Position Detection Research Articles

Robot Tracking Navigation Based on Visual SLAM

Visual Simulation Localization and Mapping technology is an important research direction in the fields of computer vision and robotics. The research background of this technology mainly stems from the insufficient accuracy of traditional positioning and map construction methods in the face of environmental changes. With the development of artificial intelligence, this technology has been involved in multiple disciplinary fields. Since 2015, researchers have begun to focus on the combination with deep learning to improve algorithm robustness, dynamic scene planning and other aspects. This article which introduces the existing tracking car system and visual SLAM (Simultaneous Localization and Mapping) system framework explores the robot tracking and navigation technology based on visual SLAM. Visual SLAM technology provides robots with an autonomous navigation solution that does not require external sensors through feature extraction, real-time positioning, mapping, and closed-loop detection. This article also discusses the advantages and disadvantages of visual SLAM navigation, and offers insights into the future development of VSLAM technology in robot tracking navigation.

Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks

The Mobile Cognitive Radio Network (MCRN) are an alternative to spectrum scarcity. However, like any network, it comes with security issues to analyze. One of the attacks to analyze is the Primary User Emulation (PUE) attack, which leads the system to give the attacker the service as a legitimate user and use the Primary Users’ (PUs) spectrum resources. This problem has been addressed from perspectives like arrival time, position detection, cooperative scenarios, and artificial intelligence techniques (AI). Nevertheless, it has been studied with one PUE attack at once. This paper implements a countermeasure that can be applied when several attacks simultaneously exist in a cooperative network. A deep neural network (DNN) is used with other techniques to determine the PUE’s existence and communicate it with other devices in the cooperative MCRN. An algorithm to detect and share detection information is applied, and the results show that the system can detect multiple PUE attacks with coordination between the secondary users (SUs). Scenarios are implemented on software-defined radio (SDR) with a cognitive protocol to protect the PU. The probability of detection (PD) is measured for some signal-to-noise ratio (SNR) values in the presence of one PUE or more in the network, which shows high detection values above 90% for an SNR of -7dB. A database is also created with the attackers’ data and shared with all the SUs.

Motion Path Study Based on Isometric Solenoids and Discrete Simulation

This paper focuses on the position, speed change and collision detection problems during the traveling process of the bench dragon from both physical and mathematical perspectives. Firstly, based on the equidistance solenoid equation and the mechanical arm model, this paper iteratively recursively starts from the dragon head and sequentially solves the position of each section of the bench in the traveling process. Secondly, this paper combines the velocity analysis of the rigid rod and the nature of isometric solenoid, and also solves the position of each section of the bench in the traveling process sequentially in the form of iterative recursion. Finally, this paper discretizes the disk-in trajectory and carries out collision detection, so as to obtain the time, position, velocity and other information at the termination moment. The study shows that the model can effectively solve the complex optimization problem in the marching process of the dragon dance team.

Modified trident-shaped electrode design for particle lateral position detection in microfluidic impedance flow cytometry

Modified trident-shaped planar electrodes were developed for the detection of particles and their lateral positions inside a microfluidic flow channel. The electrodes generated time-of-flight measurements of individual moving particles in the form of a double peak, where each peak varies in amplitude and duration depending on particle trajectories. The amplitude ratio of the two peaks indicates the lateral positions of particles when crossing the electrode pairs. In particular, the ratio is 1 for particles near the channel centerline, >1 for particles displaced laterally to one-half of the channel, and < 1 for particles to the opposite half. This was confirmed by numerical simulations and detection of microspheres (6 – 10 µm ø) using proposed electrodes embedded in a polydimethyl siloxane (PDMS) microfluidic device. The device could count individual microspheres at a rate of at least 1151 per minute while identifying microspheres of the same material but differing in size based on peak amplitudes.

Research on intelligent three-dimensional anchor position detection method for ships utilizing Traversal and Monte Carlo algorithms

As intelligent ship technology advances, the importance of intelligent anchor position detection, as one of the key technologies, can ensure the safe anchoring of ships and enhance the efficiency of port operation. At present, most of the anchor position selection and detection algorithms are mainly based on two-dimensional planes, and there is a lack of research on the intelligent detection of safe water depth for ship anchoring in three-dimensional space. It not only restricts the full utilization of anchorage resources but also affects the safety and environmental adaptability of anchoring operations. To address these issues, this study proposes a three-dimension anchor position detection method. Firstly, based on the establishment of a three-dimensional ocean model, the possible anchor positions selected by the ship are simulated using the Monte Carlo algorithm. Secondly, the simulated anchor positions are optimized using a Traversal algorithm to filter out the optimal anchoring position that meets the requirements, the safety distance between each point and the existing ship is calculated, and the anchor position is determined according to the corresponding required safety spacing. Finally, to verify the applicability and effectiveness of the method under different sea conditions and different ship types, this study conducts a series of simulation experiments with 5000 random samples. These experiments compare the demand of anchor position selection for anchoring ships with changing water depths in the case of empty and full load drafts, and visualize the impact of varying water depth parameters on the selection of anchor positions for anchoring ships in various ship types. The outcomes of the experiment indicate that the algorithm’s detection area encompasses the whole anchorage area, ensuring both the anchorage area’s usage rate and the accuracy of anchor position detection. This study demonstrates that the Traversal and Monte Carlo Algorithms effectively improve the accuracy of the selection of anchoring position of the ship, makes full use of the resources of anchorage, and further improves the safety and efficiency of the anchoring operation.

An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN

For the blasting scenario, our research develops an emulsion explosive grasping and filling system suitable for tunnel robots. Firstly, we designed a system, YOLO-SimAM-GRCNN, which consists of an inference module and a control module. The inference module primarily consists of a blast hole position detection network based on YOLOv8 and an explosive grasping network based on SimAM-GRCNN. The control module plans and executes the robot’s motion control based on the output of the inference module to achieve symmetric grasping and filling operations. Meanwhile, The SimAM-GRCNN grasping network model is utilized to carry out comparative evaluated on the Cornell and Jacquard dataset, achieving a grasping detection accuracy of 98.8% and 95.2%, respectively. In addition, on a self-built emulsion explosive dataset, the grasping detection accuracy reaches 96.4%. The SimAM-GRCNN grasping network model outperforms the original GRCNN by an average of 1.7% in accuracy, achieving a balance between blast holes detection, grasping accuracy and filling speed. Finally, experiments are conducted on the Universal Robots 3 manipulator arm, using distributed deployment and manipulator arm motion control mode to achieve an end-to-end grasping and filling process. On the Jetson Xavier NX development board, the average time consumption is 119.67 s, with average success rates of 87.1% for grasping and 79.2% for filling emulsion explosives.

All-in-one, flexible, diversified self-powered sensors through embedded 3D printing

Abstract Triboelectric nanogenerators (TENGs) are characterized by zero power consumption and are often employed as self-powered sensors. However, the complex manufacturing process and expensive equipment limit the further promotion and application of self-powered sensors, which have become urgent challenges in this field. Here, a simple strategy using embedded 3D printing is proposed to enable the fabrication of diverse self-powered sensors in one-step, reducing production costs while increasing design flexibility. Specifically, the designed sensors composed of the silicone as the triboelectric layer and silicone/multi-walled carbon nanotubes as flexible electrodes with excellent all-in-one structures. Meanwhile, diversified self-powered sensors with different complex structures (e.g., planar array sensors and helical icosahedron sensors) were developed to meet the diverse needs of different applications, verifying the capability of the proposed embedded 3D printing method to design and customize sensors with various shapes and structures. In addition, the applications of these functionalized self-powered sensors in cryptographic simulation, pressure position detection, and impact force recognition have been successfully demonstrated. Therefore, this self-powered sensor based on embedded 3D printing has a promising future in human-computer interaction, collision detection and other fields.

FORTALECENDO O CONTROLE PÉLVICO PARA UMA VIDA SEM LIMITAÇÕES: UMA REVISÃO DE LITERATURA SOBRE A EFICÁCIA DA FISIOTERAPIA COM EXERCÍCIO DE KEGEL NO TRATAMENTO DA INCONTINÊNCIA URINÁRIA

The pelvic floor muscles support organs such as the bladder and uterus, and can weaken due to pregnancy and aging, which results in problems such as urinary incontinence or prolapse. The latter affects millions of women, being associated with factors such as age and number of births. Evaluation, especially through ultrasound, is crucial. Treatments include Kegel exercises, behavioral therapy, and mechanical devices. Innovative strategies, such as the gamification of Kegel exercises, have shown promise for improving adherence and treatment outcomes. The importance of pelvic floor muscles and their dysfunctions, such as urinary incontinence, was addressed, highlighting the need for effective and innovative approaches to improve patients' quality of life. An analysis of the efficacy of Kegel exercise on urinary incontinence in women was conducted, reviewing studies, synthesizing results, and offering recommendations for future research. A systematic review of the literature, conducted between February and May 2024, was adopted to analyze studies on Kegel exercise in urinary incontinence in women, searching for articles in databases such as VHL, PubMed, ScienceDirect, and JSTOR, with strict inclusion criteria. Studies on treatments for pelvic floor dysfunctions, such as prolapse and urinary incontinence, have revealed several approaches. Some have shown effectiveness in personalized training of the muscles, while others have compared methods such as electrical stimulation and active training. In addition, innovative techniques such as position detection during Kegel exercises and mechanotherapy were explored. The efficacy of simple interventions, such as Kegel exercises combined with behavioral therapy, was highlighted. These studies highlighted the complexity of pelvic floor dysfunctions and the importance of multifaceted and individualized approaches to treatment, highlighting the challenges faced and the need for more effective interventions to improve patients' health and quality of life. Authors have emphasized the importance of pelvic muscles and treatments such as training and behavioral therapy, while innovative strategies such as gamification have shown promise for promoting better physical, emotional, and social well-being and outcomes.

Acoustofluidic Tweezers Integrated with Droplet Sensing Enable Multifunctional Closed-Loop Droplet Manipulation.

Droplet manipulation technologies with surface acoustic waves attract significant attention for applications in fluid handling and bioanalysis. However, existing technologies face challenges in automation, precision, and functional integration, limiting broader applications. In this work, a highly integrated droplet-sensing acoustofluidic tweezer is developed, incorporating orthogonally arranged slanted finger interdigital transducers and a custom-designed control and detection circuit system. Using a single acoustic device, this tweezer enables switchable acoustic droplet manipulation and detection, providing multifunctional closed-loop manipulation of on-chip microliter-scale droplets. The platform takes advantage of the wideband frequency response characteristics of the transducers, along with an automated droplet detection algorithm, enabling high-precision detection of central positions, edge positions, contact diameters, and the number of droplets. With this feedback, automated closed-loop control of various droplet manipulation functions, including transportation, merging, mixing, splitting, and internal particle enrichment, is achieved for the first time on a single acoustic platform. This significantly enhances the precision, efficiency, and fault tolerance of the manipulation process. This droplet-sensing acoustofluidic tweezer provides an innovative acoustic solution for droplet manipulation technologies in fields such as fluid processing and biosensing, demonstrating significant application potential.

Magnetic field influence on electron transport in planned GEM-based radiated power diagnostic for DEMO

This work relates to development of diagnostics for radiation power (Prad) and soft x-ray (SXR) intensity measurements in DEMO. A modular, multi-chamber, multi-channel SXR detection system based on gas electron multiplier technology in lateral configuration is currently in conceptual design phase. The high magnetic fields expected at the planned detector positions in the diagnostic ports of the tokamak must be considered in the design. The present study examines two contributions of its influence. The first one pertained to the electric field drifts of thermalized electrons. These were found to be significant, but mostly linear and mitigatable. The second contribution pertained to the impact of magnetic fields on the initial photoelectrons. This was particularly effective in the second detector chamber, which had been optimized for higher photon energies. The size and shape of the electron cloud in a working gas mixture created by primary ionizations have been evaluated as a function of x-ray energy, magnetic field strength, and angle. Moreover, a more direct analysis concerning the loss of electrons in the second chamber has been performed. In this context, the magnetic field was found to have a slightly beneficial effect on the operation of the detector, as it limited the range of ionization perpendicular to its direction and decreased the probability of a primary electron leaving the drift region. Potential design changes resulting from these findings were discussed.

A sensing system and solving method for dynamic detection of relative pose of hydraulic support group

The precise detection of the pose of hydraulic support groups is crucial for the construction of intelligent coal mines. Addressing current challenges in comprehensive position sensing of support groups, such as one-sided descriptions, missing information, and limited detection capabilities, this paper, based on the concept of digital twin, proposes a sensing system and solving method for dynamic detection of the relative pose of hydraulic support group. Initially, a relative pose detection model, based on the six-point localization principle, is proposed by combining existing sensor information. Then, a sensing system is designed, incorporating a photoelectric detection device for monitoring key position parameters and virtual sensors for determining pose information. Consequently, a method for dynamically deriving relative positions, driven by the fusion of real-virtual iterative solving, real-virtual error feedback, and iterative condition optimization, is established. Finally, a relative pose dynamic detection system for a hydraulic support prototype is constructed in a laboratory environment. Experimental results demonstrate that the sensor system and the relative position detection method designed in this paper achieve a relative position accuracy of over 95.32 % for the hydraulic support, with a dynamic reconstruction time of less than 0.2 s. This verifies the rationality of the sensor system and the feasibility and accuracy of the dynamic deduction method. To sum up, this work provides a theoretical foundation and technical support for the autonomous perception of hydraulic support group, autonomous frame adjustment between frames, and overall straightness adjustment. It also lays the groundwork for further research and the advancement of information-physical systems in the fully-mechanized mining face.

Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph.

Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble model for tooth position detection, tooth outline segmentation, tooth tissue segmentation, periodontal bone loss and periodontitis stage prediction using dental panoramic radiographs. The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists. The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P <​ 0.001). ​The ICC value was 0.806 (P <​ 0.001). The total diagnostic accuracy of the DL method was 89.45%. The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services. The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.

Benefits of front coating crystalline scintillator screens for phase-contrast synchrotron micro-tomography.

Transparent crystalline scintillators such as cerium-doped YAG or LuAG are widely used in X-ray imaging for the indirect detection of X-rays. The application of reflective coatings on the front side to improve the optical gain is common practice for flat panel detectors with CsI or Gd2O2S powder scintillators but still largely unknown for crystalline scintillators such as LuAG. This work shows experimentally and quantitatively how a black and reflective coating on the X-ray side of a 2 mm LuAG:Ce scintillator improves the image quality compared to a 2 mm LuAG:Ce scintillator without a coating. The measurements have been done for two different distances, with 2 m and 29.7 m on the BM18 beamline of the European Synchrotron. The Modulation Transfer Function (MTF) and the Signal-to-Noise-Ratio (SNR2) power spectrum as well as contrast-to-noise ratio are used for comparing image quality. Propagation-based phase contrast strongly enhances the SNR2 amplitudes (gain ≈10 from 2 m to 29.7 m object-detector distance) of the raw images' spectrum independent of the scintillator coating. For both detector positions, the reflective coating is able to raise SNR2 by up to 80% through the improved optical gain, while black coating does the opposite (decrease SNR2 by 20%) with respect to no coating. With the tested optical setups, changes in MTF /sharpness between the coatings are minor. Comparing CNR2 in CT scans of a multi-material sample, in this case an electric motor, we observe the reflective coating yielding better material contrast for plastic and air. Application and effect of Wiener-deconvolution, along with Paganin-type phase retrieval, are also discussed in the context of CT image quality.

Monitoring of Broccoli Flower Head Development in Fields Using Drone Imagery and Deep Learning Methods

For different broccoli materials, it used to be necessary to manually plant in a large area for the investigation of flower ball information, and this method is susceptible to subjective influence, which is not only time-consuming and laborious but may also cause some damage to the broccoli in the process of investigation. Therefore, the rapid and nondestructive monitoring of flower heads is key to acquiring high-throughput phenotypic information on broccoli crops. In this study, we used an unmanned aerial vehicle (UAV) to acquire hundreds of images of field-grown broccoli to evaluate their flower head development rate and sizes during growth. First, YOLOv5 and YOLOv8 were used to complete the position detection and counting statistics at the seedling and heading stages. Then, UNet, PSPNet, DeepLabv3+, and SC-DeepLabv3+ were used to segment the flower heads in the images. The improved SC-DeepLabv3+ model excelled in segmenting flower heads, showing Precision, reconciled mean F1-score, mean intersection over union, and mean pixel accuracy values of 93.66%, 95.24%, 91.47%, and 97.24%, respectively, which were 0.57, 1.12, 1.16, and 1.70 percentage points higher than the respective values achieved with the DeepLabv3+ model. Flower head sizes were predicted on the basis of the pixel value of individual flower heads and ground sampling distance, yielding predictions with an R2 value of 0.67 and root-mean-squared error of 1.81 cm. Therefore, the development rate and sizes of broccoli flower heads during growth were successively estimated and calculated. Compared with the existing technology, it greatly improves work efficiency and can help to obtain timely information on crop growth in the field. Our methodology provides a convenient, fast, and reliable way for investigating field traits in broccoli breeding.

A GCN and Transformer complementary network for skeleton-based action recognition

Graph Convolution Networks (GCNs) have been widely used in skeleton-based action recognition. Although there are significant progress, the inherent limitation still lies in the restricted receptive field of GCN, hindering its ability to extract global dependencies effectively. And the joints that are structurally separated can also have strong correlation. Previous works rarely explore local and global correlations of joints, leading to insufficiently model the complex dynamics of skeleton sequences. To address this issue, we propose a GCN and Transformer complementary network (GTC-Net) that allows parallel communications between GCN and Transformer domains. Specifically, we introduce a graph convolution and self-attention combined module (GAM), which can effectively leverage the complementarity of GCN and self-attention to perceive local and global dependencies of joints for the human body. Furthermore, in order to address the problems of long-term sequence ordering and position detection, we design a position-aware module (PAM), which can explicitly capture the ordering information and unique identity information for body joints of skeleton sequence. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets are conducted to evaluate our proposed method. The results demonstrate that our method can achieve competitive results on both datasets.

Review of Key Technologies of the High-Speed Permanent Magnet Motor Drive

The high-speed permanent magnet motor has been widely used in the industrial field because of its high-power density, fast dynamic response, and wide speed range. High efficiency and stable operation are the premises for the high-speed permanent magnet motor to exert its advantages. Firstly, this article analyzes the problems existing in the drive technology of high-speed permanent magnet motors from three aspects: current harmonics, rotor position detection, and low carrier ratios. Aiming to address the above problems, the status of the latest research is summarized from the aspects of harmonic suppression, position sensorless, and control strategies. In addition, future research trends for high-speed permanent magnet motors are discussed.

E-waste driven eco-friendly highly sensitive wearable capacitive sensors for physical activity monitoring and sign language recognition

Owing to the continual and rapid advancement in technology, there has been a significant surge in electronic waste (e-waste) in recent decades, resulting in a harmful impact on our environment. Due to the growing demand for battery-powered devices, non-rechargeable batteries are a significant source of e-waste. This study describes fabricating a paper-based graphite strain sensor that is eco-friendly, inexpensive, biodegradable, and sensitive to the applied strain. Readily accessible materials are employed to design and fabricate MIDC strain sensors. The Modified Interdigital Capacitor (MIDC) structure is formed on basic printing paper, and the conductive substance made with the ink of a gel pen and graphite from a drained dry cell is painted on the paper and used as electrode material. The suggested sensor has a high gauge factor value of 1998.578 and fast response and recovery time of 112 ms and 100 ms, respectively. The proposed prototype has also been utilized to inspect fitness exercises and physical activities on the human body, inspiring the potential for its integration into wearable systems for various applications. Reliable detection and differentiation of finger positions are achieved, highlighting the sensor's potential for more advanced sign language applications. It’s a good option to integrate the suggested strain sensor in wearable systems, such as telemedicine systems, electromechanical detection, healthcare, and physical activity monitoring of humans.

Research on the NI-MLA Method for Enhancing the Spot Position Detection Accuracy of Quadrant Detectors Under Atmospheric Turbulence

The distorted spots induced by atmospheric turbulence significantly degrade the spot position detection accuracy of the quadrant detector (QD). In this paper, we utilize angular measurement and homogenization characteristics of non-imaging microlens array (NI-MLA) systems, effectively reducing the distortion degree of the spots received on the QD target surface, thereby significantly enhancing the spot detection accuracy of the QD. First, based on the principles of geometric optics and Fourier optics, it is proved that the NI-MLA system possesses the angular measurement characteristic (AMC) within the paraxial region while deriving and verifying the focal length of the system. Then, the QD computation curve characteristics of the system under non-turbulence are explored. This study further elucidates the mathematical principle of the NI-MLA system for mitigating the spot position detection random error of QD (SPDRE-QD) and discusses in depth the relationship between the NI-MLA system’s capability to mitigate the SPDRE-QD and the system’s parameters under various turbulence intensities. Finally, it is experimentally verified that the root-mean-square error (RMSE) of the QD computation values using the NI-MLA system are reduced by a significant improvement of at least 2.44 times and up to 17.36 times compared with that of the conventional optical system of QD (COS-QD) under turbulence conditions ranging from weak to strong.

Vibration Position Detection of Robot Arm Based on Feature Extraction of 3D Lidar.

In the process of construction, pouring and vibrating concrete on existing reinforced structures is a necessary process. This paper presents an automatic vibration position detecting method based on the feature extraction of 3D lidar point clouds. Compared with the image-based method, this method has better anti-interference performance to light with reduced computational consumption. First, lidar scans are used to capture multiple frames of local steel bar point clouds. Then, the clouds are stitched by Normal Distribution Transform (NDT) for preliminary matching and Iterative Closest Point (ICP) for fine-matching. The Graph-Based Optimization (g2o) method further refines the precision of the 3D registration. Afterwards, the 3D point clouds are projected into a 2D image. Finally, the locations of concrete vibration points and concrete casting points are discerned through point cloud and image processing technologies. Experiments demonstrate that the proposed automatic method outperforms ICP and NDT algorithms, reducing the mean square error (MSE) by 11.5% and 11.37%, respectively. The maximum discrepancies in identifying concrete vibration points and concrete casting points are 0.059 ± 0.031 m and 0.089 ± 0.0493 m, respectively, fulfilling the requirement for concrete vibration detection.

Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management

A key best practice to prevent and treat pressure injuries (PIs) is to ensure at-risk individuals are repositioned regularly. Our team designed a non-contact position detection system that predicts an individual’s position in bed using data from load cells under the bed legs. The system was originally designed to predict the individual’s position as left-side lying, right-side lying, or supine. Our previous work suggested that a higher precision for detecting position (classifying more than three positions) may be needed to determine whether key bony prominences on the pelvis at high risk of PIs have been off-loaded. The objective of this study was to determine the impact of categorizing participant position with higher precision using the system prediction F1 score. Data from 18 participants was collected from four load cells placed under the bed legs and a pelvis-mounted inertial measurement unit while the participants assumed 21 positions. The data was used to train classifiers to predict the participants’ transverse pelvic angle using three different position bin sizes (45°, ~30°, and 15°). A leave-one-participant-out cross validation approach was used to evaluate classifier performance for each bin size. Results indicated that our prediction F1 score dropped as the position category precision was increased.