, there is a continuous decrease in the base pressure despite the nozzles being highly under-expanded. The lower aspect ratio of the rib tends to reduce the base pressure, whereas a higher aspect ratio effectively increases the base pressure for an area ratio of 6.25. Later simulations considered a single rib instead of five ribs, varying the rib's heights from 1 mm to 5 mm. Results show that the base pressure increases considerably when rib heights are 4 mm and 5 mm. The influence of ribs at two duct diameters (25 mm and 18 mm) is studied to assess the impact of a decrease in the area ratio and, hence, a decrease in the relief available to the flow. Results of duct 18 mm show that passive control becomes very effective when a rib of 3 mm height is located at a 3D position. The differences in the base pressure, velocity, and pressure field for each case are explored. The simulation results indicate that the rib breaks the primary vortex at the base and forms multiple vortices. Turbulent kinetic energy increases in the presence of ribs more than without a rib.

A subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image

The paper presents an intelligent tunnel 3D laser image acquisition system. The system can quickly acquire the 3D point cloud and tunnel surface image of the whole tunnel section. The paper proposes the subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image. Firstly, the method extracts the homonymous points from the mapped image generated from the 3D point cloud and the camera image based on the maximum information coefficient method. Secondly, the 3D point cloud positions corresponding to the homonymous points are determined. The optimization of camera parameters is achieved based on the gradient descent method. Then, the tunnel point cloud is expanded to generate a gray scale image based on spherical projection. Based on the optimized parameters, the point cloud is projected to the camera image to determine the depth value of each camera image pixel point. Finally, the pixel values of the camera image are projected based on the spherical projection to obtain the stitching result images. This method stitches individual images together to form a more complete tunnel surface image. The larger tunnel surface image helps in identifying tunnel defects.

A novel 3D MRI-based approach for assessing supraspinatus muscle length

Rotator cuff (RC) tears are a common source of pain and decreased shoulder strength. Muscle length is known to affect muscle strength, and therefore evaluating changes in supraspinatus muscle length associated with RC pathology, surgical repair, and post-operative recovery may provide insights into functional deficits. Our objective was to develop a reliable MRI-based approach for assessing supraspinatus muscle length. Using a new semi-automated approach for identifying 3D location of the muscle–tendon junction (MTJ), supraspinatus muscle length was calculated as the sum of MTJ distance (distance between 3D MTJ position and glenoid plane) and supraspinatus fossa length (distance between root of the scapular spine and glenoid plane). Inter- and intra-operator reliability of this technique were assessed with intraclass correlation coefficient (ICC) and found to be excellent (ICCs > 0.96). Muscle lengths of 6 patients were determined before RC repair surgery and at 3- and 12-months post-surgery. Changes in normalized muscle length (muscle length as a percentage of pre-surgical muscle length) at 3 months post-surgery varied considerably across patients (16.1 % increase to 7.0 % decrease) but decreased in all patients from 3- to 12-months post-surgery (0.3 % to 17.2 %). This study developed a novel and reliable approach for quantifying supraspinatus muscle length and provided preliminary demonstration of its utility by assessing muscle length changes associated with RC pathology and surgical repair. Future studies can use this technique to evaluate changes over time in supraspinatus muscle length in response to clinical intervention, and associations between muscle length and shoulder function.

Infrared-based temperature measurement in three dimensions

A highly accurate and low-cost mobile platform for multiple object temperature measurement in 3D coordinate positions is introduced. The key idea relies on not only a combination of a 3D optical depth sensor and the 2D infrared thermal imager but also a determined distant-dependent compensation model. As a result, a cost-effective yet wider (>100.0∘C) temperature measurement range under the fluctuation of working distant shift is realized with a low standard deviation of 0.16°C at the working distance of 1.0 m. Accurate temperature measurement at different positions of objects along the suitable working distance is also demonstrated.

An Image-Based Augmented Reality System for Achieving Accurate Bone Resection in Total Knee Arthroplasty.

Background and objective With the steady advancement of computer-assisted surgical techniques, the importance of assessing and researching technology related to total knee arthroplasty (TKA) procedures has increased. Augmented reality (AR), a recently proposed next-generation technology, is expected to enhance the precision of orthopedic surgery by providing a more efficient and cost-effective approach. However, the accuracy of image-based AR in TKA surgery has not been established. Therefore, this study aimed to determine whether accurate bone resection can be achieved in TKA surgery using image-based AR. Methods In this study, we replaced traditional CT imaging and reconstructions for creating a bone 3D modelby direct 3D scanning of the femur and tibia. The preoperative planning involved identifying anatomical landmarks and determining the surgical details. During surgery, markers were employed to create a local coordinate system for an AR-assisted surgical system using a Polaris camera. This approach helped minimizediscrepancies between the 3D model and actual positioning, ensuring accurate alignment. Results The AR-assisted surgery using the image method resulted in fewer errors [average error: 0.32 mm; standard deviation (SD): 0.143] between the bone resection depth of the preoperative surgical plan and the bone model test results. Conclusions Our findings demonstrated the accuracy of bone resectioning by using image-based AR-assisted navigation for TKA surgery. Image-based AR-assisted navigation in TKA surgery is a valuable tool not only for enhancing accuracy by using smart glasses and sensors but also for improving the efficiency of the procedure. Therefore, we anticipate that image-based AR-assisted navigation in TKA surgery will gain wideacceptance in practice.

A 3D Evaluation of Condyle Position of Skeletal Class I and III Patients: A Cone-Beam Computed Tomography Technique

Purpose: The present study aims to assess the differences in the condyle position for two skeletal classes using Cone-Beam Computed Tomography (CBCT) reconstructions for both sides and genders.
 Materials and Methods: In this cross-sectional descriptive study, the CBCT images of 96 patients (20-60 years) were assessed. The participants were divided according to their Angle malocclusion classifications (Angle Classes I and III). The variables of the Anterior-Posterior position of the Condyle (APC), condylar angle in the axial plane (ACA), the Lateral Position of the Condyle in the axial plane (LPC), the Vertical Position of the Condyle (VPC), condylar angle in coronal dimension (CCA), and the difference of APC and VPC on both sides were measured. The measurements were analyzed using a one‑way ANOVA and Tukey’s post hoc test.
 Results: The variables of APC, LPC, ACA, VDC, and the difference of the APC on both sides in the two skeletal classes were similar. The VPC and CCA were greater in Class III than in Class I. All variables representing the 3D position of the condyle were similar in men and women, as well as on the right and left in both skeletal classes, I and III.
 Conclusion: Based on the 3D evaluation results of the condylar position, the skeletal classes III and I differed in the VPC and CCA; however, for the rest variables, there were no statistical differences.

An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated errors in multi-floor scenes. The proposed calibration method involves two steps: motion pattern recognition and position matching-based calibration. The motion pattern recognition aims to detect different motion patterns, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion patterns and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR, achieving accurate 3D positioning. The average error is reduced from 6.60 m to 1.37 m.

A Novel Position-Sensitive Linear Winding Silicon Drift Detector.

A novel position-sensitive linear winding silicon drift detector (LWSDD) was designed and simulated. On the frontside (anode side), the collecting anodes were set on both sides of the detector, and an S-shaped linear winding cathode strip was arranged in the middle, which can realize independent voltage division and reduce the complexity of external bias resistor chain compared with the traditional linear silicon drift detector. The detectors were arranged in a butterfly shape, which increased the effective area of the detectors and improved the collection efficiency. The linear winding silicon drift detector can obtain one-dimensional position information by measuring the drift time of electrons. The 2D position information of the incident particle is obtained from the anodes coordinates of the readout signal. One-dimensional analytically exact solutions of electric potential and field were obtained for the first time for the linear winding silicon drift detector. The simulation results show that the electric potential distribution inside the detector is uniform, and the "drift channel" inside the detector points to the collecting anodes on both sides, which proves the reasonable and feasible design of the linear winding silicon drift detector.

Testing and Analysis of Selected Navigation Parameters of the GNSS/INS System for USV Path Localization during Inland Hydrographic Surveys.

One of the main methods of the path localization of moving objects is positioning using Global Navigation Satellite Systems (GNSSs) in cooperation with Inertial Navigation Systems (INSs). Its basic task is to provide high availability, in particular in areas with limited access to satellite signals such as forests, tunnels or urban areas. The aim of the article is to carry out the testing and analysis of selected navigation parameters (3D position coordinates (Northing, Easting, and height) and Euler angles (pitch and roll)) of the GNSS/INS system for Unmanned Surface Vehicle (USV) path localization during inland hydrographic surveys. The research used the Ellipse-D GNSS/INS system working in the Real Time Kinematic (RTK) mode in order to determine the position of the "HydroDron" Autonomous Surface Vehicle (ASV). Measurements were conducted on four representative routes with a parallel and spiral arrangement of sounding profiles on Lake Kłodno (Poland). Based on the obtained research results, position accuracy measures of the "HydroDron" USV were determined using the Ellipse-D GNSS/INS system. Additionally, it was determined whether USV path localization using a GNSS/INS system working in the RTK mode meets the positioning requirements for inland hydrographic surveys. Research has shown that the Ellipse-D system operating in the RTK mode can be successfully used to position vessels when carrying out inland hydrographic surveys in all International Hydrographic Organization (IHO) Orders (Exclusive, Special, 1a/1b and 2) even when it does not work 100% correctly, e.g., loss of RTK corrections for an extended period of time. In an area with limited coverage of the mobile network operator (30-40% of the time the receiver operated in the differential mode), the positioning accuracy of the "HydroDron" USV using the Ellipse-D GNSS/INS system working in the RTK mode was from 0.877 m to 0.941 m for the R95(2D) measure, depending on the route travelled. Moreover, research has shown that if the Ellipse-D system performed GNSS/INS measurements using the RTK method, the pitch and roll error values amounted to approx. 0.06°, which is almost identical to that recommended by the device manufacturer. However, when working in the differential mode, the pitch and roll error values increased from 0.06° to just over 0.2°.

A Framework for Modeling, Optimization, and Musculoskeletal Simulation of an Elbow–Wrist Exosuit

The light weight and compliance of exosuits are valuable benefits not present rigid exoskeleton devices, yet these intriguing features make it challenging to properly model and simulate their interaction with the musculoskeletal system. Tendon-driven exosuits adopt an electrical motor combined with pulleys and cable transmission in the actuation stage. An important aspect of the design of these systems for the load transfer efficacy and comfort of the user is the anchor point positioning. In this paper, we propose a framework, whose first purpose is as a design methodology for the synthesis of an exosuit device, achieved by optimizing the anchor point location. The optimization procedure finds the best 3D position of the anchor points based on the interaction forces between the exosuit and the upper arm. The computation of the forces is based on the combination of a mathematical model of a wrist–elbow exosuit and a dynamic model of the upper arm. Its second purpose is the simulation of the kinematic and physiological effects of the interaction between the arm, the exosuit, and the complex upper limb musculoskeletal system. It offers insights into muscular and exoskeleton loading during operation. The presented experiments involve the development and validation of personalized musculoskeletal models, with kinematic, anthropometric, and electromyographic data measured in a load-lifting task. Simulation of the exosuit operation—coupled with the musculoskeletal model—showed the efficacy of the suit in assisting the wrist and elbow muscles and provided interesting highlights about the impact of the assistance on shoulder muscles. Finally, we provide a possible design of an elbow and wrist exosuit based on the optimized results.

Factors Influencing Accuracy of Estimating Position of Objects in a Multi-camera System

Nowadays, research focusing on robotics, autonomous vehicles, and scene analysis shows a clear need for the ability to accurately reconstruct three-dimensional environments. One of the methods allowing to conduct such a reconstruction is to use a set of cameras and image processing techniques. This is a passive method. Despite being, in general, less accurate than its active counterparts, it offers significant advantages in numerous applications in which active systems cannot be deployed due to limited performance. This paper provides a theoretical analysis of the accuracy of estimating 3D positions of objects present at a given scene, based on images from a set of cameras. The analysis assumes a known geometrical configuration of the camera system. The important limiting factor in the considered scenario is the physical resolution of sensors - especially in the case of systems that are supposed to work in real time, with a high FPS rate, as the use of high-resolution cameras is difficult in such circumstances. In the paper, the influence of the geometric arrangement of the cameras is studied and important conclusions about the potential of three-camera configurations are drawn. The analysis performed and the formulas derived help predict the boundary accuracy values of any system using a digital camera. The results of an experiment that confirm the theoretical conclusions are presented as well.

Development of a vision system integrated with industrial robots for online weld seam tracking

This paper develops a laser-structured light vision system for the industrial welding manipulator. The designed structured light vision system consisting of a line laser projector and an industrial camera is a decent 3D object data acquisition platform. By applying the principle of triangulation, the depth value of the object can be obtained via the laser plane in the captured image. According to hand-eye calibration and the principle of triangulation, the 3D position of the objects in the robot coordinate system is calculated. Based on the discriminated convolution tracker and deep reinforcement learning that increases the accuracy and processing time of the tracker, allowing simultaneous scanning for seam data acquisition and welding along the weld path, a real-time seam tracking algorithm is developed to handle the square-groove butt joint with small gaps (i.e., <1 mm). Experiments are undertaken using a Yaskawa industrial welding robot integrating the designed laser-structured light vision system. Experiment results show the significant performance of the developed system in welding square-groove butt joints with curved profiles and small gaps, i.e., the welding absolute mean error is 0.31 mm, whereas in the case of using convolution tracker only, the welding absolute mean error 1.7 mm.

Webcam-based gaze estimation for computer screen interaction.

This paper presents a novel webcam-based approach for gaze estimation on computer screens. Utilizing appearance based gaze estimation models, the system provides a method for mapping the gaze vector from the user's perspective onto the computer screen. Notably, it determines the user's 3D position in front of the screen, using only a 2D webcam without the need for additional markers or equipment. The study presents a comprehensive comparative analysis, assessing the performance of the proposed method against established eye tracking solutions. This includes a direct comparison with the purpose-built Tobii Eye Tracker 5, a high-end hardware solution, and the webcam-based GazeRecorder software. In experiments replicating head movements, especially those imitating yaw rotations, the study brings to light the inherent difficulties associated with tracking such motions using 2D webcams. This research introduces a solution by integrating Structure from Motion (SfM) into the Convolutional Neural Network (CNN) model. The study's accomplishments include showcasing the potential for accurate screen gaze tracking with a simple webcam, presenting a novel approach for physical distance computation, and proposing compensation for head movements, laying the groundwork for advancements in real-world gaze estimation scenarios.