Distant Objects Research Articles

Research on personalized Japanese language teaching model based on big data technology

Abstract In order to better improve students’ Japanese language performance and help them progress, this paper proposes an analysis of personalized Japanese language teaching in a big data technology environment. We build a model of learner characteristics and use collaborative filtering techniques to push learning information from learners with the same or similar interest and preference characteristics. Adjust teaching strategies based on visual user information in the information panel. Build a personalized teaching model based on big data, extract and visualize the data of each student’s learning behavior, find out the gap between the teaching objectives and the preset ones, and record the students’ learning methods. Based on data to dynamically update the teaching content, assign the most appropriate learning tasks, determine the distance of teaching objectives, and improve students’ learning ability and learning effectiveness. Through inductive learning inference on the training dataset, the probabilities of all subsets are calculated, the relevant data subset intervals are merged, and the business sensitivity index is introduced to measure the whole source, which makes the improved decision tree gradually reduce the uncertainty of the division and decrease the weight degree of the attributes, which makes the personalized Japanese teaching better. The analysis results showed that the improved decision tree model reached 71% accuracy in 80 cases and stayed above 73% afterwards, and the ratio of students’ mastered knowledge to the number of knowledges learned was 0.4. Personalized Japanese language teaching under this method led to a significant improvement in students’ performance.

Mobile Surveillance Siren Against Moving Object as a Support System for Blind People

Visually impaired people can use smartphone navigation applications to arrive at their destination. However, those applications do not provide the means to detect moving objects. This paper presents an Android application that uses the smartphone’s camera to provide real-time object detection. Images captured by the camera are to be processed digitally. The model then predicts objects from the processed image using a Convolutional Neural Network (CNN) stored in mobile devices. The model returns bounding boxes for each of the detected objects. These bounding boxes are used to calculate the distance from the object to the camera. The model used is SSD MobileNet V1, which is pre-trained using the Common Objects in Context (COCO) dataset. System testing is divided into object distance and accuracy testing. Results show that the margin of error for calculating distance is below 5% for distances under 8 meters. The mean average precision is 0.9393, while the mean average recall is 0.4479. It means that the system can recognize moving objects through the embedded model in a smartphone.

Evaluation of public transportation station area accessibility based on walking perception

Public transportation (PT) often fails to provide door-to-door service. Passengers often have to walk a distance to reach their destination after getting off the public transportation station. Therefore, the walking accessibility of the station area directly affects the attractiveness of the PT. For walking, accurate calculation or prediction of accessibility should consider not only the objective distance, but also the environment and psychological perception factors of pedestrians. This paper aims to map the pedestrian perceived cost to the transportation environment to evaluate the walking accessibility of the public transportation station area accurately. From the perspective of psychological perception of walking environment, four key impedance factors are selected and a pedestrian perceived impedance model is established. Then an evaluation model of station area accessibility is set employing POIs (Point of Interests) based on the accumulative opportunity method. Finally, the case is given to show the application of the model. The results show that the number of crosswalks with signal lights, mixed use of sidewalk and non-motorized lane, the obstacle quantity and the vehicle entrance quantity on sidewalks can increase perceived impedance significantly. For example, pedestrians are willing to spend 4.21 extra minutes to adopt routes with one fewer obstacle per 100 meters. Within 10 minutes of walking time, walking perception has a greater impact on station area accessibility. The perceived walking time thresholds for evaluating bus and rail transit station area accessibility are recommended to be 15 minutes and 20 minutes, respectively. The evaluation results can provide a reliable basis for improving the walking network around public transportation station.

Bending of electric field lines and light-ray trajectories in a static gravitational field

It is well known that the light-ray trajectories follow a curved path in a gravitational field. This has been confirmed observationally where light rays coming from distant astronomical objects are seen to get bent in Sun’s gravitational field. We explore here the bending of electric field lines due to gravity. We determine, from a theoretical perspective, not only the exact shapes of the bent trajectories of light rays, emitted isotropically by a source supported in a gravitational field, but also demonstrate that the electric field lines of a charge, supported in a gravitational field, follow exactly the trajectories of light rays emitted isotropically from a source at the charge location. From a detailed examination of the electrostatic field of a charge accelerated uniformly in the instantaneous rest frame, exploiting the strong principle of equivalence, we determine the bending of the electric field lines of a charge in a gravitational field. The fraction of electric field lines crossing a surface, stationary above or below the charge in the gravitational field, are shown to be exactly similar to the fraction of light-ray trajectories intersecting that surface, emanating from a source lying at the charge location. On the other hand, for a freely falling charge in the gravitational field there is no such bending of electric field lines. The field lines continue to extend in radial straight lines from the instantaneous ‘present’ position of the charge, as do the trajectories of light rays spreading away from the instantaneous position of a freely falling source in the gravitational field. The electric field configuration of a freely falling charge in the gravitational field is shown to be exactly the same as that of a charge moving uniformly in an inertial frame with velocity equal to the instantaneous ‘present’ velocity of the freely falling charge.

The Orbital Properties of Black Holes: Exploring the Relationship between Orbital Velocity and Distance

This research explores the concept of black holes in the physics of general relativity, including its formation and properties. The study focuses on the relationship between the orbital velocity and orbital distance of objects around a black hole, which is measured in units of the speed of light (c) and kiloparsecs (kpc), respectively. Using observational techniques, the study produces a plot showing the relationship between orbital velocity and orbital distance, which follows Kepler's law modified by the Newtonian theory of gravity and general relativity. The study also highlights the effective potential of particles in orbit around a black hole, which combines the effects of kinetic energy and gravitational potential. The effective potential shows the gravitational and relativistic properties of black holes, such as the photon orbit radius, ISCO, and the spin parameter. The resulting plot demonstrates the characteristics of the Milky Way black hole and how its spin parameter and Schwarzschild radius affect the orbital properties of surrounding particles. The study concludes that the closer the orbital distance is to the black hole, the more the orbital velocity increases, and particles with high spin parameters and small Schwarzschild radii are unlikely to escape the black hole's gravity.

Performing arts metaverse: The effect of perceived distance and subjective experience

As a new paradigm for experiencing virtual worlds, metaverses are beginning to gain traction as a new way to enjoy the performing arts. Additionally, as its feasibility as a new market for the distribution of performing arts has been confirmed, there have been various attempts to better enable audiences to experience performing arts in a metaverse. However, metaverse performing arts still require several improvements for active user participation and better enjoyment. It is also necessary to understand the barriers that people perceive in experiencing metaverse performing arts. Based on the construal level theory, this study explores and identifies the causal relationship in factors that influence viewing metaverse performing arts, such as the user's psychological distance, objective distance, subjective experience, and resistance. The results demonstrate that artistic distance is a significant factor that strongly affects resistance to viewing performances. Spatial, social, economic distance, and subject experience also impact resistance. The results also uncover the issues that must be discussed to vitalize metaverse-based performing arts.

Continuous Vat Photopolymerization for Optical Lens Fabrication

Optical lenses require feature resolution and surface roughness that are beyond most (3D) printing methods. A new continuous projection-based vat photopolymerization process is reported that can directly shape polymer materials into optical lenses with microscale dimensional accuracy (< 14.7µm) and nanoscale surface roughness (< 20nm) without post-processing. The main idea is to utilize frustum layer stacking, instead of the conventional 2.5D layer stacking, to eliminate staircase aliasing. A continuous change of mask images is achieved using a zooming-focused projection system to generate the desired frustum layer stacking with controlled slant angles. The dynamic control of image size, objective and imaging distances, and light intensity involved in the zooming-focused continuous vat photopolymerization are systematically investigated. The experimental results reveal the effectiveness of the proposed process. The 3D-printed optical lenses with various designs, including parabolic lenses, fisheye lenses, and a laser beam expander, are fabricated with a surface roughness of 3.4nm without post-processing. The dimensional accuracy and optical performance of the 3D-printed compound parabolic concentrators and fisheye lenses within a few millimeters are investiagted. These results highlight the rapid and precise nature of this novel manufacturing process, demonstrating a promising avenue for future optical component and device fabrication.

Entanglement-assisted multi-aperture pulse-compression radar for angle resolving detection

Entanglement has been known to boost target detection, despite it being destroyed by lossy-noisy propagation. Recently, Zhuang and Shapiro (2022 Phys. Rev. Lett. 128 010501) proposed a quantum pulse-compression radar to extend entanglement’s benefit to target range estimation. In a radar application, many other aspects of the target are of interest, including angle, velocity and cross section. In this study, we propose a dual-receiver radar scheme that employs a high time-bandwidth product microwave pulse entangled with a pre-shared reference signal available at the receiver, to investigate the direction of a distant object and show that the direction-resolving capability is significantly improved by entanglement, compared to its classical counterpart under the same parameter settings. We identify the applicable scenario of this quantum radar to be short-range and high-frequency, which enables entanglement’s benefit in a reasonable integration time.

The radiation dose, clinical and anatomical implications of erect lumbar spine radiography: A single centre pre-post implementation evaluation

IntroductionLumbar radiographs remain a common imaging examination despite strategies to reduce their use. Many authors have demonstrated benefits in changing from traditional supine and recumbent lateral projections to a prone and/or erect orientation. Despite evidence of clinical and radiation dose optimisation, widespread adoption of these strategies has stalled. This article describes the single-centre implementation and evaluation of erect PA and lateral projections. MethodThis was an observational study pre- and post-implementation of an erect imaging protocol. Patient BMI, image field size, source image and source object distances and DAP were collected together with assessment of radiographic spinal alignment and disc space demonstration. Effective dose was calculated with organ specific doses. Results76 (53.5%) patients were imaged in the supine AP and recumbent lateral position, 66 (46.5%) had erect PA and lateral radiographs. Despite the larger BMI of the erect cohort and similar field sizes, effective dose was lower in the PA position by an average of 20% (p < 0.05), however, no significant difference in lateral dose. Anatomical improvements were evident with greater visualisation of intervertebral disc spaces in the PA erect (t = −9.03; p < .001) and lateral (t = −10.298; p < .001) orientations. Erect PA radiographs demonstrated a leg length discrepancy in 47.0% (0.3–4.7 cm) and a scoliosis in 21.2% of cases, with a significant link between these findings (r (64) = 0.44; p < .001). ConclusionErect lumbar spine radiography provides information on clinical outcomes not available with recumbent projections. The improvements in anatomical visualisation and radiation dose reduction supports the local change in practice. Implications for practice- Erect imaging can reduce effective dose with an optimised acquisition protocol- additional pathological information can be revealed by the erect posture.- postural awareness is critical to interpret images accurately.

Spectral characteristics analysis of typical ground objects in Poyang Lake wetland based on “Zhuhai-1” hyperspectral data

Hyperspectral image data provides new ideas and methods for the fine classification of wetland. Research on wetland spectral characteristics lays the foundation for wetland classification and improves classification accuracy. Based on “Zhuhai-1” hyperspectral image data, combined with the Pure Pixel Index (PPI), this paper extracted the average spectral curves of 11 typical ground objects in Poyang Lake wetland. Two mathematical transformation methods, namely, the continuum removal and the first derivative, were used to analyze the spectral characteristics of the ground objects before and after transformation, in combination with spectral characteristic parameters. The method of combining the mean distance of ground objects with the range of data error was used to select characteristic wavelengths before and after spectral transformation. Finally, the Mahalanobis distance method was used to verify the recognition effect. The results demonstrate that: (1) Spectral transformation can effectively amplify the differences between ground objects. The continuum removal led to an increase in the characteristic bands of four types of vegetation and the emergence of characteristic bands of four types of other ground objects. The first derivative method extended the characteristic bands of four vegetation from red-edge to near-infrared. (2) The method of combining the mean distance and the data error range can effectively screened out the spectral characteristic bands of typical ground objects in Poyang Lake wetland, resulting in hyperspectral dimensionality reduction. (3) The Mahalanobis distance method validates the recognition effect of the characteristic bands. In the screened characteristic bands, the Mahalanobis distance between different ground objects is larger than that between the same ground object, except between shoaly land and road. This study offers valuable insights into spectral recognition and classification of freshwater lake wetlands, and also demonstrates the potential of “Zhuhai-1” hyperspectral image data in wetland research.

Fusion of an RGB camera and LiDAR sensor through a Graph CNN for 3D object detection

Despite the recent development of RGB camera and LiDAR sensor fusion technology using deep learning, fusion without loss of information is a very difficult problem because the structural data characteristics of the two sensors are different. To solve this problem, we use a graph convolutional neural network (Graph CNN) to fuse RGB and LiDAR sensors. The proposed method creates a fusion feature by supplementing the geometric information of each feature in the process of fusing the features of two different sensors. Based on the experimental data, the proposed method has higher accuracy in detecting distant objects and complex situations than the existing method.

Enhanced depth of field of integral imaging display using bifocal microlens array fabricated by two‐step lithography

AbstractDue to the limitation of traditional microlens arrays (MLAs) in integral imaging display, the depth of field (DOF) is restricted in space and the center depth plane is difficult to extend in a large range. Here, we propose a microfabrication method based on bifocal MLAs to improve DOF. The bifocal MLAs for extended DOF were fabricated by using two‐step photolithography and thermal reflow. This method allows diverse microlenses of high to low numerical aperture to achieve high spatial resolution as well as accurate depth estimation. Microlenses of different focal lengths were simultaneously deposited on a substrate by repeated photolithography with multiple photomasks with alignment mark to define micro‐posts of different thicknesses. Hexagonally packaged bifocal MLAs clearly show the DOF extended from 0.004 to 4.908 mm for 57.6 μm in lens diameter, and their corresponding object distance ranges from 0.125 to 0.165 mm. Based on the proposed scheme, this method provides potential applications in integral imaging 3D display or light field display.

Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene.

Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25-30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm's robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm's estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios.

Scale-adaptive three-dimensional imaging using Risley-prism-based coherent lidar.

We present a scale-adaptive three-dimensional (3D) imaging architecture for coherent light detection and ranging (lidar) that incorporates Risley-prism-based beam scanning. An inverse design paradigm from beam steering to prism rotation is developed for demand-oriented beam scan pattern generation and prism motion law formulation, which allows the lidar to perform 3D imaging with adaptive scale and configurable resolution. By combining flexible beam manipulation with simultaneous distance and velocity measurement, the proposed architecture can achieve both large-scale scene reconstruction for situational awareness and small-scale object identification against long range. The experiment results demonstrate that our architecture enables the lidar to recover a 3D scene in a ±30° field of view and also focus on distant objects at over 500 m with spatial resolution up to 1.1 cm.

Improving 3D Vulnerable Road User Detection With Point Augmentation

Point clouds have been a popular representation to describe 3D environments for autonomous driving applications. Despite accurate depth information, sparsity of pointsresults in difficulties in extracting sufficient features from vulnerable objects of small sizes. One solution is leveraging self-attention networks to build long-range connections between similar objects. Another method is using generative models to estimate the complete shape of objects. Both approaches introduce large memory consumption and extra complexity to the models while the geometric characteristics of objects are overlooked. To overcome this problem, this paper proposes Point Augmentation (PA)- RCNN, focusing on small object detection by generating efficient complementary features without trainable parameters. Specifically, 3D points are sampled with the guidance of object proposals and encoded through the 3D grid-based feature aggregation to produce localised 3D voxel properties. Such voxel attributes are fed to the pooling module with the aid of fictional points, which are transformed from sampled points considering geometric symmetry. Experimental results on Waymo Open Dataset and KITTI dataset show a superior advantage in the detection of distant and small objects in comparison with existing state-ofthe- art methods.

IMPLEMENTASI METODE CNN UNTUK KLASIFIKASI OBJEK

Objects can be interpreted as all inanimate and living things that have various shapes and sizes. For humans to determine the presence of objects, to classify and estimate the distance of objects around them is not difficult. But for a computer to do the work mentioned above with an accuracy level that reaches up to greater than 90% is not easy. Object detection is important in the field of computer vision because it is used to monitor and track objects, while robots that use cameras as sensors are used to avoid obstacles, follow objects, classify and so on. Therefore, the purpose of this study was to determine the level of accuracy of the CNN method in classifying objects. The steps used to complete this research were literature study, collecting digital image data, determining training, and testing data, designing the CNN program, conducting training and testing. From the results of testing the CNN method that has been carried out, it is known that the level of accuracy in classifying objects reaches 98%.

Autofocusing of Fresnel zone aperture lensless imaging for QR code recognition.

Fresnel zone aperture (FZA) lensless imaging encodes the incident light into a hologram-like pattern, so that the scene image can be numerically focused at a long imaging range by the back propagation method. However, the target distance is uncertain. The inaccurate distance causes blurs and artifacts in the reconstructed images. This brings difficulties for the target recognition applications, such as quick response code scanning. We propose an autofocusing method for FZA lensless imaging. By incorporating the image sharpness metrics into the back propagation reconstruction process, the method can acquire the desired focusing distance and reconstruct noise-free high-contrast images. By combining the Tamura of the gradient metrics and nuclear norm of gradient, the relative error of estimated object distance is only 0.95% in the experiment. The proposed reconstruction method significantly improves the mean recognition rate of QR code from 4.06% to 90.00%. It paves the way for designing intelligent integrated sensors.

Flexible Long-Reach Robotic Limbs Using Tape Springs for Mobility and Manipulation

Abstract Conventional mobile robots have difficulty navigating highly unstructured spaces such as caves and forests. In these environments, a highly extendable limb could be useful for deploying hooks to climb over terrain, or for reaching hard-to-access sites for sample collection. This article details a new form of a multimodal mobile robot that utilizes a novel tape spring limb named EEMMMa (elastic extending mechanism for mobility and manipulation). Its innovative U-shaped tape structure allows it to handle loads in tension as well as compression. It can also bend using mechanical multiplexing for a lightweight and compact design that is well suited for mobile robots. For mobility, the limb can extend prismatically to deploy grappling hook anchors to suspend and transport the main body, or even serve as legs. For manipulation, the limb can morph its shape to bend around or over obstacles, allowing it to retrieve distant objects or position cameras around corners. The EEMMMa-1 prototype detailed in this article successfully demonstrates climbing ladders and shelves in 1.5 body lengths per second, and can bend up to 100 deg. A simplified model of the bending kinematics is developed and analyzed. This article concludes by detailing future EEMMMa applications and theories to strengthen the model in future studies.

Transfer Learning Approach for Indoor Localization with Small Datasets

Indoor pedestrian localization has been the subject of a great deal of recent research. Various studies have employed pedestrian dead reckoning, which determines pedestrian positions by transforming data collected through sensors into pedestrian gait information. Although several studies have recently applied deep learning to moving object distance estimations using naturally collected everyday life data, this data collection approach requires a long time, resulting in a lack of data for specific labels or a significant data imbalance problem for specific labels. In this study, to compensate for the problems of the existing PDR, a method based on transfer learning and data augmentation is proposed for estimating moving object distances for pedestrians. Consistent high-performance moving object distance estimation is achieved using only a small training dataset, and the problem of the concentration of training data only on labels within a certain range is solved using window warping and scaling methods. The training dataset consists of the three-axes values of the accelerometer sensor and the pedestrian’s movement speed calculated based on GPS coordinates. All data and GPS coordinates are collected through the smartphone. A performance evaluation of the proposed moving pedestrian distance estimation system shows a high distance error performance of 3.59 m with only approximately 17% training data compared to other moving object distance estimation techniques.

An Infrared Image Stitching Method for Wind Turbine Blade Using UAV Flight Data and U-Net

Infrared image stitching of wind turbine blades is a challenging task due to weak texture and low contrast. To address this issue, an infrared image stitching method for wind turbine blades based on unmanned aerial vehicle (UAV) flight data and u-shaped neural network (U-Net) is proposed in this article. Remove complex background using U-Net semantic segmentation network and retain the complete area of the blades to be stitched. The rotation angle of blades is calculated to unify the orientation of the blades in all images to be horizontal, the increasing pixel between adjacent images is calculated by the velocity, diagonal field of view (DFOV), and object distance of UAV, and the scaling coefficient is calculated to align the upper and lower edges of blades. Based on the above parameters, the panoramic infrared images of wind turbine blades can be finally obtained. Through comparative analysis and quantitative evaluation with the widely used image stitching algorithms, it is shown that the proposed method can obtain better quality results.