Distant Objects Research Articles

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.

Co-relation Between Memory Functions and Vision Impairment Amongst Individuals with Spectacles: A Cross Sectional Study

Background :- Vision impairment includes the weakness in the eyesight, not able to recognize distant objects, difficulty in remembering things, troubling reading and writing which affect the ability in making decisions, carry out particular tasks, and their satisfaction with their memory even having the spectacles and also, using them from long duration. Aim: - To correlate the memory impairment in the people having spectacles Methods - A cross sectional study was carried out among 73 individuals of the age group between 18-25 years. This study was done to find out the correlation between the memory impairment in the people using spectacles. The study included individuals having spectacles, power ranging from 0.5 and above. The Individuals having psychological problems and not wearing spectacles on daily basis were excluded from the study. The outcome measure used for the study was Multifactorial memory questionnaire which includes the components of ability, satisfaction, and strategy. Result: - Spearman Correlation Analysis was used for assessing eyesight of the right side and left side with MMQ scale. The results obtained proved to be statistically non- significant. (p-value >0.05). Conclusion: - The study demonstrated that there was no significant correlation between people wearing spectacles and memory in the young population.

ЗАХОДИ ПОПУЛЯРИЗАЦІЇ АСТРОНОМІЇ

Astronomy is a natural-mathematical science that opens unknown pages of both our Solar system and Universe. However, most people have the impression that astronomy is only about the stars, romance and astrological. Popular scientific events can accelerate this opinion, namely: scientific exhibitions, locations, popular scientific magazines, films and video clips, excursions to observatories, scientific and educational institutions. The actual problem of natural and mathematical training of students contributed to the growing popularity of entertaining and popular science events. It able be organize interactive lectures, where a trained scientist will tell interesting information in a language accessible to the audience, and answer their questions. If you add demonstrations of objects of the universe to the performance, you can create the illusion of full involvement of the listeners in scientific discoveries and observations. Another way of actively perceiving information is the organization of interactive astronomical locations, such as quests, quizzes, mazes, intelligence tasks, rebuses. It will be appropriate to involve modern digital technologies. Astronomical models and instruments add more visuality to the locations. Students of higher education institutions take part in such events with great pleasure, prepare models, information brochures, draw posters, and create planetary systems. Posters or brochures can be made in the form of applications or developed own electronic makets. This scientific and educational material is used and demonstrated at various events and interests not only students but also their parents. In addition to communication and inspection of models at mass popular science events, visitors can be offered to independently make their own template (for example, a gnomon or a sundial), draw certain constellations, assemble models of the solar system or star constellations, adjust astronomical instrument (for example a telescope for observing distant terrestrial objects or the Sun), and learn to tell time using a self-made watch. Popularization of astronomy as a science is not only interesting, but also contributes to increasing education among the younger generation, students and adults. Properly selected equipment and good material will be able to interest the listener and increase interest in astronomy, as an important component of natural and mathematical science.

Optical simulations of the impact of vault increase in scleral contact lenses in healthy eyes

PurposeTo investigate by using computational simulations the optical impact of the change in the vault of two geometries of scleral contact lenses (SCLs). MethodsRay-tracing simulations were performed using specialized software in three eye models with different levels of primary SA (6 mm pupil). Two different geometries of SCL were used in such simulations characterized by the conic constants of the anterior surface of the lens (K1, −0.1 and −0.3). Likewise, the fitting of the SCL was simulated for different vaults (50–250 µm). The impact on the quality of the images through the eye models was assessed by analyzing the modulation transfer function (MTF) at different spatial frequencies (10 Lp/mm, 30 Lp/mm, and 50 Lp/mm). This impact was not only simulated for a distant object, but also for intermediate and near objects (vergence demands from 0.00 to 3.00 D). All these optical simulations were performed assuming a centered SCL, but also assuming a downward vertical decentration of 0.5 mm. ResultsThe thinnest vault (50 µm) provided the best ocular optical quality in all three eye models for low vergence demands. For medium and high vergence demands, Lens 1 (K1 = −0.3, K2 = −0.4) resulted in a considerable improvement in optical quality in Eye 2 (C40 = −0.078 µm), while for eyes 1 (C40 = 0.408 µm) and 3 (C40 = −0.195 µm), this improvement only tended to happen for medium vergence demands. Overall, all the aberrations increased after lens fitting. Lens decentration did not cause significant variations in the results obtained with the well-centered lenses. ConclusionsChanges in the vault of a SCL have an impact on the optical quality achieved for different vergence demands independently on the level of SA of the eye in which it is fitted. The clinical relevance of such impact should be investigated further.

СЛАБОЕ ГРАВИТАЦИОННОЕ ЛИНЗИРОВАНИЕ ЧЕРНЫМИ ДЫРАМИ С ЗАРЯДОМ

The deflection of light due to gravity was predicted by general relativity and first confirmed by observations in 1919. In the following decades, various aspects of the gravitational lensing effect were theoretically explored, including the possibility of obtaining multiple or annular images of background sources, the use of lensing as a gravitational telescope on very faint and distant objects, and the possibility of determining the Hubble constant using lensing. Gravitational lensing is the deflection of light by gravity caused by the action of compact astrophysical objects. Various objects, such as a galaxy, stars, black holes, wormholes, and others, can act as a gravitational lens. In our study, as a gravitational lens, we chose black holes described by the Gibbons-Maeda-GarfinkleHorowitz-Strominger solutions in the string frame. The two solutions in the string frame are magnetically and electrically charged black holes with an event horizon. The work will investigate the effect of electric and magnetic charges on the angle of deflection of light.

PEMERIKSAAN PENDERITA PRESBYOPIA DENGAN STATUS REFRAKSI HYPERMETROPIA, MYOPIA, ASTIGMAT DAN EMETROPIA MASYARAKAT CILANDAK PERIODE JANUARI 2023

The inability of a person to see close objects clearly can occur for several reasons, including presbyopia. Meanwhile, a person's inability to see distant objects clearly can occur for several reasons, including because his eyes have hypermetropic refractive status, myopia, astigmatism and emmetropia. This research was carried out using a descriptive method through a quantitative approach. The results showed that of the 53 Cilandak people who received a subjective refraction examination with optic 688, they were presbyopic patients with right (26.4%) and left (28.3%) hypermetropia refractive status, right and left myopia (41.5%), left com astigmatism (22.6%), right com astigmatism (18.9%), right sim astigmatism (13.2%), left sim astigmatism (15.1%), and right emmetropia (31.1%) and Emmetropia left 28.3%). In the examination of subjective refraction, determining the size of spectacle lenses for far vision for patients with hypermetropia is based on a concept, namely by providing the largest spherical lens that can produce the best vision. Meanwhile, for myopia, the weakest spherical correction lens is provided. For astigmatism, cylindrical correction lenses are given, determining the size of spectacle lenses for near vision for presbyopia sufferers with hypermetropia refractive status, which is the accumulation of lens diopters for far vision and addition. It can be concluded that the ultimate goal of examining subjective refraction in patients with presbyopia with hypermetropia, myopia and astigmatism is to determine the appropriate lens size, so that glasses made with these sizes can function as a visual aid.

Tempat Sampah Pintar Berbasis IoT

The behavior of people who often litter is one of the factors that affect environmental cleanliness. For this reason, an innovative tool is needed, namely the Smart Trash Can that is able to manage waste using a water sensor and determine the capacity and distance of the waste object using an ultrasonic sensor and can perform automatic control of the type of waste using a servo output. This Smart Trash can make it easier for users to manage waste on a small scale. How this tool works uses NodeMCU as a microcontroller to retrieve data generated by rain sensors, ultrasonic sensors, and micro servos. Because NodeMCU already has a wifi module, the data is sent to telegram for garbage notification and communication between trash bins.

Solar Energy Based Object Detection and Automatic Speed Reducing Intelligence Robot Using Sensors

The average human life span has decreased since ancient times. Because the number of automobiles on the road is growing every day, the death rate from accidents has dramatically grown. Because brake failure causes so many accidents, we can lessen their impact when the brake is controlled automatically. A receiver and an emitter make up an ultrasonic setup that is put up in front of the vehicle. When an obstacle is recognized, the ultrasonic waves are always emitted by the ultrasonic emitter and are reflected back to the receiver, which then picks up the signal. The Arduino controller receives the signal from the reflected wave, and depending on the object's distance, it activates the buzzer and stops the car with LED indicators. motor used to move the vehicle.

Improved LeGO-LOAM method based on outlier points elimination

Based on the analysis of the shortcomings of Lightweight and Ground-Optimized Lidar Odometry and Mapping (LeGO-LOAM) algorithm, an improved LeGO-LOAM method based on outlier points elimination (LeGO-LOAM-OE) was proposed. The original point cloud data generated by Light Detection and Ranging (LiDAR) was analyzed and the four types of outlier points were eliminated, which made the extracted feature points more effective and improved the accuracy of pose estimation. In ground segmentation, the levelness of the ground is constrained so that the extracted ground points are more abundant and accurate. In dynamic point elimination, dynamic objects are eliminated by the moving distance of the same cluster objects in adjacent frames, and the calculation method is simple. The simulation results show that the trajectory accuracy generated by LeGO-LOAM-OE is higher than that generated by LeGO-LOAM. Compared with LeGO-LOAM, LeGO-LOAM-OE can effectively reduce the maximum error and average error of three axes. The total maximum error is reduced by 14.3%, and the total mean error is reduced by 7.6%. LeGO-LOAM-OE had the most significant effect on reducing the Y-axis error (vertical direction), with an average error reduction of 8.5%. An experimental platform was built to test the positioning accuracy and mapping effect of the proposed algorithm in an outdoor environment. Experimental results showed that the proposed method has high accuracy in both online and offline simultaneous localization and mapping (SLAM). The maximum error of the calculated position is less than 1 m, and the average error is less than 0.1 m. Moreover, the generated point cloud map can highly reproduce the environment without deviation.

Plastic Classification Using Optical Parameter Features Measured with the TMF8801 Direct Time-of-Flight Depth Sensor.

We demonstrate a methodology for non-contact classification of five different plastic types using an inexpensive direct time-of-flight (ToF) sensor, the AMS TMF8801, designed for consumer electronics. The direct ToF sensor measures the time for a brief pulse of light to return from the material with the intensity change and spatial and temporal spread of the returned light conveying information on the optical properties of the material. We use measured ToF histogram data of all five plastics, captured at a range of sensor to material distances, to train a classifier that achieves 96% accuracy on a test dataset. To extend the generality and provide insight into the classification process, we fit the ToF histogram data to a physics-based model that differentiates between surface scattering and subsurface scattering. Three optical parameters of the ratio of direct to subsurface intensity, the object distance, and the time constant of the subsurface exponential decay are used as features for a classifier that achieves 88% accuracy. Additional measurements at a fixed distance of 22.5 cm showed perfect classification and revealed that Poisson noise is not the most significant source of variation when measurements are taken over a range of object distances. In total, this work proposes optical parameters for material classification that are robust over object distance and measurable by miniature direct time-of-flight sensors designed for installation in smartphones.

Turbulence video simulation using the spatiotemporal cross correlation of distortion fields.

Previous simulations of atmospheric turbulence in videos are computationally complex. The purpose of this study is to develop an efficient algorithm for simulating spatiotemporal videos affected by atmospheric turbulence, given a static image. We extend an existing method for the simulation of atmospheric turbulence in a single image by incorporating turbulence properties in the time domain and the blurring effect. We accomplish this through analysis of the correlation between turbulence image distortions in time and in space. The significance of this method is the ease with which it will be possible to produce a simulation, given properties of the turbulence (including turbulence strength, object distance, and height). We apply the simulation to low and high frame rate videos, and we show that the spatiotemporal cross correlation of the distortion fields in the simulated video matches the physical spatiotemporal cross correlation function. Such a simulation can be useful when developing algorithms that apply to videos degraded by atmospheric turbulence and require a large amount of imaging data for training.

ODRP: a new approach for spatial street sign detection from EXIF using deep learning-based object detection, distance estimation, rotation and projection system

ODRP: a new approach for spatial street sign detection from EXIF using deep learning-based object detection, distance estimation, rotation and projection system