Orientation Detection Research Articles

CAS3D: 3D quantitative morphometry on Second Harmonic Generation image volumes from single skeletal muscle fibers

The CAS3D image processing method intuitively applies a combination of Fourier space and real space 3D analysis algorithms to volumetric images of single skeletal muscle fiber Myosin II Second Harmonic Generation (SHG) XYZ image data. Our developed tool automatically quantifies the myofibrillar orientation in muscle samples by determining the cosine angle sum of intensity gradients in 3D (CAS3D) while determining the mean sarcomere length (SL) and sample orientation. The expected CAS3D values could be reproduced from ideal artificial data sets. Applied random noise in artificial images lowers the detected CAS3D value, and for noise levels below 20%, the correlation can be approximated by a linear function with a slope of −0.006 CAS3D/noise%. The deviations in SL and orientation detection were determined on ideal and noisy artificial data sets and were statistically indistinguishable from 0 (null hypothesis t-test P > 0.1). The software was applied to a previously published data set of single skeletal muscle fiber volumetric SHG image data from a rat intensive care unit (ICU) model of ventilator-induced diaphragm dysfunction (VIDD) with treatment regimens involving the small anti-inflammatory molecules BGP-15, vamorolone, or prednisolone. Our method reliably reproduced the results of the previous work and improved the standard deviation of the cosine angle sum detection in all sample groups from a mean of 0.03 to 0.008. This improvement is achieved by applying analysis algorithms to the whole volumetric images in 3D in contrast to the previously common method of slice-wise XY analysis.

Image-based corn seed embryo orientation detection and adjustment for precision planting

Research has indicated that positioning a corn seed with its embryo side facing upward and its long axis perpendicular to a seeding row leads to the corn leaves growing perpendicular to the seeding row. This positioning results in an increased rate of light interception and yield for the crop. An identification and adjustment prototype was designed to rearrange corn seeds into the aforementioned seed orientation. The main parts are the teeth plate, V-shaped groove, photo sensor, camera, LED light, and pneumatic solenoid valve. When a corn seed enters the prototype, it will first be led to a leaning state, then go through an image acquisition and processing process, where the template matching method will be used to find a region of interest (ROI) on a corn seed image. The embryo side will be identified based on the average normalized saturation value in the ROI. The pneumatic solenoid valve will then be activated to adjust the seed accordingly to the targeted seed orientation. Trial results showed a success rate of 84 % and an average adjustment time of 1.438 s. The proposed method of embryo side direction identification and adjustment will contribute to the development of corn-oriented seeding technology.

Construction Activity Recognition Method Based on Object Detection, Attention Orientation Estimation, and Person Re-Identification

Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker’s activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency.

Multi-DOF Orientation Detection of a Permanent Magnet Spherical Motor Based on Back EMF

Multi-DOF Orientation Detection of a Permanent Magnet Spherical Motor Based on Back EMF

Tac-scope: An endoscope integrated with soft sensor for real-time tactile feedback

In order to improve the safety of endoscopic surgery, a flexible endoscope with real-time tactile feedback (Tac-scope) has been proposed. It integrates a soft sensor at the end embedded with fiber Bragg grating (FBG) to detect the contact force with the lumen wall, while retaining the original function of the endoscope. In this article, theoretical analysis, simulated analysis, and experiments have been implemented, obtaining the mapping from FBG wavelength shifts to the force magnitude and orientation. To verify the real-time detection and feedback capability, tests are conducted on the bronchial model. The results show an average reduction of 60% in contact force and 30% in operation time. The linearity of Tac-scope force detection is 0.991, the repeatability error is 6.46%, the resolution is 8.8 mN, the sensitivity is 0.693 nm/N, the average error is 4.4%, and the error of orientation detection is 3.57%. Such merits indicate that the proposed Tac-scope has huge potential in endoscopic surgeries.

Non-destructive testing by eddy current sensor for crack orientation detection using HHO optimizer by differential probe

In this research, the design of non-destructive eddy current evaluation systems was improved using Harris Hawks optimization (HHO). To determine the orientation of cracks from eddy current data, a numerical approach was developed based finit elements methode FEM and on an iterative inversion technique that minimizes a non-linear least squares objective function that evaluates the overall difference between forecasts and observationsin axisymmetric model. In this paper the HHO optimization and steepest descent methods were compared and studied in order to minimize the objective function. To measure the precision of the estimated crack orientation, the lower bounds of the HHO optimization on crack orientation were obtained. This work explores the concept of an HHO algorithm that searches for the optimal solution within a large solution space for a specific crack orientation problem.The algorithm starts its search at any point in the solution space and looks for better solutions by searching in the "local" area surrounding the start point. The algorithm ends when it finds what it believes to be the best solution or when predetermined stopping criteria are met.

A Hardware-Based Orientation Detection System Using Dendritic Computation

Studying how objects are positioned is vital for improving technologies like robots, cameras, and virtual reality. In our earlier papers, we introduced a bio-inspired artificial visual system for orientation detection, demonstrating its superiority over traditional systems with higher recognition rates, greater biological resemblance, and increased resistance to noise. In this paper, we propose a hardware-based orientation detection system (ODS). The ODS is implemented by a multiple dendritic neuron model (DNM), and a neuronal pruning scheme for the DNM is proposed. After performing the neuronal pruning, only the synapses in the direct and inverse connections states are retained. The former can be realized by a comparator, and the latter can be replaced by a combination of a comparator and a logic NOT gate. For the dendritic function, the connection of synapses on dendrites can be realized with logic AND gates. Then, the output of the neuron is equivalent to a logic OR gate. Compared with other machine learning methods, this logic circuit circumvents floating-point arithmetic and therefore requires very little computing resources to perform complex classification. Furthermore, the ODS can be designed based on experience, so no learning process is required. The superiority of ODS is verified by experiments on binary, grayscale, and color image datasets. The ability to process data rapidly owing to advantages such as parallel computation and simple hardware implementation allows the ODS to be desirable in the era of big data. It is worth mentioning that the experimental results are corroborated with anatomical, physiological, and neuroscientific studies, which may provide us with a new insight for understanding the complex functions in the human brain.

User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning.

In this paper, the issue of detecting a user's position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for the binary classification of user orientation, neural networks achieved accuracy that was more than 9% higher than that for the well-known threshold method. In addition, the classification of user position angles relative to the reference node was analyzed. It was proven that, using the proposed deep learning approach and the channel impulse response, it was possible to estimate the angle of the user's position in relation to the antenna geometry. Absolute user orientation angle errors of about 4-7° for convolutional neural networks and of about 14-15° for multilayer perceptrons were achieved in approximately 85% of the cases in both tested scenarios.

Passive Visual Odometry of Mobile Transport Robots in Production Areas Using Anchor Points

The article considers the problem of determining the orientation of mobile transport robots, which are now actively used in almost all areas of industry. They make it possible to intensify production, free personnel from performing routine operations, and exclude the influence of the human factor from work. Based on the analysis of various variants of active orientation methods involving the use of binocular video systems installed on the vehicles themselves, it is proposed to use a passive orientation detection system. It includes a single stationary monocular video system, the replacement of onboard video systems on mobile robots with simple radiation sensors, as well as the use of anchor points in the area of their movement. The order of choice of anchor points in solving the problem of orientation of a mobile transport robot using a monocular video system, in particular for a rectangular interior space, is considered. Calculation formulas for determining the coordinates of the sensor from the pixel coordinates of its image obtained from a monocular video camera are determined. The general sequence of actions for determining the orientation of a mobile robotic platform is also considered. Unlike active systems, this method makes it possible to significantly simplify the hardware, significantly facilitate the analysis of the current position of the mobile robot and thereby reduce the computational complexity of calculations due to the fact that there is no need to use a complex mathematical apparatus. It is being replaced by simpler two-dimensional geometric calculations. This approach, due to the unified management, makes it possible to effectively coordinate the actions of a group of mobile transport robots when they are used together, greatly simplifies the solution of a number of tasks for optimizing the intra-shop movement of mobile vehicles.

Crop Guidance Photography Algorithm for Mobile Terminals

The issues of inadequate digital proficiency among agricultural practitioners and the suboptimal image quality captured using mobile smart devices have been addressed by providing appropriate guidance to photographers to properly position their mobile devices during image capture. An application for crop guidance photography was developed, which involved classifying and identifying crops from various orientations and providing guidance prompts. Three steps were executed, including increasing sample randomness, model pruning, and knowledge distillation, to improve the MobileNet model for constructing a smartphone-based orientation detection model with high accuracy and low computational requirements. Subsequently, the application was realized by utilizing the classification results for guidance prompts. The test demonstrated that this method effectively and seamlessly guided agricultural practitioners in capturing high-quality crop images, providing effective photographic guidance for farmers.

Visible Light Communication-Enabled Simultaneous Position and Orientation Detection for Harnessing Multipath Interference and Random Fading

Visible Light Communication-Enabled Simultaneous Position and Orientation Detection for Harnessing Multipath Interference and Random Fading

Quantitative Automated Detection of Voids, Pores, Cracks, and Fibre Orientation in Scanning Electron Microscopy Images Utilising Mask Convolutional Neural Networks (M-CNN) for Natural Fibre Composite Characterisation

Quantitative Automated Detection of Voids, Pores, Cracks, and Fibre Orientation in Scanning Electron Microscopy Images Utilising Mask Convolutional Neural Networks (M-CNN) for Natural Fibre Composite Characterisation

A Scalable Target Orientation Detection Method for Remote Sensing Images Based on Improved YOLOX Algorithm

A Scalable Target Orientation Detection Method for Remote Sensing Images Based on Improved YOLOX Algorithm

A Fully-Passive Frequency Diverse Lens-Enabled mmID for Precise Ranging and 2-Axis Orientation Detection in Next-Generation IoT and Cyberphysical Systems

A Fully-Passive Frequency Diverse Lens-Enabled mmID for Precise Ranging and 2-Axis Orientation Detection in Next-Generation IoT and Cyberphysical Systems

Optical Detection of Dynamic Polarization of Nuclei in Semiconductors

Much research has been performed by now on optical pumping of spin-polarized electrons in semiconductors [1–6]. In these experiments, the spin-oriented carriers were excited from the valence band into the conduction band by absorption of circularly-polarized light. The most convenient for the detection of the spin orientation of the electrons is an optical method [2, 3] with investigation of the recombination-luminescence polarization. For III–V crystals, the selection rules for interband transitions yield a simple connection S = 0.5P between the degree of circular polarization of the luminescence (S) and the degree of stationary orientation of the electrons (P) at the bottom of the conduction band. We have investigated, also by an optical method, a number of effects that arise when the electron spins interact strongly with the magnetic moments of the nuclei in the crystal.

Saliency-guided stairs detection on wearable RGB-D devices for visually impaired persons with Swin-Transformer

Accuracy stairs detection is crucial for people with visual impairment, as it can reduce the potential unforeseen risks of falling on stairs. Wearable RGB-D technology can assist blind and visually impaired individuals. However, existing stair detection algorithms on RGB-D images face difficulties in the stair material, texture, lighting, and direction. In this study, we proposed a saliency-guided stairs detection method based on Swin-Transformer to address the challenges mentioned above. First, saliency detection based on RGB-D images is used to learn spatial information for fast stair localization. Furthermore, we use the Swin-Transformer that incorporates key depth features of the stairs to solve orientation detection deficiencies. To evaluate the performance of our proposed method, we collected 3,290 RGB-D images, including the indoor and outdoor staircases. Experiments on our dataset show that our method can achieve high performance in terms of detection accuracy.

OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images

OP06.07: Machine learning‐based detection of fetal anatomical orientation in second trimester ultrasound images

A deep dive into the use of local positioning system in professional handball: Automatic detection of players' orientation, position and game phases to analyse specific physical demands.

The objective of this study is to automate and analyse the quantification of external load during an elite men's handball match. This study was carried out using data from a local positioning system and inertial measurement units. The literature review leads us to assume that physical demands are different depending on position, player specialty and phases of the game. In order to do this analysis, raw data was used from professional competitors of a Spanish club during National and European competition matches. First, a game phase algorithm was designed to automate phase recognition. Then, a descriptive evaluation of the means and standard deviation was performed with the following variables: total distance, total time, total Accel'Rate, the percentages of distance and time per speed and displacement direction. A Kruskal Wallis test was applied to normalized distance and normalized Accel'Rate. Defensive play showed the highest values on covered distance (930.6 ± 395.0 m). However, normalized distance showed significant differences (p<0.05) across all phases with defensive play (558.8 ± 53.9 m/10min) lower than offensive play (870.3 ± 145.7 m/10min), offensive transition (1671.3 ± 242.0 m/10min) or defensive transition (1604.5 ± 242.0 m/10min). Regarding position, wing players covered the most distance (2925.8 ± 998.8 m) at the second highest intensity (911.4 ± 63.3 m/10min) after offensive back players (1105.0 ± 84.9 m/10min). Significant difference in normalized requirements were found between each playing position: goalkeepers, wings, versatile backs, versatile line players, offensive backs and defensive backs (p<0.05), so a separation between offensive or defensive specialists is plausible and necessary. In conclusion, as physical demands differ for each game phase, activity profile among players is modulated by their playing position and their specialty (offense, defense or none). This study may help to create individual training programs according to precise on-court demands.

Robustness Improvement for Chipless RFID Reading Using Polarization Separation

In this article, a novel method to improve the readability of chipless tags is presented. When the EM wave falls upon the tag’s surface at normal incidence or with a known incidence, the proposed approach permits to obtain a higher signal-to-noise ratio (SNR) of resonators whose orientation with respect to the normal to the plane in which the tag is positioned is unknown and, thus, to increase the reading distance of the tag. It introduces a technique based on the detection of this tag orientation in relation to the antennas; a projection of the signals will allow correcting this misalignment in order to always be able to decode the tag identifier on a signal that corresponds to an ideal alignment between the tag and the antennas. With this principle, it is also possible to separate the resonance mode of the resonator that is connected to the identifier from other parasitic resonance modes that may appear due to a possible misalignment between the tag and the antenna. This principle is applied to loop resonators commonly used in chipless radio frequency identification (RFID), which can present strong parasitic modes of resonance. Also, this method makes the reading orientation invariant, allowing to read the resonator whatever its orientation with respect to the normal to the plane in which the tag is positioned. Likewise, the proposed method can be also used to sense this resonator orientation. This method requires a dual-polarization antenna and a two-port vector network analyzer (VNA). Results are validated in simulation and with real-environment measurements for a single resonator and multiple resonators, as it is classically used in chipless RFID technology. Measurements in a harsh environment, as well as comparisons with classical techniques, such as time gating (TG), are presented. A study on the improvement of the read range is achieved to highlight the potential of the proposed technique. A reading distance of more than 80 cm was, thus, obtained, which represents 20 cm more than with the classic use of a postprocessor based on time gating. Finally, the principle of the measurement is generalized by applying it to a tag with a ground plane and considering that the antenna is not necessarily placed in normal incidence with respect to the plane of the tag.

The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images

Human visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental process in the visual cortex where neurons respond to linear stimuli in specific orientations, plays a pivotal role in both fields. In this study, we propose a novel orientation detection mechanism for local neurons based on dendrite computation, specifically designed for grayscale images. Our model comprises eight neurons capable of detecting local orientation information, with inter-neuronal interactions facilitated through nonlinear dendrites. Through the extraction of local orientation information, this mechanism effectively derives global orientation information, as confirmed by successful computer simulations. Experimental results demonstrate that our mechanism exhibits remarkable orientation detection capabilities irrespective of variations in size, shape, or position, which aligns with previous physiological research findings. These findings contribute to our understanding of the human visual system and provide valuable insights into both biological and computer vision. The proposed orientation detection mechanism, with its nonlinear dendritic computations, offers a promising approach for improving orientation detection in grayscale images.