Precise Distance Research Articles

Anatomical Measurements of the Malar Bone for Safe Zygomatic Implant Placement: A Study on Donated Bodies

Background: The malar bone provides an anchorage point for zygomatic implants, avoiding invasive reconstructive surgeries in the fixed rehabilitation of fully edentulous and severely atrophic maxillae. The limited bone volume, however, requires precise implant placement to prevent complications related to nearby anatomical structures. This observational cross-sectional study aims to measure the malar and zygomatic arch bones and their distances from critical anatomical landmarks to guide surgeons in safe zygomatic implant placement. Methods: Dissections were performed bilaterally on 29 heads from human donated bodies in a cross-sectional observational study. Key landmarks evaluated include the infraorbital foramen (IF), pyriform nasal aperture (PNA), infraorbital margin (IM), zygomaticofacial foramen (ZFF), anterior end (A), and the most protruding point of the zygomatic arch (B). Measurements included IF-PNA, IF-IM, IF-ZFF, ZFF-IM, A-B, and orbital floor depth (OFD). Results: Significant findings showed IF-PNA was greater in males (18.66 ± 2.63 mm, p = 0.001), and IF-ZFF varied between sides (26.72 ± 8.7 mm, p = 0.002). ZFF-IM was larger in males (7.43 ± 2.09 mm, p < 0.001). Heights and thicknesses were also assessed, with significant side differences observed. Conclusions: These findings underscore the importance of understanding precise anatomical distances for successful implant placement. The study provides essential data to enhance surgical planning and training, ensuring safer procedures and minimizing the risk of complications.

Enhancing Surveillance Systems with Cost-Effective Ultrasonic Radar for Object Detection and Tracking

Object detection is critical in surveillance systems because it enables timely identification of unauthorized objects, which is essential for maintaining security and mitigating risks. Existing approaches to surveillance typically suffer from limited coverage, variable detection range, moderate accuracy, high cost, and complex setup requirements. This article presents an approach to object detection using microcontroller-based ultrasonic sensors. Our approach leverages four distinct processing phases to create a radar-like system capable of real-time detection and autonomous decision-making. In this study, we conducted experiments by integrating more than six peripheral modules with the Arduino platform. By evaluating 17 diverse test cases in various scenarios and environments, the approach demonstrated enhanced object tracking and robustness compared to existing methods. The system effectively detects objects within the targeted area, providing precise distance measurements (around 50 cm) and position information (0 to 360 degrees). Moreover, our approach enabled the identification of three risk zones—medium, high-risk, and mild danger—within the critical region, contributing to improved security and risk management. The results reveal the system’s effectiveness in real-time detection, accurate distance estimation, and comprehensive risk assessment. Overall, our study significantly contributes to the advancement of object detection technology by effectively addressing the limitations of existing methods and offering a cost-effective and practical solution. Our research has the potential to monitor objects in surveillance systems across various domains, with significant implications for enhancing security, risk mitigation, and monitoring in industries reliant on effective object detection systems. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 157−171

Distance and stellar parameter estimations of solar-like stars from the LAMOST spectroscopic survey

The Gaia mission has opened up a new era for the precise astrometry of stars, thus revolutionizing our understanding of the Milky Way. However, beyond a few kiloparseconds from the Sun, parallax measurements become less reliable, and even within 2 kpc, there still exist stars with large uncertainties. Our aim was to determine the distance and stellar parameters of 521,424 solar-like stars from LAMOST DR9; these stars lacked precise distance measurements (uncertainties higher than 20<!PCT!> or even without any distance estimations) when checked with Gaia. We proposed a convolutional neural network (CNN) model to predict the absolute magnitudes, colors, and stellar parameters ( and directly from low-resolution spectra. For spectra with signal-to-noise ratios at $g$ band ($ S/N_g $) greater than 10, the model achieves a precision of 85 K for 0.07 dex for 0.06 dex for 0.25 mag for and 0.03 mag for The estimated distances have a median fractional error of 4<!PCT!> with a standard deviation of 8<!PCT!>. We applied the trained CNN model to 521,424 solar-like stars to derive the distance and stellar parameters. Compared with other distance estimation studies and spectroscopic surveys, the results show good consistency. Additionally, we investigated the metallicity gradients of the Milky Way from a subsample, and find a radial gradient ranging from $-0.05 < R < 0.0\ dex\ $ and a vertical gradient ranging from $-0.26 < Z < -0.07\ dex\ We conclude that our method is effective in estimating distances and stellar parameters for solar-like stars with limited astrometric data. Our measurements are reliable for Galactic structure studies and hopefully will be useful for exoplanet researches.

An adaptive learning based aberrance repressed multi-feature integrated correlation filter for Visual Object Tracking (VOT)

Target tracking via Correlation Filter (CF) is a hot research area of computer vision domain, and offers various credible benefits. Existing CF algorithms face challenges when there are target appearance variations due to background noise, scale and illumination changes, occlusion, and fast motion, which severely degrades the overall tracker performance. To get maximum benefits, an object tracker should perform well with the less computational burden in the presence of real time challenging situations. To address this issue, a novel visual object trackeris proposed based on multi feature fusion and adaptive learning technique with aberrance suppression. At first, multiple features i.e., Histogram of gradient (HOG), Color Naming (CN), saliency, and gray level intensities are combined using feature fusion technique. Further, based on the evaluation of final fused response map using Peak-to-Sidelobe Ratio (PSR), an adaptive learning strategy is integrated to improve the learning phase of tracker. Tracking results show that the proposed strategy beats the other modern CF trackers with Distance Precision (DP) scores of 88.2%, 85.9%, and 74.1% and 64.7% over OTB2013, OTB2015, and TempleColor128 and UAV123 datasets respectively.

Uncovering the first-infall history of the LMC through its dynamical impact in the Milky Way halo

ABSTRACT The gravitational interactions between the LMC and the Milky Way cause dynamical perturbations in the MW halo, leading to biased distributions of stellar density and kinematics. We run 50 high-resolution N-body simulations exploring varying masses and halo shapes of the MW and LMC to study the evolution of LMC-induced perturbations. By measuring mean velocities of simulated halo stars, we identify a discontinuity between the first-infall and second-passage scenarios of the LMC’s orbital history. In the first-infall, the Galactocentric latitudinal velocity hovers around 16 km s$^{-1}$ for stars at 50–100 kpc, while it subsides to about 8 km s$^{-1}$ in the second-passage scenario. We demonstrate that, this reduced perturbation magnitude in the second-passage scenario is mainly due to the short dynamical times of the Galactic inner halo and the lower velocity of the LMC during its second infall into the MW. Using a subset of $\sim 1100$ RR Lyrae stars located in the outer halo (50 kpc $\le R_{\mathrm{GC}}\lt $ 100 kpc) with precise distance estimates from Gaia, we find the mean latitudinal velocity ($v_{b}$) in the Galactocentric frame to be $\langle v_{b} \rangle =18.1 \pm 4.1$ km s$^{-1}$. The observation supports the first-infall scenario with a massive LMC ($\sim$$2.1 \times 10^{11} \, \mathrm{M}_{\odot }$) at infall, an oblate MW halo with a virial mass $M_{200}\lt 1.4\times 10^{12}\,\mathrm{M}_{\odot }$ and a flattening parameter $q\gt 0.7$. Our study indicates that LMC-induced kinematic disturbances can reveal its orbital history and key characteristics, as well as those of the MW. This approach shows promise in helping determine fundamental parameters of both galaxies.

PCR And Sequencing Technique Detection Of Gardnerella Spp Associated With Urinary Tract Vaginosis

Introduction: Gardnerella is a bacteria originally described in bacterial vaginosis, which can colonize the urinary tract and is often associated with vaginosis when present as a single-species flora. Commonly found in vaginal tracts, and it is also associated genital tract problems. Methods: The current study was to identify the genetic variation of 16SrRNA gene in Gardnerella spp. which isolated from urine samples. In this study, 100 samples of urine from of women suffering from Urinary tract problems collected between August to September 2023 in Thi-Qar province of Iraq. Results: The PCR technique results noted that (90%) of urine samples had Gardnerella sp. isolates by 16SrRNA gene. The bacterial samples recorded in NCBI (LC774556, LC774557, LC774558, LC774559, LC774560 LC7745561, LC774562 and LC774563) a 278bp genetic fragment that partially encompassed a coding region of the 16SrRNA gene was enlarged in this study. The phylogenetic tree was built in order to evaluate the precise phylogenetic distances, in addition to other related bacterial genomes. The present results exhibited that Gardnerella spp. sequences showed around 99% homology. Genetic variations of 16SrRNA gene were identified, the major variations were variably distributed in the (LC774556, LC774557) of the studied samples; while the variation in (G176A) was identified only in LC774556 sample. Also, the deletion mutation (A116 del) was noticed in all studied samples, while (T129 del) was showed in (LC774556, LC774558, LC774560) samples. Conclusion: The infection with Gardnerella vaginalis. may be related with Bacterial vaginosis tract; Also, the DNA sequencing used to detect the genetic variation of Gardnerella vaginalis.

Environmental transport and sorting of glass retroreflective microbeads and their potential as proxies for road marking paints

Road marking paints are a potentially important contributor to the global microplastic pool but very little reliable information is available on their erosion or environmental distributions. As potential carriers of or proxies for road paints, we determine the concentrations and sorting of retroreflective glass microbeads in marking materials and in fractionated (< 5 mm) local dusts, soils and sediments. As an aid to our investigation, we also determine the concentrations of metals of geochemical significance or components of road paint pigments in markings and geosolids. Concentrations of beads up to 92,800 kg−1 were observed in street dusts, with a median diameter (350 μm) greater than that in road marking samples (270 μm). Few beads were found in adjacent (< 5 m) or more remote soils (six beads in ten 50-g samples) and none were detected in replicates of a sample of roof dust, suggesting that aeolian transport is limited. Concentrations up to 3700 kg−1 were found in estuarine sediments close to bridges or stormwater runoff effluents, and with increasing sediment depth concentrations and median diameter decreased; beads were not, however, detected in sediments 400 m away from any significant roads or runoff effluents. These observations suggest that bead accumulation is constrained locally but that the precise distance travelled and extent of burial in sediments are inversely related to size. Road marking paints sampled from urban streets readily fragmented into pieces smaller than glass microbeads, suggesting that while beads might carry small quantities of paint, transport and dispersion of the two particle types may not be directly coupled. Environmental ratios of V to Bi and Cr to Pb, as markers for BiVO4- and PbCrO4-pigmented yellow paints, respectively, did not correlate with bead distribution, presumably because these metals have a multitude of additional anthropogenic sources. However, an inverse relationship between bead concentrations and K:Ca suggests that this ratio might be a useful proxy of road marking paint in regions that are geologically similar.

Experimental research on strain distribution measurement of PC beams based on weak grating array sensing technology

The strain and deformation of bridges are important parameters to reflect the state of bridge structures. The traditional measurement methods are to obtain the partial strain of a bridge by point strain sensor or to obtain the deformation of some special bridge positions by static level and other methods. These methods can not realize the continuous distributed measurement of bridge stress state. The grating array is a new sensing technology with the characteristics of large capacity, long distance, distribution, and high precision. In this paper, the grating array strain optical cable is installed on PC (pre-stressed concrete) beams to conduct loading and unloading experiments under various working conditions compared with other measurement methods. The experimental results show that the grating array strain sensing technology can realize the distributed measurement of bridge strain, and offer a what we believe is a novel approach to bridge health monitoring.

Arduino-Controlled Multi-Function Robot with Bluetooth and nRF24L01+ Communication

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system’s design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17.

Phage-induced “one-to-many” FRET sensor for highly sensitive detection of Escherichia coli O157:H7

As a foodborne pathogen capable of causing severe illnesses, early detection of Escherichia coli O157:H7 (E. coli O157:H7) is crucial for ensuring food safety. While Förster resonance energy transfer (FRET) is an efficient and precise detection technique, there remains a need for amplification strategies to detect low concentrations of E. coli O157:H7. In this study, we presented a phage (M13)-induced “one to many” FRET platform for sensitively detecting E. coli O157:H7. The aptamers, which specifically recognize E. coli O157:H7 were attached to magnetic beads as capture probes for separating E. coli O157:H7 from food samples. The peptide O157S, which specifically targets E. coli O157:H7, and streptavidin binding peptide (SBP), which binds to streptavidin (SA), were displayed on the P3 and P8 proteins of M13, respectively, to construct the O157S-M13K07-SBP phage as a detection probe for signal output. Due to the precise distance (≈3.2 nm) between two neighboring N-terminus of P8 protein, the SA-labeled FRET donor and acceptor can be fixed at the Förster distance on the surface of O157S-M13K07-SBP via the binding of SA and SBP, inducing FRET. Moreover, the P8 protein, with ≈2700 copies, enabled multiple FRET (≈605) occurrences, amplifying FRET in each E. coli O157:H7 recognition event. The O157S-M13K07-SBP-based FRET sensor can detect E. coli O157:H7 at concentration as low as 6 CFU/mL and demonstrates excellent performance in terms of selectivity, detection time (≈3 h), accuracy, precision, practical application, and storage stability. In summary, we have developed a powerful tool for detecting various targets in food safety, environmental monitoring, and medical diagnosis.

Accuracy improvement of laser tracker registration based on enhanced reference system points weighting self-calibration and thermal compensation.

Improving the accuracy of large measurement systems consisting of multiple laser trackers and Enhanced Reference System (ERS) points is technically challenging. In practice, standard devices with precise distance limits are often used to improve the registration accuracy of laser trackers. However, these standard devices are expensive and need to be calibrated by the Coordinate Measuring Machine (CMM). In addition, the stability of ERS points can significantly affect registration errors. Therefore, this paper proposes a laser tracker registration method based on ERS point-weighted self-calibration and thermal deformation compensation. First, a self-calibration method for simple standard devices based on multilateration measurements is presented, which only utilizes large measurement systems without additional high-precision measurement instruments. Based on this, a weighted registration optimization algorithm for the registration process of a relocation laser tracker is proposed. Then, the position errors of ERS points caused by temperature changes are calculated and compensated based on the thermal deformation coefficient of large structural components. The compensated ERS points are used for the registration of the laser trackers. Finally, the effectiveness of the proposed method is demonstrated by a field measurement experiment on a large spherical shell. Compared with the most widely used benchmark method, the proposed method reduces the average registration error of all ERS points from 0.103 to 0.02mm.

Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction.

Objective.We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction.Approach.57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed.Main results.The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVMPE), mU-Net, and Deeplabv3. The HD95 was 2.75 ± 0.44 mm and 3.91 ± 0.83 mm for the 10 patients used in the model construction and 7 patients used for external testing, respectively. The ablation test showed that all five MR sequences contributed to the performance of the final model, with T1ce contributing the most. Convergence analysis, time efficiency analysis, and visualization of the intermediate results further discovered the characteristics of the proposed method.Significance. We present an advanced MFFE learning framework, MFFE U-Net, for effective voxel-wise GBM recurrence prediction. MFFE U-Net performs significantly better than the state-of-the-art networks and can potentially guide early RT intervention of the disease recurrence.

Finding the precise distance: Self-differentiation, marital relationship and trauma among ex-combatants’ spouses

BackgroundSpouses of ex-combatants often experience psychological distress due to sharing their lives with a partner who endured traumatic experiences, a phenomenon known as “secondary traumatization”. Self-differentiation is the emotional distance one takes, while keeping an amount of togetherness with significant others. The couple's relationship and self-differentiation can impact the manner and expanse of secondary traumatization experienced by the spouse. ObjectiveThis study aimed to examine the role of marital adjustment and self-differentiation (an enmeshed vs detached differentiation style), in secondary traumatization among spouses of ex-combatants. MethodsThis study is part of a longitudinal study examining psychological implications of war among Israeli ex-combatants and their spouses. Overall, 267 spouses have been assessed three times (2003;2011;2016). Participants completed self-report questionnaires evaluating secondary PTSD (SPS), general psychiatric distress (GPD), self-differentiation and dyadic adjustment (DAS). Cross-path models were used to assess associations between the variables longitudinally. ResultsAssociations were found between both self-differentiation and dyadic adjustment and SPS and GPD. Path models showed that fusion\\cutoff differentiation predicted more SPS\\GPD over time and vice-versa. Furthermore, dyadic adjustment mediated the association between fusion\\cutoff differentiation and SPS\\GPD. In other words, fusion\\cutoff differentiation in time 1 predicted dyadic adjustment in time 2, which in turn predicted SPS\\GPD in time 3. ConclusionsOur results can be interpreted in conjunction with original theories about secondary traumatization, emphasizing the importance of keeping a balanced emotional distance in the marital relationship as a way of reducing distress and PTSD symptoms following traumatic events.

Review on Photometric Study of Variable Stars

This literature review focuses on variable stars, which exhibit changes in brightness over time. This topic is vital in astronomical research, particularly due to the abundance of telescope data available in archives. The photometric study of variable stars is significant for understanding stellar evolution, classifying stars, and contributing to cosmological research. It enables precise distance measurements, examines exoplanetary systems, and identifies rare astronomical phenomena, thereby expanding our understanding of the universe. The study begins by outlining the fundamentals of variable stars and three methods for analyzing them: photometry, spectroscopy, and spectrophotometry. However, the focus of this study is solely on photometric studies. This work presents an overview of the photometric study of variable stars. Addressing research gaps in photometric studies of variable stars is crucial for improving our understanding of stellar behavior and evolution, with broader implications for astrophysics and cosmology. Future directions include conducting photometric studies on variable stars using machine learning to develop an automated variable star classification system utilizing online telescopic data archives.

A search for Galactic post-asymptotic giant branch stars in Gaia DR3

When low- and intermediate-mass stars leave the asymptotic giant branch (AGB) phase, and before they reach the planetary nebula stage, they enter a very brief and rather puzzling stellar evolutionary stage called post-AGB stage. The post-AGB phase lasts very briefly, about a few thousand years at most. The number of objects that are confirmed in this phase therefore is really small, and our understanding of this elusive stellar evolutionary stage is accordingly very limited. We provide a reliable catalogue of Galactic post-AGB stars together with their physical and evolutionary properties obtained through Gaia DR3 astrometry and photometry. As an added product, we provide information for a sample of other types of stellar objects, whose observational properties mimic those of post-AGB stars. Post-AGB stars are characterised by their infrared excesses and high luminosities. The publication of precise parallaxes in Gaia DR3 made it possible to calculate accurate distances and to revise the derivation of luminosities for post-AGB candidates, so that objects outside the expected luminosity range can be discarded. We started by identifying post-AGB stars or possible candidates from the bibliography, and we then searched for their Gaia DR3 counterpart sources. Using the available photometry, interstellar extinction, spectroscopically derived temperatures or spectral types and parallax-derived distances from the literature, we fitted their spectral energy distributions and estimated their luminosities and circumstellar extinctions. By a comparison to models, the luminosity values allowed us to determine which objects are likely post-AGB stars from other target types. Their position in the Hertzsprung-Russell diagram allows a direct comparison with updated post-AGB evolutionary tracks and an estimation of their masses and evolutionary ages. We obtained a sample of 69 reliable post-AGB candidates that meet our classification criteria, which provide their coordinates, distances, effective temperature, interstellar and circumstellar extinction, luminosity, mass, and evolutionary age. In addition, we provide similar data for other stellar objects in our initial compilation, such as supergiant stars and young stellar objects. Our identifications and parameters are compared with others found in the recent literature for the subject. We selected the data with the best precision in parallax and distance to obtain more accurate luminosities, which allowed us to confidently classify the objects of the sample in different stellar phases. In turn, this allowed us to provide a small but reliable sample of post-AGB objects. The derived mean evolutionary time and average mass values agree with theoretical expectations and with the mean mass value obtained in a previous work for the subsequent evolutionary stage, the planetary nebula stage.

ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model

ObjectiveTo develop deep learning methods with high accuracy for segmenting irregular corneas and detecting the tear fluid reservoir (TFR) boundary under the scleral lens. Additionally, this study aims to provide a publicly available cornea with scleral lens OCT dataset, including manually labeled layer masks for training and validation of segmentation algorithms. This study introduces ScLNet, a dataset comprising cornea with Scleral Lens (ScL) optical coherence tomography (OCT) images with layer annotations, and a multi-task network designed to achieve rapid, accurate, automated segmentation of scleral lens with regular and irregular corneas. MethodsWe created a dataset comprising 31,360 OCT images with scleral lens annotations. The network architecture includes an encoder with multi-scale input and a context coding layer, along with two decoders for specific tasks. The primary task focuses on predicting ScL, TFR, and cornea regions, while the auxiliary task, aimed at predicting the boundaries of ScL, TFR, and cornea, enhances feature extraction for the main task. Segmentation results were compared with state-of-the-art methods and evaluated using Dice similarity coefficient (DSC), intersection over union (IoU), Matthews correlation coefficient (MCC), Precision, and Hausdorff distance (HD). ResultsScLNet achieves 98.22 % DSC, 96.50 % IoU, 98.13 % MCC, 98.35 % Precision, and 3.6840 HD (in pixels) in segmenting ScL; 97.78 % DSC, 95.66 % IoU, 97.71 % MCC, 97.70 % Precision, and 3.7838 HD (in pixels) in segmenting TFR; and 99.22 % DSC, 98.45 % IoU, 99.15 % MCC, 99.14 % Precision, and 3.5355 HD (in pixels) in segmenting cornea. The layer interfaces recognized by ScLNet closely align with expert annotations, as evidenced by high IoU scores. Boundary metrics further confirm its effectiveness. ConclusionWe constructed a dataset of corneal OCT images with ScL wearing, which includes regular and irregular cornea patients. The proposed ScLNet achieves high accuracy in extracting ScL, TFR, and corneal layer masks and boundaries from OCT images of the dataset.

Rapid and precise distance measurement with hybrid comb lasers

Rapid and precise distance measurement with hybrid comb lasers

VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation.

In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community.

Monocular Vision Guidance for Unmanned Surface Vehicle Recovery

The positioning error of the GPS method at close distances is relatively large, rendering it incapable of accurately guiding unmanned surface vehicles (USVs) back to the mother ship. Therefore, this study proposes a near-distance recovery method for USVs based on monocular vision. By deploying a monocular camera on the USV to identify artificial markers on the mother ship and subsequently leveraging the geometric relationships among these markers, precise distance and angle information can be extracted. This enables effective guidance for the USVs to return to the mother ship. The experimental results validate the effectiveness of this approach, with positioning distance errors of less than 40 mm within a 10 m range and positioning angle errors of less than 5 degrees within a range of ±60 degrees.

Exploring the Maximum Magnitude versus Rate of Decline Relation for Novae in M31

The results of a two-decade-long R-band photometric survey of novae in M31 are presented. From these data, R-band light curves have been determined for 180 novae with data sufficient for estimating the peak brightness and subsequent rate of decline. The data show a weak correlation of peak brightness with fade rate consistent with the well-known maximum magnitude versus rate of decline (MMRD) relation. As generally appreciated for Galactic novae, the large scatter in the MMRD relation precludes its use in determining distances to individual novae. The novae at maximum light are distributed with standard deviation σ = 0.89 mag about a mean R-band absolute magnitude given by 〈M R 〉 = −7.57 ± 0.07. The overall M31 luminosity distribution is in excellent agreement with that found for Galactic novae suggesting that the nova populations in M31 and the Galaxy are quite similar. The notion that all novae can be characterized by a standard luminosity 15 days after maximum light (M 15) is also explored. Surprisingly, the distribution of M 15 values is characterized by a standard deviation only slightly smaller than that for novae at maximum light and thus offers little promise for precise extragalactic distance determinations. A dozen faint and fast novae that are likely to be previously unidentified recurrent novae have been identified from their position in the MMRD plot and in the M 15 distribution.